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Deriving Housing Quality Indicators to Estimate Rehabilitation Needs: An Analysis in the Gainesville Area

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Deriving Housing Quality Indicators to Estimate Rehabilitation Needs: An Analysis in the Gainesville Area
Creator:
HARRIS, MATTHEW A.
Copyright Date:
2008

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Assessed values ( jstor )
Censuses ( jstor )
Cost estimates ( jstor )
Counties ( jstor )
Estimated taxes ( jstor )
Housing ( jstor )
Housing conditions ( jstor )
Housing units ( jstor )
Rehabilitation facilities ( jstor )
Value appraisal ( jstor )
Alachua County ( local )

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University of Florida
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University of Florida
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Copyright Matthew A. Harris. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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12/31/2008
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80443970 ( OCLC )

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DERIVING HOUSING QUALITY INDICATORS TO ESTIMATE REHABILITATION NEEDS: AN ANALYSIS IN THE GAINESVILLE AREA By MATTHEW A. HARRIS A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN BUILDING CONSTRUCTION UNIVERSITY OF FLORIDA 2003

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ACKNOWLEDGEMENTS I would like to thank my family for their love and support. My achievements mean so much more being able to share them with the ones I love. I thank my dad, for showing me what it means to be genuine person. I have looked up to my dad all my life. Throughout my own development in becoming a man, I learned how difficult it is to be someone of such high moral obligation not only to others but oneself. His honesty, integrity, and work ethic have encouraged me to become a better person. I thank my mom for being such a special person in my life. Her attention and love have given me the strength to accomplish anything I set my mind to. Although he can be difficult like any sibling, I thank my younger brother for being there through thick or thin. My brother’s loyalty will forever be remembered. I thank my late older brother, for showing me to have no fear and for teaching me life lessons no one else could. I would also like to express my sincere gratitude to my committee members, Dr. Mark Smith, Dr. Bob Stroh, and Dr. Leon Wetherington, for serving as my committee members and their suggestions for finalizing my thesis. I thank Douglas White for his help with the survey and analysis section of my thesis. My thanks also go to Dr. R. Raymond Issa of the M.E. Rinker School of Building Construction for his guidance in the establishment of the research project and advice on my study progress. Finally, I would like to thanks my friends for there humor and light heartedness that have helped me get through the stressful times of my life. I thank in no particular ii

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order Matt Hines, Andres Castro, Joel Perez, Chris Sidote, Josh Doktor, Wes Osburne, Elie Andary, Bobby Motley, Brad Williams, Carlos Bulnes, Danny Frank, Fransisco Montelegra, Maysoram Prashard, Jae-Mo Jung, Tavaine Green, Juan and Shawn, Rachael Scott, Melanie Johnson, and Melissa Mazer. iii

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TABLE OF CONTENTS page ACKNOWLEDGEMENTS................................................................................................ii LIST OF TABLES.............................................................................................................vi LIST OF FIGURES..........................................................................................................vii ABSTRACT.....................................................................................................................viii CHAPTER 1 INTRODUCTION.....................................................................................................1 Statement of the Problem..........................................................................................1 Objective of the Study...............................................................................................3 Hypothesis Statement................................................................................................6 Research Methodology..............................................................................................6 Overview...................................................................................................................7 2 LITERATURE REVIEW..........................................................................................9 Introduction...............................................................................................................9 Importance of Rehabilitation.....................................................................................9 Benefits of Rehabilitation........................................................................................11 Sources of Data on Housing Condition...................................................................12 Sources that Develop Measures of Housing Condition..........................................15 3 METHODOLOGY..................................................................................................18 Introduction.............................................................................................................18 Sources of Data.......................................................................................................18 Housing Condition Survey............................................................................19 Property Appraiser Data................................................................................20 Description of Data Analysis..................................................................................21 4 DATA ANALYSIS AND RESULTS.....................................................................24 General Information on Units Surveyed.................................................................25 The Relationship between Housing Quality Variables...........................................26 iv

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Rehab Cost Analysis for Future Work....................................................................29 5 CONCLUSIONS AND RECOMMENDATIONS..................................................35 APPENDIX A HOUSING CONDITION SURVEY.......................................................................37 B PROPERTY APPRAISAL DATA FOR ALACHUA COUNTY...........................38 LIST OF REFERENCES..................................................................................................39 BIOGRAPHICAL SKETCH............................................................................................41 v

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LIST OF TABLES Table page 4-1 Average age surveyed per age group....................................................................25 4-2 Quality by year built .............................................................................................27 4-3 Range of value ratios by year built.......................................................................30 4-4 Range of unit value by year built..........................................................................32 vi

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LIST OF FIGURES Figure page 4-1 Units surveyed by year built....................................................................................26 4-2 Quality by year built................................................................................................27 4-3 Square footage by year built....................................................................................29 4-4 Range of value ratios by year built..........................................................................31 4-5 Range of unit value by year built............................................................................33 vii

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Abstract of Thesis Presented to the Graduate School Of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science in Building Construction ABSTRACT DERIVING HOUSING QUALITY INDICATORS FROM HOUSING AUTHORITY DATA FOR MARKETING OPPURTUNITIES IN REHABILITATION: AN ANALYSIS IN THE GAINESVILLE AREA By Matthew Harris December 2003 Chair: Dr. Mark T. Smith Major Department: School of Building Construction The goal of this thesis is to develop a fundamental procedure of identifying substandard housing by comparing survey data to county property appraiser data. Such a procedure will improve the awareness of deteriorating neighborhoods and create a consistent process for maintaining properly funded programs to improve the quality of life in these neighborhoods. The use of the procedure will provide local government and nonprofit organizations the ability to target resources to areas of greatest need as well as create a potential market for contractor opportunity to fulfill the need for housing rehabilitation programs. The objective of this research is to link housing quality measures found in a housing condition survey conducted by the Alachua County Housing Authority with data gathered by the county property appraiser. Contractors will have an easily derived estimate of the number of units below standard housing quality in cities, counties, and viii

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neighborhoods. To address these issues and provide a method that allows estimates of the number of units in an area that are substandard or in need of rehabilitation, this paper describes a methodology based on the use of parcel data. It provides a standard approach that could be used statewide or nationally to the extent that data are available and consistent. The approach that has been taken in this analysis is to consider the possibility of using housing unit characteristics that are available from the county property appraiser database and the Alachua County housing condition survey to develop proxies for housing condition. ix

