Citation
An Examination of Assessed Valuation to Income for Funding Public Education in Florida

Material Information

Title:
An Examination of Assessed Valuation to Income for Funding Public Education in Florida
Creator:
Jackson Smith, Sharda L
Place of Publication:
[Gainesville, Fla.]
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (241 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ed.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Educational Leadership
Human Development and Organizational Studies in Education
Committee Chair:
WOOD,R C
Committee Co-Chair:
ELDRIDGE,LINDA BURNEY
Committee Members:
THERRIAULT,DAVID JAMES
DANA,THOMAS M
Graduation Date:
8/5/2017

Subjects

Subjects / Keywords:
education -- income -- property
Human Development and Organizational Studies in Education -- Dissertations, Academic -- UF
City of Homestead ( local )
Median income ( jstor )
Assessed valuation ( jstor )
Household income ( jstor )
Genre:
Electronic Thesis or Dissertation
born-digital ( sobekcm )
Educational Leadership thesis, Ed.D.

Notes

Abstract:
The purpose of this examination was to determine whether the state of Floridas assessed valuation and income were correlated because the Commissioners Required Local Effort calculation uses school district property assessed valuation as the measure of wealth (i.e. funding capacity). This study retrospectively determined the degree of the relationship of the variables over time and considered that the state assessment differential policy, Save Our Homes, interfered with the degree of robustness in using property assessed valuation as the sole wealth indicator. This study concluded that wealth, a measure of fiscal capacity that is based on tangible assets, is comprehensive and should weigh both property assessed valuation and income. The results of the study determined that the association between property assessed valuation and median household income was exceptionally weak and although the Pearson Product-Moment Correlation Coefficient was always positive, it was not identical year to year. More convincingly, the results were not statistically significant and likely due to chance for the past decade. The outcome of this study provided the education finance field with further research that property assessed valuation is not a complete gauge of wealth for the state of Florida and highly suggests an income factor be added to the state education funding formula if it seeks to provide an equitable education despite economic and geographic differences. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ed.D.)--University of Florida, 2017.
Local:
Adviser: WOOD,R C.
Local:
Co-adviser: ELDRIDGE,LINDA BURNEY.
Statement of Responsibility:
by Sharda L Jackson Smith.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Applicable rights reserved.
Classification:
LD1780 2017 ( lcc )

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AN EXAMINATION OF ASSESSED VALUATION TO INCOME FOR FUNDING PUBLIC EDUCATION IN FLORIDA By SHARDA JACKSON SMITH A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF EDUCATION UNIVERSITY OF FLORIDA 2017

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2017 Sharda Jackson Smith

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To my father, Arnette Jackson

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4 ACKNOWLEDGMENTS I am indebted to the University of Florida and former President James Bernard Machen for providing an opportunity to those of a modest background, which inspired me to pursue degrees in the field of education and ultimately select the topic of this dissert thank my Dissertation Chair, Dr. R. Craig Wood, for his direct personality and the hours of limitless wisdom he has afforded me. Without his guidance, my understanding of education finance would be surface level. I would also like to tha nk the remainder of my committee: Dr. Thomas Dana for offering perspective, Dr. David Therriault for offering advice, and Dr. Linda Eldridge for her command of educational practice and lifelong learning. In addition, I would also like to acknowledge Dr. Ro se Pringle for inspiring me to go further in my education, Dr. Jann MacInnes for sharing her expertise involving methodology throughout the years and Dr. Robert Tauber, from Penn State, for presenting a real world view of education theory and beyond. I ad mire my extended family for their support and desire to see me succeed. I thank my extraordinary mother for her foresight, poise, and belief in my purpose, my sister for continuously reminding me of my capabilities, and my daughter for giving me life and motivation. I thank my husban d for his patience, intelligence and everlasting love. Lastly, I praise Almighty God for seeing me through to the end.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ............... 4 LIST OF TABLES ................................ ................................ ................................ ........................... 8 LIST OF FIGURES ................................ ................................ ................................ ......................... 9 LIST OF ABBREVIATIONS ................................ ................................ ................................ ........ 20 ABSTRACT ................................ ................................ ................................ ................................ ... 21 CHAPTER 1 DEFINITION OF PROBLEM ................................ ................................ ................................ 23 Background ................................ ................................ ................................ ............................. 23 Problem Statement ................................ ................................ ................................ .................. 28 Purpose Statement ................................ ................................ ................................ .................. 28 Significance of the Study ................................ ................................ ................................ ........ 29 Methodology ................................ ................................ ................................ ........................... 30 Research Questions ................................ ................................ ................................ ......... 30 Research Design ................................ ................................ ................................ .............. 30 Definition of Terms ................................ ................................ ................................ ................ 32 Organization of the Study ................................ ................................ ................................ ....... 33 Su mmary ................................ ................................ ................................ ................................ 33 2 REVIEW OF LITERATURE ................................ ................................ ................................ 36 Introduction ................................ ................................ ................................ ............................. 37 Part I: Education Finance Programs ................................ ................................ ....................... 38 Education Funding Litigation ................................ ................................ ................................ 38 Fiscal Revenue and Capacity ................................ ................................ ................................ .. 43 Federal Revenue ................................ ................................ ................................ .............. 43 State Revenue ................................ ................................ ................................ .................. 44 Local Revenue ................................ ................................ ................................ ................. 45 Local Fiscal Capacity ................................ ................................ ................................ ...... 46 Florida Education Funding Program ................................ ................................ ...................... 47 ................................ ................................ 47 ................................ ................................ ............ 49 Part II. The Property Tax ................................ ................................ ................................ ........ 51 Property Tax Climate ................................ ................................ ................................ .............. 52 Property Rate and Tax Limitations ................................ ................................ ......................... 54 Housing Market ................................ ................................ ................................ ...................... 56 Assessment Equity ................................ ................................ ................................ .................. 61 Part III: Property Value, Income, and Save Our Homes ................................ ........................ 62

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6 Property Value and Income in Florida ................................ ................................ .................... 62 Property Value ................................ ................................ ................................ ................. 62 Income ................................ ................................ ................................ ............................. 63 es Assessment Limitation ................................ ................................ 64 ................................ ................................ ..................... 67 Part IV: Similar Studies and Topics ................................ ................................ ....................... 69 Summary ................................ ................................ ................................ ................................ 76 3 METHODS ................................ ................................ ................................ ............................. 87 Methodological Approaches ................................ ................................ ................................ ... 87 Property Assessed Valuation (PAV) ................................ ................................ ............... 87 Median Household Income (MHI) ................................ ................................ .................. 88 Purpose of the Study ................................ ................................ ................................ ............... 90 Research Design ................................ ................................ ................................ ..................... 91 Research Questions ................................ ................................ ................................ ......... 91 Research Design ................................ ................................ ................................ .............. 91 Description of Measure ................................ ................................ ................................ ... 92 Validity and Reliability of the Measure ................................ ................................ .......... 94 Description of Analysis ................................ ................................ ................................ ... 95 Setting and Participants ................................ ................................ ................................ .......... 95 Data Sources and Organization ................................ ................................ ............................... 96 Property Assessed Valuation ................................ ................................ ........................... 96 Median Household Income ................................ ................................ .............................. 97 Data Processing and Analysis ................................ ................................ ................................ 98 Summary ................................ ................................ ................................ ................................ 99 4 PRESENTATION OF RESULTS ................................ ................................ ........................ 100 Pu rpose of Study ................................ ................................ ................................ ................... 100 Demographics ................................ ................................ ................................ ....................... 100 2006 Correlation Results ................................ ................................ ................................ ...... 100 Results for 2006 ................................ ................................ ................................ ............. 100 Interpreta tion of Results 2006 ................................ ................................ ....................... 101 2007 Correlation Results ................................ ................................ ................................ ...... 101 Results for 2007 ................................ ................................ ................................ ............. 101 Interpretation of Results 2007 ................................ ................................ ....................... 101 2008 Correlation Results ................................ ................................ ................................ ...... 101 Results for 2008 ................................ ................................ ................................ ............. 101 Interpreta tion of Results 2008 ................................ ................................ ....................... 102 2009 Correlation Results ................................ ................................ ................................ ...... 102 Results for 2009 ................................ ................................ ................................ ............. 102 Interpreta tion of Results 2009 ................................ ................................ ....................... 102 2010 Correlation Results ................................ ................................ ................................ ...... 103 Results for 2010 ................................ ................................ ................................ ............. 103 Interpretation of Results 2010 ................................ ................................ ....................... 103 2011 Correlation Results ................................ ................................ ................................ ...... 103

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7 Results for 2011 ................................ ................................ ................................ ............. 103 Interpretation of Results 2011 ................................ ................................ ....................... 103 2012 Correlation Results ................................ ................................ ................................ ...... 104 Results for 2012 ................................ ................................ ................................ ............. 104 Interpreta tion of Results 2012 ................................ ................................ ....................... 104 2013 Correlation Results ................................ ................................ ................................ ...... 104 Results for 2013 ................................ ................................ ................................ ............. 104 Interpretation of Results 2013 ................................ ................................ ....................... 104 2014 Correlation Results ................................ ................................ ................................ ...... 105 Results for 2014 ................................ ................................ ................................ ............. 105 Interpretation of Results 2014 ................................ ................................ ....................... 105 2015 Correlation Results ................................ ................................ ................................ ...... 105 Results for 2015 ................................ ................................ ................................ ............. 105 Interpreta tion of Results 2015 ................................ ................................ ....................... 105 Correlation Results of 2006 2015 ................................ ................................ ......................... 106 Correlation Coefficient Results for 2006 2015 ................................ ............................. 106 Interpretation of Results 2006 2015 ................................ ................................ .............. 106 Summary ................................ ................................ ................................ ............................... 108 5 DISCUSSION AND RECOMMENDATIONS ................................ ................................ ... 116 Introduction ................................ ................................ ................................ ........................... 116 Summary of Findings ................................ ................................ ................................ ........... 117 Implications for Practice ................................ ................................ ................................ ....... 118 Re commendations for Research ................................ ................................ ........................... 120 Conclusion ................................ ................................ ................................ ............................ 123 APPENDIX A PROPERTY TAX LIMITATIONS ACROSS THE UNITED STATES ............................. 128 B SAVE OUR HOMES VALUE HISTORY 2005 2015 ................................ ........................ 129 C SPSS OUTPUT RESULTS ................................ ................................ ................................ .. 135 D INTERNAL REVENUE SERVICE ZIP CODE DATA DOCUMENTATION GUIDE .... 225 BIBLIOGRAPHY ................................ ................................ ................................ ........................ 230 BIOGRAPHIC AL SKETCH ................................ ................................ ................................ ....... 241

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8 LIST OF TABLES Table page 1 1 Florida Demographic Statistics: Population, Housing, Income, Poverty, and Land ......... 35 2 1 State Funding Formulas ................................ ................................ ................................ ..... 78 2 2 Florida School Districts Schedule of Millage Rates ................................ .......................... 79 2 3 Florida Education Finance Program Formula ................................ ................................ .... 80 2 4 Gross State and Local FEFP Components ................................ ................................ ......... 81 2 5 2008 Constitutional Amendment Impact (2009 2015) ................................ ...................... 82 2 6 2016 Statewide Just, Assessed, Exemption, and Taxable Values, by Property Type ........ 83 2 7 Annual Homestead Portability Impact ................................ ................................ ............... 84 4 1 List of Counties / School Districts Used in the Study ................................ ..................... 109 4 2 Table of Primary Descriptive Statistics, by Year ................................ ............................. 110 4 3 Table of Descriptive Statistics withou t Outliers, by Year ................................ ............... 111 5 1 Save Our Homes Annual Increases, 2006 2016 ................................ .............................. 127 A 1 Property Tax Limitations Across the United States ................................ ......................... 128 B 1 Save Our Homes Value History (2005 2008) ................................ ................................ .. 129 B 2 Save Our Homes Value History (2009 2012) ................................ ................................ .. 131 B 3 Save Our Homes Value History (2013 2015) ................................ ................................ .. 133

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9 LIST OF FIGURES Figure page 2 1 The Income Effect. ................................ ................................ ................................ ............. 85 2 2 Florida Average Annual Wages as a Percent of the United States ................................ .... 86 4 1 Graphical Representation of the PPMCC Fluctuation, 2006 2015 ................................ .. 112 4 2 Graphical Represen tation of the P Value Fluctuation, 2006 2015 ................................ .. 113 4 3 Graphical Representation of the PPMCC Fluctuation (without Outliers), 2006 2015 .... 114 4 4 Graphical Representation of the P Value Fluctuation (without Outliers), 2006 2015 .... 115 C 1 2006 Descriptive Statistics for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......................... 135 C 2 2006 Correlations for Median Household Income and Property Assessed Valuation. .... 135 C 3 Scatterplot Results for 2006. ................................ ................................ ............................ 135 C 4 Histogram Results for 2006 Median Household Income. ................................ ................ 136 C 5 Histogram Results for 2006 Property Assessed Valuation. ................................ ............. 136 C 6 2007 Descriptive Statistics for Me dian Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......................... 137 C 7 2007 Correlations for Median Household Income and Property Assessed Valuation. .... 137 C 8 Scatterplot Results for 2007. ................................ ................................ ............................ 137 C 9 Histogram Results for 2007 Median Household Income. ................................ ................ 138 C 10 Histogr am Results for 2007 Property Assessed Valuation. ................................ ............. 138 C 11 2008 Descriptive Statistics for Median Household Income and Property As sessed Valuation. ................................ ................................ ................................ ......................... 139 C 12 2008 Correlations for Median Household Income and Property Assessed Valuation. .... 139 C 13 Scatterplot Results for 2008. ................................ ................................ ............................ 1 39 C 14 Histogram Results for 2008 Me dian Household Income. ................................ ................ 140 C 15 Histogram Results for 2008 Property Assessed Valuation. ................................ ............. 140

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10 C 16 2009 Descriptive Statistics for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......................... 141 C 17 2009 Correlations for Median Household Income and Property Assessed Valuation. .... 141 C 18 Scatterplot Results for 2009. ................................ ................................ ............................ 141 C 19 Histogram Results for 2009 Median Household Income. ................................ ................ 142 C 20 Histogram Results for 2009 Property Assessed Valuation. ................................ ............. 142 C 21 2010 Descriptive Statistics for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......................... 143 C 22 2010 Correlations for Median Household Income and Property Assessed Valuation. .... 143 C 23 Scatterplot Results for 2010. ................................ ................................ ............................ 143 C 24 Histogram Results for 2010 Median Household Income. ................................ ................ 144 C 25 Histogram Results for 2010 Property Assessed Valuation. ................................ ............. 144 C 26 2011 Descriptive Statistics for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......................... 145 C 27 2011 Correlations for Median Household Income and Property Assessed Valuation. .... 145 C 28 Scatterplot Results for 2011. ................................ ................................ ............................ 145 C 29 Histogram Results for 2011 Median Household Income. ................................ ................ 146 C 30 Histogram Results for 2011 Property Assessed Valuation. ................................ ............. 146 C 31 2012 Descriptive Statistics for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......................... 147 C 32 2012 Correlations for Median Household Income and Property Assessed Valuation. .... 147 C 33 Scatterplot Results for 2012. ................................ ................................ ............................ 147 C 34 Histogram Results for 2012 Median Household Income. ................................ ................ 148 C 35 Histogram Results for 2012 Property Assessed Valuation. ................................ ............. 148 C 36 2013 Descriptive Statistics for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......................... 149 C 37 2013 Correlations for Median Househol d Income and Property Assessed Valuation. .... 149

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11 C 38 Scatterplot Results for 2013. ................................ ................................ ............................ 149 C 39 Histogram Results for 2013 Median Household Income. ................................ ................ 150 C 40 Histogram Results for 2013 Property Assessed Valuation. ................................ ............. 150 C 41 2014 Descriptive Statistics f or Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......................... 151 C 42 2014 Correlations for Median Household Income and Property As sessed Valuation. .... 151 C 43 Scatterplot results for 2014. ................................ ................................ ............................. 151 C 44 Histogram Results for 2014 Median Household Income. ................................ ................ 152 C 45 Histogram Results for 2014 Property Assessed Valuation. ................................ ............. 152 C 46 2015 Descriptive Statistics for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......................... 153 C 47 2015 Correlations for Median Household Income and Property Assessed Valuation. .... 153 C 48 Scatterplot Results for 2015. ................................ ................................ ............................ 153 C 49 Histogram Results for 2015 Median Household Income. ................................ ................ 154 C 50 Histogram Results for 2015 Property Assessed Valuation. ................................ ............. 154 C 51 2006 Correlations without Outliers for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......... 155 C 52 Scatterplot without Outliers Results for 2006. ................................ ................................ 155 C 53 2007 Correlations without Outliers for Median Household Income and Property Assessed Valuatio n. ................................ ................................ ................................ ......... 156 C 54 Scatterplot without Outliers Results for 2007. ................................ ................................ 156 C 55 2008 Correlations without Outliers for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......... 157 C 56 Scatterplot without Outliers Results for 2008. ................................ ................................ 157 C 57 2009 Correlations without Outliers for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......... 158 C 58 Scatterplot without Outliers Results for 2009. ................................ ................................ 158

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12 C 59 2010 Correlations without Outliers for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......... 159 C 60 Scatterplot without Outliers results for 2010. ................................ ................................ .. 159 C 61 2011 Correlations without Outliers for Median Household Income and Property Assessed Valuatio n. ................................ ................................ ................................ ......... 160 C 62 Scatterplot without Outliers Results for 2011. ................................ ................................ 160 C 63 2012 Correlations without Outliers for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......... 161 C 64 Scatterplot without Outliers Results for 2012. ................................ ................................ 161 C 65 2013 Correlations without Outliers for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......... 162 C 66 Scatterplot without Outliers Results for 2013. ................................ ................................ 162 C 67 2014 Correlations without Outliers for Median Household Income and Property Assessed Valuation. ................................ ................................ ................................ ......... 163 C 68 Scatterplot without Outliers Results for 2014. ................................ ................................ 163 C 69 2015 Correlations without Outliers for Median Household Income and Pr operty Assessed Valuation. ................................ ................................ ................................ ......... 164 C 70 Scatterplot without Outliers results for 2015. ................................ ................................ .. 164 C 71 2006 Test for Normal Distribution for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ ......................... 165 C 72 2006 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 165 C 73 2006 Normal Q Q Plot for Median Household Income. ................................ .................. 166 C 74 2006 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ ......................... 166 C 75 2006 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 167 C 76 2006 Normal Q Q Plot for Property Assessed Valuation. ................................ ............... 167 C 77 2006 Test for Normal Distributi on without Outliers for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ .... 168

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13 C 78 2006 Test for Normal Distribution without Outli ers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 168 C 79 2006 Normal Q Q Plot without Outliers for Median Household Income. ...................... 169 C 80 2006 Test for Normal Distribution without Outliers for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ .... 169 C 81 2006 Test for Normal Distribution without Outliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 170 C 82 2006 Normal Q Q Plot without Outliers for Property Assessed Valuation. .................... 170 C 83 2007 Test for Normal Distribution for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ ......................... 171 C 84 2007 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 171 C 85 2007 Normal Q Q Plot for Median Household Income. ................................ .................. 172 C 86 2007 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ ......................... 172 C 87 2007 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 173 C 88 2007 Normal Q Q Plot for Property Assessed Valuation. ................................ ............... 173 C 89 2007 Test for Normal Distributi on without Outliers for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ .... 174 C 90 2007 Test for Normal Distribution without Outli ers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 174 C 91 2007 Normal Q Q Plot without Outliers for Median Household Income. ...................... 175 C 92 2007 Test for Normal Distribution without Outliers for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ .... 175 C 93 2007 Test for Normal Distribution without Outliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 176 C 94 2007 Normal Q Q Plot without Outliers for Property Assessed Valuation. .................... 176 C 95 2008 Test for Normal Distribution for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ ......................... 177

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14 C 96 2008 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 177 C 97 2008 Normal Q Q Plot for Median Household Income. ................................ .................. 178 C 98 2008 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ ......................... 17 8 C 99 2008 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 179 C 100 2008 Normal Q Q Plot for Property Assessed Valuation. ................................ ............... 179 C 101 2008 Test for Normal Distrib ution without Outliers for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ .... 180 C 102 2008 Test for Normal Distribution without Outliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 180 C 103 2008 Normal Q Q Plot wit hout Outliers for Median Household Income. ...................... 181 C 104 2008 Test for Normal Distribution without Outliers for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ .... 181 C 105 2008 Test for Normal Distribution without Outliers for Property Assessed Valuation ( Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 182 C 106 2008 Normal Q Q Plot without Outliers for Property Assessed Va luation. .................... 182 C 107 2009 Test for Normal Distribution for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ ......................... 183 C 108 2009 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 183 C 109 2009 Normal Q Q Plot for Median Household Income. ................................ .................. 184 C 110 2009 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ ......................... 184 C 111 2009 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 185 C 112 2009 Normal Q Q Plot for Property Assessed Valuation. ................................ ............... 185 C 113 2009 Test for Normal Distribution without Outliers for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ .... 186

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15 C 114 2009 Test for Normal Distribution without Outliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 186 C 115 2009 Normal Q Q Plot without Outliers for Median Household Income. ...................... 187 C 116 2009 Test for Normal Distribution without Outliers for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ .... 187 C 117 2009 Test for Normal Distribution without Outliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 188 C 118 2009 Normal Q Q Plot without Outliers for Property Assessed Valuation. .................... 188 C 119 2010 Test for Normal Distribution for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ ......................... 189 C 120 2010 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 189 C 121 2010 Normal Q Q Plot for Median Household Income. ................................ .................. 190 C 12 2 2010 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ ......................... 190 C 123 2010 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 191 C 124 2010 Normal Q Q Plot for Property Assessed Valuation. ................................ ............... 191 C 125 2010 Test for Normal Distrib ution without Outliers for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ .... 192 C 126 2010 Test for Normal Distribution without Outliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 192 C 127 2010 Normal Q Q Plot wit hout Outliers for Median Household Income. ...................... 193 C 128 2010 Test for Normal Distribution without Outliers for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ .... 193 C 129 2010 Test for Normal Distribution without Outliers for Property Assessed Valuation ( Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 194 C 130 2010 Normal Q Q Plot without Outliers for Property Assessed Va luation. .................... 194 C 131 2011 Test for Normal Distribution for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ ......................... 195

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16 C 132 2011 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 195 C 133 2011 Normal Q Q Plot for Median Household Income. ................................ .................. 196 C 134 2011 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ ......................... 196 C 135 2011 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 197 C 136 2011 Normal Q Q Plot for Property Assessed Valuation. ................................ ............... 197 C 137 2011 Test for Normal Distribution without Outliers for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ .... 198 C 138 2011 Test for Normal Distribution without Outliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 198 C 139 2011 Normal Q Q Plot without Outliers for Median Household Income. ...................... 199 C 140 2011 Test for Normal Distribution without Outliers for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ .... 199 C 141 2011 Test for Normal Distribution without Outliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 200 C 142 2011 Normal Q Q Plot without Outliers for Property Assessed Valuation. .................... 200 C 143 2012 Test for Normal Distribution for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ ......................... 201 C 144 2012 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 201 C 145 2012 Normal Q Q Plot for Median Household Income. ................................ .................. 202 C 146 2012 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ ......................... 202 C 147 2012 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 203 C 148 2012 Normal Q Q Plot for Property Assessed Valuation. ................................ ............... 203 C 149 2012 Test for Normal Distrib ution without Outliers for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ .... 204

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17 C 150 2012 Test for Normal Distribution without Outliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 204 C 151 2012 Normal Q Q Plot wit hout Outliers for Median Household Income. ...................... 205 C 152 2012 Test for Normal Distribution without Outliers for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ .... 205 C 153 2012 Test for Normal Distribution without Outliers for Property Assessed Valuation ( Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 206 C 154 2012 Normal Q Q Plot without Outliers for Property Assessed Va luation. .................... 206 C 155 2013 Test for Normal Distribution for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ ......................... 207 C 156 2013 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 207 C 157 2013 Normal Q Q Plot for Median Household Income. ................................ .................. 208 C 158 2013 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ ......................... 208 C 159 2013 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 209 C 160 2013 Normal Q Q Plot for Property Assessed Valuation. ................................ ............... 209 C 161 2013 Test for Normal Distribution without Outliers for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ .... 210 C 162 2013 Test for Normal Distribution without Outliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 210 C 163 2013 Normal Q Q Plot without Outliers for Median Household Income. ...................... 211 C 164 2013 Test for Normal Distribution without Outliers for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ .... 211 C 165 2013 Test for Normal Distribution without Outliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 212 C 166 2013 Normal Q Q Plot without Outliers for Property Assessed Valuation. .................... 212 C 167 2014 Test for Normal Distribution for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ ......................... 213

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18 C 168 2014 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 213 C 169 2014 Normal Q Q Plot for Median Household Income. ................................ .................. 214 C 170 2014 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ ......................... 214 C 171 2014 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 215 C 172 2014 Normal Q Q Plot for Property Assessed Valuation. ................................ ............... 215 C 173 2014 Test for Normal Distrib ution without Outliers for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ .... 216 C 174 2014 Test for Normal Distribution without Outliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 216 C 175 2014 Normal Q Q Plot wit hout Outliers for Median Household Income. ...................... 217 C 176 2014 Test for Normal Distribution without Outliers for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ .... 217 C 177 2014 Test for Normal Distribution without Outliers for Property Assessed Valuation ( Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 218 C 178 2014 Normal Q Q Plot without Outliers for Property Assessed Va luation. .................... 218 C 179 2015 Test for Normal Distribution for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ ......................... 219 C 180 2015 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 219 C 181 2015 Normal Q Q Plot for Median Household Income. ................................ .................. 220 C 182 2015 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ ......................... 220 C 183 2015 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ................................ 221 C 184 2015 Normal Q Q Plot for Property Assessed Valuation. ................................ ............... 221 C 185 2015 Test for Normal Distribution without Outliers for Median Household Income (Descriptive Statistics). ................................ ................................ ................................ .... 222

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19 C 186 2015 Test for Normal Distribution without Outliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 222 C 187 2015 Normal Q Q Plot without Outliers for Median Household Income. ...................... 223 C 188 2015 Test for Normal Distribution without Outliers for Property Assessed Valuation (Descriptive Statistics). ................................ ................................ ................................ .... 223 C 189 2015 Test for Normal Distribution without Outliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). ................................ ......... 224 C 190 2015 Normal Q Q Plot without Outliers for Property Assessed Valuation. .................... 224 D 1 Internal Revenue Service Zip Code Data Documentation Guide ................................ .... 229

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20 LIST OF ABBREVIATIONS ACS CPI American Community Survey Consumer Price Index DOE Department of Education DOR Department of Revenue F.S. Florida Statutes FDOE Florida Department of Education FDOR Florida Department of Revenue FEFP FTE Florida Education Finance Program Full Time Equivalent HE Homestead Exemption IBM International Bureau Machines IRS Internal Revenue Service MHI OEDR Median Household Income Office of Economic and Demographic Research PAV Property Assessed Valuation PPMCC Pearson Product Moment Correlation Coefficient PT Portability Transfer RLE Required Local Effort SOH SPSS Statistical Package for the Social Sciences USCB USPAP VAB United States Census Bureau Uniform Standards of Professional Appraisal Practices Value Adjustment Board

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21 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Education AN EXAMINATION OF ASSESSED VALUATION TO INCOME FOR FUNDING PUBLIC EDUCATION IN FLORIDA By Sharda Jackson Smith August 2017 Chair: R. Craig Wood Major: Educational Leadership The purpose of this examination was to determine whether the state of calculation uses school district property assessed valuation as the measure of wea lth (i.e. funding capacity ). This study retrospectively determined the degree of the relationship of the variables o ver time and considered that the state assessment differential policy, Save Our Homes, interfered with the degree of robustness in using property assessed valuation as the sole wealth indicator. This study concluded that wealth, a measure of fiscal capacity that is based on tangible assets, is comprehensive and should weigh both property assessed valuation and income. The results of the study determined that the association between property asses sed valuation and median household income was exceptionally weak and although the Pearson Product Moment Correlation Coefficient was always positive, it was not identical year to year. More convincingly, the results were not statistically significant and l ikely due to chance for the past decade. The outcome of this study provided the education finance field with further research that property assessed valuation is not a complete gauge of wealth for the state of Florida and highly suggests

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22 an income factor b e added to the state education funding formula if it seeks to provide an equitable education despite economic and geographic differences.

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23 CHAPT ER 1 DEFINITION OF PROBLEM Background A writer for the New York Times a source of po pular literature, conc luded of education finance in the United States is a feature of the system, not a bug, stemming from its 1 Ironically, fo rty years earlier, the Florida Legislature established the intent of the state education fina nce program and promised to guarantee facilities that would provide public education system the availability of an educational environment appropriate to his or her educational needs e qual to that available to any similar student, notwithstanding geographic differences and varying local economic factors 2 Today, the field of education still seeks to ensure that public education has exercised an equitable system. Economically, the Unit Government Tax Collection s taxpayers paid more than half of all government income. 3 The Florida Tax Watch Research Institute concluded that t he state relied ] more heavily on local governments to fund public services that any other state; [and] 55 percent of all government revenues in Florida [were] raised 1 New York Times Nov ember 5, 2013, accessed April 15, 2017, http://www.nytimes.com/2013/11/06/business/a rich childs edge in public education.html 2 FLA. STAT. § 235 .002 (1)(a) (2001) 3 Florida Tax Watch Research Institute, How Florida Compares Taxes: State and Local Tax Rankings for Florida and the Nation (Florida Tax Wa tch Research Institute, 2015), 2 3 accessed April 15, 2017 http://www.floridataxwatch.org/resources/pdf/2015_HFCTaxes_Final.pdf ; United States Department of Commerce accessed April 15, 2017 https://www.census.gov/govs/statetax.

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24 by local governments which was the highest percentage in the nation. 4 Furthermore, alth ough Florida homeownership 5 was greater than the national average, the median household income was lower than the national average ($47,507 to $53,889 respectively ) 6 The special economic and demographic structure of Florida requires stakeholders in the fi eld of education to continually evaluate whether education and tax policies satisfy their intended motive. Table 1 1 Bureau. The Florida tax system attempts to regulate the vast amount of population differences and needs that are present within the state through exemption s and assessment differentials. Save Our Homes, a petition initiated amendment, limited increases in the asses sment of homestead property to t hree percent per year or the percent change in the Consumer Price Index [CPI] whichever i s lower. 7 Presently, widowed, senior citizen, blind, disabled, and veteran populations are granted estate exemptions to relieve propert y taxation, in addition to the Save 4 Ibid 3. 5 The United States Census Bureau, American Community Survey defines a housing unit as owner occupied if the owner or co owner lives in the unit, even if it is mortgaged or not fully paid for. The homeownership rate is computed by dividing the number of owner occupied housing units by the number of occupied housing units or households. 6 Uni ted States Department of Commerce accessed April 15, 2017 https://www.census.gov/quickfacts/table/PST045215/12 ; Data derived from Population Estimates, American Community Survey, Census of Population and Housing, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits 7 Budget Subcommittee on Finance and Ta x, Property Tax Update, Fla. S. Rep. No. 2012 207, at 7 (2011), accessed April 15, 2017, https://www.flsenate.gov/PublishedContent/Session/2012/InterimReports/2012 207ft.pdf ; FLA. STAT § 193.155 (2016)

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25 Our Homes a ssessment differential 8 With exceptions, 9 the income of these populations are not directly factored into exemption qualification, but many researchers believe that their status has an inherent negative effect on their household income. Because exemptions are largely voluntary, unlike taxes, the f inancial abilities of these populations become increasingly complicated to decipher through taxable pr operty assessed valuation. Yet, assessed valuation is the gauge of local district wealth in the state of Florida, especially pertaining to education finan ce. Just as the Florida property tax system compensates for population differences for individuals, its public education funding formula also attempts to counterbalance variation among districts. The F lorida Legislature mandated [ e ] f the state total required local effort [be] determined by a statutory procedure that is initiated by certification of 10 The 8 Florida Department of Revenue accessed April 15, 2017, floridarevenue.com/dor/property/taxpayers/exemptions.html for a complete list of individual, family, fallen heroes, and other property tax exemptions; FLA. STAT. § 196 (2016) 9 tations (FLA. STAT. § 196 (2016) and F LA C ONST preceding Florida Department of Revenue accessed April 15, 2017, floridarevenue.com/dor/property/resources/limitations.html 10 Florida Department of Education, 2015 2016 Funding for Florida School Districts: Statistical Report (Florida Department of Education, 2015), 2, accessed April 15, 2017, http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf

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26 Commissioner, 11 School Board, 12 and Voter Referendu m 13 has the authority to adjust millage rates in a manner that is unique to each district. However, with a 10 millage maximum 14 for operations, Florida school districts are confined to specific allocation mandates that aim to satisfy state statutes but also meet the needs of each district. valuation as an appropriate measure of district financial capability. Millage rates, assessment differentials, and exemptions, which are al l rarely based on income, disrupt the premise that property assessed valuation is interchangeable with both income or property taxes. The central issue is that income, from which taxes are paid, may or may not correlate to property assessed valuation but m ay cause distribution of taxpayer dollars to become lost in aggregation. If income were equally proportional throughout the state, via property assessed valuation, the higher the correlation and the more likely the education finance formula will satisfy th e goal of educational and financial equity. The Florida Departmen t of Revenue reported the 2016 just ($2,431.2 billion), assessed ($2,055.2 billion), e xem ptions ($447.7 billion) and taxable ($1,607.2 billion) values by property t ype. 15 The Save Our Homes as sessment d ifferential totaled $231.7 billion. 16 Assessed as a 11 FLA. STAT.§ 1011.62(4) (2016), FLA. STAT. § 1011.62(4)(e) (2016). 12 FLA. STAT.§ 1011.71(1) (2016), FLA. STAT.§ 1011.71(2) (2016), FLA. STAT.§ 1011.71(3)(a) (2016). 13 FLA. STAT.§ 1011.73(1) (2011), FLA. STAT.§ 1011.73(2) (2011), FLA. STAT.§ 200.001(3)(e) (2016), FLA. CONST. art. VII, § 12. 14 FLA. S TAT.§ 200.065(5 ) (2016). 15 Florida Department of Revenue accessed April 15, 2017, http://floridarevenue.com/dor/property/resources/data.html 16 Ibid.

