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Estimating the Impact of Foreclosures on Housing Prices in Orlando, Florida

Permanent Link: http://ufdc.ufl.edu/UFE0045556/00001

Material Information

Title: Estimating the Impact of Foreclosures on Housing Prices in Orlando, Florida A Hedonic Modeling Approach
Physical Description: 1 online resource (76 p.)
Language: english
Creator: Xia, Yibin
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2013

Subjects

Subjects / Keywords: foreclosure -- hedonic -- housing
Geography -- Dissertations, Academic -- UF
Genre: Geography thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Foreclosures pose a significant threat to the market valueof residential properties, and are partly responsible for driving down thesales price of units in areas where foreclosure density is relativelyhigh.  After the bust of housing bubble,there has been a profound increase in the number of foreclosures in certaingeographic markets.  The foreclosurephenomenon has negatively affected housing prices in the United States, and especiallyin the state of Florida. This thesis uses data on single-family housing unittransactions in Orlando, FL for 2011 and 2012 to estimate the impact(s) offoreclosures on residential housing prices. A series of hedonic regression models are used to estimate the effectsof foreclosures on market value. A foreclosure intensive index is alsointroduced, and estimates of the average loss of market value for foreclosedproperties are obtained under various scenarios. The results suggest that, inthe Orlando market, foreclosures have significant negative effects on the priceof single-family housing units and nearby property values. Foreclosures alsoaffect property value negatively when they are associated with other submarketfactors, suggesting that the impact of foreclosures on market price issomething that is not constant over various locations or submarkets. Forexample, in housing submarkets with high median household income, the negativeeffects of foreclosures on the price of single-family housing units aresignificantly greater than the negative effect of foreclosures in the overallmarket (on average). The approach adopted in this thesis can be applied toother urban housing markets to estimate the negative impact of foreclosures onaverage market price, and to explain variability in the impact of foreclosuresin various housing submarkets.
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.
Statement of Responsibility: by Yibin Xia.
Thesis: Thesis (M.S.)--University of Florida, 2013.
Local: Adviser: Fik, Timothy J.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2013
System ID: UFE0045556:00001

Permanent Link: http://ufdc.ufl.edu/UFE0045556/00001

Material Information

Title: Estimating the Impact of Foreclosures on Housing Prices in Orlando, Florida A Hedonic Modeling Approach
Physical Description: 1 online resource (76 p.)
Language: english
Creator: Xia, Yibin
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2013

Subjects

Subjects / Keywords: foreclosure -- hedonic -- housing
Geography -- Dissertations, Academic -- UF
Genre: Geography thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Foreclosures pose a significant threat to the market valueof residential properties, and are partly responsible for driving down thesales price of units in areas where foreclosure density is relativelyhigh.  After the bust of housing bubble,there has been a profound increase in the number of foreclosures in certaingeographic markets.  The foreclosurephenomenon has negatively affected housing prices in the United States, and especiallyin the state of Florida. This thesis uses data on single-family housing unittransactions in Orlando, FL for 2011 and 2012 to estimate the impact(s) offoreclosures on residential housing prices. A series of hedonic regression models are used to estimate the effectsof foreclosures on market value. A foreclosure intensive index is alsointroduced, and estimates of the average loss of market value for foreclosedproperties are obtained under various scenarios. The results suggest that, inthe Orlando market, foreclosures have significant negative effects on the priceof single-family housing units and nearby property values. Foreclosures alsoaffect property value negatively when they are associated with other submarketfactors, suggesting that the impact of foreclosures on market price issomething that is not constant over various locations or submarkets. Forexample, in housing submarkets with high median household income, the negativeeffects of foreclosures on the price of single-family housing units aresignificantly greater than the negative effect of foreclosures in the overallmarket (on average). The approach adopted in this thesis can be applied toother urban housing markets to estimate the negative impact of foreclosures onaverage market price, and to explain variability in the impact of foreclosuresin various housing submarkets.
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.
Statement of Responsibility: by Yibin Xia.
Thesis: Thesis (M.S.)--University of Florida, 2013.
Local: Adviser: Fik, Timothy J.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2013
System ID: UFE0045556:00001