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CHAPTER 1 INTRODUCTION This project explains the creation of a procedure of identifying substandard housing by using appraiser data. As a housing unit ages, the need for that unit to be remodeled or rehabilitated begins to become an increasing concern. The surge in single family housing construction from the post – WWII period has meant a large number of units are reaching an age which such improvements are necessary. This trend creates opportunity for contractors to build a business around the market of rehabilitation. In certain portions of the market, government funds will be used to subsidize the construction. Identifying neighborhoods likely to be targets will facilitate the growth of a business. The use of the method will improve the ability of local government and nonprofit organizations to target resources to areas of greatest need as well as demonstrate the need for housing rehabilitation programs. The improved awareness will help identify business opportunities for contractors to perform the work needed for housing rehabilitation. Contractors will benefit from this process because of the business opportunity rehab presents. This analysis will focus on the need for contractors to be the advocates for more resources for rehabilitation to be available. Statement of the Problem Without better measures of housing condition, it is difficult to estimate the extent of need and opportunity for work in rehab. There is not an adequate method of easily 1

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2 assessing and adequately specifying the amount of need or target and make estimates of the resources required for demolition and replacement. The availability of data has been a concern in the continued tracking of housing condition at the neighborhood level. The Census provides data for block groups and census tracks, but the data is only collected every ten years and the variables used are of limited use. As a result of the Census data topography not fitting neighborhood boundaries local developers of neighborhood indicators have made extensive use of property assessor files. This data source provides parcel level information on housing units and other land uses. A typical property assessor file includes the taxable value and estimated market value of the property, sales price and date, square footage of building, year built of building, address, tenure, and lot size. With this data, it is possible to define a neighborhood boundary and compile information on homeownership, appreciation in sales prices, number of transactions, and abandoned properties. The data are based on recorded transactions and can be updated annually, or more frequently in some cases. Estimates of the number of substandard housing units in a locality would facilitate policy, advocacy, resource allocation, and targeting business decisions as well as provide information to facilitate grant applications. To address these issues and provide a method that allows estimates of the number of units in an area that are substandard or in need of rehabilitation, this paper describes a methodology based on the use of parcel data. It provides a standard approach that could be used statewide or even nationally to the extent that data are available and consistent.

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3 Objective of the Study Because measuring housing condition at the local level is important and there is not an inexpensive and consistent method currently available to develop a set of condition measures for all jurisdictions in a state or the nation, this research is intended to develop such an indicator. The approach that has been taken in this analysis is to use housing unit characteristics that are available from the county property appraiser database to develop proxies for housing condition. In this way, this research can develop gross estimates of the need for rehabilitation or replacement in the locality. Variables that are anticipated to be important indicators of housing quality and that are available from the appraiser database include age of the unit, size of the unit, whether the unit is owner occupied, and the market value of the unit. Housing affordability is a large concern in the United States today; it affects the economy and the standard of living throughout the nation. An extensive literature examines indicators involved with analyzing the standards of housing at the national level (see, for example, Bogdan and Can, 1997, Nelson, 1994, and U.S. Department of Housing and Urban Development, 1994, 1999). When measuring housing need the idea of housing condition is given less attention on the national level, however the work of many community development corporations and other nonprofit housing entities, as well as local government programs, focus on the rehabilitation of housing. For example, the major shares of Community Development Block Grant (CDBG) and HOME Investment Partnerships (HOME) funds are devoted to housing rehabilitation in many communities, and they contribute to the rehabilitation of some 200,000 units annually (U.S. Department of Housing and Urban Development, 2001a). There is a need

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4 for local government and nonprofit organizations to be able to target resources to areas of greatest need as well as demonstrate the need for housing rehabilitation programs in an environment of limited resources, increased questioning of the value of local programs, and competing interests. The decreasing concern for housing condition in the national context is due to the overall quality of the housing stock improving dramatically over the past 60 years by common measures. These measures include the average size of units and the percent of units without kitchen facilities, complete plumbing, or heating. For example, in 1940 over fifty percent of housing units in the United States were lacking some or all plumbing; by 1991 that number had fallen to two and one half percent (Kutty, 1999). Thus, technically the conditions that existed in the 1940s are no longer a cause for significant national concern. Yet approximately $100 to $200 billion of rehabilitation is carried out in the United States each year, an amount that is close to that being spent in new construction (HUD, 2001a). It is estimated that about 765,000 homeowners live in small properties that are severely inadequate, and an additional 1.6 million are in homes with moderate problems (Belsky, 2002). The historically extreme circumstances of housing condition that were once accepted as humane are still being used as the same criteria for evaluating housing condition in today’s housing stock. Our standard for living has increased at such a rapid pace that it is giving a false identity of the housing condition of today being better than it actually is. At the local level the problem is that lacking complete plumbing, lacking heating facilities, or lacking kitchen facilities are the only variables that have been available in the Census since 1970 to provide an indicator of housing condition. These variables do

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5 not reflect the structural integrity of the unit or aspects of the unit subject to wear and tear. It is units whose structural, physical condition is deteriorated that require interventions in the form of rehabilitation programs, or possibly their removal from the housing stock. Units that are in need of rehabilitation are generally concentrated in particular neighborhoods, so that the occurrence in specific neighborhoods is significantly higher than the average for the metropolitan area or the nation. These units that are not being looked at as detailed as they should be are an important affordable housing resource, and their condition influences the quality of life in any given neighborhood. It is necessary to estimate rehabilitation needs in a community to aid in planning and to make the case for funding to local officials (Belsky, 2002). Better indicators of housing condition than those available through the Census are required for providing evidence of the quality of the local housing stock as well as facilitating local housing policy decisions. Without better indicators of housing condition, planners, program administrators, and nonprofit housing organizations cannot adequately identify need numbers in their housing plans, measure progress in addressing needs, or target and make estimates of the resources required for demolition and replacement. Estimates of the amount of substandard housing units in a local area would facilitate policy, advocacy, resource allocation, and targeting decisions as well as provide information to facilitate grant applications. The ability to approve funding will give contractors the market to do the work needed to rehab substandard housing. Better estimates of substandard housing are necessary to facilitate the preparation by local governments of the required housing elements of comprehensive plans as well as federal Consolidated Plans. A common estimation method that can be applied across