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27 percentage of just v al u e was 84.5 percent. Exemptions as a percentage of assessed v alue was 21.7 percent. T axable as a percentage of just v al ue was 66.2 and of assessed v alue was 78.2 percent. Th is amount of precision per county and state allows taxpayers to differentiate the amount of collection and frame s the trillions of dollars that circulate through the state and local government. Stakeholders dispute whether equity exists in not only educ ati on spending, but in revenue dispersion Recent literature has debated whether the property tax has been regressive or progressive for particular communities, whether changing millage rates to offset changes in the tax base is beneficial for all, and how to measure the amount of tax burden that has been placed on school districts. It has also argued how much of education funding should be placed on the property tax, the relationship between income mobility and quality of education, and whether property value equates to property taxes. 17 Stakeholders for education finance must consider the possibility that the housing bubble of the Great Recession 18 may have changed the economic climate in a manner that equates to a funding formula that requires an adjustment. Because the property tax is the link that connects 17 Journal of Real Estate Research 24, no. 2 (2002) ; James cal Government Reliance on Regional Science and Urban Economics 41, no. 4 (2011): 320 31; Keith R. Cityscape 15, no. 1 (2013): 255 59; Mark Skidmore, Laura Reese, and Sung Regional Science and Urban Economics 42, no. 1 2 (2012): 351 63, accessed April 15, 2017 http://dx.doi.org/10.1016/j.regsciurbeco.2011.10.008; Dean Stansel, Gary Jackson, and J. Based Property Tax: The Journal of Housing Research 16, no. 2 (2007) 18 United States Department of Labor, Bureau of Labor Statistics, BLS Spotlight on Statistics: Recession of 2007 2009 (United States Department of Labor, 2012), accessed April 15, 2017, http://www.bls.gov/spotlight/2012/recession/pdf/recession_bls_spotlight.pdf

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28 property assessed valuation and household income, the argument considered the vast amount of population differences, and possibly assessment differentials exercised, in the state of Florida that create an unbalanced arrangement of taxation and, consequently, education funding. If property were the means in which wealth were measured in public education, property assessed valuation abstractly yields a positive relationship that parallels median household inc ome, another form of wealth measurement. This study essentially determined if equity were present in terms of the primary variable that dictates the funding formula and taxpayer ability to exert the required local effort. Problem Statement The root of the problem rests in education finance methodology and tax code. Controversy encompasses the property tax as a percent of personal income. Literature is limited when it comes to the relationship of these variables through a correlational design Also, although the available previous research has focused on property value and income, recent research has not focused greatly enough on the correlation of property valuation and median household income after the implementation of an assessment differe ntial for educational funding purposes, especially post Great Recession and specifically for the state of Florida. This research related to the current literature by focusing the discussion of the impact of assessment limitations, income and property on ed ucation funding. The research problem is to investigate the extent to which property assessed valuation post assessment limitation is the most equitable measurement of school district wealth in Florida overtime. Purpose Statement Glomm, Ra vikumar, and Schi opu stated redi stribution between groups (e.g. between rich and poor between old and young ), the political decisions have to aggregate conflicting preferences regarding taxation, redistribution, or income

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29 19 As a response, this study openly addressed existing theory and evidence that predicated the premise that property assessed valuation without regard for income was the most faultless measure of district wealth in public school finance. Generally, although the proportion of income may be similar, the amount of property taxes a lower income household pays has a greater impact on their total earnings than a higher income household due to the increasingly limited amount of disposable income. In addition, it is not uncommon for households to have high income but low property value or households to have low income but high property value. The same theory stands for school districts. If property assessed valuation and median household income wer e consistently correlated in Florida, this testified that property valuation was an authentic measure of district wealth. If property assessed valuation and median household income were not consistent in Florida, using assessed valuation as a measure of ab ility was less valid. Significance of the Study Some believe that meaningful reform not only requires restoration of the public education system but the tax system from which it is funded. However, what is more likely than a dismantling of a state tax syst em is an education funding formula adjustment. Providing state aid in a manner th at ignores income neglects the probable extent to which property assessed valuation may differ from median household income. Public education has the duty of providing a prope r funding structure for all students in the wake of the Great Recession, being mindful of shifting environmental circumstances and socioeconomic status. 19 Chapter 9: The Political Economy of Handbook of the Economics of Education ed. Eric A. Hanushek, Ste phen J. Machin, Ludger Woessmann (Waltham : Elsevier, 2011), 617, accessed April 15, 2017, http://dx.doi.org/10.1016/B978 0 444 53444 6.00009 2

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30 Florida has a distinct population with a series of implications based on its demographics that directl y affect local government funding. This study added to existing literature considering the interchangeability of property assessed valuation and median household income within the state of Florida. It considered that althoug h ad valorem property taxes have been an acceptable foundation for local revenue, it may fall short as an absolute measure of income for public education purposes. Otherwise, the probable extent to which income may differ from assessed valuation and the unmeasured burden it places on pop ulations when various exemptions and assessment differentials are exercised is continually ignored. This study contemplated the possible funds that taxpayers are capable of yielding, regardless of their in come. At its conclusion, policy makers learned whet her to consider an adjustment added to the public education funding formula that weighed whether a median household income measure was Methodology Research Questions 1. Is there a correlation between propert y assessed valuation and median household income among school districts in the state of Florida over a 10 year span? H 0 : = 0 H A : 2. How consistent is the correlation between property assessed valuation and median household income amongst school districts in the state of Florida over a 10 year span? Research Design This study sought to determine how property assessed valuation and median household income correlated amongst school districts in the state of Florida over the last decade of available data (2006 2015). A bivariate correlational test was used to examine the relationship between two variables per district, with a significance level (p value) of .05. The variables of

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31 interest were Property Assessed Valuation (PAV) and Median Household Inco me (MHI) One PAV item and one MHI item were entered into the Statistical Package for the Social Sciences (SPSS) a predictive analytic software, for each available school district in the state of Florida for each year. The Pearson Product Moment Correlati on Coefficient was used to determine the direction and strength of association between each variable due to the interval scales of measurement for both variables. Median income was used, as opposed to mean income, because it is a more robust measure of cen tral tendency that is efficient and has little bias. It was expected that there would be a strong, consistent correlation between PAV and MHI, which would be statistically significant. A significant strong correlation shows that PAV and MHI are related bu t not that one variable caused changes in another variable. Nevertheless, the researcher focused on the consistency of the correlation between the variables over a 10 year span. In determining the strength of the association between the variables, the corr elation coefficient best depicted the relationship between PAV and MHI because it standardized the variables. PAV was secured from the Florida Department of Revenue (FDOR). 20 Property assessed value, which was measured in real, personal, and centrally asses sed, was based on the annual property appraisal reported to FDOR. MHI was secured from the United States Cen sus Bureau. Demographic data were derived from the United States Census Bureau American Community Survey and the Decennial Census Long Form. 20 Flori da Department of Revenue accessed April 15, 2017 http://floridarevenue.com/dor/property/resources/data.html

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32 Defini tion of Terms Equity the outcome of practices that result in the same outcomes for members of a group. 21 Florida Education Finance Program (FEFP) 22 Median Household Income (MHI) based on the United States Census Bureau, American Community Survey 1 Year Estimate; based on the distribution of the total number of households and families including those with no income; based on individuals 15 years old and over with income; computed on the basis of a standard d istribution. 23 Millage Rate the amount per $1,000 used to calculate taxes on property; one one of taxes or to the cumulative of all levies. 24 Property Tax the local government tax on real estate. based 25 Property Assessed Valuation (PAV) the difference of mark et value and assessment differentials (i.e. Save Our Homes ); an annual determination of: (a) The just or fair market value of an item or property; (b) The value of property as limited by Article VII of the Florida Constitution; or (c) The value of propert y in a classified use or at a fractional value if the property is assessed solely on the basis of character or use or at a specified percentage of its value under Article VII of the Florida Constitution. 26 21 Randall Lindsey, Kikanza Nuri Robins, and Raymond D. Terrell, Cultural Proficiency: A Manual for School Leaders 3 rd ed. (Thousand Oaks: Corwin of Sage Publications, 2009), 166. 22 Florida Department of Education, 2015 2016 Funding for Florida School Districts: Statistical Report (Florida Department of Education, 2015 ), 1, accessed April 15, 2017, http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf 23 United States Department of Commerce, United States C ensus Bureau accessed April 15, 2017, http://quickfacts.census.gov/qfd/meta/long_INC110213.htm 24 FLA. STAT.§ 192.001(10) (2016). 25 FLA. STAT. § 192.001(1) (2016). 26 FLA. STAT. § 192.001(2) (2016); The Department of Revenue reports other exceptions that make up the difference of Just Value and Assessed Valuations. They include a ten percent Non

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33 School Tax the product of taxable property assessed valuation and millage rate; total tax liability 27 Organization of the Study The first chapter of this dissertation presented a purpose of the study while previewing the literature, methodology, and significance of the study. The next chapter over viewed relevant literature and provide d connect ions to the study. Chapter 3 of this study identified the design, participants, setting, instruments, procedures, and the process used to analyze the se data. The fourth chapter inc luded data presentation, anal yse s, and interpretation. The last chapter of this dissertation reported the findings in context while presenting implications and recommendations. Summary Researchers agree [t] ax systems are hugely complex and interrelated, and generally speaking, effo rts to make them fairer and more equitable usually result in making them more complicated and more difficult for laypersons to understand hence 28 Under these circumstances, Florida has managed to adopt an extensive funding formula. Yet, the debate persists involving a more equitable and adequate education funding structure that is sensitive to economic factors and current legislation. This argument deserves education based academic attention if taxable property assessed valuation will continu e to be the single measure of wealth in the state of Florida. Researchers have continued to address income as a legitimate factor in Homestead Assessment Increase Cap, Agricultural Classification, Pollution Control Devices, Conservation Lands and Working W aterfronts. The Save Our Homes assessment d ifferential makes up the greatest difference. 27 Florida Department of Revenue, accessed April 15, 2017, http://dor.myflorida.com/dor/property/taxpayers 28 David C. Thompson Faith E. Crampton, and R. C raig Wood Money and Schools 5 th ed. (New York: Routledge, 2012 ), 101.

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34 determining stat e education funding capacities for districts. 29 T he goal is for Florida legislators to consider whether the state education funding formula de serves an adjustment because of advancing population differences, a changing economic environment and the implementation of dynamic policy that only an income factor can alleviate. Determining the extent to which property assessed valuation and median household income were correlated contributed to the field of education by adding to the body of knowledge that connects taxpayers to the quality of education that students receive. The correlation between the variables confro nted whether the means in which the local government funds schools was geographically and economically judicious. This study, specific to Florida, examined existing policy that claimed to presently be sensitive to the wealth of households by using the meas urement of property assessed valuation as the sole contributor to local funding of education. This study sought to confirm that concept through the observation of fiscal capacity The next chapter provided background information to help illustrate the clim ate of the education field as it pertains to education finance, income, property, and assessment limitations. 29 E .g., Roe L. Johns, Edg ar Morphet, and Kern Alexander, The Economics and Financing of Education 4th ed. (Englewood Cliffs: Prentice Hall, 1983); Ellwood Cubberly, The History of Education (Boston: Houghton Mifflin, 1920); George D. Strayer and Robert M. Haig, The Financing of E ducation in the State of New York (New York: Macmillan, 1923); Paul Mort, State Support for the Public Schools (New York: Teachers College Press, Columbia University, 1926); Percy Burrup, Financing Education in a Climate of Change (Boston: Allyn and Bacon, 1974); the Needs of High Property Wealth School Districts with Low on Education and Cultural Affairs, ME, August 2013), accessed April 15, 2017 http://www.maine.gov/legis/opla/MaineFiscalCapacityMeasuresPaper73013.pdf ; Vern Brimley Deborah A. Verstegen, and Rulon R. Garfield, Financing Education in a Climate of Change 12 th ed (Boston: Pearson, 2016).

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35 Table 1 1. Florida Demographic Statistics: Population, Housing, Income, Poverty, and Land Category Value Population estimates, July 1, 2016, (V2016) 20261439 Population estimates base, April 1, 2010, (V2016) 18804592 Population, Census, April 1, 2010 18801310 Persons under 5 years, percent, July 1, 2015, (V2015) 5.4 Persons under 18 years, percent, July 1, 2015, (V2015) 20.3 Persons 65 years and over, percent, July 1, 2015, (V2015) 19.4 Veterans, 2011 2015 1507738 Housing units, July 1, 2015, (V2015) 9209857 Housing units, April 1, 2010 8989580 Owner occupied housing unit rate, 2011 2015 65.3 Median value of owner occupied housing units, 2011 2015 $159000 Median selected monthly owner costs with a mortgage, 2011 2015 $1438 Median selected monthly owner costs without a mortgage, 2011 2015 $463 Median gross rent, 2011 2015 $1002 Median household income (in 2015 dollars), 2011 2015 $47507 Per capita income in past 12 months (in 2015 dollars), 2011 2015 $26829 Persons in poverty, percent 15.7 Population per square mile, 2010 350.6 Land area in square miles, 2010 53624.76 Source: Information adapted from Quick Fa cts: Florida, United States Census Bureau accessed April 15 2017, http://www.census.gov/quickfacts/table/PST045215/12 Note: The vintage year (e.g., V2015) refers to the final year of the series (2011 thru 2015).

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36 CHAPTER 2 REVIEW OF LITERATURE Scholars discuss a variety of factors that interfe re with the most genuine assessed value of property, leading to what may be defined as less than uniform assessment. While the Florida financial capacity, taxation impacts the discretiona ry income of households resulting in a compromised proportionality of assessed value to income ratio. What currently exists is an education system that bases district financial ability on one historically reliable but yet indirect variable: Property Assess ed Valuation. If there were evidence to support that the variable used was consistent ly or inconsistent ly associated with the most untouched form of Florida taxpayer wealth (i.e. income), greater support can be had for an equitable education finance formu la. This study sought to determine if property assessed value were correlated to income, despite recent policy that limits property assessment in the state of Florida. The purpose of the literature review was to provide justification of the study through an examinat ion of the field. T he information noted was secured from scholarly sources. Those whom were identified as popular literature were noted as such and were occasionally provided to document ubiquitous discourse. The literature review was organized into four parts. First, this chapter discussed school finance programs and how it influenced the research question. It then provided a brief analysis of property tax limitations and assessment equity. Afterward, the current state of assessed value and inco me, and the inherent effect of the assessment limitation in Florida were discussed. Last, the review evaluated approaches to similar topics.

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37 Introduction 1 (SOH) assessment differential, requires that a particular hom percent of its assessed value or the percentage change in the Consumer Price Index (CPI) of the prior year, whichever is lower annual SOH value totaled over $184 billion, peaked at over $427 bil lion and fell to as little as $56 billion within the last decade of reported data. 2 This study implementation. Concerning education finance, the purpose of a school dis (RLE) is to appraise its share toward the Florida Education Finance Program (FEFP) calculation, all the while sensitive to the abilities of the school district. School taxable value, a Department of Revenue (DOR) computation u sed to determine RLE, is based on the SOH influenced value of at the assessment level relative to tangible affluence. Measuring wealth from a wielded figure that varies from household to household jeopardizes the idea of equity for any one student, household, or district. With equity in mind, this study made an observ ation overtime of the correlation between two variables that are commonly used to determine wealth. It sought to discover whether there was a consistent 1 FLA. STAT.§ 193.155 (2016) and FLA. ADMIN. CODE R. 12D 8.0062 (1995); Beginning in 2009, assessment increases for non homestead p roperty were limited to 10 percent, for purposes of non school taxation. 2 Fl Florida Department of Revenue Property Tax Oversight, Research and Analysis accessed April 15, 2017 http://floridarevenue.com/dor/property/resources/data.html

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38 relationship between property assessed valuation and median household income, despite the fluctuating a ssessment differential It considered that tax policy possibly weakened the relationship as time continued If the correlation between assessed valuation and income were not significant and consistent over time, there is a need to discuss whether the Flori da Legislature should collect more precise records at the household level to compensate for authentic financial prosperity and to accurately measure local ability within the state education finance distribution formula. Part I: Education Finance Programs C rampto n, Wood and Thompson stated chools compete at all government levels for tax revenues because there are practical limits on the amount of tax dollars that can be generated and those same dollars must be apportioned among the many worthy programs that serve the 3 Acknowledging this contingency, the next subsection outlined education funding programs within the United States. It discussed the motivation and avenues from which revenue for the federal, state, and local governments are col lected and ended with a description of the Education Funding Litigation The United States has the daunting task of creating a multidimensional education funding system that seeks to provide an equitable opport unity for all students. The Every Student Succeeds Act 4 year old Elementary and Secondary Education Act 5 3 Faith E. Crampton, R. Craig Wood, and David C. Thompson, Money and Schools 6 th ed. (New York : Routledge 2015), 85. 4 Every Student Succeeds Act Pub. L. No. 114 95 129 Stat. 1802 (2015). 5 Elementary and Secondary Education Act Pub. L. No. 89 10, 27 Stat. 79 (1965).

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39 6 Although a federal law, its policy affects both state and local government. State 7 policy makers are seeking to meet these goals through education fin ance programs that exercise the concept of equity and adequacy. Verstegen and Knoeppel summarized that states fund education finance s tructures in the form of flat grants, full state funding, foundation programs, distr ict power equalization 6 United States Department of Education accessed April 15, 2017, ht tp://www.ed.gov/essa 7 State Department of Education Websites: Alabama (https://www.alsde.edu/); Alaska (https://education.alaska.gov/); Arizona (http://www.azed.gov/); Arkansas (http://www.arkansased.gov/); California (http://ww w.cde.ca.gov/) Colorado (http://www.cde.state.co.us/); Connecticut (http://www.sde.ct.gov/sde/site/default.asp); Delaware (http://www.doe.k12.de.us/site/default.aspx?PageID=1); Florida (http://www.fldoe.org/) Georgia (http://www.gadoe.o rg/Pages/Home.aspx); Hawaii (http://www.hawaiipublicschools.org/Pages/Home.aspx); Idaho (http://sde.idaho.gov/ Illinois (http://www.isbe.net/); Indiana (http://www.doe.in.gov/); Iowa (https://www.educateiowa.gov/); Kansas (http://www.ksde.org/); Kentucky (http://education.ky.gov/Pages/default.aspx); Louisiana (http://www.louisianabelieves .com/); Maine (http://www.maine.gov/doe/); Maryland (http://www.marylandpublicschools.org/); Massachusetts (http://www.doe.mass.edu/) Michigan (https://www.michigan.gov/mde); Minnesota (http://education.state.mn.us/mde/index.html) Mississippi (http://www.mde.k12.ms.us/); Missouri (https://dese.mo.gov/); Montana (http://opi.mt.gov/) Nebraska (https://www.education.ne.gov/); Nevada (http://www.doe.nv.gov/); New Hampshire (http://education.nh.gov/); New Jersey (http://www.state.nj.us/education/); New Mexico (http://ped.state.nm.us/ped/index .html); New York (http://schools.nyc.gov/default.htm) North Carolina (http://www.dpi.state.nc.us/); North Dakota (https://www.nd.gov/dpi) Ohio (http://education.ohio.gov/); Oklahoma (http://sde.ok.gov/sde/); Oregon (http://www.ode.state.or.us/home/); Pennsylvania (http://www.education.pa.gov/Pages/default.aspx#.VvCxrmQrIfE); Rhode Island (http://www.ride.ri.gov/); South Carolina (http://ed.sc.gov/); South Dakota (http://doe.sd.gov/); Tennessee (https://www.tn.gov/education); Texas (http://tea.texas.gov/); Utah (http://www.schools.utah.gov/main /); Vermont (http://education.vermont.gov/) ; Virginia (http://www.doe.virginia.gov/); Was hington (http://www.k12.wa.us/); West Virginia (https://wvde.state.wv.us/); Wisconsin (http://dpi.wi.gov/); Wyoming (http://edu.wyoming.gov/).

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40 systems, and combination approaches. 8 state 9 Flat grants are funds that are absolute equalization grants increase state aid to local districts with the least fiscal capacity. 10 Full state tax so that it can be pooled at the state level and redistributed as aid to schools without regard to 11 Multi tiered grants are a combination of plans. In addition, state educational doctrine also seek to provide equity via vertical adjustments through statutes. Currently, t here are thirty seven state legislative policies that provide foundational funding, two that have adopted district power equalization systems, one that uses flat grants, one that uses full state funding, and nine that have adopted the multi tiered approach to funding schools. 12 Table 2 1 summarizes the types of funding formulas that each state has adopted. What makes equity throughout the states rigorous and fluid is that it seeks to coagulate education finance through taxation. History has shown how arduou s it is to balance economic 8 Journal of Education Finan ce 38, no. 2 (2012): 164. 9 State Finance Policy Annual Conference, San Antoni o, TX, March 2014), 2, accessed April 15, 2017 https://schoolfinancesdav.files.wordpress.com/2014/04/aefp 50 stateaidsystems.pdf 10 Faith E. Crampton, R. Cr aig Wood, and David C. Thompson, Money and Schools 6 th ed. (New York : Routledge 2015), 89 and 93. 11 Ibid 96. 12 presented at the Association for Education Finance Policy Annual Conference, San Antonio, TX, March 2014), 2, accessed April 15, 2017 https://schoolfinancesd av.files.wordpress.com/2014/04/aefp 50 stateaidsystems.pdf

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41 theory that supports the belief that individuals with similar income and assets should pay the same amount in taxes and the theory that supports taxes paid should progressively increase with the amount of earned income. Scholars refer to these concepts as vertical and horizontal equity. Horizontal equity suggests that 13 Vertical equity suggests that taxpayers with the greater ability to pay should pay more tax 14 Dishman and Redish declared [ San Antonio Independent School District v. ] Rodriguez 15 finance litigation cases moved to state courts, initially advancing under a state y method in 16 More recently Verstegen claimed characteristics and providing additional funding for 17 13 Yale Law and Policy Review 24, no. 1 (2006): 43, accessed April 15, 2017, http://digitalcommons.law.yale.edu/ylpr/vol24/iss1/3 14 American Institute of Certified Public Accountants, Tax Policy Concept Statement Guiding Principles of Good Tax Policy: A Framework for Evaluating Tax Proposals (Tax Division of the American Institute of Certified Public Accountants, 2017), 10, accessed April 15, 2017, http://www.aicpa.org/advocacy/tax/downloadabledocum ents/tax policy concept statement no 1 global.pdf 15 San Antonio Independent School District v. Rodriguez 411 U.S. 1 (1973). 16 Mike Dishman cy Litigation and the Quest for Equal Peabody Journal of Education 85, no. 1 (2010): 16. 17 r presented at the Association for Education Finance Policy Annual Conference, San Antonio, TX, March 2014), 1, accessed April 15, 2017 https://schoolfinance sdav.files.wordpress.com/2014/04/aefp 50 stateaidsystems.pdf

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42 Adequacy is another concept that is embraced by the stat es. Adequacy is present when 18 often set by federal and state education laws. Odden believed that adequacy grants schools that will enable them to make substantial improvements in student performance over [time] as progress toward ensuring that all, or almost all, student 19 Yinger stated that stakeholders a gree that the with a foundation level based on a generous notion of educational adequacy, a required minimum tax rate, and some kind of educa tional cost adjustment that provides extra funds for high 20 With this in mind, state legislators still struggle to arrive at a consensus on the best way to provide and measu re an equitable and adequate educ ation finance program within a state. Evidence within judicial and educational literature show that state legislation require an education finance formula that is tailored for its specific economic and demographic condition s, likely resulting in different definitions of equity and adequacy for the circumstance. Wood considered the complexity of statistically similar school districts serving statistically similar students [that] produce significantly differing results within a state that exhibits a high degree of 18 John Yinger ed. Helping Children Left Behind: State Aid and the Pursuit of Educational Equity (Cambridge : MIT Press, 2004) 9 19 Allan R. Odden, Lawrence O. State Strategy to Achieve Educational Policy 24, no. 4 (2010): 630, http:doi.org/10.1177/0895904809335107 20 John Yinger ed. Helping Childr en Left Behind: State Aid and the Pursuit of Educational Equity (Cambridge : MIT Press, 2004) 46

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43 21 Across America, school and tax systems work simultaneously to derive a solution of financial support to satisfy equity and adequacy for all. Fiscal Revenue and Capacity Federa l Revenue Controversy accompanying Serrano v. Priest 22 led to the consciousness of state and district wide facilitation of adequate education funding. On these terms, the federal, state, and local governments have intertwined roles. S chool districts receive funds from the federal government directly and through the state as an administering agency; districts receive federal funds from various departments such as the Department of Education Veterans Administration, Department o f Interi or, Department of Labor Department of Defense and Department of Agriculture. 23 Federal funding aids state programs associated with a number of legislation. Support programs are often associated with the No Child Left Behind Act 24 the Individuals with Disabilities Education Act 25 the Workforce Investment Act 26 and the Carl D. Perkins Vocational 21 The Kentucky Law Journal 98, no. 4 (2010): 776. 22 Serrano v. Priest, 487 P.2d 1241, 5 Cal. 3d 584 (1971); Serrano v. Priest, 557 P.2d 929, 18 Cal. 3d 728 (1976); Serrano v. Priest, 569 P.2d 1303, 20 Cal. 3d 25 (1977). 23 Florida Department of Education, 2015 2016 Funding for Florida School Districts: Statistical Report (Florida Depa rtment of Education, 2015), 6, accessed April 15, 2017, http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf 24 No Child Left Behind Act Pub. L. No. 107 110, 115 Stat. 1425 (2001). 25 Individuals with Disabilities Education Act Pub L. No. 101 476, 1103 Stat. 104 (1990). 26 Workforce Investment Act Pub L. No. 105 220 112 Stat. 936 (1998).

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44 and Technical Education Act 27 The American Recovery and Reinvestment Act of 2009 (ARRA) 28 opportunity to save hundreds of thousands of jobs, support states and school districts, and advance reforms and improvements that [seek to] create long lasting results for students and the nation including early learning, K 12, and post secondary education 29 Initiatives like Pell grants, work study, independent living services, teacher incentives, teacher quality, education for homeless youth, and statewide data systems are supported by the federal government. 30 State Revenue In education finance, the state serves as the liaison between the local and federal governments, as needed. Although state legislation has the option of determining the manner in which to provide education finance to school districts, they are still under the jurisdiction of federal leg islation. State legislators have the liberty of constructing funding formulas that are then implemen ted by local governments. State legislators often have the goal to provide revenue methods that are malleable, sensitive to regional conditions, and that wi den the dissemination of the tax burden to consumers. 27 Carl D. Perkins Vocational and Technical Education Act Pub. L No. 109 2 70, 120 Stat. 683 (2006). 28 American Recovery and Reinvestment Act Pub. L. No. 111 5, 115 Stat. 123 (2009). 29 United States Department of Education March 7, 2009, accessed April 15, 2017, http://www2.ed.gov/policy/gen/leg/recovery/implementation.html 30 United States Department of Education, Fiscal Year 2017 Budget Summary and Background Information (United States Department of Education, 2016), accessed April 15, 2017, https://www2.ed.gov/about/overview/budget/budget17/summary/17summary .pdf

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45 Local Revenue Local government is the frontline of education funding. For instance, the Florida Department of Revenue (FDOR) acknowledged education funding an d 30 percent of its local government revenues come from property taxes. 31 Crampto n, Wood, and Thompson state, ax systems derive a large percentage of its revenues from real property taxation, and a significant portion is used by schoo l districts 32 Customarily, property taxes are derived from assessed valuation. The Florida Administrative Code defines assessed value as: The price at which a proper ty, if offered for sale in the open market, with a reasonable time for the seller to find a purchaser, would transfer for cash or its equivalent, under prevailing market conditions between parties who have knowledge of the uses to which the property may be put, both seeking to maximize their gains and neither being in a position to take advantage of the exigencies of the other. 33 Most local school districts across America extract revenue based on millage rates imposed on local districts. A mill is defined as one one thousandth of a dollar. 34 The millage rate district/state commissioner) to meet the needs of fiscal conditions and projections. Not all states grant scho 31 Florida Department of Revenue Property Tax Oversight (Florida Department of Revenue), 1, accessed April 15, 2017, http://floridarevenue.com/dor/property/taxpa yers/pdf/ptoinfographic.pdf 32 Faith E. Crampton, R. Craig Wood, and David C. Thompson, Money and Schools 6 th ed. (New York: Routledge, 2015), 85. 33 FLA. ADMIN. CODE R. 12D 1.002[2] (1996). 34 FLA. STAT.§ 192.001 (2016).

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46 local taxes are determined by applying millage levies to 96 percent of the school taxable value of property. School board adoption of millage levies is governed by the adv ertising and public 35 Table 2 2 illustrates the types of millage rates funds can be distributed. Local Fiscal Capaci ty S tate legislators vary the measurement of local ability. Yet, there are themes present. State statutes use pupil/population, property, income, sales, motor, excise or a combination of such (most of which equalized) to determine how much school districts are able to supply for education funding. 36 Measuring local ability across the United States has quite a degree of diversification pending the circumstance, all of which contingent on the value of property. S tate statutes may require the collection of loca l revenue from particular taxes, it does not mean that local fiscal capacity is measured using those same taxes. Although several state constitutions prohibit collecting state income taxes, some states base its fiscal capacity on income taxes in addition t o property taxes. In the state of Florida, local fiscal capacity (or RLE) is based on the taxable assessed valuation of property for school purposes. The FDOE reports: The Florida Department of Revenue provides the Commissioner with its most recent determi each district and for the state. A millage rate is computed based on the positive or negative variation of each district from the state average assessment level. The millage rate result ing from application of this equalization factor is added to the 35 Florida Department of Education, 2015 2016 Funding for Florida School Districts: Statistical Report ( Florida Department of Education, 2015 ), 5, accessed April 15, 2017 http://www.fldoe.org/core/fileparse.php/7 507/urlt/Fefpdist.pdf 36 National Center for Education Statistics, Education Finance Statistics Center accessed April 15, 2017 http://nces.ed.gov/edfin/st ate_financing.asp ; Website offers descriptions of funding systems arranged by state. Taxes that are not named may also be used.