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1 ES TIMATING THE IMPACT OF FORECLOSURES ON HOUSING PRICES IN ORLANDO, FLORIDA: A HEDONIC MODELING APPROACH By YIBIN XIA A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR TH E DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2013

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2 2013 Yibin Xia

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3 To my Mom and Dad, Fang Liu and Kang Xia

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4 ACKNOWLEDGMENTS I am deeply grateful to a number of people withou t whose encouragement and assistance this thesis would not have been completed. I am greatly indebted to my supervisor, Dr. Timothy Fik for his constant encouragement and insightful guidance throughout my thesis process. In the preparation of the thesis, he has spent much time reading through each draft and provided me with inspiring advice. Without his consistent and illuminated instruction, the completion of this thesis could not have been possible. I would like to ex press my gratitude to Dr. Eric K eys f or serving on my thesis committee and offering my valuable suggestions in the academic studies. I have learned much analysis skills and academic writing fro m him.I feel grateful to Dr. David Ling at Department of Finance Insurance and Real Estate for servi ng on my thesis committee. I have benefited a lot from his devoted teaching and enlightening lectures. I am profoundly indebted to Dr. Steven Perz at Department of Sociology for supporting me to finish my two year academic study. His broad and profound kn owledge gave me great impression as well as great help. I would like to give my he arty thanks to Zhuojie Huang, Yang Yang and Jing Sun for helping me work out my problems and for their valuable suggestions and critiques during the process of my thesis. Fi nally, I would like to thank my parents for their loving consideration and great confidence in me all through these years. They have always been helping me out of difficulties and supporting without a word of complaint.

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5 TABLE OF CONTENTS P age ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 6 LIST OF FIGURES ................................ ................................ ................................ .......... 7 ABSTRACT ................................ ................................ ................................ ..................... 8 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 10 2 LITERATURE REVIEW ................................ ................................ .......................... 17 Impact of F oreclosure on Housing Price ................................ ................................ 17 Foreclosure in Submarkets ................................ ................................ ..................... 21 Hedonic Price Model ................................ ................................ ............................... 23 3 SURVEY OF STUDY AREA AND DATA ................................ ................................ 28 Research Statement and Objectives ................................ ................................ ...... 28 Study Area ................................ ................................ ................................ .............. 29 Hypotheses ................................ ................................ ................................ ............. 32 Data Source ................................ ................................ ................................ ............ 33 Data and Variables ................................ ................................ ................................ 34 4 METHODOLO GY ................................ ................................ ................................ ... 47 Summary of Modeling Strategies ................................ ................................ ............ 47 Foreclosure Contagion M odel ................................ ................................ ................. 48 Foreclosure Intensive Index Model ................................ ................................ ......... 49 5 RESULTS AND INTERPRETATION ................................ ................................ ...... 52 Global market ................................ ................................ ................................ ......... 52 High Median Household Income Submarkets ................................ ......................... 59 6 CONCLUSION AND DISCUSSION ................................ ................................ ........ 70 Conclusion ................................ ................................ ................................ .............. 70 Limitation and Future Research ................................ ................................ .............. 71 LIST OF REFERENCES ................................ ................................ ............................... 73 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 76

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6 LIST OF TABLES Table P age 3 1 Definition and descriptive statistics of variables in the global market ................. 39 3 2 Descriptive statistics of variables in high median household income submarkets ................................ ................................ ................................ ......... 41 5 1 Regression results of foreclosure contagion model with non interactive approach for the global market ................................ ................................ ........... 65 5 2 Regression results of foreclosure contagion model with interactive approach for the global market ................................ ................................ ........................... 65 5 3 Regression results of foreclosure intensive index model for the global market .. 66 5 4 Regression results of foreclosure contagion model with non interactive approach for the high income submarkets ................................ .......................... 66 5 5 Regression results of foreclosure contagion model with interactive approach for the high income submarkets ................................ ................................ .......... 67 5 6 Regression results of foreclosure intensive index model for the high income submarkets ................................ ................................ ................................ ......... 67