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6 jurisdictions would also increase the consistency of identifying problematic areas. Nationally, 27 percent of the housing units in the U.S., and 39 percent in central cities, are over 50 years old, an age at which substantial rehabilitation is often needed (HUD, 2001b). The focus of this research is on Florida, specifically the Gainesville region. The aging of the Florida housing stock, particularly in slower growing and rural counties, has resulted in the condition of the housing stock becoming increasingly an issue of concern. The selected county for analysis is Alachua County which has an older housing stock than the average Florida area. For this analysis the city of Gainesville will be used; this city was settled earlier than most other areas of the state. Hypothesis Statement The hypothesis tested for the Gainesville area was as follows: Ho: There are consistent links in the housing quality indicators found in the housing condition survey conducted by the Alachua County Housing Authority with variables gathered by the property appraisal data. Research Methodology This analysis is primarily descriptive. It attempts to link housing quality indicators found in the housing condition survey with data gathered by the county tax assessor. The objective is to provide contractors with an easily derived estimate of the number of units of below standard housing quality in cities, counties, and neighborhoods. To address these issues and provide a method that allows estimates of the number of units in an area that are substandard or in need of rehabilitation, this paper describes a methodology based on the use of parcel data. It provides a standard approach that could be used statewide or even nationally to the extent that data are available and consistent.

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7 Overview Chapter 2 presents a literature review on a variety of data that has been collected on the subject of housing condition. There has been a limited literature on using unpublished data sources to develop measures of housing condition, and there is little work at the neighborhood scale that is of interest in this research. The review will include the reason for variables of housing characteristics to be used to assess need for rehabilitation and a discussion of early data and past research pertaining to the use of housing quality indicators. Chapter 3 provides the methodology used to conduct the research. The methodology determines and analyzes the link between housing quality variables found in the housing condition survey have with variables gathered by the county tax assessor. This chapter will describe the data collected from the housing condition survey of Alachua County and the property appraisal information to develop a process for estimating substandard housing. Chapter 4 provides a discussion of the analysis performed on the results of the attempt to link the housing quality indicators found in the housing condition survey with data gathered by the county tax assessor. A discussion of the analysis used to study the data collected is presented. The hypothesis is then accepted or rejected based on the results of the statistical analysis. Next, the objective of the research outlined in this chapter is reviewed to decide if it has been adequately satisfied. Finally, comparisons of the results to the literature review findings and additional observations deemed significant but not included in the original scope of work are presented.

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8 Chapter 5 provides a conclusion of the research and results by summing up the study conducted in this research analysis. Finally, recommendations and practical suggestions for implementation of results and for future research on a simple yet accurate method by which housing quality could be estimated in a local jurisdiction.

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CHAPTER 2 LITERATURE REVIEW Introduction The literature review for this analysis provides an introduction into why rehabilitation is the answer to the problem of decreasing housing quality and an analysis of previous research that has been conducted on data that has been collected on the subject of housing condition. There has been a limited literature on using non-published data sources to develop measures of housing condition, and there is little work at the neighborhood scale that is of interest in this research. The use of indicators gives an opportunity to assess the potential market for contractor opportunity to fulfill the need for housing rehabilitation programs. The review will include the importance of rehabilitation programs needed to improve housing quality and a discussion of data and past research pertaining to the use of housing quality indicators. Importance of Rehabilitation Improving neighborhoods often involves a slow and continual struggle to recycle abandoned homes and rescue others from deterioration. By helping individual homeowners finance needed home repairs, replacements and alterations, community groups are at the forefront of sustaining hard-won home-ownership gains and protecting low-income neighborhoods from decline. Rehabilitation of small-family homes is important but all too easy for policymakers to ignore. Caught up in the glamour of the large and the new, federal 9

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10 policy has often favored development and rehabilitation of large multifamily properties. Indeed, the last remaining major production program is the low-income housing tax credit and is nearly 100 percent dedicated to multifamily rental housing. It has only been during the last few years that another giant federal program – Section 8 – has been used to assist a limited number of homebuyers. The fraction of HOME and Community Development Block Grant monies spent on repair and rehabilitation of small properties pales by comparison to the sums spent on the tax credit, public housing, and Section 8 programs. And they all target large multifamily properties (Belsky, 2002). In our nation’s housing policy there is an emphasis on large rental properties, and there also is an ingrained tendency to count progress in terms of the number of new homes built and the number of new owners added. It is not normal to count progress in terms of the number of existing homes saved from abandonment and loss or the number of homeowners saved from either losing their homes or living in unsafe and unhealthy conditions. This is not to say that adding to the nation’s rental stock is not critical, or that adding to the ranks of low-income and minority homeowners is unimportant. Of course, these activities are also vital and grossly under-funded. Policymakers must be reminded of how important the costly, overhead-intensive process of restoring and preventing deterioration of small properties is to achieving national housing goals (Belsky, 2002). The importance of helping lower-income homeowners improve their properties and rehabilitating small properties is underscored by a few simple facts that are stated in Rehabilitation Matters: Improving Neighborhoods One Home at a Time (Belsky, 2002): About one-fifth of the nation’s nearly 25 million low-income homeowners have a tough time accessing credit for home repair and improvements.

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11 Forty-five percent of the nation’s seven million extremely low-income homeowners have difficulty properly maintaining their homes. Over two years in 1998 and 1999, 42 percent of homeowners reported spending less than $500 on upkeep of their homes. Not counting uninhabitable properties due to fires and other disasters, about 400,000 units of housing in small properties are abandoned or otherwise uninhabitable. They pose an extreme threat to housing markets and impediments to neighborhood revitalization. Approximately 765,000 homeowners live in small properties that the federal government deems severely inadequate, and 1.6 million are exposed to unhealthy or unsafe homes with moderate problems. Only about half of low-income elderly homeowners spent an average of $46 a year or less on home repairs and improvements between 1984 and 1993. Tenants spend more than half of their income on other housing costs and do not have enough capital to rehabilitate housing on their own. For this reason it is important to develop a consistent process to assess these types of neighborhoods using practical variables for analysis. Benefits of Rehabilitation The rehabilitation of existing single-family housing stock can accomplish national housing goals in areas of public health, energy conservation, community impact, asset protection, and economic benefits as stated in Belsky’s, Rehabilitation Matters: Improving Neighborhoods One Home at a Time (Belsky, 2002). First, rehabilitation can reduce public health problems. An estimated 26 million children are exposed to lead hazards. Exposure to lead in youth impairs cognitive development and lifelong prospects for economic success. Addressing home health and safety hazards are not only a matter of common decency, it also can lower costly demands on the nation’s liability insurance companies, Medicare and Medicaid systems, and hospital emergency rooms. Second, rehabilitation can help achieve national energy conservation goals. Although most new homes are now built to very stringent energy standards, the overwhelming majority of the existing stock was built to lesser standards and