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47 state average required local effort millage. The sum of these two rates becomes 37 Florida Education Funding Program The purpose of Flor sensitive to local property tax bases, education program costs, costs of living, and costs for equivalent educational programs due to sparsity and dispersion of student population. 38 Ov er fifty years ago, the Florida Legislature enacted the FEFP and established the state policy on equalized funding to guarantee facilities to public education system the availability of an educational environment appropriate to his or that available to any similar student, notwithstanding geographic differences and varying local economic factors 39 uniqueness of the populat ion therein through adjustments. Table 2 3 illustrates the different components of the entire FEFP. The FEFP levies the RLE from taxable value for school purposes. The Ful l Time Equivalent (FTE) student is the primary method in which the FEFP determines ne ed and eventually disperses the appropriate funds. Florida funding for school districts is elaborate, unifying a combination of federal, state, and local funding. The FDOE reported that school funding consisted financial support from state sources, 45.93 percent from local sources (including the RLE portion 37 Florida Department of Education, 2015 2016 Funding for Florida School Districts: Statistical Report ( Florida Department of Edu cation, 2015), 19, accessed April 15, 2017 http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf 38 Ibid 1. 39 Ibid; FLA. STAT.§ 235.002(1)(a) (2001).

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48 40 The FTE, calculated five times per year before arriving at the ultimate allotment, is the state education finance program base that makes it primarily foundational. For the 2015 2016 school year, the state legislature earmarked $7,758,617,374 41 for the FEFP. 42 These funds, accrued from the General Revenue Fund, Educational Enhancement Tru st Fund, and the State School Trust fund, were mostly obtained from the state sales tax on goods and services. More specifically, the Florida Legislature set the amount of $7,605,422,572 43 as the adjusted local fiscal capacity for the state. The statewide d istrict millage set by the Commissioner determined each district s share of local contribution that is needed to fund K 12 a part of the larger FEFP calculation. The Florida Department of Education reports that [f] unds for state support to school districts are provided primarily by legislative appropriations and l ] ocal revenue for school support which is derived almost entirely from property taxes levied by Flo 44 However, there are portions of school district millage rates that are relatively unrestricted. 45 Millage types that are established by voter referendum have undefined application, although limited to year 40 Ibid 1. 41 Ibid; This amount consisted of $7,488,209,041 from the General Revenue Fund, $219,369,431 from the Educational Enhancement Trust Fund and $51,038,902 from the State School Trust Fund; Florida Department of Education. 42 Ibid 2. 43 Ibid 1; 2015 2016 Scho ol Year. 44 Ibid. 2. 45 FLA. STAT § 1011.73 (2011) outlines portions of the schedule of millage rates that are subject to Voter Referendum rather than the School Board or Commissioner of Education.

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49 based time frames. 46 Table 2 4 defines the different components that when joined together make up the Gross State and Local FEFP dollars. has also provi ded security for those who pay for education. The history of m illage rate limitations 47 within the state of Florida is fairly extensive. The state constitution does not allow school districts to collect an income tax or an income tax surcharge; It also proh ibits a state income tax and state property tax. 48 Local governments, however, have greater access to tax structures. School districts receive funding from several different avenues. For instance, Florida school boards are authorized to levy a sales surtax of 0.5 percent for capital outlay purposes, if 46 E.g., Debt service is established by voter referendum an d is limited to debt service. 47 county purposes, 10 mills for municipal purposes, and 10 mills for school purposes. These rates could be exceeded for not more than two years if approved by the voters, or to repay bonds phased increase in the homestead exemption for other taxes, contingent on compliance with fair market assess (TRIM) legislation was intended to provide information to taxpayers that would shift taxpayer concern over the level of taxes away from the assessment process and toward the local budgetary processes where millage rates were set. Under this legislation, proposed tax rates are compared to a tax rate which will, if applied to the same tax base, provide the same amount of property tax revenue for each taxing authority as was lev ied during the prior tax year. This is referred to as the rolled back rate. A millage rate higher than the rolled back rate must be advertised as a tax that, year maximum levies required reductions in taxes levied for most jurisdictions; going forward the maximum is based on the rolled back rate and the change in per capita Florida income. The maximum levy may be exceeded by a super majority vote or Budget Subcommittee on Finance and Ta x, Property Tax Update, Fla. S. Rep. No. 2012 207, at 2 (2011), accessed April 15, 2017, https://www.fls enate.gov/PublishedContent/Session/2012/InterimReports/2012 207ft.pdf ]. 48 FLA. STAT.§ 220.02 (2016).

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50 approved by referendum. 49 A portion of state motor vehicle license tag proc eeds is dedicated to school board debt service or capital outlay. 50 As discussed prior, the Commissioner, School Board, and Voter Refere ndum allow adjustments to millage rates. The state constitution provides for a homestead exemption of $25,000 on the assessed value of residential property for school purposes. 51 Gross revenue from the sale of lottery tickets and other earned revenues are d eposited into the Educational Enhancement Trust Fund. 52 Also, districts may derive revenue from the collection of the gross receipts tax on utilities. 53 Florida school districts are restricted to supplying no more than 90 percent of funding from local revenu e. 54 For the 2015 2016 School Year the following was reported by the Florida Department of Education pertaining to millage rate that satisfies this constraint : Based on the 2015 tax roll provided by the Florida Department of Revenue, the Commissioner certif ied the required millage of each district on July 14, 2015. The state average millage was set at 4.984 and certifications for the 67 school districts varied from 5.132 mills (Gulf) to 1.802 mills (Monroe) due to the assessment ratio adjustment and the 90 p ercent limitation. The 90 percent limitation reduced the required local effort of seven districts. The districts and their adjusted millage rates were: Collier (3.229), Franklin (3.551), Martin (4.848), Monroe (1.802), Sarasota (4.504), Sumter (3.791) and Walton (2.707). 55 49 FL A. STAT.§ 211.055(6) (2016). 50 FLA. CONST. art. XII, § 9(d). 51 FLA. CONST. art. VII, § 6. 52 FLA. STAT. § 1010.70 (2011); FLA. STAT.§ 24.121 (2016). 53 FLA. CONST. art. XII, § 9(a)(2). 54 FLA. STAT.§ 1011.66 (2016). 55 Florida Department of Education, 2015 2016 Funding for Florida School Districts: Statistical Report (Florida Department of Education, 2015), 3, accessed April 15, 2017, http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf

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51 millage rates. Its Homestead Exemption (HE) is a portion of the value of the home that is exempted from local school property taxes. It also exempts bus iness inventories from local school taxes. Other than the HE and SOH, homesteaders can receive exemptions for qualification as a widow(er), blind person, totally and permanently disabled person, senior citizen, veteran, and more. 56 Yet, overtime, some 57 have adopted the view that exemptions and assessment differentials have created a false perception of home value and distribution of tax effort. This concept directly effects education funding in a formula that uses property value as the sole basis of determin ing local ability to support education. Part II. The Property Tax Blanke nau and Skidmore agreed valuating the effects of education finance reform without jointly considering tax and expenditures limitations could lead to biased estimates of the effects 58 Thusly, this subsection began with a description of the property tax and the argument of the general consensus of its need. Appropriately, the idea of the portability of property tax limi tations and its implications were revi ewed. Afterward, the discussion shifted toward the housing market, acknowl edging the income effect and how it influences assessed value and income. Last, assessment equity was discussed. 56 FLA. STAT. § 196 (2016) 57 p Journal of Property Tax Assessment & Administration 7, no. 1 (2010) ; It is the belief that o ne group of property owners has to pay an increased tax burden if another group of property owners is allowed to pay less than they would have had to p ay if there were no cap in place. 58 Contemporary Economic Policy 22, no. 1 (2004): 128.

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52 Property Tax Climate P roperty taxes have been an attractive source t o fund education. T he property tax is accommodating of the elasticity of income when compared to o ther taxes. Kenyon conceded on capital, can be progressiv e. Furthermore, the property tax is more progressive than the sales 59 Alm also reported 60 Because education fundin g is ongoing, support th rough local residents is desirable not only because residents are able to have a direct impact on the funding its youngest citizens receive but because property is an everlasting revenue source. Reliance upon property not only aids the individual consumer but it also benefits the local government, particularly in times of economic hardship such as a recession or depression Alm stated d espite the overall decline in property values in the United States attributable to the bursting of the housing bubble before the start of the Great Recession, the experiences of local 61 He further supp orted this claim by stating ocal government reliance on the property tax rather than on more elastic revenue sources like income, sales, and 59 Daphne Kenyon, The Property Tax, School Fun ding Dilemma (Cambridge: Lincoln Institute of Land Policy, 2007), 3. 60 Cityscape: A Journal of Policy Development and Research 15, no. 1 (2013): 243. 61 Ibid., 244. See Regional Science & Urban Economics 41, no. 4 (2011)

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53 experienced by many other governments in the Great Recessi on 62 Lutz, Molloy, and Sha n believed that this stability wa s [wa s ] unlikely that property tax revenues [would] 63 Such firm characteristics of the property tax makes assessed value a likely factor in a thorough funding formula. Despite the stability of the property tax, many researchers believe that property taxes create disparities in the quality of education of particular school districts. Kenyon believed that this disparity is impro perly measured proclaiming that [ p ] roperty tax rates are not a good measure of property tax burden because high tax rates can reflect a high level of local government services or restrictive zoning practices rather than low fiscal capacity; high tax rates can also redu 64 Amendment 1 65 impacted government revenue. It added an additional $25,000 homestead exemption for non school taxes, a $25,000 tangible personal property (TPP) exemption for business owners, a 10 percent non homestead assessment increase limitation, and as it pertains to this study, homest ead portabilit y. Since then, the FDOR reports the effect of the constitutional amendment on a yearly basis following its implementation. Table 2 5 outlined the effect of Amendment 1 between the years of 2009 and 2015. This legislation effected Florida scho ol district education funding in that the por tability transfer 62 Ibid. 63 Byron Lutz, Raven Molloy Regional Science & Urban Economics 41, no. 4 (2011): 318, accessed April 15, 2017, http://dx.doi.org/10.1016/j.regsciurbeco.2011.03.009 64 Daphne Kenyon, The Property Tax, School Funding Dilemma (Cambridge: Lincoln Institute of Land Policy, 2007), 3. 65 Modified F LA C ONST art. VII, § 3, F LA C ONST art. VII, § 4, and F LA C ONST art. VII, § 6; F LA C ONST art. XII, § 27.

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54 portion lowered the assessment of property in a school district, once again possibly distorting the perception of fiscal capability and thusly capacity. Property Rate and Tax Limitations Assess ed value limitations restrict the amount that an assessed value can increase in a 66 Sirmans Tax and expenditure limitations 67 most often appeal to homeo wners who 66 Journal of Property Tax Assessment & Administration 7, no. 1 (2010) : 57 67 People's Initiative to Limit Property Taxat ion ) allowed property owners to be able to estimate future property taxes by placing a l imit on the amount of rates at one percent of full cash value at the time of acquisition and allowed asses sments to rise by no more than two percent per year until the property was resold. [ Cali. Const. art XIII § 1(a)] ; homeowners in California to analyze the differential impacts of Proposition 13 resulting from the cap on increases i n assessed values National Tax Journal 47, no. 4 (1994): 721 31.] As with many high profile pieces of legislation, there were amendments to make the law more comprehensive. Sonstelie and Richardson In approving that initiative, voters began a process that effectively shifted control over the property tax (and school revenues) from the local to the state level. Jon Sonstelie and Peter Richardson, eds., School Finance and California's Master Plan for Education (San Francisco: Public Policy Institute of California, 2001) 127] [ Right to Vote on Taxes Act, Cali. Const. art XIII § (c) (1996) and Cali. Const. art. XIII § (d) (1996) ; also known as Proposition 218 ] ; Another popular limitation was the Mass. Gen. L. c. 59, § 21 C ]; [ had ] a disproportionate impact on poorer towns [which were] faced with redu cing expenditures (e.g. teacher layoffs) or passing overrides to in Fiscal Conditio ns: The Impact of Proposition 2 in Regional Science and Urban Economi cs 41, no. 4 (2011): 383, accessed April 15, 2017, http://dx.doi.org/10.1016/j.regsciurbeco.2011.03.008 ].

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55 feel overtaxed and underserved or who feel that local governments are not efficient in providing services. 68 A chart of property tax limitations nation wide is provided in Appendix A. 69 68 G. Stacy Sirmans Journal of Housing Research 21, no. 1 (2012): 1. 69 As a whole, some state statutes impose different rates on different jurisdictions. State legislation also has the option of imposing an overall property tax rate limitation. Mikhailov and assessment increases; Otherwise, these limits can be circumvented by altering assessment practices ( or through interfund transfers for specific services [for specific property tax rate Nikolai Mikhailov and Jason Kolman, Types of Property Tax and Assessment Limitations and Tax Relief Programs ( Lincoln Institute of Land Policy 1998), 3, acces sed April 15, 2017, https://www.leg.state.nv.us/73rd/otherDocuments/PTax/lincoln institute property tax relief.pdf ]. At the turn o f the century, states exercised variations of property limitations. Appendix A summarized the types of property tax limitations imposed in different states across the United States. State Department of Revenue Websites: Alabama (http://www.ador.alabama.gov/); Alaska (http://dor.alaska.gov/); Arizona (https://www.azdor.gov/); Arkansas (http://www.dfa.arkansas.gov/Pages/default.aspx) California (http://www.taxes.ca.gov/); Colorado (https: //www.colorado.gov/revenue) Connecticut (http://www.ct.gov/drs/site/default.asp); Delaware (http://revenue.delaware.gov/) Florida (http://dor.myflorida.com/Pages/default.aspx); Georgia (https://dor.georgia.gov/); Hawaii (http://tax.hawaii.gov/); Idaho (http://tax.idaho.gov/); Illinois (http://www.revenue.state.il.us/#&panel1 1); Indiana (http://www.in.gov/dor/); Iowa (https://tax.iowa.gov/) Kansas (http://www.ksrevenue.org/); Kentucky (http://revenue.ky.gov/); Louisiana (http://www.rev.state.la.us/); Maine (http://www.maine.gov/revenue/) Maryland (http://dat.maryland.gov/Pages/default.aspx); Massachusetts (https://www.mass.gov/dor/) Mich igan (http://www.michigan.gov/treasury/0,4679,7 121 -8483 -,00.html); Minnesota (http://www.revenue.state.mn.us/Pages/default.aspx); Mississip pi (http://www.dor.ms.gov/Pages/default.aspx); Missouri (http://dor.mo.gov/) Montana (https://revenue.mt.gov/); Nebraska (http://www.revenue.nebraska.gov/); Nevada (http://tax.nv.gov/); New Hampshire (http://revenue.nh.gov/); New Jersey (http://www.state.nj.us/treasury/taxation/); New Mexico (http://www.tax.newmexico.gov/) New York (https://www.tax.ny.gov/); North Carolina (http://www.dornc.com/); North D akota (https://www.nd.gov/tax/); Ohio (http://www.tax.ohio.gov/); Oklahoma (https://www.ok.gov/tax/) Oregon (http://www.oregon.gov/dor/Pages/index.aspx); Pennsylvania (http://www.revenue.pa.gov/Pages/default.aspx#.VvLK0GQrIfE); Rhode I sland (http://www.tax.ri.gov/) South Carolina (https://dor.sc.gov/); South Dakota (http://dor.sd.gov/); Tennessee (https://www.tn.gov/revenue); Texas (http://comptroller.texas.gov/taxinfo/sales/);

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56 S ave Our Homes, enacted over twe nty years ago, sou ght to limit homestead 70 provide s several exemptions and limitations on property taxes. SOH, a property rate limitation, is specific to jurisdictions and requires a popular vote in order t o be lifted. Moore claimed [ H ] orizontal equity and vertical equity deteriorated in Florida between 1995 and 2004, and a simula tion using actual data indicated that a constitutional amendment 71 approved by voters in January 2008 resulted in even greater i 72 Housing Market Understanding the housing market is essential in preparing an education funding formula that will withstand the test of time. The past two decades of the housing market has undoubtedly been subject to the oscillation of the econom y. Scopelliti depicted the cause of the most recent economic crisis by stating Utah (http://tax.utah.gov/); Vermont (http://tax.vermont.gov/) Virginia (tax.virginia.gov); Washington (http://dor.wa.gov/); West Virginia (http://www.wvrevenue.gov/) Wisconsin (https://www.revenue.wi.gov/); Wyom ing (http://re venue.wyo.gov/) By including the property tax rate levied by other local governments (counties, school districts), Wu and Hendrick found that tax competition exists for property tax among neighboring municipalities (horizontal) as well as be tween municipalities and other local governments (vertical). Florida Local Public Finance Review 37, no. 3 (2009): 289, http://dx.doi.org/10.1177/1091142109332054 ]. 70 FLA. CONST. art. VII. 71 The amendment extended the homestead exemption to $50,000, rather than $25,000. The second $25,000 does not apply to school taxe s. 72 Exemptions for Owner Occupied Single Journal of Property Tax Assessment & Administration 5, no. 3 (2008): 55

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57 began to burst. In late 2007 the economy fell into recession. The housing market continued to 73 The housing marked effected the value of property and discretionary income. Although the income effect 74 is not regarded as a property to income specific model, it is an economic theory that supports the side effect that people who have income typically purchase within their price range and to the point where they are more likely to purchase items that are of a greater quality than an inferior quality. The income effect is, would have resulted if prices had stayed the same, but incomes had risen or fallen sufficiently to bring the consumer to the sam 75 Figure 2 1 show s a graphical representation of this theory T he price to income ratio 76 has helped measure the wellbeing of the housing market. This measure compares the price of a home stead to the median annual income of a given area. 77 73 fore, United States Department of Labor, Bureau of Labor Statistics accessed April 15, 2017, http://www.bls.gov/spotlight/2014/housing/home.htm 74 P income and how that change will impact the quantity demanded of a good or service; The relat ionship between income and the quantity demanded is a positive one, as income increases, so does the quantity of goods and Investopedia accessed April 15, 2017, http://www.investopedia.com/terms/i/incomeeffect.asp ]. 75 John Black, Nigar Hashimzade, and Gareth Myles A Dictionary of Economics ( Oxford University Press, 2012) 198. 76 The price to See Shelly Dreim an, Using the Price to Income Ratio to Determine the Presence of Housing Price Bubbles (Federal Housing Finance Agency, 2000), accessed April 15, 2017, http://www.fhfa.gov/DataTools/Downloads/Documents/HPI_Focus_Pieces/2000Q4_HPIFocus_ N508.pdf 77 Forbes (popular literature) stated that historically med ian home in the U.S. cost 2.6 times as much as the median annual income. [ High Home Price to Income Ratios Hiding Behind Low Forbes April 16, 2013, accessed April 15, 2017

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58 Researchers have discussed the connectedness of income to house prices, household vulnerability to income effects, and income inequality to house prices. Gallin contrasted the literature that supports that hou sing prices are co integrated with income. He argued that the evidence does not support a long run eq uilibrium relationship and that he level s regressions found in the literature are likely spurious and the associated error correction models may be inapp 78 S tudies acknowledge the relatedness of the housing market and income. Guo and Hardin maintained that wealth composition is a significant determinant of consump tion. Their study found ancial assets have much lower income effects, have substantially higher marginal 79 effects associated with stock holdings and 80 ncome effects for groups with the smallest amounts of relative financial wealth are dramatically higher 81 This suggests that the housing market impacts the purchasing power of those with lower financial wealth and that the disposable income for th ose with lower financial wealth is significantly different than households with http://www.forbes.com/sites/zillow/2013/04/16/high home price to income ratios hiding behind low mortgage rates / 73dc3c99378d .] 78 Run Relationship Between House Prices and Income: Evidence Real Estate Economics 34, no. 3 (2006): 417 accessed April 15, 2017 http://dx.doi.org/10.1111/j.1540 6229.2006.00172.x 79 Williams, Marginal Effects for Continuous Variables (University of Notre Dame, 2016), accessed April 15, 2017, https://www3.nd.edu/~rwilliam/stats3/Margins02.pdf 80 nd Journal of Real Estate Finance and Economics 48, no. 2 (2014): 221, accessed April 15, 2017, http:dx.doi.org/10.1007/s11146 012 9390 z 81 Ibid.

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59 [ d ] a framework for analyzing 82 Their nine year study observed that increased income inequality has a negative impact on average house prices in six U.S. metropolitan areas. 83 House prices are directly related to the assessed valuation of property so acknowledging its connection to income is appropriate, e specially in terms of a cap. Researchers agree that: Properties that increase in value due to external market forces at a rate greater than the assessed value limit or cap rate received favorable treatment from the cap, while properties that increased in v alue due to external market forces at a rate equal to or less than the assessed value limit or tax cap received unfavorable treatment. 84 Epple, Romano, and Sieg discussed how the market, income, and education effects taxpayer mobility by explaining the demand for public education and the willingness to support high quality education at the ballot box is at least partially determined by income, households with higher income tend to locate in communities with higher expenditures and 85 As it pertains to assessment differentials, the enactment of the SOH amendment has raised issues of tax burden equity across households in different income groups occupying different property types. 86 82 ncome Distribution and Housing Prices: An Assignment Journal of Economic Theory 151 (2014): 403, accessed April 15, 2017, http://dx.doi.org/10.1016/j.jet.2014.01.003 83 Ibid., 381 ; See Jesse M. Abraham Journal of Housing Research 7, no. 2 (1996): 191 208. 84 Journal of Property Tax Assessment & Administration 7, no. 1 (2010): 58 85 Journal of Public Economics 96 (2012): 255. 86 Wayne R. Archer, Brian B uckles, David A. Denslow, Jr., James F. Dewey, Dean H. Gatzlaff, Lynne Holt, Tracy L. Johns, Babak Lotfinia, David A. Macpherson, Gabriel Montes Rojas,

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60 With respect to taxation, alternative housing scenarios exist that must be considered when discussing property and income. For instance, some property owners have multiple homes. In some Florida districts (i.e. counties), properties that are used as vacation or rental homes are as sessed differently. Restrictions are based on how often the home is inhabited and how much the owner or their tenants use the property. Also, some properties are tax delinquent, vacant, or/and foreclosed. Each district is unique in the degree of property t ypes and delinquency, all of which impacting property assessed valuation and therefore levied taxes. S ome districts have a great degree of polarization within the scope of property value or income. So, although the property value for the district may be in dicative of wealth (for funding schools) in some school districts, the income of the district may contrast that value. Additionally, while a district may have significant income, it does not mean that individual households will have an income that is relat ively equivalent. These reasons serve as the basis of the factors that make up the FEFP but still fall short as the most comprehensive of district financial capacity. Recently, the Florida Legislature presented information, reported via the Office of Econo mic and Demographic Research, that recognized that the homeownership rate was below he 2015 percentage of 64.8 [was] the lowest since 1989, and [was] below the long term average for Florida. Second quarter data for 2016 [showed] a f urther decline to 6 3.8 percent. If this level [held] for the year, it [would] be the lowest level for Florida in the Stefan C Norrbin, Donald E. Schlagenhauf, Michael J. Scicchitano, G. Stacy Sirmans, Robert C. Stroh, Sr ., Anne R. Williamson, Analytical Services Relating to Property Taxation Part 1: Assessment Component (Bureau of Economic and Business Research, 2007), 17, accessed April 15, 2017 http://edr.state.fl.us/Content/special research projects/property tax study/Report Assessment.pdf

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61 thirty two 87 This type of market effected both house prices and assessment values. Assessment Equity Assessed v aluation is dependent on property appraisal. Scholars recognize how important accuracy of assessed valuation is for the Department of Revenue. Sirmans, Gatzlaf f, and MacPherson projected tween assessed value and market value and is, to some extent, a problem of basic econometrics relative 88 Payton stated property tax system, valuation becomes the root from whic h all other components of the property 89 Assessment equity is significant because property taxes 90 Zhu and Pace found [ E ] xperienced and licensed appraisers provide materially more accurate valuations. Unlicensed, inexperienced appraisers have an error rate approximately four 91 In 2008, Florida enacted legislation 92 that 87 Florida Legislature, Office of Economic and Demograp hic Research, Florida: Economic Overview (Florida Legislature, 2 016), accessed April 15, 2017, http://edr.state.fl.us/Content/presentations/economic/FlEconomicOverview_8 24 16.pdf 88 Journal of Real Estate Literature 16, no. 2 (2008): 168. 89 r Journal of Regional Analysis and Policy 36, no. 2 (2006): 182. 90 Ibid 192. 91 Shuang Zhu and Journal of Real Estate Finance and Economics 44 (2012): 153, a ccessed April 15, 2017, http:dx.doi.org/10.1007/s11146 010 9290 z 92 and procedures manual and to provide training for special magistrates; changed the make up of VABs (Value Adjustment Board) to include 2 citizen members; imposed several conditions on

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62 sought to further transparency within the appraisal process par tly because critics view property assessed valuation as subjective. Part II I: Property Value, Income, and Save Our Homes When considering that consumers absorb taxes of all kinds, the wealth of the taxpayer is a significant factor when determining the lea st intrusive but adequate amount of funding to delegate toward public education. Floridians unremittingly support the lack of a state income tax, showing their apprehension toward the measure, at least in the way that it may affect their discretionary inco me. The following subsection began with a discussion of the state of property value and income in Florida. It then discussed the effects of the (SOH) assessment d ifferential. Last, this section discussed wealth as a method of ensur ing equity. Property Value and Income in Florida Property Value In general, the assessed value of property is the difference of its just value and assessment limitations while taxable value is the difference of assessed value and tax exemptions. The total tax obligation of a taxpayer is the product of taxable value and the millage rate established by the taxing authority. Ultimately, the sum of the total tax liabilities is the amount for which any the qualifications for special magistrates and board counsel; and expressed the intent of the Legislature th [ c ] sment of value. by a preponderance of the evidence that the assessment was arrived at by complying with s. 193.011, F.S. However, a taxpayer who challenges an assessment is entitled to a determination Budget Subcommittee on Finance and Ta x, Property Tax Update, Fla. S. Rep. No. 2012 207, at 3 (2011), accessed April 15, 2017, https://www.flsenate.gov/PublishedContent/Session/2012/InterimReports/2012 207ft.pdf ].

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63 property owner is legally responsible. If the legislature of a given state believes that certain populations are in need of relief from particular taxes, property exemptions or circuit breakers are instituted. Exemptions may be specific to school taxes, non school taxes, or to both and possibly in dif ferent amounts M illage rate s and legally supported exemptions are what separate school taxes from non school taxes and the amount at which each taxpayer is responsible post property assessment. 93 The FDOR reported the 2016 statewide just, assessed, exemption, and taxabl e values by property type. Table 2 6 illustrates the data reported for real, personal, and centrally assessed property types. Income Income in the state of Florida varies from year to year but trends are present. Unlike the relatively stagnant nature of pr operty value, income is generally more yielding In 2017, the elasticity of personal income: Florida grew above the national average of 4.4%, recording growth of 5.2% and ranking 6th in capita income was below the nation as a whole and ranked Florida 28th in the Unit ed States. Newly released Florida data for the third quarter of 2016 showed a slight weakening relative to the second quarter, dropping Florida to a ranking of 22nd in the country. 94 93 Florida Department of Revenue accessed April 15, 2017, http://dor.myflorida.com/dor/property/taxpayers 94 Florida Legislature, Office of Economic and Demographic Research, Florida: Economic Overview (Florida Legislature, 2017), accessed April 15, 2017, http://edr.state.fl.us/Content/presentations/economic/FlEconomicOverview_2 9 17.pdf

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64 ally been 87.2 percent of the US average. 95 96 Figure 2 2 illustrates th e average annual wage as a percent of the United States from the year 2001 to 2015. The outcome of assessment limitations on local property tax revenues in an area is chiefly between the rate of appreciation and any binding assessment cap; the percentage of properties that are homesteaded in a community; the frequency of sales uncon 97 95 In 2013, the average was 87.6 percen t (lowest percentage since 2001). 96 Florida Legislature, Office of Economic and Demographic Research, Florida: Economic Overview ( Florida Legislature, 2016), accessed April 15, 2017 http://edr.state.fl.us/Content/presentations/economic/FlEconomicOverview_1 26 16.pdf 97 Wayne R. Archer, Brian Buckles, David A. Denslow, Jr., James F. Dewe y, Dean H. Gatzlaff, Lynne Holt, Tracy L. Johns, Babak Lotfinia, David A. Macpherson, Gabriel Montes Rojas, Stefan C. Norrbin, Donald E. Schlagenhauf, Michael J. Scicchitano, G. Stacy Sirmans, Robert C. Stroh, Sr., Anne R. Williamson, Analytical Services R elating to Property Taxation Part 1: Assessment Component (Bureau of Economic and Business Research, 2007), 18, accessed April 15, 2017, http: //edr.state.fl.us/Content/special research projects/property tax study/Report Assessment.pdf About a decade ago, the Department of Revenue analyzed the impact of SOH on public school property taxes. Their study, which compared the amount of tax roll that would be collected with SOH assessment limitation result[ed] in a change in taxable value greater than the statewide average would experience an increase in the RLE dollars levied and counties with a roll change Department of Revenue, Florida's Property Tax Structure: An Analysis of save out Homes and Truth in Millage, Pursuant to 2006 311, L.O.F. (Florida Department of Revenue, 2007): 34, accessed April 15, 2017, http://dor.myflorida.com/dor/property/trim/ptsreport/pdf/ptaxstructure.pdf ] Because of the 90/10

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65 Considering that limitations were created to have positive effects, its nature greatly influences budget making decisions which will continue to require inspection as time continues the growth of local revenues and expenditures, 98 progressivity in the sta properties are shouldering an increasing proportion of the property tax burden relative to the 99 ion, legislation later allowed a Portability Transfer 100 for those who desired to relocate within the state of Florida. Cheu ng and Cunningham stated of state and thus Rule [FLA. STAT.§ 1011.66 (2016)], school districts that would have to lower their millage rate, reduction in the millage required due to t directly addressed RLE, the measurement of district fiscal capacity for education funding. The significance of this study suggested that there was an anticipation of some effect of SOH on educati 98 Limi tations, and Education Spending, Contemporary Economic Policy 22, no. 1 (2004): 128. 99 M Journal of Real Estate Research 31, no. 1 (2009): 81. 100 FLA. ST AT.§ 193.155(8) (2016); This statute a llows homestead property owners to transfer up to $500,000 of Save Our Homes assessment differential to a new homestead if the property owner had received a homestead exemption within either of the 2 years immediately preceding the establishment of the new homestead.