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7 LIST OF FIGURES Figure P age 1 1 The United States foreclosure re sales ................................ .............................. 15 1 2 U.S. foreclosure rate as of August 2012 ................................ ............................. 16 3 1 Orlando base map ................................ ................................ .............................. 43 3 2 Distribution of single family housing units ................................ ........................... 43 3 3 Housing density surface ................................ ................................ ..................... 44 3 4 Foreclosure density surface ................................ ................................ ................ 44 3 5 M odeling the impact of foreclosure on property value ................................ ........ 45 3 6 Distribution of single family housing units along with sale pri ce ......................... 45 3 7 Single family housing submarkets ................................ ................................ ...... 46 4 1 Flowchart of models for both global market and high income submarket ........... 51 5 1 ................................ ..................... 68 5 2 Percent below poverty ................................ ................................ ........................ 68 5 3 Median household income ................................ ................................ .................. 69 5 4 Number of households with public assistance income ................................ ....... 69

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8 Abstract of Thesis Presented to the Graduate School of the Univer sity of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science ES TIMATING THE IMPACT OF FORECLOSURES ON HOUSING PRICES IN ORLANDO, FLORIDA: A HEDONIC MODELING APPROACH By Yibin Xia May 2013 Chair: Timoth y Fik Major: Geography Foreclosures pose a significant threat to the market value of residential properties, and are partly responsible for driving down the sales price of units in areas where foreclosure density is relatively high. After the bust of h ousing bubble, there has been a profound increase in the number of foreclosures in certain geographic markets. The foreclosure phenomenon has negatively aff ected housing prices in the United States and especially in the state of Florida. This thesis use s data on single family housing unit transactions in Orlando, FL for 2011 and 2012 to estimate the impact(s) of foreclosures on residential housing prices. A series of hedonic regression models are used to estimate the effects of foreclosures on market va lue. A foreclosure intensive index is also introduced, and estimates of the average loss of market value for foreclosed properties are obtained under various scenarios. The results suggest that, in the Orlando market, foreclosures have significant negative effects on the price of single family housing units and nearby property values. Foreclosures also affect property value negatively when they are associated with other submarket factors, suggesting that the impact of foreclosures on market price is somethi ng that is not constant over various locations or submarkets. For

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9 example, in housing submarkets with high median household income, the negative effects of foreclosures on the price of single family housing units are significantly greater than the negative effect of foreclosures in the overall market (on average). The approach adopted in this thesis can be applied to other urban housing markets to estimate the negative impact of foreclosures on average market price, and to explain variability in the impact of foreclosures in various housing submarkets.

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10 CHAPTER 1 INTRODUCTION The U .S. housing market was regarded to be prosperous over the past decade and began to decline in the middle of 2007. During the periods of housing market prosperity, the homeownershi p rate was approximately 70 % and more people than ever were able to purchase homes through various mortgage products, loose underwriting standards, and improved services (Cutts and Green 2004; LaCour Little 2000) A growing credit risk was inevitable along with this achievement (Lin, Rosenblatt, and Yao 2009) For instance, subprime mortgages have grown to serve those with weak payment capacity and blemished credit who would not qualify for a mortgage in the prime market (Lin, Rosenblatt, and Y ao 2009) $665 billion of such loans were originated in 2005, and the reached 23 % in 2006 (Lin, Rosenblatt, and Yao 2009) According to National and Twelfth District Developments (2007) a sharp increase in de linquencies and foreclosures took place in 2007 as the subprime market has captured the public spotlight. Due to the outbreak of the subprime credit collapse, the U S housing market was in a crisis th rough 2007 and 2008, which substantially increased foreclosures and largely declined the housing prices in over half of the states in the U S especially in Arizona, California, Florida, Michigan, Ohio, and Nevada (Calomiris, Longhofer, and Miles 2008) According to Humphries (2012) foreclosure re sales increased to a new high making up 20.5 % of all sales in March 2012 (Figure 1 1 ). It is expected that foreclosure re sales will continue their steady increase based on the status over the last severa l months (Humphries 2012)