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12 never has been retrofitted to achieve significant conservation. Yet, the residential sector accounts for about one-fifth of the nation’s energy consumption. Third, maintaining and rehabilitating small properties can help spare communities from a downward spiral of abandonment and loss that, in turn, contributes to steep social and economic costs of trapping poor people in undesirable areas. Preventing properties from falling into disrepair, therefore, is essential to thwarting the progressive deterioration of neighborhoods. Similarly, significant reinvestment in rundown housing can breathe new life into distressed communities. Fourth, helping homeowners obtain subsidies or financing on fair and prudent terms for needed repairs and renovations can benefit them in a number of ways. It can reduce their borrowing costs, increase their chances of successfully repaying their loans, and protect them from scam artists. Finally, housing repair and rehabilitation give a lift to local economies and businesses. Like new construction, rehabilitation work often is financed, relies on local contractors, and uses materials purchased locally. Remodeling is much more labor-intensive than new construction, so it generates more local employment. Nationwide, building-material manufacturers and suppliers also benefit from housing reinvestment. The use of housing quality indicators will improve the obvious need for local government and nonprofit organizations to be able to target resources to areas of greatest need as well as demonstrate the need for housing rehabilitation programs. The uses of housing rehabilitation programs to assist individual homeowners and restore small properties are vital to achieving multiple objectives. This will affect the local contractors by identifying housing in need of rehab. Sources of Data on Housing Condition The Census is used to provide government planners, policy makers and administrators with data on which to base their social and economic development plans and programs. Among the objectives of the Census are: Obtain comprehensive data on the size, composition and distribution.

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13 Take stock of the housing units existing in the country and to get information about their geographic location, structural characteristics, available facilities, etc. Gather migration and fertility data. Classify the population according to ethnic origin and religious affiliations and determine their geographic distribution; and gather data on usual occupation and industry (US Census Bureau, 2003). The Census provides data for block groups and census tracks, but the data is only collected every ten years and the variables used are of limited use for condition. As a result of the Census data topography not fitting neighborhood boundaries local developers of neighborhood measures have made extensive use of property assessor files. At the local level the problem is that lacking complete plumbing, lacking heating facilities, or lacking kitchen facilities are the only variables that have been available in the Census since 1970 to provide an indicator of housing condition. These variables do not reflect the structural integrity of the unit or aspects of the unit subject to wear and tear. According to US Census Bureau, the Census data is collected on whether the housing units have kitchens, heating units, and electrical systems and, if so, how well these work. Information on the costs incurred for mortgage payments, real estate taxes, property insurance, utilities, and garbage collection allow comparisons of housing costs from one year to another or between geographic areas. Data collected on income can be used in conjunction with annual housing expenditures to estimate the average percentage of families and primary individuals' incomes that is spent on housing. Households that have moved in the last 12 months prior to enumeration are asked to provide comparative information on the current and previous residences of household heads. Information is collected on whether employment, family, or other factors such as changes in

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14 neighborhood or financial conditions influenced the decision to move. Demographic information, including age, sex, race, marital status, income, and relationship to householder is collected for each household member. Years of school completed, Spanish origin, length of residence, and tenure is provided for the householder. The American Housing Survey (AHS) provides more information, but this information is based on a survey. This survey is conducted by the Bureau of the Census for the Department of Housing and Urban Development (HUD). The American Housing Survey (AHS) collects data using quality variables on the nation's housing, including apartments, single-family homes, mobile homes, vacant housing units, household characteristics, income, housing and neighborhood quality, housing costs, equipment and fuels, size of housing unit, and recent movers. National data are collected in odd numbered years, and data for each of 47 selected Metropolitan Areas are collected currently every six years. The national sample covers an average 55,000 housing units. Each metropolitan area sample covers 4,100 or more housing units. The sample does not allow estimates at the jurisdiction level for Florida’s over 400 local jurisdictions. Therefore, the AHS is useful at the national and metropolitan level to estimate housing condition but not at the local level. Further, the expectation is that housing condition is a greater problem in rural areas where the housing stock is older, but where AHS data is limited. Another approach is to undertake windshield or other surveys of condition, but they are expensive, time consuming, and may not allow cross-jurisdiction comparisons due to different definitions and standards used by surveyors in different communities. In

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15 Florida, the county property appraisers provide an estimate of housing condition as one of the data points in their data on properties in a county. These assessments are of limited usefulness because there is wide variation across counties in the percentage of units considered substandard, well greater than the expected difference in quality. In fact, one county in which most of the housing stock has been built in the last twenty years is rated by its county appraiser as having the highest percentage of below average housing units in the state. Sources that Develop Measures of Housing Condition There is a limited literature on using secondary data sources to develop measures of housing condition, and there is little work at the neighborhood scale that is related to the objective of this thesis. Using county property appraiser data and testing the findings of that data with fieldwork using windshield surveys, Schneider and Zwick attempted to develop a measure of substandard housing in two Florida localities (1988, 1995). They found a set of variables that were potential predictors of substandard housing including age, size, and value of the unit. Koebel used tax assessor data to measure housing quality and created a “housing quality index” which was then compared to housing code violations in Louisville, Kentucky (Koebel 1986). The index showed potential as a tool for classifying poor quality housing, but it misclassified many units. The work of Koebel was based on two earlier papers (Stegman and Sumka, 1976) and (Sumka, 1977) that also demonstrated the use of tax assessor data in relation to housing quality.

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16 The American Housing Survey develops a housing quality variable that is based on multiple occurrences across a number of individual variables that are measures of housing deficiencies, such as leaks, electrical breakdowns, or plumbing breakdowns. There is precedence from previous research, such as Kutty (1999) using variables as a measure of housing quality data from seven metropolitan areas to estimate a model of the determinants of structural adequacy. She used the variable age of the building as the most important physical attribute determining structural adequacy. This variable is significant yet her measure of age is a categorical variable with the oldest category being over twenty years old. That is a premature measure of an age variable, structural quality would decline at forty years age. Important factors determining structural adequacy in Kutty’s model include unit type, tenure, income of the occupants, and vehicle ownership. The variable of tenure has been explored as a variable in several papers including Gatzlaff et al. (1998), Galster, (1983) and Henderson and Ioannides (1983). It has been documented that owner-occupants have an increased incentive to maintain their units than renters, and that relative maintenance effort would be reflected in appreciation rates and in housing condition. Galster finds strong evidence of the relationship that owner-occupied housing is in better condition; while Gatzlaff et al. finds inconsistent evidence. The experimental research done on tenure directs its research to single family housing stock. The data set that is being used to develop local estimates of housing condition is county property appraiser data. This data set includes characteristics of the housing unit but not of the household occupying the unit. The focus of this thesis will be on single-family housing units because the appraiser data does not have information on units in a

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17 multifamily structure, only the structure itself and in the Gainesville area the use of multi-family housing for students gives a false indication of the norm. Consistent with Kutty’s (1999) variables, information on age of the unit and tenure has been acquired as well as other variables.