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66 d support is lower when mobility in the rest of the tax jurisdiction 101 They argue that voters alter assessment rules to minimize their tax share. Some say that property tax limitations create a lock in effect. Researchers have studied tenure as a result of portability litigation. Stansel, Jackson, and Finch 102 examined housing tenure at two points in time to see whether housing tenure has changed in the state of Florida as a result of assessment limitations. Their research studied the percentage dif ference between the just value and assessed value between twenty counties who se geographical and demographic composition varied. The results of their study rejected the notion that acquisition based property tax systems increase house tenure. The researche rs note limitations 103 to their study that could have influenced their results such as that the study used only residential properties that received the Homestead Exemption, less than a and data from only two points in time. Re searchers found that the SOH differential created significant differences in the property tax burdens of individual homeowners with properties having similar market values [ and ] that these occurrences were due to differences in individual house price appreciation and length of tenure. 104 Archer et al ., concluded 101 Lock Regional Science and Urban Economics 41, no. 3 (2011): 173. 102 Dean Stansel, Ga ry Jackson, and J. Howard Finch, nure and Mobility with an Acquisition Journal of Housing Research 16, no. 2 (2007): 117 29. 103 Ibid. 104 Wayne R. Archer, Brian Buckles, David A. Denslow, Jr., James F. Dewey, Dean H. Gatzlaff, Lynne Holt, Tracy L. Jo hns, Babak Lotfinia, David A. Macpherson, Gabriel Montes Rojas, Stefan C. Norrbin, Donald E. Schlagenhauf, Michael J. Scicchitano, G. Stacy Sirmans, Robert C. Stroh, Sr., Anne R. Williamson, Analytical Services Relating to Property Taxation Part 1:

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67 had a minimal effect on a property selling at relatively low SOH saving s levels. However, the effect is non linear. As the SOH saving grows, the deterrent effect becomes progressively 105 The implementation of assessment limitations across the country has prompted the education field to study the effects of these limi tations on education finance. 106 Save Our Homes SOH legislation was created to provide tax relief for Florida citiz ens. Yet, Thomas warned gratification of appe asing the masses by way of a pr 107 The will have on the ability of counties, cities, and municipalities to provide basic infrast ructure services such as water, sewer, law enforcement, rescue services, schools, and parks and 108 Assessm ent Component (Bureau of Economic and Business Research, 2007), 10, accessed April 15, 2017, http://edr.state.fl.us/Content/special research p rojects/property tax study/Report Assessment.pdf 105 Ibid 10. 106 E.g. government on education spending. The study found that limitations cuts are n ot viable as time court and federal mandates that require additional spending on education, economic fluctuations that reduce the ability of state budgets to maintain a given share of education spending, and demands for local control to allow school d istricts to spend more or less than state Are State Journal of Public Budgeting, Accounting & Financial Management 15, no. 4 (2003): 593]. 107 Stetson Law Review 35, no. 2 (2006): 516. 108 Ib id

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68 In the state of Florida, the effects of SOH across the counties were fairly diverse. 109 As he evidence reveal [ ed] that following the housing boom, the state average 110 Researchers found impact of SOH varie [ d] by county and region depending on the real property value appreciation that occurred in 111 They concluded certainly impressive in some coastal counties, especially Brevard, Broward, Miami Dade, Martin, Pinellas and Palm Beach. At the other extreme, it [had] a very small impact in the central and n 112 Sonnier and Lassar state d dramatically declined in Florida causing a substantial loss in the economic fortunes of many individuals and businesses and resulting in significant decreases in the property tax base of 113 Sirmans and Sirmans state that, b y definition, because [an assessment differential] calls for homestead properties to be reassessed at market value after any change in ownership, 109 Appendix B for specific Save Our Homes data extracted from the Florida Depar tment of Florida Department of Revenue Property Tax Oversight, Research and Analysis accessed April 15, 2017 http://floridarevenue.com/dor/property/resources/data.html 110 Wayne R. Archer, Brian Buckles, David A. Denslow, Jr., James F. Dewey, Dean H. Gatzlaff, Tracy L. Johns, David A. Macpherson, Stefan C. Norrbin, Donald E. Sc hlagenhauf, Michael J. Scicchitano, Stacy Sirmans, Robert C. Stroh Sr., Anne R. Williamson, Analytical Services Relating to Property Taxation Part 2: Revenue Component (Bureau of Economic and Business Research, 2007), 100, accessed April 15, 2017, http://edr.state.fl.us/Content/special research projects/property tax study/Report Revenue Revised.pdf 111 Ibid 99. 112 Ibid. 113 Blaise M. S onnier Relief Measure and Inflation Protection for Non Journal of State Taxation 26, no. 6 (2008): 45

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69 differences can occur in the assessmen t equity among comparable homestead properties. 114 Sirmans, Gatzlaff, and MacPherson agree that a s a result, the ratio of assessed value to market value is not constant across different value ranges. 115 The Florida Senate conceded that SOH acquired an unint ended impression on the market: While SOH allowed long term residents with a fixed income to be able to afford to stay in their homes without being hit by large tax increases as their property value increases, it had consequences that may not have been ful ly anticipated by its proponents, and many of these consequences were aggravated by changes in the residential real estate market during the early years of the new century. 116 Amendment 1 sought to alleviate the unintended strain. Table 2 7 lists the effec t in dollars of the Portability Transfer for the past five years. Theory charges that SOH is only beneficial if the just value of a property transcends the taxable value. SOH could potentially create a greater gap between measured income and housing, makin g property value a less accurate measure of fiscal capacity for any household or district. Part IV: Similar Studies and Topics Baker claimed on state education finance programs included a loss of [ income ] in many states, thus a greater loss to state general fund revenues, a [ collapse ] of housing [ markets ] or at least leveling of growth of taxable property wealth, but also involved a substantive 117 T his subsection discussed the 114 G. Stacy Sirmans Journal of Housing Research 21, no. 1 (2012): 5. 115 Journal of Real Estate Literature 16, no. 2 (2008): 168. 116 Budget Subcommittee on Finance and Ta x, Property Tax Update, Fla. S. Rep. No. 2012 207, at 8 (2011), accessed April 15, 2017, https://www.flsenate.gov/PublishedContent/Session/2012/InterimReports/2012 207ft.pdf 117 Education Policy Analysis Archives 22, no. 91 (2014): 1, accessed April 15, 2017,

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70 appro priateness of studying the relationship of assessed valuation and income, the research that has been conducted on similar terms, and the variables i research question. For well over a century scholars have debated whether property is demonstrative of the capacity to pay. Early developers 118 of th is concept in education finance debated the most proper way to define district fiscal capacity and therefore fiscal capacity, current measure of s chool district capacity being based on property assessed valuation, scholars are forced to evaluate whether it is symbolic of a distric policy. Income, being a common measure of financial prominence, is often compared to prop erty assessed valuation, a common measure of fiscal capacity. The philosophical basis of this study rests on an approach to fiscal capability that and involved comparisons of state or local taxing jurisdictions 119 on the basis of such 120 The argument for and against income as a wealth indicator in public school http://dx.doi.org/10.14507/epaa.v22n91.2014 118 E.g., Roe L. Johns, Edgar Morphet, Cornell Francis, Herbert Meyer, Puff Clinton, George Strayer, Robert Haig Paul Mort 119 Although the United States Department of Treasury (Internal Revenue Service) measures and levies income for federal income tax purposes, the state of Florida does not levy a state income tax. 120 Fi Association, Chicago, IL, April 1974) 1

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71 finance ha s prevailed 121 Perspectives of proper financing have been reviewed, 122 rewritten, 123 and restated 124 by the leaders of education finance including discussion ab out fiscal capacity and capability 125 126 claim that it is a theoretical flaw to determine fiscal capability via inc ome for states like Flor ida, a state that do es not collect income taxes. They argue that school district fiscal capability should be measured only by constitutionally unrestricted tax structures and believe that it alternatively places an improper 121 E.g., Ellwood Cubberly, School Funds and their Apportionment (New York: Teachers College Press, Columbia University, 1906) an d The History of Education (Boston: Houghton Mifflin, 1920); George D. Strayer and Robert M. Haig, The Financing of Education in the State of New York (New York: Macmillan, 1923); Paul Mort, State Support for the Public Schools (New York: Teachers College Press, Columbia University, 1926). 122 E.g., Roe L. Johns, Edgar Morphet, and Kern Alexander, The Economics and Financing of Education 4th ed. (Englewood Cliffs: Prentice Hall, 1983); Wood, R. Craig, review of The Economics and Financing of Education 4th ed. by Roe L. Johns, Edgar L. Morphet, Kern Alexander, Journal of Education Finance 9, no. 1 (1983): 133 6, accessed April 15, 2017, http://www.jstor.org/stable/40703400 123 E.g., Percy Burrup, Financing Education in a Climate of Change (Boston: Allyn and Bacon, 1974; Vern Brimley, Deborah A. Verstegen, and Rulon R. Garfield, Financing Education in a Climate of Change 12 th ed (Boston: Pearson, 2016). 124 E.g., Kern Alexander, Richard G. Salmon, and F. King Alexander, Financing Public Schools: Theory, Policy, and Practice (New York: Routledge, 2015); Faith E. Crampton, R. Craig Wood, and David C. Thompson, Money and Schools 6 th ed. (New York: Routledge 2015); Allan Odden and Larry Picus, School Finance: A Policy Perspective, 5 th ed. (New York : McGraw Hill, 2014); Bruce D. Baker, Preston C. Green, and Craig E. Richards. Financing Education Systems (Upper Saddle River: Pearson/Merrill/Prentice Hall, 2008). 125 Journal of Education Finance 2, no. 3 (1977): 356 79 and Journal of Education Finance 3, no. 1 (1977): 98 100. 126 Kern Alexander, Richard G. Salmon, and F. King Alexander, Financing Public Schools: Theory, Policy, and Practice (New York: Routledge, 2015) 156 7 Currently, states typically ability through accessible tax sources.

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72 burden on taxpayers. 127 Advocates argue that fiscal capability does not have to be based on a of the tax base upon which they are legally accountable. 128 S cholars persist in presenting both si des of the argument, resting on equity for both student and taxpayer. Researchers continue to advance ideas to further define fiscal capacity as they pertain to state funding structures. Griffith et al ., does not take services, is more likely to result in low income, high property wealth districts being treated as if they have a greater tax capacity tha n the local community believes it ca 129 Scholars and practitioners must accept that assessed value, income and education are interconnected via funding formulas based on data from the Department of Education and the Department of Revenue, in spite of state taxation practices. The Re search Committee International Association of Assessing Officers addressed wealth, equalization, and assessment limitations as it pertains to school funding: Because of requirements to provide an adequate level of education for all citizens, regardless of whether they live in property rich or poor school districts, states typically equalize school funding, adding state funds when sufficient local funds are not available. Assessment limits artificially distort this system. If constrained value is used to eq ualize school funding, districts with large market value increases may appear poor and may receive larger shares of state funds, despite more market value wealth. If full market value is used to equalize school funding, 127 Ibid. 128 Ibid. 129 Address the Needs of High Property Wealth School Districts with Low (paper presented to the Affairs, ME, August 2013), 2, accessed April 15, 2017, http://www.maine.gov/legis/opla/MaineFi scalCapacityMeasuresPaper73013.pdf

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73 school property tax rates may be dis parate, giving the impression of unequal treatment. 130 In a variety of situations, similar studies have sought to discuss or determine the relationship between property and income, 131 tax burden and tax effort, 132 school funding and income, 133 income and school qu ality, 134 property tax and millage rate, 135 and income tax and property tax. 136 These studies can often be characterized by age, location, and the intended audience by academic discipline. 137 130 Journal of Property Tax Assessment & Administration 7, no. 1 (2010): 61 131 E.g., : The Courts and Public School Finance: Judge Made nd ed, in Handbook of the Economics of Education ed. Eric A. Hanushek and Fenis Welsh (Amsterdam: North Holland, 2006); Rhys Davies, Michael Orton, and Dereck Bosswo Environment and Planning C: Government and Policy 25, no. 5 (2007); Run Relationship between House Prices and Income: Evidence from Local Housing Market Real Estate Economics 34, no. 3 (2003). 132 E.g., Public Finance and Management 14, no. 4 (20 14). 133 E.g., Public Budgeting & Finance 29, no. 3 (2009). 134 E.g., Contemporary Issues in Education Research 7, no. 2 (2014). 135 E.g., Educational Funds April 15, 2017 136 E.g., Cas April 15, 2017 ; y Taxes: National Tax Journal 51, no. 2 (1998). 137 Studies within the last few decades attempt to address the relationship between the variables. valuation and median household income to inform education finance, t hey still provide insight into the association of the variables. For instance, Gallin sought to determine the relationship of house prices, income, and population in 95 metropolitans across the United States, six of whom

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74 were Florida cities [Joshua Gallin, Run Relationship Between House Prices and Real Estate Economics 34, no. 3 (2006): 434, accessed April 15, 2017, http://dx.doi.org/10 .1111/j.1540 6229.2006.00172.x ]. Regression measured wealth per pupil valuation of property of School Finance Reform on W ealth Neutrality Applied Economics 19, no. 12 (1987): 1690] and Goodspeed found that in New Jersey there was no relationship between property and income Rel ationship Between State Income Taxes and Local Property Taxes: Education Finance in New National Tax Journal 51, no. 2 (1998)]. More current economics studies continue to support this phenomenon. Jordan, Chapman, and Wrobel defined tax effort (th e total local property tax receipts divided by capacity, where capacity is equal to assessed value multiplied by millage; revenue collected relative to property taxes levied) and tax burden (the amount the districts taxpayers paid in property taxes relativ e to wealth; revenue collected relative to wealth or assessed value) in the context of property and Districts: The Property Tax Equity Impact of Arkansas School Finance E Public Finance and Management 14, no. 4 (2014): 146]. Their study used the Mann Whitney U analysis to measure data that was distributed in quintiles. Thornton and Arbogast used regression analysis to distinguish several factors that affect th e variability in school quality. [Barry Thornton and Contemporary Issues in Education Research 7, no. 2 (2014)] Of those qualities, lie income, property, and tax, all of which yielding a low c oefficient but statistically significant value. Some studies provide insight on the relation ship between property value and taxes For instance, trends support that property values and taxes are not positively simultaneous. Through quasi experimental desig n, Gallagher, Kurban, Persky, considered the significance of small home taxation and public school funding. Although their study had a d hen benefits are reasonably controlled for, property taxes are found to be negatively, a nd quite strongly, capitalized into Regional Science & Urban Economics 43, no. 2 (2013): 426, accessed April 15, 2017 http://dx.doi.org/10.1016/j.regsciurbeco.2013.01.001 ]. Studies outside of the United States attempted to discuss property value and income that provide insight into the relationship bet ween the variables. Orton and Davies studied the relationship between household income and property value for owner occupiers and whether there was evidence of people living in high value properties that have low incomes. The researchers were unable to sec [ d ] for a direct analysis of the relationship between household instead used multivariate analysis for two of three measures of property value s that were based Warwick Institute for Employment Rese arch 75 (2004) : 4 ] They stated, 1].

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75 Within the past decade education finance studies have addressed property and income through equity ideology. Kenyon stated 138 low per pupil property values may be high income communities just as communities with high per pupil property values can be low 139 Despite the similarities of the studies previously mentioned, very little research in the past decade have specifical ly questioned the relationship between property assessed valuation and median household income through a correlational design, with a Florida education funding focus, and in response to economics based public policy that effects wealth Nevertheless, deter mining, understanding, and applying this association from education theory is essential to Another study by Orton and Davies, along side Bossworth, further emphasized the relationship of by American based scholars. [ Environment and Planning C: Government and Policy 25, no. 5 (2007) : 756 elasticity of property prices with respect to income is not constant, but follows a bell shaped distributi on which is skewed to the right. [Ibid.] They used a variety of measures for the value of property. One measure involved an average of over 50,000 households but another measure inflated prices to a desired year and included about 55 percent of the original sample population. Davies, Orton, and Bosswort h agreed that there was an insufficient amount of empirical evidence for a relationship. A decade ago, in a policy analysis, Fischel warned stakeholders about the possibility of a lack of representation of particular populations in revamped funding formulas. He claimed that property relation between income communities : The Courts and Public School Finance: Judge Made Centralization and nd ed, in Handbook of the Economics of Education ed. Eric A. Hanushek and Fenis Welsh (Amsterd am: North Holland, 2006), 1280]. 138 Daphne Kenyon, The Property Tax, School Funding Dilemma (Cambridge : Lincoln Institute of Land Policy, 2007), 15. 139 Ibid 3.

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76 practice. In Florida, property value is the present measure of wealth for education fundi ng purposes and this practice presents the assumption that property value en compasses preference and financial condition With a changing economy and as education funding formulas seek to become more equitable, this research is fund amental. Summary The Florida Senate explained, eliable 140 All the same, upon examination of various factors (i.e. assessment limitations) there is reason to believe that this may not be present to the degree expected because of assessment differentials that are tailored for particular populations within a district By recognizing this disconnectedness and striving to create a more inclusive state ed ucation finance distribution, education has the possibility of receiving more equitable and adequate funding regardless of economic factors, as promised. If there were a consistently positive correlation between the two variables over time, more sup port fo r property tax equity would be established in terms of local education funding in the state of Florida. If not, the promise of an equalized educational opportunity that guarantees to each ilable to any similar student 141 becomes unlikely. The next chapter outlined the methodology of this study. It defined the setting, participants, instrumentation, procedures, and the process used to analyze these data. It examined 140 Budget Subcommittee on Finance and Ta x, Property Tax Update, Fla. S. Rep. No. 2012 207, at 5 (2011), accessed April 15, 2017, https://www.flsenate.gov/PublishedContent/Session/2012/InterimReports/2012 207ft.pdf 141 FLA. STAT.§ 235.002(1)(a) (2001).

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77 the trend based on the implementation of particular policy to inform decisions that are likely be made in the future.

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78 Table 2 1. State Funding Formulas State Type of Funding Formula State Type of Funding Formula Alabama Foundation Montana Combination / Tiered System Alaska Foundation Nebraska Foundation Arizona Foundation Nevada Foundation Arkansas Foundation New Hampshire Foundation California Foundation New Jersey Foundation Colorado Foundation New Mexico Foundation Connecticut Foundation New York Foundation Delaware Foundation North Carolina Flat Grant Florida Foundation North Dakota Foundation Georgia Combination / Tiered System Ohio Foundation Hawaii Full State Funding Oklahoma Combination / Tiered System Idaho Foundation Oregon Foundation Illinois Combination / Tiered System Pennsylvania Foundation Indiana Foundation Rhode Island Foundation Iowa Foundation South Carolina Foundation Kansas Foundation South Dakota Foundation Kentucky Combination / Tiered System Tennessee Foundation Louisiana Combination / Tiered System Texas Combination / Tiered System Maine Foundation Utah Combination / Tiered System Maryland Combination / Tiered System Vermont District Power Equalizing Massachusetts Foundation Virginia Foundation Michigan Foundation Washington Foundation Minnesota Foundation West Virginia Foundation Mississippi Foundation Wisconsin District Power Equalizing Missouri Foundation Wyoming Foundation Source: Schools? An Update of a 50 the Association for Education Finance Policy Annual Conference, San Antonio, TX March 2014), 2, accessed April 15, 2017 https://schoolfinancesdav.files.wordpress.com/2014/04/aefp 50 stateaidsystems.pdf

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79 Table 2 2. Florida School Districts Schedule of Millage Rates Type of Millage Statutory Authority Established By Uses Required Local Effort (RLE) FLA. STAT. § 1011.62(4) Commissioner Operating Prior Period Adjustment Formula FLA. STAT. § 1011.62(4)(e) Commissioner Operating Current Operating Discretionary Maximum 0.748 Mills FLA. STAT. § 1011.71(1) School Board Operating Local Capital Improvement Maximum 1.50 Mills FLA. STAT. § 1011.71(2) School Board Capital improvements Capital Improvement Discretionary Maximum 0.25 Mills FLA. STAT. § 1011.71(3) School Board Lease purchase payments or to meet other critical fixed capital outlay needs in lieu of operating discretionary millage Operating or Capital (Not to Exceed Two Years) FLA. STAT. § 1011.73(1) Voter Referendum Not specified Additional Millage (Not to Exceed Four Years) FLA. STAT. § 1011.73(2) Voter Referendum Not specified Debt Service FLA. STAT. § 200.001(3)(e); F LA C ONST art. VII, § 12 Voter Referendum Debt service Source: Information adapted from Florida Department of Education, 2015 2016 Funding for Florida School Districts: Statistical Report (Florida Department of Education, 2015), 2, accessed April 15, 2017 http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf

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80 Table 2 3. Florida Education Finance Program Formula Category Formula Weighted FTE Students (FTE Students) X (Program Cost Factors) Base Funding (Weighted FTE Students) X (Base Student Allocation [BSA]) X (District Cost Differential [DCD]) Gross State and Local FEFP Dollars (Base Funding) + (DJJ Supplement) + (Declining Enrollment) + (Sparsity Supplement) + (State Funded Discretionary Contribution) + (0.748 Dis cretionary Compression) + (Safe Schools) + (Reading Program) + (Supplemental Academic Instruction) + (ESE Guaranteed Allocation) + (Instruction Materials) + (Teachers Classroom Supply Assistance) + (Student Transportation) + (Virtual Education Contribution ) + (Digital Classrooms Allocation) Net State FEFP Allocation (Gross State FEFP) + (Adjustments) Total State F unding (Net State FEFP Allocation) + (Categorical Program Funds) Source: Information adapted from Florida Department of Education, 2015 2016 Funding for Florida School Districts: Statistical Report (Florida Department of Education, 2015), 8 9, accessed April 15, 2017 http://www.fldoe.org/core/fileparse.php/7507/urlt /Fefpdist.pdf Note : FTE (Full Time Equivalent [student]), DJJ (Department of Juvenile Justice), ESE (Exceptional Student Education)

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81 Table 2 4. Gross State and Local FEFP Components FEFP Categorization Definition Base Funding Product of the weighted FTE students multiplied by the Base Student Allocation and the District Cost Differential ; FLA. STAT § 1011.62; FTE (Full Time Equivalent), the basis of the funding formula, is the quantification of one student who is enrolled in at least one FEFP program for a given school year. Program Cost Factors defines a category for each FTE student. The sum of the weighted FTE, the Base Student Allocation and the District Cost Differential equals the Base Funding. Department of Juvenile Justice (DJJ) Supplement Th e total K 12 weighted FTE student membership in juvenile justice education program in each school district shall be multiplied by the amount of the state average class Declining Student Supplement Compares the unweighted FTE for the current year to the unweighted FTE of the prior year. Sparsity Supplement Divides the FTE of the district by the number of permanent senior high school centers. State Funded Discretionary Contribution FLA. STAT § 1002.32(9), FLA. STAT § 1011.71(1) 0.748 Mills Discretionary Compression FLA. STAT § 1011.62(5) Safe Schools Base funding appropriated to each district; Of the remaining funds, 67 percent shall be allocated based on the latest official Florida Cri me Index provided by the Florida Department of Law Enforcement and 33 percent shall be allocated based on Reading Program K 12 comprehensive, district wide system of research based reading instruction; FLA. STAT § 1008.22(3), 1011.62(9); FLA. STAT § 1008.32 Supplemental Academic Instruction Districts with one or more of the 300 lowest performing elementary schools based on the statewide, standardized English Language Arts assessment provide an additional hour of instruction beyond the normal school day for each day of the entire school year for intensive reading instruction for the students in each of these schools ESE Guaranteed Allocation ESE services for students whose level of service is less than Support Levels 4 and 5 Instructional Materials Instructional content, as well as electronic devices and technology equipment and infrastructure. Teachers Classroom Supply Assistance Allocation to each school district based o n the prorated total of each school 12 unweighted FTE student enrollment; FLA. STAT § 1012.71. Student Transportation Equitable distribution of funds for safe and efficient transportation services in school districts in support of student learning, Virtual Education Contribution FLA. STAT § 1011.62(11) Digital Classrooms Allocation to support school and district efforts and strategies to improve outcomes related to student performance by integrating technology in cl assroom teaching and learning. Federally Connected Student Supplement for school districts to support the education of students connected with federally owned military installations, National Aeronautics and Space Administration property, and Indian lands. Source: Information adapted from Florida Department of Education, 2015 2016 Funding for Florida School Districts: Statistical Report (Florida Department of Education, 2015), 17 20, accessed April 15, 2017 http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf

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82 Table 2 5. 2008 Constitutional Amendment Impact (2009 2015) 2009 2010 2011 2012 2013 2014 2015 Total 109,763,958 ,199 104,767,650 ,017 102,234,039 ,946 104,038,479 ,741 111,756,447 ,044 135,598,622 ,449 165,119,972 ,954 Add. $25K Ex. 91,832,647, 069 87,962,853, 837 84,198,498, 206 81,251,969, 966 80,691,798, 384 81,369,346, 256 82,764,644, 264 TPP $25K Ex. 8,448,822,5 19 8,098,463,3 00 7,765,836,6 67 7,705,343,1 93 7,716,750,5 91 7,772,966,3 67 7,829,244,3 43 10% Cap 7,205,266,0 94 7,671,415,6 27 9,707,982,0 64 14,615,646, 273 22,840,323, 397 45,566,414, 883 72,786,742, 187 Portabi lity 2,277,222,5 17 1,034,917,2 53 561,723,009 465,520,309 507,574,672 889,894,943 1,739,342,1 60 Source: Data from Florida Ad Valorem Valuation and Tax Data Flor ida Department of Revenue accessed April 15, 2017 http://dor.myflorida.com/dor/property/resources/data.html No te

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83 Table 2 6. 2016 Statewide Just, Assessed, Exemption, and Taxable Values by Property Type Property Type Number of Parcels / Accounts Just Value (JV) in Dollars Assessed Value (AV) in Dollars Exemptions (E) in Dollars Taxable Value (TV = AV E) in Dollars Real 10,198,467 2,265,383,628,563 1,895,186,823,046 399,235,061,006 1,495,951,762,040 Personal 1,213,937 164,180,260,401 158,386,019,619 47,623,330,592 110,762,689,027 Centrally Assessed 1,641,927,080 1,639,613,248 69,335,318 1,570,277,930 Total 11,412,404 2,431,205,816,044 2,055,212,455,913 446,927,726,916 1,608,284,728,997 Property Type % of Total Number of Parcels JV as percent of Total JV AV as percentage of JV E as percentage of AV TV as percentage of JV Real 89.4 93.2 83.7 21.1 66.0 Personal 10.6 6.8 96.5 30.1 67.5 Centrally Assessed 0.1 99.9 4.2 95.6 Total 100.0 84.5 21.7 66.2 Source: Florida Department of Revenue accessed April 15, 2017 http://dor.myflorida.com/dor/property/resources/data.html

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84 Table 2 7. Annual Homestead Portability Impact Year Homestead Portability Impact (in dollars) Annual Percent Increase 2015 1739342160 95.5 (2014 2015) 2014 889894943 75.3(2013 2014) 2013 507574672 9 (2012 2013) 2012 465529309 17.1(2011 2012) 2011 561723009 45.7 (2010 2011) Source: Data Florida Department of Revenue accessed March 4, 2017, http://floridarevenue.com/d or/property/resources/data.html

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85 Figure 2 1. The Income Effect. Source : Image from Income Effect 2009. 3rd ed. Oxford University.

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86 Figure 2 2. Florida Average Annual Wages as a Percent of the United States Source: Florida Legislature, Office of Economic and Demographic Research, Florida: Economic Overview (Florida Legislature, 2017), accessed April 15, 2017 http://edr.state.fl.us/Content/presentations/economic/FlEconomicOverview_2 9 17.pdf Note : Blue Average Annual Wage; Red Average Percen tage 2001 2015

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87 CHAPTER 3 METHODS One could logically project that overtime policy that affects property valuation c ould disproportionately impact measurement of fiscal capacity P roperty tax limitations like the Save Our Homes policy by statu tory definition, become less equitable as time progresses if uncompensated for in later legislation and policy It is likely that it will become increasingly difficult for education stakeholders to measure and control which populations the limitat ion impac ts and to what degree within the education finance program formula T he duty is to accurately gauge financial prosperity to dictate fiscal accountability. Therefore, measuring the relationship of Median Household Income (MHI) and Property Ass essed Valuation (PAV), despite SOH implementation over time will constitute using the variables interchangeably. Methodological Approaches The approach of this study is to use correlation to imply lack of proportionality between property assessed valuati on and income based on the concept that assessment limitations can create economic inequity before taxable value is determined. Property Assessed Valuation (PAV) This study used property assessed valuation data secured from the Florida Department of Revenu e ( FDOR ) Property assessed val uation was chosen because the office of the Commissioner of Education uses the derivative of the exact measure to help establish the Florida Education Finan ce Program ( FEFP ) Critics view property assessed valuation as subjective, a claim that is outside the scope of this study. Nevertheless, scholars recognize how important accuracy is for the Department of Revenue. Uniform Standards of Professional Appraisa l Practice (USPAP), authorized by Congress as the source of appraisal standards and qualifications, The Appr aisal

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88 Foundation defines value as the monetary relationship between properties and those who buy, sell, or use those properties; value expresses an economic concept [that] is never a fact but always an opinion of the worth of a property at a given time in accordance with a specific definition of value. 1 competently and in a 2 Median Household Income (MHI) The Unit ed States Census Bureau (USCB) provided a complete measure of median household income for most districts for each state. 3 The USCB reported that e median household income was $47,507. 4 This source was chosen to minimize manipulation of data on behalf of the researcher, for ease of duplication for other rese archers, and because its data are reliable 5 Many other researchers have secured their quanti fication of household i ncome from indirect sources such as the Federal School Lunch Program enrollment, etc. M edian household income was also chosen as the measure of fiscal wealth is because of its degree of robustness. T he paradox of using a measure of central tendency is that although a district may have a caliber of income, it does not mean that this calculation is representative of any one household (and vice versa). However, the goal is to establish what the status of a district 1 The Appraisal Foun dation, 2016 17 Uniform Standards of Professional Appraisal Practice (Washington D.C.: Appraisal Foundation, 2015), 36 5. 2 Ibid., 1. 3 Not all median household income statistics for each county in a state is represented. 4 United States Department of Commerce accessed April 15, 2017 https://www.census.gov/quickfacts/table/PST045215/12 5 United States Department of Commerce, United States Census Bureau accessed April 15, 2017 http://www.census.gov/econ/census/help/methodology_disclosure/reliability_of_data.html

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89 is in term s of the average individual student, taxpayer, etc. With a focus on equity, the researcher chose the value of median because it is the widely accepted measure of central tendency. Chiripanhura explained the appeal of median income by stating: Median income is the income available to the household in the middle of the household. It has been shown that in most instances, median income is lower than mean income, and that rising inequality causes median income to lag behind mean income. The latter is influenced by high values at the top of the income distribution, thus giving an impression of high living standards even though this may not be the case. 6 Scholars have used median household income to evaluate economic trends to justify factors impacting school qu ality in Florida. For instance, Thornton and Arbogast 7 used median per capita income for each Florida county and Jordon, Chapman, and Wrobel 8 measured the effort to determine whether there was a decrease in burden and effort post reform. 9 as an alternative robust measure of central tendency. Geometric means are often used to observe nonlinear data and is usually exe rcised when data possesses different ranges or times, comparatively speaking. Although possible to interpret through the Statistical Package for Social Sciences (SPSS) and the National 6 The ir Implications for Material Living Standards and National Well Economic and Labour Market Review 5, no. 2 (2011): 16, accessed April 15, 2017, http:dx.doi.org/10.1057/elmr.2011.17 7 Contemporary Issues in Education Research 7, no. 2 (2014). 8 The Property Tax Equity Public Finance and Management 14, no. 4 (2014): 408. 9 Siam Journal on Control and Optimization 51, no. 5 (2013) : 3386 414, accessed April 15, 2017, http:dx.doi.org/10.1137/110847482

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90 Center for Education Statistics, the USCB has not supplied this particu lar measure of raw data. evaluate how the median has changed over time between one variable but rather to determine how the correlation of PAV to MHI has changed over time. The researcher considered that PAV represents the entire p opulation of the variable where as MHI represents a portion of the population of the variable, the drawback being that the MHI variable lacks representation of non residential property. The limitations of the design are largely due to policy created ambiguity that seeks to authorize moral value via fiscal equity. income or property value, a ll student s are due quality education. Because taxpayers are accountable to property taxes through their income and state education legislation places value on equity, the statistical design of this examination attempts to consider these complexitie s through variables that quantify magnitude and central tendency. Purpose of the Study The objective of this study was to use a bivariate correlational design to determine the extent to which PAV and MHI were correlated amongst school districts in the state of Florida as time progressed. I ts purpose was to observe the relationship between the variables as they occur in the population. 10 By design, Pearson correlational tests cannot be used to imply causation or to develop conclusions that are beyond the scope of the data. Income, a relative measurement of wealth from which taxes are paid, and property assessed valuation, a measurement used to determine school district wealth from which all taxes are derived, were analyzed amongst each other to determine if there were a significant level of in terconnectedness to justify the validity of 10 Barry Cohen, Explaining Psychological Statistics (Hoboken: John Wiley & Sons, 2008), 276.