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11 In the 1990s, two theories of foreclo sures were prevalent to model the factors associated with foreclosures (Grover, L. Smith, and Todd 2008) The options theory of foreclosures indicated that foreclosures occur when the value of the property falls far below the outstanding loan balance that the borrower finds it profitable to put the collateral to the lender instead of continuing payments. These foreclosures are often associated with homes whose market value s have declined (Ambrose and Capone 1998) Th e trigger event theory indicated that foreclosures occur when the borrower experiences financial setbacks, such as job loss, which make s it difficult to continue making payments. T hese foreclosures are often associate d with low credit scores or high unemployment rates (Ambrose and Capone 1998) Based on the report from RealtyTrac (2012) about 2 million homes are facing foreclosure process. According to the Mortgage Bankers Association, the number of mortgages in foreclosure or more than 90 days delinquent is at a record high of about 4.2 million (Hartley 2010) Highly (Frame 2010) According to repo rts from CoreLogic 11.3 million residential properties which representing 24 % of all residential properties with mortgages were in a negative equity position as of year end 2009 (Frame 2010) Foote Gerardi, and Willen (2008) indicated that a negative equity position is necessary for a mortga ge default and a trigger like a shock to the borro often req uired for a foreclosure. Abundant researches suggested that foreclosure s have negative effects on housing price s Foreclosed properties tend to sell at discount because they are likely to be physically damaged during the f oreclosure process or because lenders intend to sell them quickly to reduce their holding costs (Biswas 2012) Foreclosed properties also

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12 tend to lower the price of properties in the vicinity. According to Immergluck and Smith (2006) foreclosure s of single family homes have been regarded as a threat to neighborhood well being. They argue d that foreclosures, especially in lower income neighborhoods, lead to vacant properties. These properties bring physical disorder and crime in a community, which then lead to lower housi ng values of nearby properties. After the bust of housing bubble, there has been a profound increase in the number of foreclosures in certain geographic markets. The foreclosure phenomenon has negatively affected housing prices in the U.S., and especially in the state of Florida. Florida Realtors (2012) estimated that about 600,000 houses in Florida remain in a "shadow inventory which means that those properties are in the risk of default or foreclosure but not yet listed for sale The Metropolitan Statistical Area (MSA) of Orlando Kissimmee has the 7 th highest foreclosures rate (14.9 % ) in 2012 (FloridaRealtors 2012) Figure 1 2 sh ows the foreclosure rate s in the U S as of August 2012, and Florida has one of the highest foreclosure rate s as a per centage of all mortgaged homes (11.0 % ). This thesis uses data on single family housing unit transactions in Orlando, FL for 2011 a nd 2012 to estimate the impact(s) of foreclosures on residential housing prices and nearby property values. A series of hedonic regression models are used to estimate the effects of foreclosures on market value, and control for property, locational, and ne ighborhood characteristics. Based on the hedonic regression s both foreclosure contagion model and foreclosure intensive index model show good performance s. Foreclosure contagion model measures the foreclosure discount on housing price s and nearby property value s It also estimates the interactive nature between foreclosure s

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13 and other attributes such as property attributes, locational attributes, or neighborhood attributes In addition, f oreclosure intensive index model only includes foreclosure intensive index as foreclosure attributes to estimate the impact of foreclosure intensity on housing price s It provides estimation of the average loss of market value for foreclosed prop erties under various scenarios. Moreover, this thesis intends to measure the ef fects of foreclosures on property values in the high end housing submarket (i.e., in neighborhoods with relatively high income levels), given that effects of foreclosure tend to differ across submarkets. The results suggest that, in the Orlando market, fo reclosures have significant negative effects on the price of single family housing units and nearby property values. In particular, a foreclosure property lowers t s he sale price by $49,105, and each additional foreclosure within 2000 feet of a subject sale is associated with an approximately $2,500 decrease of the sale price. The interactive approach suggests that foreclosures with larger living area s have larger negative effect s on housing price s than those with smaller living area s Foreclosures with popu lation that has a higher s on housing price s than those with a significant negative effect on the housing price s as expected, for a 0.01 unit increase of the foreclosure intensive index lowe rs the housing price by $1,563, which indicates that foreclosures are partly responsible for driving down the sales price of units in areas where foreclosure density is relatively high. Forecl osures also affect property value negatively when they are associated with other submarket factors, suggesting that the impact of foreclosures on market price is something that is not constant over various