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CHAPTER 3 METHODOLOGY Introduction The research methodology used for this paper is primarily descriptive. As the methodology will show, this thesis will define and measure the key critical factors that lead to the need for rehabilitation, which will indicate the extent of a market for contractors to fulfill the work needed to increase housing quality of neighborhoods in the Gainesville area. Moreover, once the data collected from the housing condition survey of Alachua County combined with the property appraisal information are analyzed, this study will determine the current effectiveness of an easily derived estimate of the number of units of below standard housing quality and introduce a marketing possibility for contractors to perform the necessary rehabilitation work needed to improve the current housing stock. This paper will help to be aware of the decreasing housing quality that is present throughout the country and assess opportunity for work for local contractors and it will provide a standard approach that could be used statewide or nationally to the extent that data are available and consistent. Sources of Data The data for this analysis come from the housing condition survey conducted by the Alachua County Housing Authority and from the Alachua County property appraisal data. Housing condition surveys are used as a means of identifying housing stock condition. These surveys are recognized as an essential part of housing strategy 18

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19 development. The survey results indicate the level of resource needed to make an impact and identify the extent of existing problems in the housing stock. The housing condition survey is used with variables from county property appraisal information to produce a sample for assessment of current housing condition. Housing Condition Survey In the housing condition survey there were 2204 property addresses that were analyzed by windshield surveys. A random sample of dwellings was drawn from a total of 10 neighborhood areas. Within each study area the stock was identified separately to facilitate subsequent analysis of the gathered data. The study was conducted by two housing officials assessing the exterior of each unit using a survey form. The housing condition survey creates a variable that identifies housing quality to measure quality for units in the housing condition survey sample. Because there are few severely inadequate housing units, the adequacy variable was reduced from three categories, adequate, inadequate, and severely inadequate, to two categories, adequate and inadequate. The criteria used in the survey form to determine if a house is substandard are as follows: roof caving in or damaged house boarded up pulled utility meter broken windows siding heavily damaged or rotting overall appearance. Included in the form are the following codes that were used in the “comments” column: 0 Good 1 – Substandard 2 – No House / Not Located 3 – Building is commercial, or church

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20 Property Appraiser Data Property appraiser data are collected on every parcel of land in the state of Florida. These data are compiled at the county level. The county files contain a number of variables for each parcel. When a building permit is issued on a parcel, one of the appraisers will visit the property to value the improvements. At least once every three years one of the appraisers will inspect a property to verify the information on the property record card. In addition, sales of the area will be reviewed at least once every three years to determine if there has been a change in the market value. The focus of this analysis is on single-family housing units, and for these the data available include most recent and second most recent sales price, just value, assessed value, year built, square footage of the unit, land value, size of the parcel, and whether the homestead exemption is claimed. Note that there is no data on the households occupying a unit. The inclusion of parcel identification numbers permits geographic information system (GIS) mapping and allows for the examination of various spatial issues. Schneider and Zwick (1988, 1995) have used county property appraiser data in two Florida applications to test the use of this data in the identification of substandard housing. They developed a set of decision criteria to identify substandard units using the appraiser data, and then used fieldwork to test the conclusions. The county property appraiser data does not measure household characteristics that might contribute to the quality of a housing unit, such as household size, age of householder, and income. The work of Schneider and Zwick suggests that housing unit characteristics that contribute to the quality of a unit and are available from the county property appraiser data include age, value, and square footage. These variables will be

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21 tested directly and indirectly. As became apparent from the data, age is a particularly important variable in determining housing condition and is the start point for the analysis. Why is age important? As housing units age, and without reinvestment in the building, the value of the building gradually declines as a percentage of the total property value. In an area experiencing extreme problems, both housing value and land value may decline. At some point the building value declines as a result of depreciation to a point where it became desirable to tear the building down and construct a new structure. Age does not in itself result in a substandard unit, as evidenced by the older homes in prime neighborhoods in many cities, but age combined with deferred maintenance and upkeep will result in declining housing quality (Smith, 2002). Description of Data Analysis To determine the need for a consistent method of locating the amount of rehabilitation needed for specific units is possible by using a process derived from Alachua County housing condition survey and the county property appraisal data. The housing condition survey and the property appraisal data include the number of adequate to inadequate units. This analysis will cover the amount of units found to be inadequate and units that reduce in value by year built. The housing condition survey will be used to give a general estimate of the quality of units by using variables age, and size. The property appraisal data will be used to estimate a value of the amount of work available for local contractors. This will be the foundation of the analysis from the variables age, size, and value used to evaluate the units.

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22 The variables that are involved have a direct or indirect relationship to the objective of this analysis are age, value, and size. The housing condition survey and the tax appraiser data have information regarding all the variables needed for this analysis. First, regarding the age of a unit, the housing condition survey data indicates that there is a negative relationship between the age of the unit and its condition as evaluated by the adequacy variable. This correlation between age and adequacy of a unit is important to determine if the average age of a house in a certain neighborhood indicate rehabilitation opportunities. After examining the housing condition survey, the variables from the property appraiser data for Alachua County were considered in the analysis, including just value, land value, square footage, and latest sale price. In addition, several new variables were calculated. The variable land ratio is the ratio of land value to just value. Land ratio = Assessor estimate of land value / Assessor estimate of just value The reciprocal of this variable was used to estimate the proportion of total property value that is attributable to the building. Percent of value attributed to building = 1 – Land ratio Building value for each single-family unit was calculated two ways: first using the just value figure, and then using the latest sale price figure. Just value is the county property appraiser’s estimate of market value. However, if the latest sale price was recent and higher than the just value figures, it was used as a better indicator of market

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23 value. By finding the value of the building the estimate of how much it will cost to improve the structure its original price or how much it will cost to replace the structure. Building value = (Percent of value attributed to the building)(Just value) or Building value = (Percent of value attributed to the building)(Latest sale price) Square footage of the unit enters the analysis indirectly as the value of a new unit of that size was estimated as a way to approximate the replacement cost for the structure. To do this, the square foot estimate of construction cost from the RS Means: Building Construction Cost Data (2003) for the Southeast United States with an adjustment for the Gainesville area. The average construction cost per square foot multiplied by the square foot figure for each unit produced the variable unit value new. Unit value new = (Per square foot construction cost)(Square footage of unit) Lastly, the ratio of current unit value to the replacement cost, the value ratio, was calculated. Value ratio = Current building value / Unit value new The use of these value ratios will shed light for the need to improve the quality of housing in a given area and the potential market for business in rehabilitation. The current unit value was derived from the value ratio and is compared to the unit-value-new. The use of these value equations will give an accurate estimate of the amount of work available to contractors in the local area.