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91 using assessed value to measure wealth for school district fiscal aid in current Florida educational policy. Research Design Research Questions 1. Is there a correlation between property assessed valuation and media n household income among school districts in the state of Florida over a 10 year span? H 0 : = 0 H A : 2. How consistent is the correlation between property assessed valuation and median household income amongst school districts in the state of Florida over a 10 year span? Research Design ociation between two variables that are not obviously related but are predicated by some theory or past research to 11 The Pearson correlation is one method of performing a basic bivariate analysis and is often used for more so Moment Correlatio n Coefficient (PPMCC), often established whether there was a relationship between the variables as well as the extent to which they were correlated. Fouladi a nd Steiger confirm based on independent units of observation, has been a tool in the analysis of observational data. 12 The unit of measurement fo r both variables of interest were comparatively appropriate because the weight of the do llar was equal between the variables within the year for each year The data were not altered to 11 Ibid 275. 12 Rachel T. Fouladi and Jame Moment Correlation Coefficient and Its Square: Communications in Statistics Simulation and Computation 37, no. 5 (2008): 928, accessed April 15, 2017, http://dx.doi.org/10.1080/03610910801943735

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92 accommodate the current weight of the dollar. Each pair of variab les were independent of other and inclusive of all available comparable data supplied by the FDOR and USCB with very little limitations. A scatterplot, often used to illustrate the characteristics and the limitations of the correlation coefficient, is a graph in which one of the var iables is plotted on the x axis and the other variable is plotted on the y axis. 13 Typically, for the sake of validity of the statistical measure used, researchers draft the variables on a scatterplot to determine if the variables independently reveal signs of normal distribution, linearity and homoscedasticity (which are the assumptions of the PPMCC). T he significance level was provided to rule out the misinterpretation of statistical results by researchers. Description of Measure The PPMCC helped obtain a n objective analysis that uncovered the magnitude and significance of the relationship between the variables, PAV and MHI. Th is value was calculated by multiplying the z scores of each variable by one another to get the product and then calculating the average or mean value, which is called a moment of those products. Conceptually, the Pearson correlation coefficient is the ratio of the joint covariability of x and y to the va riability of x and y separately. Young explained that t he formula uses the sum of products as the measure of covariability, and the square root of the product of the sum of squares for x and the sum of squares for y as the measure of separate variability. 14 The following 13 Barry Cohen, Explaining Psychological Statistics (Hoboken: John Wiley & Sons, 2008), 258 59. 14 The Relationship of Two Var University of North Carolina accessed April 15, 2017, http://forrest.psych.unc.edu/research/vista frames/help/lecturenotes/lecture11/over view.html

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93 equation is representative of the PPMCC formula where n is equal to the number of pairs of is equal to the the sum of x is equal to the sum of the y scores is equal to the sum of squared scores, and is equal to the sum of : (3 1) was used because both variables are continuous and this technique provides the smallest standard of error. One variable, MHI, is based on an estimate, constituting the use of rather than which is customarily used with sample correlation statistical tests. Also, even though all data available are being used to test the relationship between the variables, only a portion of the district MHI has been supplied by the USCB and thusly the entir cannot be argued. Neither variable was deemed independent or dependent because of the nature of the statistical test. The range of the PPMCCs was calculated, as well. The range of the data set was determined to illustrate the amount of cor relation coefficient deviation that took place in the last decade of the relationship between property assessed valuation and median household income. Range measures the amount of variation between a given set of numbers. The fluctuation amongst the coeffi cients on a year to year basis is not reflected in the range, lowering the degree of robustness. Yet, range does help create an approximate account of consistency. The range is determined by subtracting the lowest number, in this case, the correlation coef ficient, from the highest number. The greater the range, the greater variation there is between the lowest and highest number in a set. The lesser the range, the smaller the variation between the lowest and highest number in a set. In Chapter 4 a correla tion coefficient table was presented alongside other descriptive statistics.

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94 Validity and Reliability of the Measure As with any statistical measure, there were constraints. Cohen explained measurement of correlation depends on pairs of numbe rs, correlation is especially sensitive to 15 Outliers, depending on its degree, can greatly influence measures of central tendency. Another limitation in using this particular design are its theoretical parameters. only mea sures the tendency for the pairs of variables to fall on the same straight line. 16 Thus, other relationships, like a curvilinear relationship, are possible but may display a weak Pearson correlation coefficient. The scatterplot was inspected to help interpr et the 17 Also, when analyzing the results, th e researcher considered orrelation is based not on absolute num bers, but on relative numbers (i.e. z 18 Moment Correlation was appropriate for answering the research question and for the population of interest. Because the full population of that which was availab le by the USCB was used validity and reliability of the results are as absolute as possible. stated prior, it was neither the correlational test nor the stu effect. The goal was merely to make an observation of trend during a designated scope of time. 15 Barry Cohen, Explaining Psychological Statistics (Hoboken: John Wiley & Sons, 2008), 262. 16 Ibid 260. 17 Ibid 263. 18 Ibid 261.

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95 In terms of external validity, this study was exclusive situation. It did not intend to pr oject the outcome of another location or population. Description of Analysis SPSS was used to evaluate these data through simple correlati onal analysis. PAV data, via the FDOR was entered into Microsoft Excel and later imported into SPSS. MHI data, via t he USCB was entered directly from Microsoft Excel into SPSS. There were no missing data to be coded or defined. The Shapiro Wilk Test Statistic was used to check for normal distribution of variables. The SPSS Descriptives, Tests of Normality, and Normal Q Q Plot Graphs were provided for each year in Appendix C. The researcher also evaluated school district population, property assessed valuation, and median household income via ranking and considered the impact it may have had on the correlation results. S etting and Participants population of more than 20 million. In Florida there are 67 school districts formed al ong county lines. Data for forty school districts, in which both property assessed valuation and median household income, were provided and thusly use d in the study. The state extends 53,625 square miles, 19 many of them rural. The 2015 average median household income of the state was $47, 507. 20 The state of a lue totaled $2,431.21 billion, assessed v alue totaled $2,055.21 billion, and taxable v alue totaled $1,608.28 billion. 21 19 United States Department of Commerce accessed April 15, 2017 https://www.census.gov/quickfacts/table/PST045215/12 20 Ibid. 21 Florida Department of Revenue accessed April 15, 2017 http://floridarevenue.com/dor/property/resources/data.html

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96 Baker, Bradford, Calhoun, DeSoto, Dixie, Franklin, Gadsden, Gilchrist, Glades, Gulf, Hamilton, Hardee, Hendry, Hol mes, Jackson, Jefferson, Lafayette, Levy, Liberty, Madison, Okeechobee, Suwannee, Taylor, Union, Wakulla, Walton, and Washington county/school districts were not used in the study because median household income data were not provided by the United States Census B ureau for the prospective years. These school districts had the smallest population in relation to those that w ere included in the study. The populations of these counties were less than 70,000 and some counties as few as about 8,000. 22 There were s even school districts that could never be us ed in the study because of the abstract existence. Data Sources and Organization PPMCC was used to determine the direction and strength of association between each variable due to the interval scales of measurement for both variables. The types of data involved in the correlational test, PAV and MHI, were numerical (measured in dollars ). Each variable was collected for year 2006 to year 2015 for each available district. Property Assessed Valuation Assessed valuation is dependent on the just value or market v alue of a property, which is calculated by the property appraiser for each cou nty/district. M arket value conditions are based of sale (e.g., cash, cash equivalent, or other terms); and the conditions of sale (e.g., exposure in a compet 23 22 United States Department of Commerce accessed April 15, 2017 https://www.census.gov/quickfacts/table/PST045215/12 23 The Appraisal Foundation, 2016 2017 Uniform Standards of Professional Appraisal Practice (Washington D.C.: Appraisal Foundation, 2015 ), 3 4.

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97 Generally, PAV is calculated by subtracting assessment limitations from the just value of a property. In an effort to use primary data sources, the researcher used property assessed valuation data secur ed from the F DOR. These data were accumulated from Microsoft Excel Spreadsheets for the year 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, and 2015. PAV are net values for real, personal, and centrally assessed property. Median Household Income Me dian is a widely used, robust measure of central tendency that calibrates the center of a distribution of a given set of numbers. When compared to the mean, an average of a given set of numbers, it is not as in fluenced by outliers. The data were aggregated from for the year 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, and 2015. The value presented as MHI is reported as an absolute number with an accompanying margin of error supporting the estimated range of values. The USCB used the American Commun ity Survey (ACS) to produce population, demographic and housing unit estimates. They report that the ACS is based on the the population for the nation, states, co unties, cities and towns and estimates of housing units for 24 Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the u se of a margin of error. The value [reported] is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are 24 United States Department of Commerce, United States Census Bureau accessed April 15, 2017 https://factfinder.census.gov/faces/na v/jsf/pages/programs.xhtml?program=pep

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98 subject to nonsampling error. The effect of nonsampling error is n ot represented in [the] tables. 25 The USCB repo The Board of Governor s of the Federal Reserve acknowledge i nflation is an increase in the overall price level of goods and services in the economy. 26 At fi rst it appeared as if there would be a discrepancy between the FDOE Commissioner derived RLE, which is based on current FDOR reported PAV, and the researcher using the seemingly derived MHI value reported by the USCB. C omparing the monetary value of a variable over a long rang e o f time against another variable that is not dollars represent a parti Thusly, in terms of the methodology of this study, although MHI is reported in inflation adjusted dollars, the adj ustment is only relevant to the year in which it was reported making its comparison to PAV statistically sound. Data Processing and Analysis Variable data were acquired directly from the USCB and FDOR. Free from manipulation, median household income and p roperty assessed valuation were organized chronologically by year and county/school district. Data were analyzed by detecting a pattern between each county from year to year for a series of ten years. Data were interpreted by viewing the extent to which 25 United States Department of Commerce, United States Census Bureau accessed April 15, 2017 htt p://www.census.gov/programs surveys/acs/methodology.html. 26 of Inflation? Board of the Governors of the Federal Reserve System accessed April 15, 2017 https://www.federalreserve.gov/faqs/economy_14419.htm.

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99 ea ch correlation coefficient compared to relative years. Ultimately, the range of coefficients observed in the study helped discover the possible impact of the SOH assessment differential on Florida counties over time. Summary The next chapter presented the results based on the selected research design. In total, ten analyses were performed, one for each fiscal year. Correlational results were presented for each year with interpretation. Generalizations were made for the entire study and were used to draw con clusions presented in the fifth chapter.

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100 CHAPTER 4 PRESENTATION OF RESULTS Purpose of Study The research question sought to di scover if there were a correlation and how consistent the correlation of Property Assessed Valuation (PAV) and Median Househ old Income (MHI) were over a 10 year span. These variables were measured amongst each other for strength and direction of association each year from 2006 to 2015. This chapter provided the results for preliminary scatterplot results and for the Pears on Product Moment Correlation Coefficients (PPMCC) for each year. Each section includes a subsection for the statistical results and for the interpretation of those results. The end of the chapter displays graphical depictions of the Statistical Package of the Statistical Sciences ( SPSS ) output results (see Appendix C). Demographics Table 4 1 lists the school districts (i.e. counties) that were used in the study. Seven school districts in Florida were not measured because they do not have a geographical location, and accordingly an income. They included: Florida Agricultural and Mechanical University Laboratory School, Florida Atlantic University Laboratory School, Florida State University Laboratory School, University of Florida Laboratory School, Florid a School for the Deaf and Blind, Florida Virtual School, and the Okeechobee Youth Development Center. 2006 Correlation Results Results for 2006 The Pearson Correlational statistical test was conducted to evaluate the relationship between PAV and MHI for 2 006. SPSS results concluded that there was a weak, non significant, positive association between PAV (M=52553716620.00, SD=62435336680.000) and MHI (M=45691.23, SD=6696.057), r (38 ) = .168, p = .301 (see Appendix C: SPSS Output Results).

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101 Interpretation of Results 2006 The strength of the PPMCC was very weak at .168. The sign of the coefficient indicated the direction of the relationship was positive (i.e. as one PAV item increased, so did the accompanying MHI item). There was insufficient evidence to conclude that there was a not very different from 0. The researcher could not rule out the correlation was not due to chance. 2007 Correlation Results Results for 2007 The Pearson Correlational statistical test was conducted to evaluate the relationship between PAV and MHI for 2007. SPSS results concluded that there was a weak, non significant, positive association between PAV (M=57424690700.00, SD=69588231420.000 ) and MHI (M=47765.00, SD=7129.146), r (38 ) = .179, p = .270 (see Appendix C: SPSS Output Results). Interpretation of Results 2007 The strength of the PPMCC was profoundly weak at .179. The sign of the coefficient indicated the direction of the relatio nship was positive (i.e. as one PAV item increased, so did the accompanying MHI item). There was insufficient evidence to conclude that there was a not very diffe rent from 0. The researcher could not rule out the correlation was not due to chance. 2008 Correlation Results Results for 2008 The Pearson Correlational statistical test was conducted to evaluate the relationship between PAV and MHI for 2008. SPSS resul ts concluded that there was a weak, non significant,

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102 positive association between PAV (M=54859468790.00, SD=68246548670.000) and MHI (M=47956.30, SD=7258.277), r (38 ) = .138, p = .397 (see Appendix C: SPSS Output Results). Interpretation of Results 2008 The strength of the PPMCC was essentially zero at .138. The sign of the coefficient indicated the direction of the relationship was positive (i.e. as one PAV item increased, so did the accompanying MHI item). There was insufficient evidence to conclude that there was a not very different from 0. The researcher could not rule out the correlation was not due to chance. 2009 Correlation Results Results for 2009 Th e Pearson Correlational statistical test was conducted to evaluate the relationship between PAV and MHI for 2009. SPSS results concluded that there was a weak, non significant, positive association between PAV (M=46624591530.00, SD=57174734080.000) and MHI (M=45024 .38, SD=6329.123), r (38 ) = .119, p = .464 (see Appendix C: SPSS Output Results). Interpretation of Results 2009 The strength of the PPMCC was essentially zero at .119. The sign of the coefficient indicated the direction of the relationship wa s positive (i.e. as one PAV item increased, so did the accompanying MHI item). There was insufficient evidence to conclude that there was a not very different fro m 0. The researcher could not rule out the correlation was not due to chance.

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103 2010 Correlation Results Results for 2010 The Pearson Correlational statistical test was conducted to evaluate the relationship between PAV and MHI for 2010. SPSS results concl uded that there was a weak, non significant, positive association between PAV (M=42855724210.00, SD=49302880320.000) and MHI (M=44846.13, SD=6742.450), r (38 ) = .073, p = .654 (see Appendix C: SPSS Output Results). Interpretation of Results 2010 The st rength of the PPMCC was virtually zero at .073. The sign of the coefficient indicated the direction of the relationship was positive (i.e. as one PAV item increased, so did the accompanying MHI item). There was insufficient evidence to conclude that there was a not very different from 0. The researcher could not rule out the correlation was not due to chance. 2011 Correlation Results Results for 2011 The Pearson Correlational statistical test was conducted to evaluate the relationship between PAV and MHI for 2011. SPSS results concluded that there was a weak, non significant, positive association between PAV (M=41460771510.00, SD=49037825820.000) and MHI (M=44421. 10, SD=6500 .444), r (38 ) = .109, p = .502 (see Appendix C: SPSS Output Results). Interpretation of Results 2011 The strength of the PPMCC was very weak at .109. The sign of the coefficient indicated the direction of the relationship was positive (i.e. as one PAV item increased, so did the accompanying MHI item). There was insufficient evidence to conclude that there was a MHI because the PPMCC was

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104 not very different from 0. The research er could not rule out the correlation was not due to chance. 2012 Correlation Results Results for 2012 The Pearson Correlational statistical test was conducted to evaluate the relationship between PAV and MHI for 2012. SPSS results concluded that there w as a weak, non significant, positive association between PAV (M=41120029180.00, SD=49477200530.000) and MHI (M=45565.90, SD=6538.957), r (38 ) = .098, p = .548 (see Appendix C: SPSS Output Results). Interpretation of Results 2012 The strength of the PPM CC was very weak at .098. The sign of the coefficient indicated the direction of the relationship was positive (i.e. as one PAV item increased, so did the accompanying MHI item). There was insufficient evidence to conclude that there was a significant lin MHI because the PPMCC was not very different from 0. The researcher could not rule out the correlation was not due to chance. 2013 Correlation Results Results for 2013 The Pearson Correlational statisti cal test was conducted to evaluate the relationship between PAV and MHI for 2013. SPSS results concluded that there was a weak, non significant, positive association between PAV (M=41880845740.00, SD=50301617890.000) and MHI (M=46767.53, SD=6544.037), r (3 8 ) = .198, p = .220 (see Appendix C: SPSS Output Results). Interpretation of Results 2013 The strength of the PPMCC was profoundly weak at .198. The sign of the coefficient indicated the direction of the relationship was positive (i.e. as one PAV item increased, so did

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105 the accompanying MHI item). There was insufficient evidence to conclude that there was a very different from 0. The researcher could not rule ou t the correlation was not due to chance. 2014 Correlation Results Results for 2014 The Pearson Correlational statistical test was conducted to evaluate the relationship between PAV and MHI for 2014. SPSS results concluded that there was a weak, non signi ficant, positive association between PAV (M=44157754890.00, SD=53843446140.000) and MHI (M=47170.63, SD=7675.414), r (38 ) = .097, p = .551 (see Appendix C: SPSS Output Results). Interpretation of Results 2014 The strength of the PPMCC was essentially z ero at .097. The sign of the coefficient indicated the direction of the relationship was positive (i.e. as one PAV item increased, so did the accompanying MHI item). There was insufficient evidence to conclude that there was a significant linear relations very different from 0. The researcher could not rule out the correlation was not due to chance. 2015 Correlation Results Results for 2015 The Pearson Correlational statistical test was cond ucted to evaluate the relationship between PAV and MHI for 2015. SPSS results concluded that there was a weak, non significant, positive association between PAV (M=46944039960.00, SD=58122218940.000) and MHI (M=49 623.53, SD=7388.328), r (38 ) = .121, p = .4 57 (see Appendix C: SPSS Output Results). Interpretation of Results 2015 The strength of the PPMCC was very weak at .121. The sign of the coefficient indicated the direction of the relationship was positive (i.e. as one PAV item increased, so did the

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106 accompanying MHI item). There was insufficient evidence to conclude that there was a not very different from 0. The researcher could not rule out the correlation w as not due to chance. Correlation Results of 2006 2015 Table 4 2 lists the Pearson Product Moment Correlation Coefficient and significance level for each year. Correlation Coefficient Results for 2006 2015 Table 4 3 lists the Pearson Product Moment Correlation Coefficient and significance level for each year when the three greatest outliers were removed. Interpretation of Results 2006 2015 The research question investigated the consistency of the correlati on coefficient in retrospect. Rather than solely focusing on the degree of strength and direction of each coefficient for each year, the researcher observed the descriptive statistics as they relate to one another. For each year, the researcher provided th e mean and standard deviation. The Shapiro Wilk statistics test revealed that the median household income was normally distributed, while the property assessed valuation violated the assumption for correlation with and without outliers, each year. The ra nge of the correlation coefficients was less than 0. 1 25 (with outliers) which signified that the results from year to year were quite consistent. Although the correlation between property assessed valuation and median household was minimal and not si gnifi cant with a p v alue range of .434 the association was fairly persistent. This led the researcher to conclude that the assessment limitation, Save Our Homes, had not had a significantly different effect over time amongst Florida di stricts, yet. Refer to Figure 4 1 for a graphical representation of the PPMCC (with outliers) Figure 4 2 of the p value (with outliers) Figure 4 3 for the graphical

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107 representation of the PPMCC f luctuation (without Outliers), and Figure 4 4 for the graphical representation of t he p value f luctuation (without Outliers), 2006 2015. Upon close inspection, the outliers of the study were large population counties whose geographical size were not large, comparatively speaking, as other districts in the state. Broward, Miami Dade, and Palm Beach were the three school districts that consistently sk ewed the se data via property as sessed valuation. These school districts have extr emely high property valu e due to population but income that wa s consistent with the rest of the state. The asse ssment ratio millage adjustment and the 90/10 limitation applied to seven counties for the 2015 2016 school year (five of them were included in this study [Collier, Martin, Monroe, Sarasota, Sumter]). There is the possibility that having data for MHI for r ural counties could have smoothed the distribution of data points. The researcher considered that the economic climate, however, increased the price of real estate in metropoles. When viewing these data, the stakeholders must consider a few important det ails that were not illustrated through the results. All data must be analyzed with caution because of the economic recession, the economic recovery period, and relevant policy changes. The year 2007, 2008, and 2009 were all subject to the effects of the Gr eat Recession 1 Housing Bust. Thirty seven geographic districts were not included because the median household income data were not available possibly compromising statistical power These counties had the smallest populations and property assessed valuati ons but median household incomes were diverse. These limitations could have strengthened or weakened the relationship of the variables, established a statistically significant result, and thus provided a more comprehensive picture. 1 United States Department of Labor, Bureau of Labor Statistics, BLS Spotlight on Statistics: Recession of 2007 2009 (United States Department of Labor 2012), accessed April 15, 2017 http://www.bls.gov/spotlight/2012/recession/pdf/recession_bls_spotlight.pdf.

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108 Summary The Florida Ed ucation Finance Program (FEFP) seeks to accommodate for the lack of funding due to property value or degree of rural student population. Despite these acknowledgements, the weak correlation between PAV and MHI for each year, and more importantly, the lack of statistical significance between years exposes a major fault in fiscal accountability that may or may not yet be absolutely attributable to SOH but certainly the framework itself in how fiscal capacity is defined The formula that the Florida Education Finance Program uses is sophisticated and encompasses many important segments that attempt to protect districts that may have a financial disadvantage. Yet this study urged a closer examination of the accountability of the taxpayer by the Florida Departme nt of Education. In that respect, more refinement is achievable and necessary to accurately ensure equity on behalf of the means required by each district. Chapter Five discussed the results of the study, provides implications for practice and presents rec ommendations to future researchers.

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109 Table 4 1. List of Counties / School Districts Used in the Study County/District County/District County/District County/District 1 Alachua 11 Duval 21 Manatee 31 Pinellas 2 3 4 5 6 7 8 9 10 Bay Brevard Broward Charlotte Citrus Clay Collier Columbia Miami Dade 12 13 14 15 16 17 18 19 20 Escambia Flagler Hernando Highlands Hillsborough Indian River Lake Lee Leon 22 23 24 25 26 27 28 29 30 Marion Martin Monroe Nassau Okaloosa Orange Osceola Palm Beach Pasco 32 33 34 35 36 37 38 39 40 Polk Putnam Saint Johns Saint Lucie Santa Rosa Sarasota Seminole Sumter Volusia

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110 Table 4 2. Table of Primary Descriptive Statistics, by Year Year P Value 2006 .168 .301 2007 .179 .270 2008 2009 2010 2011 2012 2013 2014 2015 .138 .119 .073 .109 .098 .198 .097 .121 .397 .464 .654 .502 .548 .220 .551 .457

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111 Table 4 3 Table of Descriptive Statistics without Outliers, by Year Year r ) P Value 2006 .234 .164 2007 .285 .088 2008 2009 2010 2011 2012 2013 2014 2015 .263 .204 .131 .171 .142 .134 .158 .224 .116 .225 .438 .312 .401 .430 .351 .182

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112 Figure 4 1. Graphical Representation of the PPMCC Fluctuation, 2006 2015

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113 Figure 4 2. Graphical Representation of the P Value Fluctuation, 2006 2015

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114 Figure 4 3. Graphical Representation of the PPMCC Fluctuation (without Outliers), 2006 2015

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115 Figure 4 4. Graphical Representation of the P Value Fluctuation (without Outliers), 2006 2015

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116 CHAPTER 5 DISCUSSION AND RECOMMENDATIONS Introduction In 1995, when Save Our Homes (SOH) 1 relatively stable. Yet, about a d ecade property disparities, prompting well intentioned legislation that stimulated both deliberate and unintended outcomes. The Portability Transfer (PT), enacted in 2008, helped further widen the spectru m of property value in communities across the state. These approaches to relieving citizens of property taxes, when paired with the Florida Education Funding Program (FEFP), compromised the allegiance of an equitable education funded and based on a distric capability. This study recognized a statute based theoretical caveat in Florida education funding: Income and assessed valuation, although treated synonymously, are dissimilar. The researcher suggests that policies like the Save Our Homes assess ment differential create variance in the perceived value of property. The study argues that by the time the Florida Department of Education (FDOE) imposes the district specific millage rate, the statewide assessment differential has adjusted the true value of property, the measure of wealth, from household to household and thusly district to district. Consequently, the education policy issue is that wealth is derived from an indirect, intricately flexible value. In an effort to close the g ap of funding ineq uity, the intention of the examination is to verify prosperity through some feasible measure that is the most reflective of wealth despite tax policy. 1 FLA. STAT §193.155 (2016)

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117 Summary of Findings Data showed a consistently weak positive correlation for each year of observable dat a between Property Assessed Valuation (PAV) and Medi an Household Income (MHI). T he scatterplots helped illustrate the high degree of u npredictability between PAV and MHI district to district and year to year, which yielded the appropriate Pearson Product Moment Correlation Coefficient (PPMCC). T he years with the least statewide SOH savings loosely imitated a decline in the PPMCC for statistical analyses that did not include the three major outliers. In additi on, the researcher evaluated the latest data by ranking the school districts in descending order by population, PAV, and MHI The tendencies observed yielded the results discover ed in the statistical analyse s. The researcher found that d istricts with the h ighest MHI were located in the center of the PAV continuum. Some districts that were located in the middle of the PAV distributi on were ranked high in MHI. Alternatively some school districts that were ranked high in PAV, were ranked in the middle of the MHI continuum. School d istricts that were in the top ten in po pulation may have ranked toward the bottom in MHI. T wo of the outliers were in the top ten of population, PAV, and MHI, while the other consistent outlier had high s in the state. When study districts and all Florida districts were ranked by PAV, the position of each district was within one position. For instance, if a district that were included i n the study ranked 30 amongst study districts, when that same distri ct was ranked against all Florida school districts, it was either positioned at 29, 30, or 31. Al though not as closely mirrored, when population was compared, the rankings were strikingly similar to PAV, showing that population has an s definition of fiscal capacity. MHI, however, varied greatly district to district. The 90/10 Limitation school districts had above average MHI with a low to average PAV. The outliers had average MHI very high population and exceptionally high PAV. The

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118 hi gher the PAV the more likely the district c ould serve as an outlier. These behaviors manifested the outcome of the examination. association between property value and incom e bu t in a manner that wa s time sensitive, specific to Florida, through correlational methods, and in response to current tax policy. Implications for Practice Although each school district was assessed at less than $2 billion in PAV, on average, school taxa ble value was about 85 percent of PAV, potentially resulting in billions of unrealized dollars of revenue statewide This study recognized that the intricacies of this phenomenon could be the difference of the quality of education a student receives. T hus, t he objective of this study was to determine the relationship betw een PAV and MHI because the FEFP uses taxable PAV to determine district wealth. The SOH policy (and its PT) immediately skew the measure of wealth of households within a population befo financial capacity is based on a distorted value, equity is threatened. Florida property tax exemptions are typically imposed in fixed dollars, regardless of the value of a home (especially for p roperties valued over $50,000) and often for the duration of the homestead. Changes in income limitations are minimal, uniform, and typically only applicabl e in the tax system for seniors and totally/permentently disabled persons. 2 Pronounced fluctuation w ithin a homestead's property tax exemptions from year to year, is unlikely, usually changing 2 Florida Department of Revenue acc essed March 6, 2017, http://floridarevenue.com/dor/property/resources/limitations.h tml ; FLA. STAT. § 196 (2016) and F LA C ONST art. VII, § 6

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119 taxes are more frequent, however, often due to changes at the asse ssment level or levied taxes. In lieu, assessment differentials are flexible, changing as frequently as the housing market and inflation rises or falls. Moreover, the implementation of the PT makes d eciphering the property value before exemptions, increas ingly difficult to determine. These concepts serve as the moral fiber of assessment cap policy but inadvertently interrupts education funding. T he target of this study was based on the equity of two m easures of prosperity that should be comparable to dete rmine whether the average taxpayer is properly represen ted in each school district. This statewide from ye ar to year. A strong co rrelation would have yielded a certain level of predictability with proper statistical analyses of PAV based on MHI or vice versa of any particular district, which based on the results of the study are nonexistent. PPMCC fluctuation wa s likely due to changes in the housing market, income, assessed valuation, and more, all responsive to a changing economy. Because of this cyclical nature changes in PPMCC could be also be due to differing percentages from year to year of the Consumer P rice Index (CPI) and thusly, the implement ation of SOH. Table 5 1 outlined the percentages from year to year for both definitions. Because the CPI is reflective of the economy through the view of the United States Bureau of Labor Statistics, the table help ed to illustrate the fluidity of the general market in conjunction with housing. Appendix B outlined the application of SOH in Florida (and accordingly the dramatic variation of the housing mar ket) throughout the past decade T he assessment growth rate fo r all property types varied greatly within the last decade. From year 2008 to 2009, the lowest school district (i.e. county) growth rate was 25.33 while

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120 from year 2014 to 2015 there were only t hree districts with a negative percent increase. 3 Percent inc reases amongst the districts in the state varied greatly within a year as well. For instance, between year 2013 and 2014 the growth rate ranged from 2.65 to 13.19, when just four years earlier the growth rate ranged from 12.93 to 83.66 between counties, signaling economic disarray. 4 This study did not seek to define legitimacy of the SOH policy overtime but rather to raise doubt about the strength and income. When analyzing the results, the researcher concluded that although changes in SOH implementation directly affected property assessment, what is likely is that the reason for these changes, the economy, has affected MHI as well but not at an e qually proport ional rate. S upposing state education funding equity is effected, recreating tax policy so that it is attentive to education funding is improbable. Presently, wealth is determined and taxes are levied from property tax data. In the future, policy makers ma y see fit that it is appropriate to continue levying taxes from property valuation but that there is also room to determine wealth by an additional, m ore precise measure, such as a federal income tax measure This method will satisfy the need to closely, a nd therefore accurately, identify the financial prosperity of a school district at the most basic level and the ne ed for policy that honors the absence of a statewide income tax. Recommendations for Research The researcher proposes five recommendations fo r future investigators. Foremost, this study should be replicated in its entirety as more recent data becomes available. As taxpayers 3 Florida Department of Revenue accessed April 15, 2017, http://floridarevenue.com/dor/property/resources/data.html 4 Ibid.

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121 migrate within and between districts, and as the economy and demographic makeup of Florida shifts, what may be equitable a t one point in time may not be at another. The SOH will continue to effect district PAV and the PPMCC will likely continue to vary year to year. Also, it is suggested that future researchers focus on how just value is correlated to median household income Data can be compared to the association of PAV and MHI. This research will add to what scholars know about how incomplete property assessed value (and in turn, taxable value) as a measure of wealth may be and provide more evidence of how the two are weak ly correlated. Just as this study observes relationship over time, so should the named recommendation because the relationship between the variables at any one time does not encompass the full status of threatened equity. Additionally, the research obser ved the variables against one another in terms of an estimated median household income. MHI data obtained from the United States Census Bureau (USCB) also provided margins of error for each county/district. Another PPMCC could be derived based on the same values for property assessed valuation from the FDOR and both values of the proposed margin of error for median household income. Although it is likely that the outcome will verify the results of this study, until the research is conducted, this assumption cannot be confirmed. This study is replicable in other states. Each state has its own DOR that is responsible for recording and reporting property assessed valuation data and the USCB provides MHI statistics for all states. Replicating the study across s tate lines, across years can solidify research that observes the impact of assessment differentials and its impact on education finance. If state legislators do not constitute assessment differentials, data could provide a baseline of the PAV and MHI assoc iation. A n investigator can choose to replicate the study for all states for the same

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122 year as one statistical test. This could likely boost the statistical significance and create a more robust study. However, the investigation would no longer be state spe cific. Because each state education finance formula is distinct the conclusions of that study would be limited, possibly only providing support for not using the variables interchangeably in a general sense, which would still require states to pursue more tailored data. Last, researchers have the option of measuring median property assessed valuation against median household income or total property assessed valuation against total household income or total income of each district. A reevaluation of the r esearch question would be necessary as t he results would be fairly predictable. Essentially, the investigator would not be measuring equity for the state of Florida. S tate legislators use total property assessed valuation, which is contingent on the number of parcels per district. If t otal median household income or total income were measured, that too would be based on the number of taxpayers. Both are measures of magnitude which would likely be consistent with the ranking of districts based on PAV and po pulation Of course, this information would provide insight of whether there is a simple association between assessed value and income in the chosen quantity but falls short of drawing conclusions about what that means for the average taxpayer against the currently established measure of wealth for public school funding All investigators must keep in mind the tax climate when discussing the results of their study. For instance, SOH cannot prevent an increase or decrease in taxation, rather that is an indi rect goal. So, when discussing the results of the study, researchers must consider that although SOH decreases the assessment of a homestead, it does not exclusively prevent an increase in property taxes due to the possibility of an increased millage rate via taxing

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123 authorities, which is why observing taxable value year to year is not as valid as it relates to education funding in the state of Florida. Conclusion In conclusion, if wealth were the accumulation of all assets (minus debts), property value and income must be factored into a comprehensive funding formula with conviction. Because the variables were only slightly correlated and that the measured correlations could greatly be due to chance, there should be a more certain manner in which the FEFP id entifies financial prosperity. The degree of non significance from year to year further supports the invalid nature of using the variables interchangeably without compensation for the other. Several states, including Connecticut, Maryland, Massachusetts, New Jersey, New York, Rhode Island, and Virginia use a combination of property value and inco me to determine fiscal capacit y. Methods range from ranking property and income for each district within a state, creating a ratio that weighs property and income comparing district income to the state average, on equalization. The severely debilitated nature of the correlational relationship does not cast anticipatio n of an association in the future, granted the current arrangement of tax policy and the state of the economy. The education finance system, however, is more pliable, especially if the lack of equity is argued, which instills hope for those populations tha t are lost in aggregation. A major concern of including an income factor by critics is the availability of data outlining the income of school districts, especially in Florida, a state that does not collect state income taxes. Yet, the Internal Revenue Service (IRS) reported that in 2015

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124 over 9.4 million Florida federal individual income tax forms were filed. 5 This information makes it more than reasonable and realistic to prepare district data. The IRS reports Statistics of Income that outlines tax data by zip code: The Statistics of Income (SOI) division bases its ZIP code data on administrative records of individual income tax returns (Forms 1040) from the Internal Revenue Service (IRS) Individual Master File (IMF) system. Included in these data are returns filed during the 12 month period, January 1, 2015 to December 31, 2015. While the bulk of returns filed during the 12 month period are primarily for Tax Year 2014, the IRS received a limited nu mber of returns for tax years before 2014 and these have been included within the ZIP code data. 6 The Internal Revenue Service Zip Code Data Documentation Guide, which frames 127 variable names with descriptions, is noted in Appendix D The future of incom e data is promising, helping to ease the responsibility of including such measurements in an education funding formula for a state that does not tax income Based on the results of this study, conversation must continue regarding the way in which property and income is used methodologically and how it is used in policy. The issue is that the median of a given set of numbers measures central tendency, what this study used for income, while the total (or sum) measures magnitude, what this study used for prop erty. Even though researchers have often been limited in their access, data are increasingly becoming publicly available. Policy writers risk modifying a formula that is less equitable because it does not fully encompass the funding abilities of the distri ct 7 Median is more robust and representative of the 5 United States Department of Treasury, Internal Revenue Service, The Internal Revenue Service Data Book: 2015 (United States Department of Treasury, 2015), 4, accessed April 15, 2017 https://www.irs.gov/pub/irs soi/15databk.pdf. 6 Individual Income Tax Statistics United States Department of Treasury, Internal Revenue Service access ed April 15, 2017 https://www.irs.gov/uac/soi tax stats individual income tax statistics 2014 zip code data soi. 7 Some scholars suggest that th e income factor be used as a multiplier to property; E.g. Michael 12 Education: How States Allocate Their Share of Education

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125 normal population while the sum represents the entire population but without regard of the variability within the population. There is a need for using both measures to describe the base in research and policy. Although c omparing median household income and median property assessed valuation or total income and total property assessed valuation may add merit to the elementary understanding of the relationship between the two variables it does not weigh l egislative principle Regardless, total relevant property is used to fund education, not those that are centered in the distribution of homes and the concern of this study and education scholars is ed. Education policy discourse within the states must continue to discuss multiple measures of both variables to fully comprehend the condition of a state. Still, because current Florida education finance policy does not yet recognize income in its school funding formula, that is the present argument. Policy makers have the task to ensure that their method of implementing an income factor actually changes district dispersion so that it is useful and certainly not a disadvantage. Regardless of legally acces sible tax structures, the compensation of communities with high property taxes ( and low income ) and c ommunities with low property taxes (and hi gh income ) should be an explicit focus of an education funding formula that utilizes one tax structure to measure wealth. The F lorida Legislature an educational student, notwithstanding geographic differences and varying local economic fac tors 8 constituting a reevaluation of the association between two important variables that merit our Education Commission of the States 14, no. 4 (2013): 4 5, accessed April 15, 2017 http://www.ecs.org/clearinghouse/01/08/47/10847.pdf. 8 FLA. STAT. § 235 .002 (1)(a) (2001)

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126 funding formula through the conversation of fiscal capacity. Synthesizing a formula that acknowledges the difference between two distinct measurements of financial prominence is critical. Adding an income factor to the FEFP formula is the most practical method in achieving greater equity in education funding formulas through income. Because its purpose is to quantify capacity through a reliable measure, th e federal income tax may provide a clearer picture of fiscal ability.