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14 locations or submarkets. A foreclosure property lo wers the sale price by $60,363, and each additional foreclosure within 2000 feet of a subject sale is associated with an approximately $3,300 decrease of the sale price. The interactive approach estimates that foreclosures with larger living area s have lar ger negative effect s on housing price s than those with smaller living area s The foreclosure intensive index has a significant negative effect on the housing price s as expected, for a 0.01 unit increase of foreclosure intensive index lowers the housing pri ce by $2,330. Based on these statistics in housing submarkets with high median household income, the negative effects of foreclosures on the price of single family housing units are significantly greater than the negative effect of foreclosures in the ove rall market. The organization of the thesis is shown as follows. Chapter 2 provides the literature reviews. Chapter 3 describes the study area and data/variables. Chapter 4 presents the hedonic regression methodology Chapter 5 discusses the results and p rovides interpretation s Chapter 6 concludes the thesis and mentions the limitation and future research.

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15 Figure 1 1. The United States f oreclosure r e s ales ( Adapted from Zillow Real Estate Research : Humphries, S. 2012. Rentals Continue to Outshine Pu rchase Market, Home Values Still Plagued By Foreclosures. Zillow Real Estate Research http://www.zillowblog.com/research/2012/04/09/rentals continue to outshine purchase market home values still plagued by foreclosures/. )

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16 Figure 1 2. U.S. f oreclosure r ate a s of August 2012 ( Adapted from CoreLogic Market Trends : Norman, D. 2012. Foreclosures in May down almost 20 % from year ago. Real Estate Industry News http://www.realestateindustrynews.com/foreclosures/foreclosures in may down almost 20 perce nt from year ago/.)

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17 CHAPTER 2 LITERATURE REVIEW Impact of Foreclosure on Housing P rice Over the past decade, numerous studies have examined the impact of foreclosures on property prices within affected neighborhoods and throughout the urban housing mark ets (Sumell 2009; Immergluck and G. Smith 2006; Mikelbank 2008; Leonard and Murdoch 20 09) Hedonic models have been widely used to quantify the effects of foreclosures on real estate values using a large number of control variables such as property, location, and neighborhood characteristics to account for various price determinants (Schuetz, Been, and I. G. Ellen 2008) These studies vary significantly in terms of t he location of the housing market, the period examined, the selection of control variables, and the model building methodology. To estimate the effect s of foreclosure s on housing price s Sumell (2009) estimated that foreclosures le a d to a 50 % discount on property price s in Cuyahoga County, Ohio, between 2004 and 2006. Similarly, Campbell, Giglio, and Pathak (2009) found a 22 % foreclosure discount for single family properties in Massachusetts during 1987 2007. Both of these studies use d hedonic price model s with a larger sample size and sufficient control variables. Clauretie and Daneshvary (2009) estimate d a foreclosure discount of less than 10 % in Clark County, Nevada, from 2004 to 2007. This analysis controlled for various property and neighborhood characteristics, occupancy status, timing on the market, and cash sales. Several studies also indicate d that larger foreclosure discounts are usually associated with homes in the lower quality range because financially distressed low income homeowners are less likely to maintain their properties (Clauretie and Daneshvary 2009; Sumell 2009)

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18 Given that foreclosed properties generally sell at discount, researchers began to concern about how foreclosure s affect housing price s in surrounding neighborhoods, which is also called the fect. Immergluck and Smith (2006) pointed out that foreclosures of conventional single family homes in Chicago in 1999 had a significant impact on the hou sing value s of surrounding properties after controlling for over 40 property and neighborhood characteristics. They indicate d that each foreclosure associated with conventional loan within one eight mile of a single family home resulted in a 0.9 % to 1.1 % decline in the home value depending on whether or not the median ho use price in the census tract was controlled for Foreclosures associated with conventional loans located between one eighth and one quarter mile away from a single family home had less negative effect s on the near by housing price s However, it was not clear why foreclosu res associated with government guaranteed loans appear ed to have no effect on nearby sales prices. Schuetz, Been, and Ellen (2008) studied single family and multifamily property sales and foreclosure s in New York City from 2000 to 2005 to identif y t he effects of foreclosure s on housing prices in the nearby neighborhood s Both physical distance (within 250 feet, 250 500 feet, and 50 1,000 feet) and time (less than and greater than eighteen months) were involved in this study. The ir results suggest e d that the proximit y to foreclosure properties i s associated with lower sales prices. They also found that with the increase of the number of foreclosures, the magnitude of the negative effects on property prices increases over time. Mikelbank (2008) examine d the spillover effects of foreclosures and vacant/abandoned properties for Franklin County, Ohio in 2006. The author found out