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CHAPTER 4 DATA ANALYSIS AND RESULTS The analysis in this chapter is from data collected from the Alachua County housing condition survey and the county property appraisal data with shared variables. The surveys are used to give an estimate of substandard housing that was described in chapter 3. Appraisal data is used to give an assessment of the most common age, value, and size of units that are in need of rehabilitation. This analysis will attempt to develop quantitative and qualitative relationships between specific variables and the characteristics of housing units. The results in this chapter are presented in three separate sections in order to clearly present the findings. The first section gives the general characteristics of the sample of houses in the condition survey, including such issues as the number of houses found inadequate per year built and the reduced value of the unit. General information is also provided on the amount of rehab available for contractors to improve structures. The second section presents findings regarding the relationship between housing quality variables. This section incorporates a summary of the age and size of units, the relationship to housing quality, and an analysis of the specific measures that surveys use to assess housing. The data from the Alachua County survey is used for this section. The last section presents the results gathered about rehabilitation needs of the structures that have declined in value or are considered to be inadequate. The data from the county property appraiser is used to produce value from an inspection of the unit. 24

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25 This data is used because the market value can be taken into consideration when the analysis is done. The market value is determined by analyzing the sales of similar properties, the cost to reproduce the property, and the ability of the property to earn income. An estimated sum will be developed by using a value ratio to indicate the amount of work (in currency) available to local contractors. The information presented shows the effect that a lack of maintenance has on the single-family housing. This section reveals the amount of business to be obtained from a marketing concept to rehabilitate housing from funded government programs, and also includes information on the type of variables involved in identifying work opportunities in rehabilitation. General Information on Units Surveyed The housing condition survey sampled single-family housing units in Alachua County. Each unit’s square footage, age, and value are variables considered and are the basis of the results in this chapter. For this analysis the survey results have been broken down into age groups. The first part of this chapter will include an analysis on the Alachua County Housing Authority survey to determine the type of units that exist in this county. In Table 1, the average age for the unit is calculated for each age group. Table 4-1 Average age surveyed per age group Year Built Average Age 1900 to 1910 100 1911 to 1920 80 1921 to 1930 71 1931 to 1940 61 1941 to 1950 53 1951 to 1960 41 1961 to 1970 35 1971 to 1980 24 1981 to 1990 14 1991 to 2000 4 Ave. age surveyed 48

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26 Based on the survey responses, the distribution of units surveyed per age groups are presented in Figure 4-1. This table shows the average age surveyed is 48, the age where deterioration begins is estimated at age 40. This gives an introduction into the average age per age group of the units surveyed. 145124334357847033421718801002003004005006007001900-19101911-19201921-19301931-19401941-19501951-19601961-19701971-19801981-19901991-2000Year BuiltTotal Units in Survey Figure 4-1 Units surveyed by year built The Relationship between Housing Quality Variables The relationship between specific age categories and the adequacy variable was then analyzed. As seen in Table 4-2, the percentage of inadequate units generally increases with age. The methodology then attempts to build a relationship between the percentage of housing condition survey units that are inadequate by age of unit and the characteristics of units of that age in the county property appraiser data set.

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27 Table 4-2 Quality by year built Year Built Total Units Adequate % Inadequate % 1900-1910 14 100 0 1911-1920 5 80 20 1921-1930 12 50 50 1931-1940 43 65 35 1941-1950 343 77 23 1951-1960 578 87 13 1961-1970 470 92 08 1971-1980 334 96 04 1981-1990 217 96 04 1991-2000 188 99 01 Average 2204 84 16 note: totals vary between tables due to missing data The total units surveyed with relationship to units with reduced value and units considered to be inadequate were broken down by year. Units reduce in value over time and often aren’t considered to be inadequate. The unit’s value from the time it was built until when it was last surveyed reduces in value. This reduced value is an estimation of the amount of work available if a unit was to be rehabilitated to its original value. This has market potential for contractor work. In Figure 4-2, a chart was used to clearly identify the total number of units surveyed, the number of units considered to be inadequate, and the number units that have a reduction in value. This chart identifies the relationship between the year the structure was built and the number of units surveyed. The amount of units per age group that have a need for work to be performed gives an estimate of the amount of rehab available for local contractors to perform.

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28 01002003004005006001900-19101921-19301941-19501961-19701981-1990Year Unit was BuiltNumber of Units Surveye d Total Inadequate Units Total Units of Reduced Value Total Units Figure 4-2. Quality by year built The quality of housing in relation to year built shows an incidence where the housing became more susceptible to inadequacies beginning at the age range of 1961 to 1970. An assessment of this survey was able to be obtained due to the amount of unit surveys that were gathered. For this reason, paired with the amount of units found inadequate and units found to be of less value when purchased, it was concluded the age group of 1961 to 1970 (30 – 39 years old) is considered to be the point where rehabilitation starts to be needed. Throughout this chapter are consistent methods of concluding an assessment of the age group and the amount of rehab or work needed to return the quality of the units back to its original state when first purchased. When evaluating the value of a unit there is a direct relationship with the size of a structure. This is to determine the amount of the work needed to be performed to improve the units housing stock. It is important with this research to clearly display the

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29 size of the properties being evaluated using the housing condition survey to begin to develop a relationship between the size and the adequacy of the structure. When evaluating the square footage of the units it is apparent that when a unit is smaller it is easier to maintain. Later in this chapter the square footage will be used in developing the method of analyzing the potential cost for rehabilitation in a given area. In Figure 4-3, the square footage is evaluated with respect to the year the unit is built. 57997817341125136112181376193616961924050010001500200025001900-19101911-19201921-19301931-19401941-19501951-19601961-19701971-19801981-19901991-2000Year BuiltSquare Footage Source: Alachua Housing condition survey Figure 4-3. Average Square footage by year built Rehab Cost Analysis for Future Work Table 4-3 illustrates the mean and standard deviation of the variable, value ratio, for single-family units. There are 6700 units that are considered to be inadequate from the county property appraisal data. These inadequate units have a mean building value at about 74 percent. The mean building value is a percentage of a new units value if it was of equal size. A number of units have ratios above one. These are housing units that are