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127 Table 5 1. Save Our Homes Annual Increases, 2006 2016 Year Consumer Price Index Change Cap Year Consumer Price Index Change Cap 2006 3.4 3.0 2012 3.0 3.0 2007 2.5 2.5 2013 1.7 1.7 2008 2.7 2.7 2014 1.5 1.5 2009 0.1 0.1 2015 0.8 0.8 2010 2.7 2.7 2016 0.7 0.7 2011 1.5 1.5 Source: Florida Department of Revenue acc essed March 6, 2017, http://floridarevenue.com/dor/property/resources/limitations.html

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128 APPENDIX A PROPERTY TAX LIMITATIONS ACROSS THE UNITED STATES Table A 1. Property Tax Limitatio ns Across the United States State Overall Counties Municipalities School Districts Alabama* x x x x Alaska x Arizona* x Arkansas* x x California* x Colorado x x x Florida* x x x Georgia* x Idaho x x x Illinois* x x x Iowa* x x x Kansas x Kentucky x x x Louisiana x x x Massachusetts x Michigan* x x Missouri x x x Montana x x x Nevada* x x x New Mexico x x x x New York* x x x North Carolina x x North Dakota x x x Ohio x Oklahoma* x Oregon* x x Pennsylvania x x x South Dakota x x x Texas* x x x Utah x x x Washington* x x x x West Virginia x x x x Wisconsin x Wyoming x x x Source: Information from Nikolai Mikhailov and Jason Kolman, Types of Property Tax and Assessment Limitations and Tax Relief Programs ( Lincoln Institute of Land Policy 1998), 3 4, accessed April 15, 2017, https://www.leg.state.nv.us/73rd/otherDocuments/PTax/lincoln%20institute%20 %20property%20tax%20relief.pdf Note: es that the state satisfies a particular type of assessment limitation; States with an asterick (*) impose limitations on assessme nt increases. (In addition to those listed in the chart, Maryland, New Jersey, South Carolina are also included.)

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129 APPENDIX B SAVE OUR HOMES VALUE HISTORY 2005 2015 Table B 1. Save Our Homes Value History (2005 2008) School District 2005 2006 20 07 2008 Alachua 1,350,504,930 1,877,892,110 2,355,631,830 2,295,775,910 Baker 87,599,027 128,062,975 197,164,279 185,253,367 Bay 1,137,956,907 3,061,880,357 3,106,897,710 2,565,356,894 Bradford 56,187,634 132,343,752 184,133,860 167,373,395 Brevard 10,765,177,610 14,595,888,600 11,170,739,060 8,286,353,750 Broward 34,025,806,342 52,417,665,175 59,326,069,219 40,527,231,354 Calhoun 10,367,796 29,692,558 61,891,418 57,030,785 Charlotte 2,874,384,298 5,183,994,390 3,748,624,181 1,697,507,958 Citrus 1,299,786,120 2,495,309,113 2,344,557,000 1,615,763,301 Clay 1,218,956,255 2,246,196,635 2,695,873,393 2,049,796,428 Collier 8,821,177,526 15,661,507,972 14,963,377,810 10,522,473,867 Columbia 167,281,933 349,414,109 455,863,881 414,861,126 Miami Dade 38,586,357,410 57,656,530,522 74,022,145,510 65,907,689,639 DeSoto 109,253,537 330,549,045 353,098,173 298,617,373 Dixie 93,517,899 78,840,613 67,487,706 71,397,492 Duval 7,188,475,624 9,664,706,456 13,390,801,942 11,698,499,856 Escambia 1,430,437,710 3,189,831,900 2,604,582,400 2,059,776,345 Flagler 1,092,445,576 1,717,916,865 1,847,523,684 1,237,240,752 Franklin 348,112,974 509,824,705 468,724,954 372,154,994 Gadsden 99,261,113 181,764,050 258,176,099 233,860,091 Gilchrist 34,690,987 101,010,034 152,855,164 140,498,004 Glades 32,789,434 80,198,994 92,474,478 82,835,308 Gulf 320,901,115 301,790,123 256,746,819 221,989,383 Hamilton 14,976,938 39,160,351 60,005,596 58,417,029 Hardee 31,854,043 95,848,002 150,025,850 137,286,632 Hendry 153,963,850 349,344,810 393,743,360 253,079,170 Hernando 1,374,292,010 2,290,345,963 2,491,811,311 1,712,878,172 Highlands 580,985,745 1,256,217,849 1,530,718,220 1,198,806,097 Hillsborough 12,276,878,890 20,187,341,354 20,271,133,053 13,373,861,957 Holmes 19,155,601 39,766,487 48,183,257 44,797,646 Indian River 2,504,791,190 3,816,745,990 2,964,387,610 2,158,769,950 Jackson 95,977,919 99,663,878 179,298,520 164,394,945 Jefferson 37,524,715 55,477,981 112,446,040 115,094,736 Lafayette 16,796,913 39,254,903 44,711,467 48,153,746 Lake 1,136,486,359 2,947,837,079 3,353,941,454 2,789,048,643 Lee 8,566,335,740 16,482,984,230 15,768,779,230 9,174,880,770 Leon 1,753,656,572 2,666,448,320 3,100,683,337 2,758,365,522 Levy 239,338,198 493,437,579 517,942,518 458,580,742 Liberty 13,104,462 29,767,649 34,000,397 31,996,860 Madison 32,349,731 64,960,885 103,863,261 122,663,594 Manatee 4,433,835,996 6,833,181,670 7,368,335,726 4,372,214,555 Marion 1,479,859,024 3,330,706,677 5,333,715,889 4,367,852,536 Martin 4,652,475,470 6,909,655,668 6,066,454,257 3,797,110,345 Monroe 4,363,418,573 6,224,777,715 5,578,491,528 4,101,252,787 Nassau 811,552,259 1,149,989,059 1,332,092,887 1,150,374,395 Okaloosa 1,929,494,340 3,783,910,660 3,582,676,899 2,621,443,608 Okeechobee 200,098,617 328,970,547 411,059,047 282,664,756

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130 Table B 1. Continued School District 2005 2006 2007 2008 Orange Osceola 7,248,637,307 1,078,216,373 15,113,273,007 2,585,936,541 19,553,574,834 3,643,195,787 13,841,116,831 2,711,704,415 Palm Beach 29,014,276,021 47,852,430,832 41,073,586,244 28,975,426,228 Pasco 3,590,739,466 6,749,056,418 7,016,844,123 4,529,764,567 Pinellas 15,657,412,902 24,626,947,671 23,713,326,637 16,431,372,239 Polk 2,597,453,712 5,559,904,889 6,991,167,460 5,740,095,417 Putnam 353,349,308 634,064,762 781,844,927 722,964,115 Saint Johns 3,113,357,349 4,806,192,905 5,370,122,738 4,165,151,300 Saint Lucie 3,088,222,988 4,942,999,073 4,233,796,452 2,069,631,163 Santa Rosa 954,414,699 1,930,805,460 1,484,495,597 1,178,464,678 Sarasota 9,728,947,032 16,369,486,988 14,252,363,423 7,995,560,591 Seminole 4,167,971,093 8,434,527,410 9,946,459,205 7,166,833,405 Sumter 507,549,291 722,731,621 1,072,240,736 920,563,225 Suwannee 163,958,068 312,835,824 367,217,078 320,531,683 Taylor 62,730,059 83,052,522 90,901,497 92,123,462 Union 21,761,676 23,007,926 57,022,864 50,630,697 Volusia 6,261,249,349 11,080,033,140 11,465,446,498 7,757,968,999 Wakulla 218,020,127 282,195,117 249,173,475 217,373,770 Walton 741,241,947 1,098,989,709 1,102,796,069 862,202,930 Washington 20,527,263 58,004,467 64,460,645 63,935,501 Statewide 246,460,668,942 404,775,082,641 427,453,977,573 313,816,741,781

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131 Table B 2. Save Our Homes Value History (2009 2012) School District 2009 2010 2011 2012 Alachua 1,731,568,060 1,116,226,290 697,574,500 457,186,880 Baker 155,144,281 76,732,622 56,369,065 33,997,124 Bay 1,946,172,739 1,501,386,992 1,118,614,926 893,348,656 Bradford 150,413,388 109,654,096 74,558,023 43,165,571 Brevard 4,334,844,770 1,852,160,030 653,825,480 518,966,500 Broward 20,449,406,705 9,353,745,860 10,149,832,510 8,898,832,000 Calhoun 56,527,904 45,471,427 41,687,490 31,317,731 Charlotte 948,671,433 437,117,411 377,839,693 300,881,443 Citrus 981,287,954 500,759,129 315,327,773 175,048,468 Clay 1,382,644,003 729,519,531 432,844,076 290,365,037 Collier 6,130,051,844 3,454,997,992 2,618,618,427 2,720,176,985 Columbia 283,456,416 196,076,643 124,352,068 79,048,904 Miami Dade 36,876,679,881 15,861,968,602 14,229,201,683 13,507,068,668 DeSoto 208,090,779 42,811,358 26,115,981 13,657,857 Dixie 80,566,557 72,086,376 62,455,910 57,862,316 Duval 8,588,538,129 5,640,021,435 3,607,745,483 2,371,836,631 Escambia 1,620,999,001 1,151,372,873 791,333,986 553,807,099 Flagler 643,186,161 225,634,139 89,102,267 49,585,832 Franklin 253,935,879 146,698,964 111,370,025 82,204,815 Gadsden 182,488,692 144,284,986 121,770,411 71,236,162 Gilchrist 102,910,548 69,426,681 46,129,992 27,817,034 Glades 54,484,813 29,652,653 13,877,724 7,556,248 Gulf 147,695,383 95,419,490 67,077,764 45,099,747 Hamilton 49,765,546 36,333,612 10,232,208 5,496,593 Hardee 110,231,456 65,105,863 16,596,801 12,463,502 Hendry 138,418,170 57,153,020 18,438,720 11,007,720 Hernando 800,298,843 223,925,263 91,930,667 48,801,146 Highlands 787,782,221 337,613,527 199,534,692 88,582,736 Hillsborough 5,731,649,834 3,092,702,961 2,114,307,279 1,426,673,599 Holmes 45,591,917 37,468,820 33,213,472 20,480,545 Indian River 1,336,187,590 848,269,100 580,534,330 425,024,930 Jackson 142,668,952 111,214,836 86,900,670 61,493,176 Jefferson 111,865,937 95,466,819 85,096,129 64,390,814 Lafayette 41,464,492 14,671,669 10,823,096 11,908,344 Lake 1,667,261,291 864,078,189 473,350,692 264,229,729 Lee 3,493,941,770 1,681,563,652 2,051,648,363 2,524,580,364 Leon 1,791,129,666 1,466,641,405 1,084,169,939 665,880,436 Levy 304,219,368 220,588,342 103,842,571 37,207,062 Liberty 31,741,567 28,866,544 26,113,947 23,813,360 Madison 95,588,791 61,500,979 40,840,465 25,644,990 Manatee 2,298,236,465 960,145,445 663,634,770 455,355,804 Marion 2,451,668,546 1,208,354,435 620,573,828 308,866,813 Martin 2,508,262,978 1,534,191,400 1,135,456,233 849,524,736 Monroe 2,574,985,439 1,554,766,723 1,393,501,623 1,349,412,058 Nassau 936,890,827 572,668,048 453,386,353 262,688,703 Okaloosa 1,707,060,981 1,041,011,032 778,153,059 529,439,432 Okeechobee 123,786,938 40,674,555 23,021,062 11,393,645 Orange 5,872,339,457 2,353,717,180 1,553,851,044 1,156,041,250

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132 Table B 2 Continued School District 2009 2010 2011 2012 Osceola 763,770,929 213,634,161 119,994,281 110,836,236 Palm Beach 14,645,705,987 7,647,612,537 7,609,083,898 6,656,283,738 Pasco 1,682,650,841 766,174,184 618,091,607 340,888,187 Pinellas 8,853,202,550 4,325,300,985 3,028,915,738 2,130,772,036 Polk 3,033,610,661 1,038,395,011 536,759,357 277,804,985 Putnam 645,247,086 515,649,922 321,189,673 196,001,707 Saint Johns 2,440,847,274 1,437,005,748 1,065,071,839 809,385,360 Saint Lucie 632,447,977 344,739,021 279,295,932 210,761,197 Santa Rosa 524,062,950 274,644,293 192,388,372 115,512,195 Sarasota 3,936,116,297 2,213,458,402 1,556,743,344 1,457,221,417 Seminole 3,411,781,417 1,711,940,706 834,895,908 484,344,536 Sumter 766,703,976 429,979,825 374,372,273 284,179,380 Suwannee 235,282,297 126,893,674 112,181,004 116,049,055 Taylor 83,769,738 69,216,519 65,171,459 45,533,863 Union 41,826,249 32,499,942 19,745,335 14,799,069 Volusia 3,212,838,106 1,391,659,876 901,596,270 841,387,527 Wakulla 177,261,272 121,442,700 92,931,011 71,281,184 Walton 562,558,631 320,000,249 258,334,689 221,571,283 Washington 59,913,234 48,029,828 32,622,608 18,274,372 Statewide 168,172,401,834 84,390,196,582 67,496,161,868 56,273,356,522

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133 Table B 3. Save Our Homes Value History (2013 2015) School District 2013 2014 2015 Alachua 357,875,800 343,572,690 671,844,670 Baker 29,272,858 52,433,806 53,172,570 Bay 761,718,501 657,464,020 586,165,028 Bradford 30,770,372 34,694,907 38,605,926 Brevard 1,687,370,700 3,250,275,480 4,925,213,190 Broward 11,298,007,980 19,530,951,300 26,263,235,270 Calhoun 26,293,358 17,524,701 16,206,390 Charlotte 630,898,309 1,177,532,206 1,560,035,298 Citrus 137,056,896 175,334,882 326,482,730 Clay 354,664,973 597,036,867 834,843,069 Collier 3,674,400,812 5,618,096,591 8,382,048,906 Columbia 73,108,086 61,551,885 64,051,267 Miami Dade 14,730,822,254 25,646,467,119 36,718,444,904 DeSoto 11,611,034 26,410,572 35,173,249 Dixie 55,114,280 51,792,420 48,238,257 Duval 1,938,219,844 3,479,592,663 4,859,002,810 Escambia 503,816,603 929,478,571 1,147,402,104 Flagler 99,919,535 378,261,573 601,360,324 Franklin 71,443,914 71,697,448 72,110,221 Gadsden 61,641,643 57,821,352 47,224179 Gilchrist 22,452,851 19,274,625 18,711,660 Glades 6,207,568 3,828,348 3,514,563 Gulf 41,841,282 40,183,465 38,396,534 Hamilton 4,980,198 4,345,685 3,790,113 Hardee 10,554,063 10,142,235 23,134,214 Hendry 15,140,470 26,443,770 41,971,420 Hernando 61,038,862 217,902,689 404,770,206 Highlands 61,984,394 56,831,521 101,546,651 Hillsborough 3,895,597,007 6,648,819,714 8,548,286,690 Holmes 17,006,661 14,690,273 13,477,107 Indian River 523,833,310 852,917,230 1,703,834,560 Jackson 51,240,922 45,356,301 44,443,685 Jefferson 56,102,218 49,303,469 41,467,981 Lafayette 10,294,960 8,945,611 10,220,344 Lake 317,323,118 647,043,873 1,042,202,844 Lee 3,748,599,666 6,110,623,884 7,417,092,555 Leon 563,440,574 696,410,936 883,442,956 Levy 24,724,459 23,385,057 40,499,641 Liberty 22,260,627 21,174,769 20,253,952 Madison 19,800,259 17,584,974 17,429,156 Manatee 824,555,642 1,645,767,156 3,128,489,783 Marion 356,119,875 665,623,086 948,681,269 Martin 943,161,820 1,388,146,458 1,910,214,321 Monroe 1,502,014,032 1,934,328,606 2,248,013,394 Nassau 263,859,517 425,238,355 629,717,935 Okaloosa 521,917,522 677,954,269 893,723,920 Okeechobee 21,758,405 23,840,583 60,502,260 Orange 1,906,287,367 5,160,147,868 8,670,531,665

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134 Table B 3. Continued School District 2013 2014 2015 Osceola 339,131,441 932,440,659 1,321,834,059 Palm Beach 9,026,775,494 16,517,810,277 23,545,729,962 Pasco 424,150,039 1,302,933,452 1,880,196,089 Pinellas 3,506,581,458 7,194,153,640 10,492,346,086 Polk 1,093,475,268 2,132,196,031 2,285,133,907 Putnam 174,731,638 161,753,322 149,274,415 Saint Johns 962,031,509 1,385,132,861 2,245,492,980 Saint Lucie 234,534,427 631,858,229 1,228,903,587 Santa Rosa 141,831,288 353,288,462 350,900,388 Sarasota 2,733,402,805 4,458,069,236 5,878,953,265 Seminole 865,685,988 2,055,693,008 2,733,909,602 Sumter 425,704,470 986,940,440 1,172,672,330 Suwannee 102,405,638 88,631,321 86,461,220 Taylor 40,816,083 38,067,016 40,009,406 Union 12,811,633 10,858,188 9,773,715 Volusia 1,230,801,669 2,520,576,930 3,749,925,205 Wakulla 51,742,452 50,298,953 70,885,906 Walton 242,453,589 346,552,999 495,943,118 Washington 14,318,246 10,303,931 10,572,190 Statewide 73,971,510,536 130,771,804,818 184,448,139,171 Source: Data adapted from Fl Florida Department of Revenue Property Tax Oversight, Research and Analysis accessed April 15, 2017 http://floridarevenue.com/dor/property/resources/data.htm l

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135 APPENDIX C SPSS OUTPUT RESULTS Descriptive Statistics, Correlation, and Scatterplot 2006 2006 Descriptive Statistics Mean Std. Deviation N 2006 Median Household Income 45691.23 6696.057 40 2006 Property Assessed Valuation 52553716620.00 62435336680.000 40 Figure C 1. 2006 Descriptive Statistics for Median Household Income and Property Assessed Valuation. 2006 Correlations 2006 Median Household Income 2006 Property Assessed Valuation 2006 Median Household Income Pearson Correlation 1 .168 Sig. (2 tailed) .301 N 40 40 2006 Property Assessed Valuation Pearson Correlation .168 1 Sig. (2 tailed) .301 N 40 40 Figure C 2. 2006 Correlations for Media n Household Income and Property Assessed Valuation. Fi gure C 3. Scatterplot R esults for 2006.

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136 Figure C 4. Histogram R esults for 2006 Median Household Income. Figure C 5. Histogram R esults for 2006 Property Assessed Valuation.

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137 Descriptive Statistics, Correlation, and Scatterplot 2007 2007 Descriptive Statistics Mean Std. Deviation N 2007 Median Household Income 47765.00 7129.146 40 2007 Property Assessed Valuation 57424690700.00 69588231420.000 40 Figure C 6. 2007 Descriptive Statistics for Median Household Income and Property Assessed Valuation. 2007 Correlations 2007 Median Household Income 2007 Property Assessed Valuation 2007 Median Household Income Pearson Correlation 1 .179 Sig. (2 tailed) .270 N 40 40 2007 Property Assessed Valuation Pearson Correlation .179 1 Sig. (2 tailed) .270 N 40 40 Figure C 7. 2007 Correlations for Median Household Income and Property Assessed Valuation. Figure C 8. Scatterplot R esults for 2007.

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138 Figure C 9. Histogram R esults for 2007 Median Household Income. Figure C 10 Histogram Results for 2007 Property Assessed Valuation.

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139 Descriptive Statistics, Correlation, and Scatterplot 2008 2008 Descriptive Statistics Mean Std. Deviation N 2008 Median Household Income 47956.30 7258.277 40 2008 Property Assessed Valuation 54859468790.00 68246548670.000 40 Figure C 11. 2008 Descriptive Statistics for Median Household Income and Property Assessed Valuation. 2008 Correlations 2008 Median Household Income 2008 Property Assessed Valuation 2008 Median Household Income Pearson Correlation 1 .138 Sig. (2 tailed) .397 N 40 40 2008 Property Assessed Valuation Pearson Correlation .138 1 Sig. (2 tailed) .397 N 40 40 Figure C 12. 2008 Correlations for Median Household Income and Property Assessed Valuation. Figure C 13. Scatterplot R esults for 2008.

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140 Figure C 14. Histogram Results for 2008 Median Household Income. Figure C 15. Histogram R esults for 2008 Property Assessed Valuation.

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141 Descriptive Statistics, Correlation, and Scatterplot 2009 2009 Descriptive Statistics Mean Std. Deviation N 2009 Median Household Income 45024.38 6329.123 40 2009 Property Assessed Valuation 46624591530.00 57174734080.000 40 Figure C 16 2009 Descriptive Statistics for Median Household Income and Property Assessed Valuation. 2009 Correlations 2009 Median Household Income 2009 Property Assessed Valuation 2009 Median Household Income Pearson Correlation 1 .119 Sig. (2 tailed) .464 N 40 40 2009 Property Assessed Valuation Pearson Correlation .119 1 Sig. (2 tailed) .464 N 40 40 Figure C 17 2009 Correlations for Median Household Income and Property Assessed Valuation. Figure C 18 Scatterplot R esults for 2009

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142 Figure C 19 Histogram R esults for 2009 Median Household Income. Figure C 20 Histogram R esults for 2009 Property Assessed Valuation.

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143 Descriptive Statistics, Correlation, and Scatterplot 2010 2010 Descriptive Statistics Mean Std. Deviation N 2010 Median Household Income 44846.13 6742.450 40 2010 Property Assessed Valuation 42855724210.00 49302880320.000 40 Figure C 21. 2010 Descriptive Statistics for Median Household Income and Property Assessed Valuation. 2010 Correlations 2010 Median Household Income 2010 Property Assessed Valuation 2010 Median Household Income Pearson Correlation 1 .073 Sig. (2 tailed) .654 N 40 40 2010 Property Assessed Valuation Pearson Correlation .073 1 Sig. (2 tailed) .654 N 40 40 Figure C 22. 2010 Correlations for Median Household Income and Property Assessed Valuation. Figure C 23. Scatterplot R esults for 2 0 10

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144 Figure C 24. Histogram R esults for 2010 Median Household Income. Figure C 25. Histogram R esults for 2010 Property Assessed Valuation.

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145 Descriptive Statistics, Correlation, and Scatterplot 2011 2011 Descriptive Statistics Mean Std. Deviation N 2011 Median Household Income 44421.10 6500.444 40 2011 Property Assessed Valuation 41460771510.00 49037825820.000 40 Figure C 26. 2011 Descriptive Statistics for Median Household Income and Property Assessed Valuation. 2011 Correlations 2011 Median Household Income 2011 Property Assessed Valuation 2011 Median Household Income Pearson Correlation 1 .109 Sig. (2 tailed) .502 N 40 40 2011 Property Assessed Valuation Pearson Correlation .109 1 Sig. (2 tailed) .502 N 40 40 Figure C 27. 2011 Correlations for Median Household Income and Property Assessed Valuation. Figure C 28. Scatterplot R esults for 2011

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146 Figure C 29. Histogram R esults for 2011 Median Household Income. Figure C 30. Histogram R esults for 2011 Property Assessed Valuation.

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147 Descriptive Statistics, Correlation, and Scatterplot 2012 2012 Descriptive Statistics Mean Std. Deviation N 2012 Median Household Income 45565.90 6538.957 40 2012 Property Assessed Valuation 41120029180.00 49477200530.000 40 Figure C 31. 2012 Descriptive Statistics for Median Household Income and Property Assessed Valuation. 2012 Correlations 2012 Median Household Income 2012 Property Assessed Valuation 2012 Median Household Income Pearson Correlation 1 .098 Sig. (2 tailed) .548 N 40 40 2012 Property Assessed Valuation Pearson Correlation .098 1 Sig. (2 tailed) .548 N 40 40 Figure C 32. 2012 Correlations for Median Household Income and Property Assessed Valuation. Figure C 33. Scatterplot R esults for 2012

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148 Figure C 34. Histogram R esults for 2012 Median Household Income. Figure C 35. Histogram R esults for 2012 Property Assessed Valuation.

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149 Descriptive Statistics, Correlation, and Scatterplot 2013 2013 Descriptive Statistics Mean Std. Deviation N 2013 Median Household Income 46767.53 6544.037 40 2013 Property Assessed Valuation 41880845740.00 50301617890.000 40 Figure C 36. 2013 Descriptive Statistics for Median Household Income and Property Assessed Valuation. 2013 Correlations 2013 Median Household Income 2013 Property Assessed Valuation 2013 Median Household Income Pearson Correlation 1 .198 Sig. (2 tailed) .220 N 40 40 2013 Property Assessed Valuation Pearson Correlation .198 1 Sig. (2 tailed) .220 N 40 40 Figure C 37. 2013 Correlations for Median Household Income and Property Assessed Valuation. Figure C 38. Scatterplot R esults for 2013

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150 Figure C 39. Histogram R esults for 2013 Median Household Income. Figure C 40. Histogram R esults for 2013 Property Assessed Valuation.

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151 Descriptive Statistics, Correlation, and Scatterplot 2014 2014 Descriptive Statistics Mean Std. Deviation N 2014 Median Household Income 47170.63 7675.414 40 2014 Property Assessed Valuation 44157754890.00 53843446140.000 40 Figure C 41 2014 Descriptive Statistics for Median Household Income and Property Assessed Valuation. 2014 Correlations 2014 Median Household Income 2014 Property Assessed Valuation 2014 Median Household Income Pearson Correlation 1 .097 Sig. (2 tailed) .551 N 40 40 2014 Property Assessed Valuation Pearson Correlation .097 1 Sig. (2 tailed) .551 N 40 40 Figure C 42 2014 Correlations for Median Household Income and Property Assessed Valuation. Figure C 43 S catterplot results for 2014

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152 Figure C 44 Histogram R esults for 2014 Median Household Income. Figure C 45 Histogram R esults for 2014 Property Assessed Valuation.

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153 Descriptive Statistics, Correlation, and Scatterplot 2015 2015 Descriptive Statistics Mean Std. Deviation N 2015 Median Household Income 49623.53 7388.328 40 2015 Property Assessed Valuation 46944039960.00 58122218940.000 40 Figure C 46 2015 Descriptive Statistics for Median Household Income and Property Assessed Valuation. 2015 Correlations 2015 Median Household Income 2015 Property Assessed Valuation 2015 Median Household Income Pearson Correlation 1 .121 Sig. (2 tailed) .457 N 40 40 2015 Property Assessed Valuation Pearson Correlation .121 1 Sig. (2 tailed) .457 N 40 40 Figure C 47 2015 Correlations for Median Household Income and Property Assessed Valuation. Figure C 48 Scatterplot R esults for 2015

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154 Figure C 49 Histogram R esults for 2015 Median Household Income. Figure C 50 Histogram R esults for 2015 Property Assessed Valuation.