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19 that the negative effect of a vacant p roperty o n a nearby house sale wa s more severe than foreclosure, but had t s of foreclosure s by contrast, were mor e moderate than vacancy, but had a significant impact up to 1,000 feet. Although this study is ab le to control for property quality, it has limited neighborhood control variables Calomiris, Longhofer, and Miles (2008) develop ed a dynamic model of the housing market at the state level to measure the size of th e effect that foreclosures have on the housing prices. The ir results suggest ed that foreclosures had significant negative effect s on housing prices. Surprisingly the magnitude of this effect was small, which indicated that home prices are quite sticky eve n in the face of a high probability of foreclosure. Leonard and Murdoch (2009) studied sales of single family homes using hed onic price model s in Dallas County, T exas, during 2006. They classified nearby foreclosures according to their physical space (250 feet, 500 feet, 1,000 feet, and 1,500 feet) of the sale, and indicate d that the effects of nearby f oreclosures on housing mar ket were negative. They found evidence that each foreclosure wi thin 250 feet negatively affected selling price by 0.5 % or approximately $1,666 The result s also suggested that there m ay be modest spillover effects even further out in the physical sp ace. Rogers and Winter (2009) investigate d the impact of foreclosures on sales prices of non foreclosed homes in St. Louis County, Missouri, between 1998 and 2007. Foreclosure ef fects were measured involving both physical distance and time. The authors indicate d that foreclosure s lead to expected decline in the sale s price s of nearby properties. On e of the most notable finding s is that the marginal impact of foreclosures on sale s price s

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20 seems to decline as the number of foreclosures increases. The weakness es of this analysis lie in the lack of temporal control variables and neighborhood control variables. Lin, Rosenblatt, and Yao (2009) examine d foreclosure spillover eff ects for the Chicago MSA in 2003 and 2006 based on a comparable properties theory. The authors indicate d that the spillover effect s of a foreclosure on the value of a neighborhood property depe nd on two factors: the discount of the foreclosure sale and the distance between the foreclosed property and the subject property. The results suggest ed that the spillo ver effects of foreclosures could lead to a decrease of nearby property value s by 8.7 % within a 0.9 kilometer radius and a 5 year period from li quidation in a bad market, such as that in 2006. The negative spillover effects reduce as the time and distance from the foreclosure increase. Moreover, the authors show ed that the s pillover effect s of foreclosure were much less in a relatively good market such as that in 2003, for the foreclosures decrease d nearby property value s only by 5 % The shortcoming s of this study lie in that only ZIP code is utilized to indicate neighborhood characteristics and the control for local property price trends i s not included. Campbell, Giglio, and Pathak (2009) use d single family property transaction s data in Massachusetts over the last 20 years to show foreclosures are sold at lower price s than regular houses (a 7 9 % foreclosure discount). After using two different in ed that foreclosures within 0.05 mile lower the price of houses by about 1 % Harding, Rosenblatt, and Yao (2009) use d a repeat sales approach to estimate the contagion effect s of foreclosed properties. Both physical space (0 300 feet, 300 500 feet, 500 1,000 feet, and 1,000 2,000 feet) and time (the stage in the foreclosure process)