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30 valued at more than the cost to construct an average unit in the Gainesville area. Values above one can occur because the unit is of higher than average quality or because the unit has already been substantially upgraded and those upgrades represent above average quality. However, in the majority of cases as the unit deteriorates, the value of the ratio declines. At some point, rehabilitation becomes necessary in order to keep the unit livable. This is important in assessing the market for this type of work and the need to pin point the age of the units in need of rehab. To do this, tax assessor data was used due to the consistent value data. Table 4-3 Range of value ratios by year built Year Built Mean Building Value % Standard Deviation 1900-1910 1911-1920 0.5836 0.1111 1921-1930 0.6398 0.0950 1931-1940 0.7436 0.1305 1941-1950 0.6894 0.1110 1951-1960 0.7099 0.0998 1961-1970 0.7106 0.0919 1971-1980 0.8282 0.1622 1981-1990 0.8629 0.1073 1991-2000 0.9103 0.0127 Owned Occupied Mean 0.7420 0.1135 6,697 Inadequate Units Source: Property Appraisal Data for Alachua County From Table 4-3, in order to estimate the age at which significant problems begin to occur, the housing quality variable was cross-tabulated with the year built (age). Refer to chapter 3 for the methodology for developing the value ratios that are used in this table. To clearly see the decrease in quality comparable to the age of the structure refer to Figure 4-4. As age increases, so did the incidence of inadequate quality. In this figure

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31 the plot begins decreasing at an increasing rate around the age group of 1961 to 1970 and is consistently maintained. The mean age for this group is 35 years. 0.50.550.60.650.70.750.80.850.90.951911-19201921-19301931-19401941-19501951-19601961-19701971-19801981-19901991-2000Year BuiltMean Building Value (% ) Figure 4-4 Average value ratios by year built The Alachua County housing survey was used to create a basis to develop and assess of the age group and size of the units that are most susceptible for rehabilitation work to be done. Then by using the Alachua property appraisal data, the research was directed to the amount of work that would need to be done in order to increase the value of the inadequate structure. The current unit value was derived from the value ratio used earlier in this chapter and is explained in full detail in chapter 3 of the methodology section of this thesis. Referring to Table 4-4, the current unit value in relationship to the unit-value-new is used to determine the amount of work needing to be performed with respect to the age group of the structure. The amount the unit costs is the difference between the current estimate (appraisal value) and amount it would cost if the structure

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32 was new. Unit-value-new was derived from the cost per square footage to rebuild the unit new the cost per square foot was referenced from RS Means: Building Construction Cost Data. The national cost per square foot average for single family homes was used against the Alachua County multiplier to get $60 per sq. ft. The greatest increase or difference between the two variables current unit value and unit value new will give an appropriate assessment of what age group and type of structure will be the most lucrative for marketing opportunities in rehabilitation. Table 4-4 Range of unit value by year built Year Built Current Unit Value Sq. Ft Unit Value New Unit Value Difference 1900-1910 1911-1920 $ 38,658.00 782 $ 46,900.00 $ 8,242.00 1921-1930 $ 37,067.68 721 $ 43,232.00 $ 6,164.32 1931-1940 $ 59,179.00 1193 $ 71,572.00 $ 12,393.00 1941-1950 $ 40,400.00 881 $ 52,876.00 $ 12,476.00 1951-1960 $ 35,318.00 743 $ 44,600.00 $ 9,282.00 1961-1970 $ 37,018.00 775 $ 46,500.00 $ 9,482.00 1971-1980 $ 52,925.00 952 $ 57,092.00 $ 4,167.00 1981-1990 $ 68,100.00 1205 $ 72,288.00 $ 4,188.00 1991-2000 $ 74,500.00 1304 $ 78,250.00 $ 3,750.00 Total $ 49,240.63 951 $ 57,034.44 $ 7,793.81 Source: Property Appraisal Data for Alachua County To further emphasize the importance of the difference between the current and the new variables for unit values, a chart has been derived from a collection of data used in the property appraiser data and the Alachua County housing quality survey, displayed in Figure 4-5 Range of Value Ratios by Year Built. The difference between the plots for the current unit value and the unit value new begin to increase at the age group of 1961-1970. This remains consistent with the data used throughout this chapter.

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33 $-$10,000.00$20,000.00$30,000.00$40,000.00$50,000.00$60,000.00$70,000.00$80,000.001900-19101921-19301941-19501961-19701981-1990Year BuiltValu e Current Unit Value Unit Value New Figure 4-5 Range of unit value by year built The total potential volume available in the Gainesville area for contractor’s to develop a market is the average unit value difference in Table 4-4 multiplied by the total units surveyed. The total inadequate units surveyed by the property appraisal data was 6,697 units, the average amount per unit of available work to be performed is $7,794. The total volume of available rehab work for this county is estimated at over 50 million dollars. This value is the total amount of rehabilitation work available if every unit was repaired to its current market value. The analysis in this chapter is from data collected from county property appraisal data and the Alachua County housing quality survey. The age, value, and size of units are used in these surveys to give a consistent and effective method of analysis to estimate and pin point where rehabilitation is needed. The analysis develops quantitative and

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34 qualitative relationships between specific variables and the characteristics of housing units to create a method to market rehab as a market for local contractors to benefit.

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CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS This research was motivated by a search for a simple yet accurate method by which housing quality could be estimated in a local jurisdiction to establish a market for contractor to perform the rehab work. The objective is to present data in an efficient way for analysis to give an estimate of the amount of rehabilitation work available in the Alachua County area using data that are easily obtainable. Tracking changes in housing quality is often inconsistent. This thesis has presented a model focusing on age groups and the relationship between the adequacies of housing from the Alachua County housing survey data. A measure, the ratio of market value to unit value new, derived from the county property appraiser data sets, is then used to test its correlation to the Alachua County housing survey relationship between condition and age. The relationship is such that it concludes this single measure, value ratio, can be used to estimate the quantity and degree of substandard housing in a community. This variable implicitly incorporates other variables thought to be important in the determination of housing condition including age, size, and tenure of the unit. This study is unique in that it establishes a single measure of housing quality, it is relatively simple to implement, and it relies on data that are readily available. While the results of this research provide valuable information about housing quality to implement rehabilitation in the housing industry, further research appears warranted. The methodology is based on the Gainesville area of Florida, and 35

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36 warrants testing in other areas in which the age and other characteristics of the housing stock differ. Further, testing of neighborhood effects on the value ratio are needed. Additionally, it is recommended that future studies focus on different sectors of the construction industry, including rental housing in order to have a broader sense of the nature of building quality in the neighborhood level. Finally, this study relied on the opinion of an individual perception of quality that was conducted in the surveys provided in the thesis. A more accurate technique that could be utilized in the future is the use of a standardized analysis of a given unit. In the judging the quality of a unit, the individuals conducting the survey only observe the exterior quality. There is a need for an interior inspection for a more complete analysis of the unit’s quality.