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155 Correlation and Scatterplot 2006 to 2015 without Outliers 2006 Correlations 2006 Median Household Income 2006 Property Assessed Valuation 2006 Median Household Income Pearson Correlation 1 .234 Sig. (2 tailed) .164 N 37 37 2006 Property Assessed Valuation Pearson Correlation .234 1 Sig. (2 tailed) .164 N 37 37 Figure C 51 2006 Correlations without O utliers for Median Household Income and Property Assessed Valuation. Figure C 52. S catterplot w ithout Outliers R esults for 2006

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156 2007 Correlations 2007 Median Household Income 2007 Property Assessed Valuation 2007 Median Household Income Pearson Correlation 1 .285 Sig. (2 tailed) .088 N 37 37 2007 Property Assessed Valuation Pearson Correlation .285 1 Sig. (2 tailed) .088 N 37 37 Figu re C 53. 2007 Correlations without O utliers for Median Household Income and Property Assessed Valuation. Figure C 54. S catterplot w ithout Outliers R esults for 2007

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157 2008 Correlations 2008 Median Household Income 2008 Property Assessed Valuation 2008 Median Household Income Pearson Correlation 1 .263 Sig. (2 tailed) .116 N 37 37 2008 Property Assessed Valuation Pearson Correlation .263 1 Sig. (2 tailed) .116 N 37 37 Figure C 55. 2008 Correlations without O utliers for Median Household Income and Property Assessed Valuation. Figure C 56. S catterplot w ithout Outliers R esults for 2008

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158 2009 Correlations 2009 Median Household Income 2009 Property Assessed Valuation 2009 Median Household Income Pearson Correlation 1 .204 Sig. (2 tailed) .225 N 37 37 2009 Property Assessed Valuation Pearson Correlation .204 1 Sig. (2 tailed) .225 N 37 37 Fig ure C 57. 2009 Correlations without O utliers for Median Household Income and Property Assessed Valuation. Figure C 58. S catterplot w ithout Outliers R esults for 2009

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159 2010 Correlations 2010 Median Household Income 2010 Property Assessed Valuation 2010 Median Household Income Pearson Correlation 1 .131 Sig. (2 tailed) .438 N 37 37 2010 Property Assessed Valuation Pearson Correlation .131 1 Sig. (2 tailed) .438 N 37 37 Fig ure C 59. 2010 Correlations without O utliers for Median Household Income and Property Assessed Valuation. Figure C 60. S catterplot w ithout O utliers results for 2010

PAGE 160

160 2011 Correlations 2011 Median Household Income 2011 Property Assessed Valuation 2011 Median Household Income Pearson Correlation 1 .171 Sig. (2 tailed) .312 N 37 37 2011 Property Assessed Valuation Pearson Correlation .171 1 Sig. (2 tailed) .312 N 37 37 Fig ure C 61. 2011 Correlations without O utliers for Median Household Income and Property Assessed Valuation. Figure C 62. S catterplot w ithout Outliers R esults for 2011

PAGE 161

161 2012 Correlations 2012 Median Household Income 2012 Property Assessed Valuation 2012 Median Household Income Pearson Correlation 1 .142 Sig. (2 tailed) .401 N 37 37 2012 Property Assessed Valuation Pearson Correlation .142 1 Sig. (2 tailed) .401 N 37 37 Fig ure C 63. 2012 Correlations without O utliers for Median Household Income and Property Assessed Valuation. Figure C 64. S catterplot w ithout Outliers R esults for 2012

PAGE 162

162 2013 Correlations 2013 Median Household Income 2013 Property Assessed Valuation 2013 Median Household Income Pearson Correlation 1 .134 Sig. (2 tailed) .430 N 37 37 2013 Property Assessed Valuation Pearson Correlation .134 1 Sig. (2 tailed) .430 N 37 37 Fig ure C 65. 2013 Correlations without O utliers for Median Household Income and Property Assessed Valuation. Figure C 66. S catterplot w ithout Outliers R esults for 2013

PAGE 163

163 2014 Correlations 2014 Median Household Income 2014 Property Assessed Valuation 2014 Median Household Income Pearson Correlation 1 .158 Sig. (2 tailed) .351 N 37 37 2014 Property Assessed Valuation Pearson Correlation .158 1 Sig. (2 tailed) .351 N 37 37 Fig ure C 67. 2014 Correlations without O utliers for Median Household Income and Property Assessed Valuation. Figure C 68. S catterplot w ithout Outliers R esults for 2014

PAGE 164

164 2015 Correlations 2015 Median Household Income 2015 Property Assessed Valuation 2015 Median Household Income Pearson Correlation 1 .224 Sig. (2 tailed) .182 N 37 37 2015 Property Assessed Valuation Pearson Correlation .224 1 Sig. (2 tailed) .182 N 37 37 Fig ure C 69. 2015 Correlations without O utliers for Median Household Income and Property Assessed Valuation. Figure C 70. Scatterplot without O utliers results for 2015

PAGE 165

165 Tests for Normality 2006 Test for Normal Distribution Statistic Std. Error 2006 Median Household Income Mean 45691.23 1058.740 95% Confidence Interval for Mean Lower Bound 43549.72 Upper Bound 47832.73 5% Trimmed Mean 45743.81 Median 44962.50 Variance 44837174.740 Std. Deviation 6696.057 Minimum 30771 Maximum 60450 Range 29679 Interquartile Range 9522 Skewness .110 .374 Kurtosis .152 .733 Figure C 71 20 06 Test for Normal Distribution for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2006 Median Household Income .100 40 .200 .976 40 .560 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 72 2006 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 166

166 Figure C 73 2006 Normal Q Q Plot for Median Household Income. Statistic Std. Error 2006 Property Assessed Valuation Mean 52553716620.00 9871893520.000 95% Confidence Interval for Mean Lower Bound 32585927230.00 Upper Bound 72521506000.00 5% Trimmed Mean 44306532810.00 Median 28807501900.00 Variance 38981712670000 00000000.000 Std. Deviation 62435336680.000 Minimum 3076767588 Maximum 277787622200 Range 274710854700 Interquartile Range 50433363300 Skewness 2.257 .374 Kurtosis 5.035 .733 Figure C 74 2006 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics).

PAGE 167

167 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2006 Property Assessed Valuation .274 40 .000 .698 40 .000 a. Lilliefors Significance Correction Figure C 75 2006 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 76 2006 Normal Q Q Plot for Property Assessed Valuation.

PAGE 168

168 2006 without Outliers Test for Normal Distribution Statistic Std. Error 2006 Median Household Income Mean 45519.89 1119.302 95% Confidence Interval for Mean Lower Bound 43249.84 Upper Bound 47789.94 5% Trimmed Mean 45549.90 Median 44951.00 Variance 46354982.100 Std. Deviation 6808.449 Minimum 30771 Maximum 60450 Range 29679 Interquartile Range 8630 Skewness .171 .388 Kurtosis .110 .759 Figure C 77 2006 Test f or Normal Distribution without O utliers for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2006 Median Household Income .109 37 .200 .971 37 .442 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 78 2006 Test f or Normal Distribution without O utliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 169

169 Figure C 79 2006 Normal Q Q Plot without O utliers for Median Household Income. Statistic Std. Error 2006 Property Assessed Valuation Mean 37458217810.00 5292845762.000 95% Confidence Interval for Mean Lower Bound 26723829070.00 Upper Bound 48192606550.00 5% Trimmed Mean 35213612970.00 Median 25640003330.00 Variance 10365260020000 00000000.000 Std. Deviation 32195123880.000 Minimum 3076767588 Maximum 112499924100 Range 109423156500 Interquartile Range 32944797610 Skewness 1.302 .388 Kurtosis .579 .759 Figure C 80 2006 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Descriptive Statistics).

PAGE 170

170 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2006 Property Assessed Valuation .215 37 .000 .816 37 .000 a. Lilliefors Significance Correction Figure C 81 2006 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 82 2006 Normal Q Q Plot without O utliers for Property Assessed Valuation.

PAGE 171

171 2007 Test for Normal Distribution Statistic Std. Error 2007 Median Household Income Mean 47765.00 1127.217 95% Confidence Interval for Mean Lower Bound 45484.99 Upper Bound 50045.01 5% Trimmed Mean 47822.58 Median 48656.00 Variance 50824722.820 Std. Deviation 7129.146 Minimum 32621 Maximum 62677 Range 30056 Interquartile Range 9658 Skewness .093 .374 Kurtosis .205 .733 Figure C 83 20 07 Test for Normal Distribution for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2007 Median Household Income .070 40 .200 .987 40 .911 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 84 2007 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 172

172 Figure C 85 2007 Normal Q Q Plot for Median Household Income. Statistic Std. Error 2007 Property Assessed Valuation Mean 57424690700.00 11002865480.000 95% Confidence Interval for Mean Lower Bound 35169294600.00 Upper Bound 79680086810.00 5% Trimmed Mean 47646708040.00 Median 30702688930.00 Variance 48425219520000 00000000.000 Std. Deviation 69588231420.000 Minimum 3507192106 Maximum 326881066000 Range 323373873900 Interquartile Range 57832415990 Skewness 2.414 .374 Kurtosis 6.145 .733 Figure C 86 2007 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics).

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173 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2007 Property Assessed Valuation .263 40 .000 .689 40 .000 a. Lilliefors Significance Correction Figure C 87 2007 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 88 2007 Normal Q Q Plot for Property Assessed Valuation.

PAGE 174

174 2007 without Outliers Test for Normal Distribu tion Statistic Std. Error 2007 Median Household Income Mean 47589.92 1196.744 95% Confidence Interval for Mean Lower Bound 45162.81 Upper Bound 50017.03 5% Trimmed Mean 47624.42 Median 48332.00 Variance 52991233.910 Std. Deviation 7279.508 Minimum 32621 Maximum 62677 Range 30056 Interquartile Range 8752 Skewness .036 .388 Kurtosis .229 .759 Figure C 89 2007 Test for Normal Distribution without O utliers for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2007 Median Household Income .099 37 .200 .982 37 .801 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 90 2007 Test f or Normal Distribution without O utliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 175

175 Figure C 91 2007 Normal Q Q Plot without O utliers for Median Household Income. Statistic Std. Error 2007 Property Assessed Valuation Mean 40654591400.00 5740912009.000 95% Confidence Interval for Mean Lower Bound 29011482190.00 Upper Bound 52297700600.00 5% Trimmed Mean 37971873290.00 Median 29958431060.00 Variance 12194486160000 00000000.000 Std. Deviation 34920604460.000 Minimum 3507192106 Maximum 132796482700 Range 129289290600 Interquartile Range 35675056420 Skewness 1.355 .388 Kurtosis .835 .759 Figure C 92 2007 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Descriptive Statistics).

PAGE 176

176 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2007 Property Assessed Valuation .221 37 .000 .822 37 .000 a. Lilliefors Significance Correction Figure C 93 2007 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 94 2007 Normal Q Q Plot without O utliers for Property Assessed Valuation.

PAGE 177

177 2008 Test for Normal Distribution Statistic Std. Error 2008 Median Household Income Mean 47956.30 1147.634 95% Confidence Interval for Mean Lower Bound 45634.99 Upper Bound 50277.61 5% Trimmed Mean 47830.50 Median 46509.50 Variance 52682578.680 Std. Deviation 7258.277 Minimum 31971 Maximum 67056 Range 35085 Interquartile Range 7712 Skewness .415 .374 Kurtosis .486 .733 Figure C 95 20 08 Test for Normal Distribution for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2008 Median Household Income .114 40 .200 .977 40 .593 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 96 2008 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 178

178 Figure C 97 2008 Normal Q Q Plot for Median Household Income. Statistic Std. Error 2008 Property Assessed Valuation Mean 54859468790.00 10790726810.000 95% Confidence Interval for Mean Lower Bound 33033163650.00 Upper Bound 76685773940.00 5% Trimmed Mean 44982878230.00 Median 28569541490.00 Variance 46575914050000 00000000.000 Std. Deviation 68246548670.000 Minimum 3657284884 Maximum 336523769600 Range 332866484800 Interquartile Range 48474306040 Skewness 2.612 .374 Kurtosis 7.586 .733 Figure C 98 2008 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics).

PAGE 179

179 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2008 Property Assessed Valuation .255 40 .000 .670 40 .000 a. Lilliefors Significance Correction Figure C 99 2008 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 100 2008 Normal Q Q Plot for Property Assessed Valuation.

PAGE 180

180 2008 without Outliers Test for Normal Distribution Statistic Std. Error 2008 Median Household Income Mean 47834.08 1226.277 95% Confidence Interval for Mean Lower Bound 45347.08 Upper Bound 50321.09 5% Trimmed Mean 47690.87 Median 46410.00 Variance 55638934.080 Std. Deviation 7459.151 Minimum 31971 Maximum 67056 Range 35085 Interquartile Range 7611 Skewness .461 .388 Kurtosis .426 .759 Figure C 101 2008 Test f or Normal Distribution without O utliers for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2008 Median Household Income .126 37 .147 .972 37 .465 a. Lilliefors Significance Correction Figure C 102 2008 Test f or Normal Distribution without O utliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 181

181 Figure C 103 2008 Normal Q Q Plot without O utliers for Median Household Income. Statistic Std. Error 2008 Property Assessed Valuation Mean 38440166780.00 5409947662.000 95% Confidence Interval for Mean Lower Bound 27468284380.00 Upper Bound 49412049180.00 5% Trimmed Mean 35670324280.00 Median 27043369870.00 Variance 10828987470000 00000000.000 Std. Deviation 32907426930.000 Minimum 3657284884 Maximum 132471028800 Range 128813743900 Interquartile Range 34191458500 Skewness 1.422 .388 Kurtosis 1.165 .759 Figure C 104 2008 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Descriptive Statistics).

PAGE 182

182 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2008 Property Assessed Valuation .233 37 .000 .821 37 .000 a. Lilliefors Significance Correction Figure C 105 2008 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 106 2008 Normal Q Q Plot without O utliers for Property Assessed Valuation.

PAGE 183

183 2009 Test for Normal Distribution Statistic Std. Error 2009 Median Household Income Mean 45024.38 1000.722 95% Confidence Interval for Mean Lower Bound 43000.22 Upper Bound 47048.53 5% Trimmed Mean 44966.36 Median 45040.00 Variance 40057794.910 Std. Deviation 6329.123 Minimum 30278 Maximum 60900 Range 30622 Interquartile Range 8673 Skewness .278 .374 Kurtosis .471 .733 Figure C 107 20 09 Test for Normal Distribution for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2009 Median Household Income .083 40 .200 .983 40 .796 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 108 2009 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 184

184 Figure C 109 2009 Normal Q Q Plot for Median Household Income. Statistic Std. Error 2009 Property Assessed Valuation Mean 46624591530.00 9040119216.000 95% Confidence Interval for Mean Lower Bound 28339224480.00 Upper Bound 64909958590.00 5% Trimmed Mean 38364790080.00 Median 24965680610.00 Variance 32689502170000 00000000.000 Std. Deviation 57174734080.000 Minimum 3526904486 Maximum 283668006100 Range 280141101700 Interquartile Range 39488412850 Skewness 2.632 .374 Kurtosis 7.710 .733 Figure C 110 2009 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics).

PAGE 185

185 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2009 Property Assessed Valuation .268 40 .000 .666 40 .000 a. Lilliefors Significance Correction Figure C 111 2009 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 112 2009 Normal Q Q Plot for Property Assessed Valuation.

PAGE 186

186 2009 without Outliers Test for Normal Distribution Statistic Std. Error 2009 Median Household Income Mean 44892.38 1066.159 95% Confidence Interval for Mean Lower Bound 42730.11 Upper Bound 47054.65 5% Trimmed Mean 44819.32 Median 44739.00 Variance 42057677.580 Std. Deviation 6485.189 Minimum 30278 Maximum 60900 Range 30622 Interquartile Range 8824 Skewness .334 .388 Kurtosis .445 .759 Figure C 113 20 09 Test for Normal Distribution without O utliers for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2009 Median Household Income .093 37 .200 .981 37 .768 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 114 2009 Test for Normal Distribution without O utliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 187

187 Figure C 115 2009 Normal Q Q Plot without O utliers for Median Household Income. Statistic Std. Error 2009 Property Assessed Valuation Mean 32862883790.00 4511918131.000 95% Confidence Interval for Mean Lower Bound 23712289690.00 Upper Bound 42013477880.00 5% Trimmed Mean 30500827840.00 Median 23496816050.00 Variance 75322399320000 0000000.000 Std. Deviation 27444926550.000 Minimum 3526904486 Maximum 113719332400 Range 110192428000 Interquartile Range 26862676240 Skewness 1.459 .388 Kurtosis 1.334 .759 Figure C 116 2009 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Descriptive Statistics).

PAGE 188

188 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2009 Property Assessed Valuation .243 37 .000 .816 37 .000 a. Lilliefors Significance Correction Figure C 117 2009 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 118 2009 Normal Q Q Plot without O utliers for Property Assessed Valuation.

PAGE 189

189 2010 Test for Normal Distrib ution Statistic Std. Error 2010 Median Household Income Mean 44846.13 1066.075 95% Confidence Interval for Mean Lower Bound 42689.79 Upper Bound 47002.46 5% Trimmed Mean 44663.83 Median 44389.00 Variance 45460626.010 Std. Deviation 6742.450 Minimum 32488 Maximum 60729 Range 28241 Interquartile Range 6998 Skewness .535 .374 Kurtosis .210 .733 Figure C 119 20 10 Test for Normal Distribution for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2010 Median Household Income .117 40 .182 .961 40 .186 a. Lilliefors Significance Correction Figure C 120 2010 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 190

190 Figure C 121 2010 Normal Q Q Plot for Median Household Income. Statistic Std. Error 2010 Property Assessed Valuation Mean 42855724210.00 7795469852.000 95% Confidence Interval for Mean Lower Bound 27087898120.00 Upper Bound 58623550290.00 5% Trimmed Mean 36129328350.00 Median 23131434150.00 Variance 24307740080000 00000000.000 Std. Deviation 49302880320.000 Minimum 3688292661 Maximum 237508768600 Range 233820475900 Interquartile Range 37645730750 Skewness 2.409 .374 Kurtosis 6.264 .733 Figure C 122 2010 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics).

PAGE 191

191 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2010 Property Assessed Valuation .275 40 .000 .693 40 .000 a. Lilliefors Significance Correction Figure C 123 2010 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 124 2010 Normal Q Q Plot for Property Assessed Valuation.

PAGE 192

192 2010 without Outliers Test for Normal Distribution Statistic Std. Error 2010 Median Household Income Mean 44748.22 1134.870 95% Confidence Interval for Mean Lower Bound 42446.59 Upper Bound 47049.84 5% Trimmed Mean 44553.95 Median 43993.00 Variance 47653370.170 Std. Deviation 6903.142 Minimum 32488 Maximum 60729 Range 28241 Interquartile Range 6495 Skewness .581 .388 Kurtosis .201 .759 Figure C 125 2010 Test f or Normal Distribution without O utliers for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2010 Median Household Income .143 37 .054 .952 37 .110 a. Lilliefors Significance Correction Figure C 126 2010 Test f or Normal Distribution without O utliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 193

193 Figure C 127 2010 Normal Q Q Plot without O utliers for Median Household Income. Statistic Std. Error 2010 Property Assessed Valuation Mean 31195254510.00 4225387683.000 95% Confidence Interval for Mean Lower Bound 22625771100.00 Upper Bound 39764737920.00 5% Trimmed Mean 28870726380.00 Median 22943329380.00 Variance 66059433960000 0000000.000 Std. Deviation 25702029870.000 Minimum 3688292661 Maximum 109419141000 Range 105730848400 Interquartile Range 23136134230 Skewness 1.506 .388 Kurtosis 1.631 .759 Figure C 128 2010 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Descriptive Statistics).

PAGE 194

194 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2010 Property Assessed Valuation .237 37 .000 .817 37 .000 a. Lilliefors Significance Correction Figure C 129 2010 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 130 2010 Normal Q Q Plot without O utliers for Property Assessed Valuation.

PAGE 195

195 2011 Test for Normal Distribution Statistic Std. Error 2011 Median Household Income Mean 44421.10 1027.810 95% Confidence Interval for Mean Lower Bound 42342.16 Upper Bound 46500.04 5% Trimmed Mean 44323.08 Median 44520.50 Variance 42255772.090 Std. Deviation 6500.444 Minimum 30440 Maximum 62510 Range 32070 Interquartile Range 8329 Skewness .280 .374 Kurtosis .518 .733 Figure C 131 20 11 Test for Normal Distribution for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2011 Median Household Income .090 40 .200 .985 40 .862 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 132 2011 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 196

196 Figure C 133 2011 Normal Q Q Plot for Median Household Income. Statistic Std. Error 2011 Property Assessed Valuation Mean 41460771510.00 7753561055.000 95% Confidence Interval for Mean Lower Bound 25777713970.00 Upper Bound 57143829060.00 5% Trimmed Mean 34583504770.00 Median 22147068560.00 Variance 24047083610000 00000000.000 Std. Deviation 49037825820.000 Minimum 3575927939 Maximum 239266976100 Range 235691048200 Interquartile Range 34288653450 Skewness 2.515 .374 Kurtosis 6.885 .733 Figure C 134 2011 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics).

PAGE 197

197 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2011 Property Assessed Valuation .286 40 .000 .677 40 .000 a. Lilliefors Significance Correction Figure C 135 2011 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 136 2011 Normal Q Q Plot for Property Assessed Valuation.

PAGE 198

198 2011 without Outliers Test for Normal Distribut ion Statistic Std. Error 2011 Median Household Income Mean 44282.68 1093.207 95% Confidence Interval for Mean Lower Bound 42065.55 Upper Bound 46499.80 5% Trimmed Mean 44158.72 Median 44310.00 Variance 44218786.170 Std. Deviation 6649.721 Minimum 30440 Maximum 62510 Range 32070 Interquartile Range 7655 Skewness .338 .388 Kurtosis .515 .759 Figure C 137 20 11 Test for Normal Distribution without O utliers for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2011 Median Household Income .088 37 .200 .982 37 .809 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 138 2011 Test for Normal Distributi on without O utliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 199

199 Figure C 139 2011 Normal Q Q Plot without O utliers for Median Household Income. Statistic Std. Error 2011 Property Assessed Valuation Mean 29766894590.00 4057512703.000 95% Confidence Interval for Mean Lower Bound 21537877420.00 Upper Bound 37995911760.00 5% Trimmed Mean 27445681310.00 Median 21923969160.00 Variance 60914614540000 0000000.000 Std. Deviation 24680886240.000 Minimum 3575927939 Maximum 107348126300 Range 103772198300 Interquartile Range 21251209010 Skewness 1.580 .388 Kurtosis 1.984 .759 Figure C 140 2011 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Descriptive Statistics).

PAGE 200

200 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2011 Property Assessed Valuation .235 37 .000 .811 37 .000 a. Lilliefors Significance Correction Figure C 141 2011 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 142 2011 Normal Q Q Plot without O utliers for Property Assessed Valuation.

PAGE 201

201 2012 Test for Normal Distribution Statistic Std. Error 2012 Median Household Income Mean 45565.90 1033.900 95% Confidence Interval for Mean Lower Bound 43474.64 Upper Bound 47657.16 5% Trimmed Mean 45321.08 Median 45091.00 Variance 42757962.350 Std. Deviation 6538.957 Minimum 34025 Maximum 61288 Range 27263 Interquartile Range 8010 Skewness .543 .374 Kurtosis .117 .733 Figure C 143 20 12 Test for Normal Distribution for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2012 Median Household Income .142 40 .040 .962 40 .191 a. Lilliefors Significance Correction Figure C 144 2012 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 202

202 Figure C 145 2012 Normal Q Q Plot for Median Household Income. Statistic Std. Error 2012 Property Assessed Valuation Mean 41120029180.00 7823032297.000 95% Confidence Interval for Mean Lower Bound 25296452790.00 Upper Bound 56943605580.00 5% Trimmed Mean 34116940290.00 Median 21821211910.00 Variance 24479933730000 00000000.000 Std. Deviation 49477200530.000 Minimum 3483005489 Maximum 242277571000 Range 238794565500 Interquartile Range 34089687970 Skewness 2.555 .374 Kurtosis 7.123 .733 Figure C 146 2012 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics).

PAGE 203

203 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2012 Property Assessed Valuation .287 40 .000 .671 40 .000 a. Lilliefors Significance Correction Figure C 147 2012 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 148 2012 Normal Q Q Plot for Property Assessed Valuation.

PAGE 204

204 2012 without Outliers Test for Normal Distribution Statistic Std. Error 2012 Median Household Income Mean 45409.86 1095.585 95% Confidence Interval for Mean Lower Bound 43187.92 Upper Bound 47631.81 5% Trimmed Mean 45148.73 Median 45009.00 Variance 44411347.510 Std. Deviation 6664.184 Minimum 34025 Maximum 61288 Range 27263 Interquartile Range 6551 Skewness .613 .388 Kurtosis .185 .759 Figure C 149 20 12 Test for Normal Distribution without O utliers for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2012 Median Household Income .164 37 .013 .949 37 .092 a. Lilliefors Significance Correction Figure C 150 2012 Test f or Normal Distribution without O utliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 205

205 Figure C 151 2012 Normal Q Q Plot without O utliers for Median Household Income. Statistic Std. Error 2012 Property Assessed Valuation Mean 29284263050.00 4037314096.000 95% Confidence Interval for Mean Lower Bound 21096210550.00 Upper Bound 37472315550.00 5% Trimmed Mean 26946243880.00 Median 21523727480.00 Variance 60309648890000 0000000.000 Std. Deviation 24558022900.000 Minimum 3483005489 Maximum 107630488600 Range 104147483100 Interquartile Range 20656405720 Skewness 1.607 .388 Kurtosis 2.112 .759 Figure C 152 2012 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Descriptive Statistics).

PAGE 206

206 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2012 Property Assessed Valuation .226 37 .000 .807 37 .000 a. Lilliefors Significance Correction Figure C 153 2012 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 154 2012 Normal Q Q Plot without O utliers for Property Assessed Valuation.

PAGE 207

207 2013 Test for Normal Distribution Statistic Std. Error 2013 Median Household Income Mean 46767.53 1034.703 95% Confidence Interval for Mean Lower Bound 44674.64 Upper Bound 48860.41 5% Trimmed Mean 46721.56 Median 46427.50 Variance 42824422.050 Std. Deviation 6544.037 Minimum 32295 Maximum 64862 Range 32567 Interquartile Range 8288 Skewness .247 .374 Kurtosis .587 .733 Figure C 155 20 13 Test for Normal Distribution for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2013 Median Household Income .069 40 .200 .986 40 .904 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 156 2013 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

PAGE 208

208 Figure C 157 2013 Normal Q Q Plot for Median Household Income. Statistic Std. Error 2013 Property Assessed Valuation Mean 41880845740.00 7953384126.000 95% Confidence Interval for Mean Lower Bound 25793607890.00 Upper Bound 57968083600.00 5% Trimmed Mean 34829852170.00 Median 22109831550.00 Variance 25302527620000 00000000.000 Std. Deviation 50301617890.000 Minimum 3498142931 Maximum 243041113800 Range 239542970900 Interquartile Range 35924229500 Skewness 2.501 .374 Kurtosis 6.720 .733 Figure C 158 2013 Test f or Normal Distribution for Property Assessed Valuation (Descriptive Statistics).

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209 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2013 Property Assessed Valuation .288 40 .000 .675 40 .000 a. Lilliefors Significance Correction Figure C 159 2013 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 160 2013 Normal Q Q Plot for Property Assessed Valuation.

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210 2013 without Outliers Test for Normal Distribution Statistic Std. Error 2013 Median Household Income Mean 46459.65 1103.021 95% Confidence Interval for Mean Lower Bound 44222.62 Upper Bound 48696.68 5% Trimmed Mean 46366.88 Median 46055.00 Variance 45016237.010 Std. Deviation 6709.414 Minimum 32295 Maximum 64862 Range 32567 Interquartile Range 7800 Skewness .376 .388 Kurtosis .587 .759 Figure C 161 20 13 Test for Normal Distribution without O utliers for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2013 Median Household Income .098 37 .200 .979 37 .686 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 162 2013 Test f or Normal Distribution without O utliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

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211 Figure C 163 2013 Normal Q Q Plot without O utliers for Median Household Income. Statistic Std. Error 2013 Property Assessed Valuation Mean 29845860550.00 4153770197.000 95% Confidence Interval for Mean Lower Bound 21421624130.00 Upper Bound 38270096970.00 5% Trimmed Mean 27401463190.00 Median 21706151050.00 Variance 63839085340000 0000000.000 Std. Deviation 25266397710.000 Minimum 3498142931 Maximum 110562335500 Range 107064192600 Interquartile Range 21787920890 Skewness 1.613 .388 Kurtosis 2.139 .759 Figure C 164 2013 Test for Normal Distributi on without O utliers for Property Assessed Valuation (Descriptive Statistics).

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212 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2013 Property Assessed Valuation .229 37 .000 .806 37 .000 a. Lilliefors Significance Correction Figure C 165 2013 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 166 2013 Normal Q Q Plot without O utliers for Property Assessed Valuation.

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213 2014 Test for Normal Distribut ion Statistic Std. Error 2014 Median Household Income Mean 47170.63 1213.590 95% Confidence Interval for Mean Lower Bound 44715.91 Upper Bound 49625.34 5% Trimmed Mean 47058.22 Median 46446.50 Variance 58911980.190 Std. Deviation 7675.414 Minimum 30765 Maximum 65976 Range 35211 Interquartile Range 9625 Skewness .229 .374 Kurtosis .129 .733 Figure C 167 20 14 Test for Normal Distribution for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2014 Median Household Income .079 40 .200 .988 40 .944 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 168 2014 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

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214 Figure C 169 2014 Normal Q Q Plot for Median Household Income. Statistic Std. Error 2014 Property Assessed Valuation Mean 44157754890.00 8513396344.000 95% Confidence Interval for Mean Lower Bound 26937785400.00 Upper Bound 61377724370.00 5% Trimmed Mean 36576174670.00 Median 23047076290.00 Variance 28991166920000 00000000.000 Std. Deviation 53843446140.000 Minimum 3521567064 Maximum 262149254400 Range 258627687300 Interquartile Range 38161096480 Skewness 2.539 .374 Kurtosis 7.001 .733 Figure C 170 2014 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics).

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215 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2014 Property Assessed Valuation .288 40 .000 .670 40 .000 a. Lilliefors Significance Correction Figure C 171 2014 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 172 2014 Normal Q Q Plot for Property Assessed Valuation.

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216 2014 without Outliers Test for Normal Distribution Statistic Std. Error 2014 Median Household Income Mean 47029.41 1295.000 95% Confidence Interval for Mean Lower Bound 44403.02 Upper Bound 49655.79 5% Trimmed Mean 46899.62 Median 46238.00 Variance 62049927.800 Std. Deviation 7877.178 Minimum 30765 Maximum 65976 Range 35211 Interquartile Range 10482 Skewness .279 .388 Kurtosis .176 .759 Figure C 173 20 14 Test for Normal Distribution without O utliers for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2014 Median Household Income .086 37 .200 .986 37 .919 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Figure C 174 2014 Test f or Normal Distribution without O utliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

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217 Figure C 175 2014 Normal Q Q Plot witho ut O utliers for Median Household Income. Statistic Std. Error 2014 Property Assessed Valuation Mean 31282726280.00 4415270520.000 95% Confidence Interval for Mean Lower Bound 22328142630.00 Upper Bound 40237309940.00 5% Trimmed Mean 28670057950.00 Median 22310003980.00 Variance 72130070920000 0000000.000 Std. Deviation 26857042080.000 Minimum 3521567064 Maximum 117176235000 Range 113654668000 Interquartile Range 23114035800 Skewness 1.621 .388 Kurtosis 2.150 .759 Figure C 176 2014 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Descriptive Statistics).

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218 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2014 Property Assessed Valuation .228 37 .000 .803 37 .000 a. Lilliefors Significance Correction Figure C 177 2014 Test f or Normal Distribution without O utliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 178 2014 Normal Q Q Plot without O utliers for Property Assessed Valuation.

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219 2015 Test for Normal Distribution Statistic Std. Error 2015 Median Household Income Mean 49623.53 1168.197 95% Confidence Interval for Mean Lower Bound 47260.62 Upper Bound 51986.43 5% Trimmed Mean 49630.86 Median 49466.50 Variance 54587388.920 Std. Deviation 7388.328 Minimum 31483 Maximum 70379 Range 38896 Interquartile Range 7851 Skewness .192 .374 Kurtosis 1.183 .733 Figure C 179 20 15 Test for Normal Distribution for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. Median Household Income .124 40 .126 .976 40 .555 a. Lilliefors Significance Correction Figure C 180 2015 Test for Normal Distribution for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

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220 Figure C 181 2015 Normal Q Q Plot for Median Household Income. Statistic Std. Error 2015 Property Assessed Valuation Mean 46944039960.00 9189929726.000 95% Confidence Interval for Mean Lower Bound 28355652550.00 Upper Bound 65532427370.00 5% Trimmed Mean 38783343810.00 Median 23823662670.00 Variance 33781923340000 00000000.000 Std. Deviation 58122218940.000 Minimum 3554300136 Maximum 282475672100 Range 278921371900 Interquartile Range 40801160370 Skewness 2.547 .374 Kurtosis 7.024 .733 Figure C 182 2015 Test for Normal Distribution for Property Assessed Valuation (Descriptive Statistics).

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221 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2015 Property Assessed Valuation .291 40 .000 .667 40 .000 a. Lilliefors Significance Correction Figure C 183 2015 Test for Normal Distribution for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 184 2015 Normal Q Q Plot for Property Assessed Valuation.

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222 2015 without Outliers Test for Normal Distribut ion Statistic Std. Error 2015 Median Household Income Mean 49474.73 1233.276 95% Confidence Interval for Mean Lower Bound 46973.53 Upper Bound 51975.93 5% Trimmed Mean 49453.16 Median 49379.00 Variance 56275908.590 Std. Deviation 7501.727 Minimum 31483 Maximum 70379 Range 38896 Interquartile Range 6167 Skewness .246 .388 Kurtosis 1.283 .759 Figure C 185 2015 Test for Normal Distribution without O utliers for Median Household Income (Descriptive Statistics). Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2015 Median Household Income .152 37 .031 .968 37 .353 a. Lilliefors Significance Correction Figure C 186 20 15 Test f or Normal Distribution without O utliers for Median Household Income (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic).

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223 Figure C 187 2015 Normal Q Q Plot without O utliers for Median Household Income. Statistic Std. Error 2015 Property Assessed Valuation Mean 33046844060.00 4766558717.000 95% Confidence Interval for Mean Lower Bound 23379814920.00 Upper Bound 42713873200.00 5% Trimmed Mean 30127675630.00 Median 22770951430.00 Variance 84064303410000 0000000.000 Std. Deviation 28993844760.000 Minimum 3554300136 Maximum 128754223100 Range 125199923000 Interquartile Range 24336738990 Skewness 1.686 .388 Kurtosis 2.503 .759 Figure C 188 2015 Test for Normal Di stribution without O utliers for Property Assessed Valuation (Descriptive Statistics).

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224 Kolmogorov Smirnov a Shapiro Wilk Statistic df Sig. Statistic df Sig. 2015 Property Assessed Valuation .226 37 .000 .797 37 .000 a. Lilliefors Significance Correction Figure C 189 20 15 Test for Normal Distribution without O utliers for Property Assessed Valuation (Kolmogorov Smirnov Statistic and Shapiro Wilk Statistic). Figure C 190 2015 Normal Q Q Plot without O utliers for Property Assessed Valuation.

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225 APPENDIX D INTERNAL REVENUE SERVICE ZIP CODE DATA DOCUMENTATION GUIDE Variable Name Description Value/Line Reference STATEFIPS The State Federal Information Processing System (FIPS) code 01 56 STATE The State associated with the ZIP code Two digit State abbreviation code ZIPCODE 5 digit Zip code AGI_STUB Size of adjusted gross income 1 = $1 under $25,000 2 = $25,000 under $50,000 3 = $50,000 under $75,000 4 = $75,000 under $100,000 5 = $100,000 under $200,000 6 = $200,000 or more N1 Number of returns MARS1 Number of single returns Filing status is single MARS2 Number of joint returns Filing status is married filing jointly MARS4 Number of head of household returns Filing status is head of household PREP Number of returns with paid preparer's signature N2 Number of exemptions 1040:6d NUMDEP Number of dependents 1040:6c TOTAL_VITA Total number of volunteer prepared returns [3] VITA Number of volunteer income tax assistance (VITA) prepared returns [3] TCE Number of tax counseling for the elderly (TCE) prepared returns [3] A00100 Adjust gross income (AGI) [2] 1040:37 / 1040A:21 / 1040EZ:4 N02650 Number of returns with total income 1040:22 / 1040A:15 / 1040EZ:4 A02650 Total income amount 1040:22 / 1040A:15 / 1040EZ:4 N00200 Number of returns with salaries and wages 1040:7 / 1040A:7 / 1040EZ:1 A00200 Salaries and wages amount 1040:7 / 1040A:7 / 1040EZ:1 N00300 Number of returns with taxable interest 1040:8a / 1040A:8a / 1040EZ:2 A00300 Taxable interest amount 1040:8 a / 1040A:8a / 1040EZ:2 N00600 Number of returns with ordinary dividends 1040:9a / 1040A:9a A00600 Ordinary dividends amount 1040:9a / 1040A:9a N00650 Number of returns with qualified dividends 1040:9b / 1040A:9b

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226 A00650 Qualified dividends amount [4] 1040:9b / 1040A:9b N00700 Number of returns with state and local income tax refunds 1040:10 A00700 State and local income tax refunds amount 1040:10 N00900 Number of returns with business or professional net income (less loss) 1040:12 A00900 Business or professional net income (less loss) amount 1040:12 N01000 Number of returns with net capital gain (less loss) 1040:13 1040A:10 A01000 Net capital gain (less loss) amount 1040:13 1040A:10 N01400 Number of returns with taxable individual retirement arrangements distributions 1040:15b / 1040:11b A01400 Taxable individual retirement arrangements distributions amount 1040:15b / 1040:11b N01700 Number of returns with taxable pensions and annuities 1040:16b / 1040A:12b A01700 Taxable pensions and annuities amount 1040:16b / 1040A:12b SCHF Number of farm returns 1040:18 N02300 Number of returns with unemployment compensation 1040:19 / 1040A:13 / 1040EZ:3 A02300 Unemployment compensation amount [5] 1040:19 / 1040A:13 / 1040EZ:3 N02500 Number of returns with taxable Social Security benefits 1040:20b / 1040A:14b A02500 Taxable Social Security benefits amount 1040:20b / 1040A:14b N26270 Number of returns with partnership/S corp net income (less loss) Schedule E:32 A26270 Partnership/S corp net income (less loss) amount Schedule E:32 N02900 Number of returns with total statutory adjustments 1040:36 / 1040A:20 A02900 Total statutory adjustments amo unt 1040:36 / 1040A:20 N03220 Number of returns with educator expenses 1040:23 / 1040A:16 A03220 Educator expenses amount 1040:23 / 1040A:16 N03300 Number of returns with self employment retirement plans 1040:28 A03300 Self employment retirement plans amount 1040:28 N03270 Number of returns with self employment health insurance deduction 1040:29 A03270 Self employment health insurance deduction amount 1040:29 N03150 Number of returns with IRA payments 1040:32 / 1040A:17 A03150 IRA payments amount 1040:32 / 1040A:17 N03210 Number of returns with student loan interest deduction 1040:33 / 1040A:18

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227 A03210 Student loan interest deduction amount 1040:33 / 1040A:18 N03230 Number of returns with tuition and fees deduction 1040:34 / 1040A:19 A03230 Tuition and fees deduction amount 1040:34 / 1040A:19 N03240 Returns with domestic production activities deduction 1040:35 A03240 Domestic production activities deduction amount 1040:35 N04470 Number of returns with itemized deductions 1040:40 A04470 Total itemized deductions amount 1040:40 A00101 Amount of AGI for itemized returns 1040:37 N18425 Number of returns with State and local income taxes Schedule A:5a A18425 State and local income taxes amount Schedule A:5a N18450 Number of returns with State and local general sales tax Schedule A:5b A18450 State and local general sales tax amount Schedule A:5b N18500 Number of returns with real estate taxes Schedule A:6 A18500 Real estate taxes amount Schedule A:6 N18300 Number of returns with taxes paid Schedule A:9 A18300 Taxes paid amount Schedule A:9 N19300 Number of returns with mortgage interest paid Schedule A:10 A19300 Mortgage interest paid amount Schedule A:10 N19700 Number of returns with contributions Schedule A:19 A19700 Contributions amount Schedule A:19 N04800 Number of returns with taxable income 1040:43 / 1040A:27 / 1040EZ:6 A04800 Taxable income amount 1040:43 / 1040A:27 / 1040EZ:6 N05800 Number of returns with income tax before credits 1040:47 / 1040A:30 / 1040EZ:10 A05800 Income tax before credits amount 1040:47 / 1040A:30 / 1040EZ:10 N09600 Number of returns with alternative minimum tax 1040:45 A09600 Alternative minimum tax amount 1040:45 N05780 Number of returns with excess advance premium tax credit repayment 1040:46/ 1040A:29 A05780 Excess advance premium tax credit repayment amount 1040:46/ 1040A:29 N07100 Number of returns with total tax credits 1040:55 / 1040A:36 A07100 Total tax credits amount 1040:55 / 1040A:36 N07300 Number of returns with foreign tax credit 1040:48

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228 A07300 Foreign tax credit amount 1040:48 N07180 Number of returns with child and dependent care credit 1040:49 / 1040A:31 A07180 Child and dependent care credit amount 1040:49 / 1040A:31 N07230 Number of returns with nonrefundable education credit 1040:50 / 1040A:33 A07230 Nonrefundable education credit amount 1040:50 / 1040A:33 N07240 Number of returns with retirement savings contribution credit 1040:51 / 1040A:34 A07240 Retirement savings contribution credit amount 1040:51 / 1040A :34 N07220 Number of returns with child tax credit 1040:52 / 1040A:35 A07220 Child tax credit amount 1040:52 / 1040A:35 N07260 Number of returns with residential energy tax credit 1040:53 A07260 Residential energy tax credit amount 1040:53 N09400 Number of returns with self employment tax 1040:57 A09400 Self employment tax amount 1040:57 N85770 Number of returns with total premium tax credit 8962:24 A85770 Total premium tax credit amount 8962:24 N85775 Number of returns with advance premium tax credit 8962:25 A85775 Advance premium tax credit amount 8962:25 N09750 Number of returns with health care individual responsibility payment 1040:61 / 1040A:38 / 1040EZ:11 A09750 Health care individual responsibility payment amount 1040:61 / 1040A:38 / 1040EZ:11 N10600 Number of returns with total tax payments 1040:74 / 1040A:46 / 1040EZ:9 A10600 Total tax payments amount 1040:74 / 1040A:46 / 1040EZ:9 N59660 Number of returns with earned income credit 1040:66a / 1040A:42a / 1040EZ:8b A59660 Earned income credit amount [6] 1040:66a / 1040A:42a / 1040EZ:8b N59720 Number of returns with excess earned income credit 1040:66a / 1040A:42a / 1040EZ:8b A59720 Excess earned income c redit (refundable) amount [7] 1040:66a / 1040A:42a / 1040EZ:8b N11070 Number of returns with additional child tax credit 1040:67 / 1040A:43 A11070 Additional child tax credit amount 1040:67 / 1040A:43 N10960 Number of returns with refundable education credit 1040:68 / 1040A:44 A10960 Refundable education credit amount 1040:68 / 1040A:44

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229 N11560 Number of returns with net premium tax credit 1040:69 / 1040A:45 A11560 Net premium tax credit amount 1040:69 / 1040A:45 N06500 Number of returns with income tax 1040:56 / 1040A:37 / 1040EZ:10 A06500 Income tax amount [8] 1040:56 / 1040A:37 / 1040EZ:10 N10300 Number of returns with tax liability 1040:63 / 1040A:39 / 1040EZ: 10 A10300 Total tax liability amount [9] 1040:63 / 1040A:39 / 1040EZ: 10 N85530 Number of returns with additional Medicare tax 1040:62a A85530 Additional Medicare tax amount 1040:62a N85300 Number of returns with net investment income tax 1040:62b A85300 Net investment income tax amount 1040:62b N11901 Number of returns with tax due at time of filing 1040:78 / 1040A:50 / 1040EZ:14 A11901 Tax due at time of filing amount [10] 1040:78 / 1040A:50 / 1040EZ:14 N11902 Number of returns with overpayments refunded 1040:75 / 1040A:47 / 1040EZ:13a A11902 Overpayments refunded amount [11] 1040:75 / 1040A:47 / 1040EZ:13a Figure D 1. Internal Revenue Service Zip Code Data Documentation Guide Source: Information adapted from SOI Tax Stats Individual Income Tax Statistics 2014 ZIP United States Department of Treasury, Internal Revenue Service, accesse d April 15 2017, https://www.irs.gov/uac/soi tax stats individual income tax statistics 2014 zip code data soi

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231 Archer, Wayne R., Brian Buckles, David A. Den slow, Jr., James F. Dewey, Dean H. Gatzlaff, Tracy L. Johns, David A. Macpherson, Stefan C. Norrbin, Donald E. Schlagenhauf, Michael J. Scicchitano, Stacy Sirmans, Robert C. Stroh, Sr., Anne R. Williamson. Analytical Services Relating to Property Taxation Part 2: Revenue Component Bureau of Economic and Business Research 2007. Accessed April 15, 2017 http://edr.state.fl.us/Content/specia l research projects/property tax study/Report Revenue Revised.pdf Baker, Education Policy Analysis Archives 22, no. 91 (2014) : 1 29 Accessed April 15, 2017 http://dx.doi.org/10.14507/epaa.v22n91.2014 Baker, Bruce D., Preston C. Green, and Craig E. Richards. Financing Education Systems Upper Saddle River: Pear son/Merrill/Prentice Hall, 2008 Black, John, Nigar Hashimzade, and Gareth Myles A Dictionary of Economics Oxford University Press, 2012. Blankenau, William and Mark Skidmore. Limi tations, and Education Spending. Contemporary Economic Policy 22 no. 1 (2004): 127 43 Brimley, Vern, Deborah A. V erstegen, and Rulon R. Garfield. Financing Education in a Climate of Change 12 th ed Boston: Pearson, 2016 Budget Subcommittee on Finance and Ta x, Property Tax Update, Fla. S. Rep. No. 2012 207 (2011). Accessed April 15, 2017. https://www.flsenate.gov/PublishedContent/Session/2012/InterimReports/2012 207ft.pdf Burrup, Percy. Financing Education in a Climate of Change Boston: Allyn and Bacon, 1974. C heung, Ron and Chris Cunningham. Lock In, Mobility and Tax Share. Regional Science and Urban Economics 41, no. 3 (2011): 173 86 Chiripanhura, Bl essing M. Their Implications for Material Living St andards and National Well Being. Economic and Labour Market Review 5, no. 2 (2011): 5 45 Accessed April 15, 2017 http:dx.doi.org/10.1057/elmr.2011.17 Cohen, Barry Explaining Psychological Statistics Hoboken, New Jersey: John Wiley & Sons 2008. N eutrality Applied Economics 19, no. 12 (1987): 1685 695. Colabella, Patrick. Funds PhD diss., University, 2008. Accessed April 1 5, 2017

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232 Crampton, Faith E., R. Craig Wood, and David C. Th ompson. Money and Schools 6 th ed. New Yo rk: Routledge 2015 Cubberly, Ellwood. School Funds and their Apportionment New York: Teachers College P ress, Columbia University, 1906. Cubberly, Ellwood. The History of Education Boston: Hought on Mifflin, 1920. Davies, Rhys, Mich ael Orton, and Dereck Bossworth. B et ween Income and Property Values. Environment and Planning C: Government and Policy 25, no. 5 (2007) : 756 72. Dishman, Mike and Traci Redish. and the Quest fo r Equal Peabody Journal of Education 85, no. 1 (2010): 16 31. Dreiman, Shelly. Using the Price to Income Ratio to Determine the Presence of Housing Price Bubbles Federal Housing Finance Agency, 2000 A ccessed A pril 15, 2017. http://www.fhfa.gov/DataTools/Downloads/Documents/HPI_Focus_Pieces/2000Q4_HPI Focus_N508.pdf Dziuban, Charles, Rich ard Rossmiller, and James H ale Fina aper presented at the American Educational Research Associa tion, Chicago, IL, April 1974 Yale Law and Po licy Review 24 no. 1 (2006): 43 90, A ccessed April 15, 2017. http://digitalcommons.law.yale.edu/ylpr/vol24/iss1/3 Epple, Dennis, Richard Romano, an d Holger Sieg. Conflict Over th e Provision of Public Education. Journal of Public Economics 96 (2012): 255 268 : The Courts and Public School Finance: Judge Made Centr alization and Economic Research. nd ed. I n Handbook of the Economics of Education edited by Eric A. Hanushek and Fenis Welsh Amsterdam: North Hol land, 2006. Florida Department of Education. 2015 2016 Funding for Florida School Districts: Statistical Report Florida Department of Education, 2015 Accessed April 15 2017. http://www.fldoe.org/core/fileparse.php/7507/urlt/Fefpdist.pdf Florida Department of Revenue. Florida's Property Tax Structure: An Analysis of Save our Homes and Truth in Mill a ge, Pursuant to 2006 311, L.O.F. Florida Department of Revenue, 2007 Accessed April 15, 2017 http://dor.myflorida.com/dor/property/trim/ptsreport/pdf/ptaxstructur e.pdf Florida Department of Revenue. Property Tax Oversight Florida Department of Revenue Accessed April 15, 2017. http://floridarevenue.com/dor/property/taxpayers/pdf/ptoinfographic.pdf

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233 Florida Legislature, Office of Ec onomic and Demographic Research. Florida: Economic Overview Florida Legislature, 2016 A cces sed April 15, 2017 http://edr.state.fl.us/Content/presentations/economic/FlEconomicOverview_1 26 16.pdf Florida Legislature, Office of Economic and Demographic Research. Florida: Economic Overview Florida Leg islature, 2016 Accessed April 15, 2017 http://edr.state.fl.us/Content/presentations/economic/FlEconomicOverview_8 24 16.pdf Florida Legislature, Office of Ec onomic and Demographic Research. Florida: Economic Overview Florida Legislature, 2017 Access ed April 15, 2017 http://edr.state.fl.us/Content/presen tations/economic/FlEconomicOverview_2 9 17.pdf Florida Tax Watch Research Ins titute How Florida Compares Taxes: State and Local Tax Rankings for Florida and the Nation Florida Tax Watch Research Institute, 2015 Accessed April 15, 2017 http://www.floridataxwatch.org/resources/pdf/2015_HFCTaxes_Final.pdf Fouladi, Rachel T. and James H. Steiger Correlation Coefficient and Its Square: Cumul ants, Moments, and Applications. Communications in Statistics Simulation and Computation 37, no. 5 (2008): 928 44. Accessed April 15, 2017 http://dx.doi.org/10.1080/03610910801943735 Gallagher, Ryan M., Haydar Kurban, and Joseph J. Persky. Property Tax Capitalization. Regional Science & Urban Economics 43, no. 2 (2013): 422 28. Accessed April 15, 2017 http://dx.doi.org/10.1016/j.regsciurbeco.2013.01.001 Gallin, Joshua. Run Relationship Between House Prices and Income: Evid ence from Local Housing Markets. Real Estate Economics 34, no. 3 (2006): 417 38. Accessed March 5, 2017. http://dx.doi.org/10.1111/j.1540 6229.2006.00172.x Glomm, Gerhard, B. Ravikumar, and Iona Schiopu. Chapter 9: The Politic al Economy of Education n Handbook of the Economics of Education edited by Eric A. Hanushek, Stephen J. Mach in, Ludger Woessmann. Waltham: Elsevier, 2011 Accessed April 15, 2017 http://dx .doi.org/10.1016/B978 0 444 53444 6.00009 2 Goodspeed, Timothy. Education Finance in New Jersey. National Tax Journal 51, no. 2 (1998). Griffith, Michael 12 E ducation: How States Allocate Their Share of Education Costs. Education Commission of the States 14, no. 4 (2013): 1 7 Accessed April 15, 2017 http://www.ecs.org/clearinghouse/01/08/47/10847.pdf Griffith Michael, Lawrence O. Picus, A llan Odden, and Anabel Aportela. the Needs of High Property Wealth School Distr icts with Low P aper presented to the Main Cult ural Affairs, ME, August 2013. Accessed April 15, 2017 http://www.maine.gov/legis/opla/MaineFisca lCapac ityMeasuresPaper73013.pdf

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234 Guo, Sheng and William G. Hardin III. g, Income and Consumption. Journal of Real Estate Finance and Economics 48, no. 2 (2014): 221 43 Accessed April 15, 2017 http:dx.doi.org/10.1007/s11146 012 9390 z Handbook of Research in Education Finance and Policy 1 st ed., edited by Helen Ladd, Edward Fiske. New York: Routledge, 2008. Handbook of Research in Education Finance and Policy 2 nd ed., edited by Helen Ladd, Margaret Goertz. New York: Routledge, 2015. Ihlanfeldt, Keith R. s a Bad Tax, but It Need Not Be. Cityscape 15, no. 1 (2013): 255 59. Journal of Education Finance 3, no. 1 (1977): 98 100. Johns, Roe L., Ed gar Morphet, and Kern Alexander. The Economics and Financing of Education 4th ed. Englewo od Cliffs: Prentice Hall, 1983. Jordan, Meagan M., David Chapman, an d S haron L. Wrobel. Property Tax Equity Impact of Arkan sas School Finance Equalization. Public Finance and Management 14, no. 4 (2014) : 399 415. Kenyon, Daphne. The Property Tax, School Funding Dilemma Cambridge: Lincoln Inst itute of Land Policy, 2007. Lindsey, Randall, Kikanza Nuri Robins, and Raymond D. Terrell. Cultural Proficiency: A Manual for School Leaders 3 rd ed. Thousand Oaks: Corwin of Sag e Publications 2009 Lutz, By ron, Raven Molloy, and Hui Shan. The Housing Crisis and State and Local Govern ment Tax Revenue: Five Channels. Regional Science & Urban Economics 41, no. 4 (2011): 306 19 Accessed April 15, 2017 http://dx.doi.org/10.1016/j.regsciurbeco.2011.03.009 M aattanen, Niku and Marko Tervio. s: An Assignment Model Approach. Journal of Economic Theory 151 (2014): 381 410. Accessed April 15, 2017 http://dx.doi.org/10.1016/j.jet.2014.01.003 Mikh ailov, Nikolai and Jason Kolman. Types of Property Tax and Assessment Limitations and Tax Relief Programs Lincoln Institute of Land Policy 1998. Accessed April 15, 2017 https://www.leg.state.nv.us/73rd/otherDocuments/PTax/lincoln institute property tax relief.pdf Moore, J. Wayne. ax Equity Implications of Assessment Capping and Homestead Exemptions for Owner Occupied Single Family Housing. Journal of Property Tax Assessment & Administration 5, no. 3 (2008): 1 36

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235 Mort, Paul, State Support for the Public Schools New York: Teacher s College Pres s, Columbia University, 1926. Muhm, April 15, 2017 http ://lib.dr.iastate.edu/rtd/15297 Nemcov a, Jana, Mihaly Petreczky, and Jan van Schuppen. Systems. Siam Journal on Control and Optimization 51, no. 5 (2013): 3 386 414. Accessed April 15, 2017 http:dx.doi.org/10.1137/110847482 Journal of Education Finance 2, no. 3 (1977): 356 79. Odden, Allan R., Lawrence O. Picus, and Michael E. Goetz. State Strategy to Achieve School Educational Policy 24, no. 4 (2010): 628 54. Accessed April 15, 2017 http:dx.doi.org/10.1177/0895904809335107 Orton, Michael and Rhys Davies. Relationship between House hold Income and Property Value. Warwick Institute for Employment Research 75 (2004 ):1 4. Arthur, Te rri Sexton, and Steven Sheffrin. s from the Assessme nt Provisions of Proposition 13. National Tax Journal 47, no. 4 (1994): 721 7 3 Payton, Seth Assessment Reform in Indiana. Journal of Regional Analysis and Policy 36 no. 2 (2006): 182 93 Research Committee International Associati on of Assessing Officers. Assessed Value Cap Overview. Journal of Property Tax Assessment & Administration 7, no. 1 (2010): 57 67 Scopelliti and After the Great Recession. States Department of La bor, Bureau of L abor Statistics. Accessed April 15, 2017. http://www.bls.gov/spotlight/2014/housing/home.htm Sirmans, G. Stacy, D ean Gatzlaff, and David MacPherson. quity in Real Property Taxation. Journal of Real Estate Literature 16, no. 2 (2008): 167 180 Sirmans G. Stacy and C. Stace Sirmans. nitiatives in the United States. Journal of Housing Research 21, no. 1 (2012): 1 13 Skidmore, Mark, Laura Reese, and Sung Hoon Kang. Education Finance Re form, and Property Value Growth. Regional Science and Urban Economics 42, no. 1 2 (2012): 351 63. A ccessed April 15, 2017 http://dx.doi.org/10.1016/j.regsciurbeco.2011.10.008

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236 Snyder, Nancy McCarthy Initiated Tax Cuts J ournal of Public Budgeting, Accounting & Financial Management 15, no. 4 (2003): 593 621. Sonnier, Blaise M. and Sharon S. Lassar. Relief Measure and Inflation Protection for Non Homestead Real Property. Journal of State Taxation 26, no. 6 (2008 ): 23 46 Sonstelie, Jon and Peter Richardson, eds., School Finance and California's Master Plan for Education San Francisco: Public Policy Institute of California, 2001. Stansel, Dan, Ga ry Jackson, and J. Howard Finch. Acquisition Based Pr operty Tax: The Case of Florida. Journal of Housing Research 16, no. 2 (2007): 117 29 Straye r, George D. and Robert M. Haig. The Financing of Education in the State of New York New York: M acmillan 1923. Thomas, Josephine Florida H omeowners and Local Governments. Stetson Law Review 35, no. 2 (2006): 509 57 Thompson, David C., Faith E. Crampton, and R. Craig Wood Money and Schools 5 th ed. New York: Routledge, 2012. Thornton, Barry and Gordon Arbogast. cting School Quality in Florida. Contemporary Issues in Education Research 7, no. 2 (2014) : 69 74 United States Departmen t of Education. Fiscal Year 2017 Budget Summary and Background Information United States Department of Education, 2016. Accessed April 15, 2017 https://www2.ed.gov/about/overview /budget/budget17/summary/17summary.pdf United States Department of Labor, Bureau of Labor Statistics. BLS Spotlight on Statistics: Recession of 2007 2009 United States Department of L abor, 2012 Accessed April 15, 2017. http://www.bls.gov/spotlight/2012/recession/pdf/recession_bls_spotlight.pdf United States Department of Treasury, Internal Revenue Service. The Internal Revenue Service Data Book: 2015 United St ates Department of Treasury, 2015 Accessed April 15, 2017 https://www.irs.gov/pub/irs soi/15databk.pdf Verstegen, Deborah A. Nevada's Public Education Finance System. Journal of Education Finance 39, no. 2 (2013): 132 49

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237 Verstegen, Deborah A. State Survey o f Finance Policies and Programs. Paper presented at the Association for Education Finance Policy Annual Conference San Antonio, TX March 2014 Accessed April 15, 2017 https://schoolfinancesdav.files.wordpress.com/2014/04/aefp 50 stateaidsystems.pdf Verstegen, De borah A. and Robert C. Knoeppel. Finance Apportionme nt Systems in the United States. Journal of Education Finance 38, no. 2 (2012): 145 66 Wallin, Bruce and Jeffrey Zabel. Impact of Pro position 2 1/2 in Massachusetts Regional Science and Urban Economics 41, no. 4 (2011): 382 93. Accessed April 15, 2017 http://dx.doi.org/10.1016/j.regsciurbeco.2011.03.008 Williams, Richard. Marginal Effects f or Continuous Variables. University of Notre Dame 2016. Accessed April 15, 2017 https://www3.nd.edu/~rwilliam/stats3/Margins02.pdf Wood, R. Craig. Review of The Economics and Financing of Education 4th ed. by Roe L. Johns, Edgar L. Morphet, Kern Alexa nder. Journal of Education Finance 9, no. 1 (1983): 133 36. Accessed April 15, 2017. http://www.jstor.org/stable/40703400 Wood, R. Craig The Kentucky Law Journal 98, no. 4 (20 10): 739 87. Public Finance Review 37, no. 3 (2009): 289 311. Accessed April 15, 2017 http:dx.doi.org/10.1177/1091142109332054 Yinger, Jo hn, ed. Helping Children Left Behind: State Aid and the Pursuit of Educational Equity C ambridge : MIT Press, 2004 Zhu Shang and Kelley Journal of Real Estate Finance and Economics 44 (201 2): 153 66. Accessed April 15, 2017 http:dx.doi.org/10.1007/s11146 010 9290 z State Department of Education Websites: Alabama (https://www.alsde.edu/) Alaska (https://education.alaska.gov/) Arizona (http://www.azed.gov/) Arkansas (http://www.arkansased.gov/) California (http://www.cde.ca.gov/) Colorado (http://www.cde.state.co.us/) Connecticut (http://www.sde.ct.gov/sde/site/default.asp) Delaware (http://www.doe.k12.de.us/site /default.aspx?PageID=1) Florida (http://www.fldoe.org/) Georgia (http://www.gadoe.org/Pages/Home.aspx)

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238 Hawaii (http://www.hawaiipublicschools.org/Pages/Home.aspx) Idaho (http://sde.idaho.gov/ ) Illinois (http://www.isbe.net/) Indiana (http://www.doe.in.gov/) Iowa (https://www.educateiowa.gov/); Kansas (http://www.ksde.org/) Kentucky (http://education.ky.gov/Pages/default.aspx) Louisiana (http://www.louisianabelieves.com/) Maine (http://www.maine.gov/doe/) Maryland (http://www.marylandpublicschools.org/) Massachusetts (http://www.doe.mass.edu/) Michigan (https://www.michigan.gov/mde) Minnesota (http://education.state.mn.us/mde/index.html) Mississippi (http://www.mde.k12.ms.us/) Missouri (https://dese.mo.gov/) Montana (http://opi.mt.gov/) Nebraska (https://www.education.ne.gov/) Nevada (http://www.doe.nv.gov/); New Hampshire (http://education.nh.gov/) New Jersey (http://www.state.nj.us/education/) New Mexico (http://ped.state.nm.us/ped/index.html) New York (http://schools.nyc.gov/default.htm) North Carolina (http://www.dpi.state.nc.us/) North Dakota (https://www.nd.gov/dpi) Ohio (http://education.ohio.gov/) Oklahoma (http://sde.ok.gov/sde/) Oregon (http://www.ode.state.or.us/home/) Pennsylvania (http://www.education.pa.gov/Pages/default.aspx#.VvCxrmQrIfE) Rhode Island (http ://www.ride.ri.gov/) South Carolina (http://ed.sc.gov/) South Dakota (http://doe.sd.gov/) Tennessee (https://www.tn.gov/education) Texas (http://tea.texas.gov/) Utah (http://www.schools.utah.gov/main/) Vermont (http://education.vermont.gov/) Virginia (http://www.doe.virginia.gov/) Washington (http://www.k12.wa.us/) West Virginia (https://wvde.state.wv.us/) Wisconsin (http://dpi.wi.gov/) Wyoming (http:/ /edu.wyoming.gov/) State Department of Revenue Websites: Alabama (http://www.ador.alabama.gov/) Alaska (http://dor.alaska.gov/) Arizona (https://www.azdor.gov/)

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239 Arkansas (http://www.dfa.arkansas.gov/Pages/default.aspx) California (http:/ /www.taxes.ca.gov/) Colorado (https://www.colorado.gov/revenue) Connecticut (http://www.ct.gov/drs/site/default.asp) Delaware (http://revenue.delaware.gov/) Florida (http://dor.myflorida.com/Pages/default.aspx) Georgia (https://dor.georgia.gov/); Hawaii (http://tax.hawaii.gov/) Idaho (http://tax.idaho.gov/) Illinois (http://www.revenue.state.il.us/#&panel1 1) Indiana (http://www.in.gov/dor/) Iowa (https://tax.iowa.gov/) Kansas (http://www.ksrevenue.org/) Kentucky (http://revenue.ky.gov/) Louisiana (http://www.rev.state.la.us/) Maine (http://www.maine.gov/revenue/) Maryland (http://dat.maryland.gov/Pages/default.aspx) Massachusetts (http s://www.mass.gov/dor/) Michigan (http://www.michigan.gov/treasury/0,4679,7 121 -8483 -,00.html) Minnesota (http://www.revenue.state.mn.us/Pages/d efault.aspx) Mississippi (http://www.dor.ms.gov/Pages/default.aspx) Missouri (http://dor.mo.gov/) Montana (https://revenue.mt.gov/) Nebraska (http://www.revenue.nebraska.gov/) Nevada (http://tax.nv.gov/) New Hampshire (http://revenue.nh.gov/) New Jersey (http://www.state.nj.us/treasury/taxation/) New Mexico (http ://www.tax.newmexico.gov/) New York (https://www.tax.ny.gov/) North Carolina (http://www.dornc.com/) North Dakota (https://www.nd.gov/tax/) Ohio (http://www.tax.ohio.gov/) Oklahoma (https://ww w.ok.gov/tax/) Oregon (http://www.oregon.gov/dor/Pages/index.aspx) Pennsylvania (http://www.revenue.pa.gov/Pages/default.aspx#.VvLK0GQrIfE) Rhode Island (http://www.tax.ri.gov/) South Carolina (https://dor.sc.gov/) Sou th Dakota (http://dor.sd.gov/) Tennessee (https://www.tn.gov/revenue) Texas (http://comptroller.texas.gov/taxinfo/sales/) Utah (http://tax.utah.gov/) Vermont (http://tax.vermont.gov/) Virginia (tax.virginia.gov) Washington (http://dor.wa.gov/) West Virginia (http://www.wvrevenue.gov/) Wisconsin (https://www.revenue.wi.gov/)

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240 Wyoming (http://revenue.wyo.gov/)

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241 BIOGRAPHICAL SKETCH Sharda Jackson Smith received her Bachelor of Arts degree in the spring of 2010 and Master of Education degree at the University of Florida in the spring of 2011. While working on her D octor of Education degree in educational l eadership, she worked full time as a classroom teacher for Marion and Alachua County Public Schools.