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APPENDIX A HOUSING CONDITION SURVEY This the Housing Condition survey conducted by the Alachua County Housing Authority. This is the windshield survey combined with the property appraiser data. The property appraiser information was implemented by the assessment the exterior appearance of an individual unit. Criteria used to determine if a house is substandard: roof caving in or damaged house boarded up pulled utility meter broken windows heavily damaged exterior or screens siding deteriorated or rotting overall appearance The following codes were used in the “common” column of the excel spreadsheets for the substandard housing study. 0 – Good 1 – Substandard 2 – No house / Not Located 3 – Building is commercial or church 37

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APPENDIX B PROPERTY APPRAISAL DATA FOR ALACHUA COUNTY The Alachua County land use survey. This is the property appraiser data of Alachua County for single-family homes. The following codes were used in the data for the Alachua County use survey. Definitions for variable: condition 0=Standard 1=Substandard Definitions for variable: landuse1 – type of housing 0=vacant 1=single-family 2=manufactured home 3=multi-family 10 or more units 4=condominium 8=multifamily less than 10 38

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LIST OF REFERENCES Belsky, Eric. 2002. “Rehabilitation Matters: Improving Neighborhoods One Home at a Time.” Bright Ideas, 21 (3), 15-26. Bogdan, Amy S. and Ayse Can. 1997. “Indicators of Local Housing Affordability: Comparative and Spatial Approaches.” Real Estate Economics, 25 (1), 43-80. Galster, George C, 1983 "Empirical Evidence on Cross-Tenure Differences in Home Maintenance and Conditions,” Land Economics, 59, 107-113. Gatzlaff, Dean. 1994. An Analysis of the Recently Enacted Save Our Homes Amendment: Its Potential Impact on the Florida Real Estate Market. Florida Real Estate Commission Education and Research Foundation. Tallahassee, FL. Gatzlaff, Dean H., Richard K. Green, and David C. Ling. 1998. “Cross-Tenure Differences in Home Maintenance and Appreciation.” Land Economic,74, 328-342. Henderson, J.V. and Y.M. Ioannides, 1983. A Model of Housing Tenure Choice. American Economic Review. 73. 98-113. Charlotte County, Florida. 2000, March 16. Property Tax Exemptions. http://www.ccappraiser.com/exemptn.htm / (retrieved November 2003). U.S. Census Bureau. 2000. Welcome to American Housing Survey. http://www.census.gov/hhes/www/ahs.html / (retrieved July 16, 2003). Kiel, K.A. and Zabel, J.E. 1999. “The Accuracy of Owner-Provided House Values: The 1978-1991 American Housing Survey.” Real Estate Economics, 27, 263-298. Koebel, C. Theodore. 1986. Estimating Substandard Housing for Planning Purposes. Journal of Planning Education and Research. 5: 191-202. Kutty, Nandinee. 1999. “Determinants of the Structural Adequacy of Dwellings.” Journal of Housing Research, 10:1. Neighborhood Reinvestment Corporation. 2001. Revitalization Through Home Ownership: Lessons from the Field. Author, Washington, DC. Nelson, Kathryn P. 1994. "Whose Shortage of Affordable Housing?" Housing Policy Debate, 5 (4), 54-116. 39

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40 Prosperi, David C. 1989. “Assessment of and Agenda for Using Property Tax Data in Urban and Regional Analysis.” 1989 URISA Proceeding, pp. 115-127. Quercia, Robert G., George W. McCarthy, Rhonda M. Ryznar, and Ayse Can Talen. 2000. “Spatio-Temporal Measurement of House Price Appreciation in Underserved Areas.” Journal of Housing Research. 11 (1), 2-8. RS Means: Building Construction Cost Data. 2003. Residential Cost Handbook. Construction Publishers and Consultants, Kingston, MA. Schneider, Richard H., and Paul D. Zwick. 1988. “Substandard Housing in Alachua County: An Inventory and Identification Methodology.” Final Report to the Alachua County Housing Authority, Gainesville, Florida. Schneider, Richard H., and Paul D. Zwick. 1995. “University of Florida – City of Largo Housing Research Project.” Final Report to the City of Largo. Stegman, Michael A., and H. J. Sumka. 1976. Non-metropolitan Urban Housing: An Economic Analysis of Problems and Policies. Ballinger Publishing Company, Cambridge, MA. Sumka, H. A. 1977. “Measuring the Quality of Housing: An Economic Analysis of Tax Appraiser Data.” Land Economics, 53,198-309. U.S. Department of Housing and Urban Development. 1994. Regional Housing Opportunities for Lower Income Households. Office of Policy Development and Research, Washington, DC. U.S. Department of Housing and Urban Development. 1999. The Widening Gap: New Findings on Housing Affordability in America. Author, Washington, DC. U.S. Department of Housing and Urban Development. 2001a. Barriers to the Rehabilitation of Affordable Housing, Volume I: Findings and Analysis. Author, Washington, DC. U.S. Department of Housing and Urban Development. Office of Policy Development and Research. 2001b, Recent Research Reports, December. Author, Washington, DC.

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BIOGRAPHICAL SKETCH Matthew Adam Harris was born on February 15, 1978, in Pittsburgh, Pennsylvania. He is the second of three sons of Rosalyn and Gary, and brother to Douglas and Bradley Harris. He moved to Coral Springs, Florida, in 1990. He received his high school diploma from Coral Springs High School in 1996. He received his bachelor’s degree from the University of Florida’s School of Architecture in 2002. Then, he entered the master’s program at the M.E. Rinker, Sr., School of Building Construction at the University of Florida where he will receive his Master of Science in Building Construction degree (MSBC) in December 2003. 41