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21 attributes were involved in this study. The results confirm ed that nearby foreclosed properties have significant negative contagion effects in house prices. A peak contagion effe ct from the closest foreclosure is approximately 1 % and the negative contagion effect diminishes rapidly as the distance between the subject prop erty and foreclosed property increases. The results also suggest that the largest negative contagion effect of a foreclosure on a nearby property occurs around the time of the foreclosure sale. A foreclosure decreases the value of a house within 300 feet o f the foreclosure by approximately 1 % at that point. Biswas (2012) us ed a dataset of housing price s in the City of Worcester from 1991 to 2008 to examine the spillover effects of foreclosures. The author proposed a new approach which enable s test ing whether and how the impacts of nearby foreclosures may diff er across types of housing. Their study focused on the effects of foreclosures of single and multi family houses on each single family transaction within 660 feet (one eighth of a mile) and within 1320 feet (one quarter of a mile). One of the m ost preferred estimates sug gested that each single family foreclosure that occurs within 600 feet from a subject single family house reduces the sale price by approximately 1 % Ano ther preferred estimate suggested that between 660 and 1320 feet of a subject property, each mul ti family foreclosure lowers the value s of the nearby single family properties by approximately 3 % Foreclosure in S ubmarket s Immergluck and Smith (2006) indicated that many cities have suffered a growth in foreclosures during the recent economic downturn. Particularly hard hit are low and moderate income and minority neighborhoods. The author s point ed out that the total number of foreclosure s ros e 238 % from 1995 to 2002. There was an increase of 215 %

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22 in census tracts with less than 10 % of minorities, while there was an increase of 544 % in census tracts with more than 90 % of minorities. One of the most notable fin ding s is that there was an over 61 % of increase in conventional foreclosures in census tracts with minority populations of 50 % or more. Hartley (2010) suggested that foreclosures affect housing prices differently in different neighborhoods (submarkets), so it may be necessary to use va rious sub market specific strategies to reduce the unwanted negative effects caused by foreclosures. T he author indicated that there are two different ways in which foreclosures can affect the nearby property value s First, foreclosures add the supply of h omes to the market and a large number of homes for sale may incur lower sale prices. Second, foreclosures have negative effects on nearby property values as foreclosures r more foreclosed properties will reduce the well being and the desirability of surrounding properties within the neighborhood, and drive down market value and prices. In short, there is a supply effect and a disamenity effect. Hartley (2010) also indicated that the supply effect and the disamenity e ffect vary across neighborhoods with different vacancy rates. It was found that foreclosures lower the nearby single family properties prices via the supply effect in neighborhoods with low vacancy rates (i.e., in tight submarkets) and by the disamenity ef fect in neighborhoods with high vacancy rates (i.e., in loose submarket). Biswas (2012) measured the spillover effects of foreclosures based on the characteristics of housing submarket s which were defined on the basis of structural and neighborhood characteristics. A statistical clustering approach, which is based on the

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23 principl e of minimizing the dissimilarities in housing characteristics within a cluster, and maximiz ing t he across cluster heterogeneity, was applied to identify the various housing submarkets. The clustering approach resulted in three definable submarkets: high, medium, and low. The results suggest ed that foreclosures have substantial impact on the subject property value s within the same submarket, for each distant foreclosure lowers a single family sale price by approximately 0.1 % within the same submarket Hedonic Price Model Hedonic price models are most widely accepted as property valuation approaches in the real estate literature s These hedonic models are applicable for illuminating the foreclosure impact s in various ways. Leonard and Murdoch (2009) estimate d the hedonic price of an increase in foreclosures beginning with an ordinary least squares regression: The dependent variable is the vector of home sale prices in 2006 ( expressed as natural log ). is a matrix of site specific characteristics. includes block group level controls for percent African American, percent Hispanic, perc ent over 65 years of age, average household size and percent owner occupied. is a matrix of dummy variables to control for any fixed effects across school districts and time (month in which the sale took place). the target v ariable, is the counts of foreclosures at various distances. Schuetz, Been and Ellen (2 008) use d a hedonic price model, controlling for property and neighborhood characteristics, to identify the effect s of foreclosure s that state on the neighboring property values. It is similar to the methodology used in the

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24 studies conducted by Ellen et a l. (2002) and Voicu and Been (2008) The regression equation is shown below: in which is a vector of variables indi cating the number of LP (a lis pendens is a notice of the intention to sue the property owner and reclaim the property if the loan is not repaid) fili ngs within a given time and dis tance interval of property ; is a vector of characteristics describing property including square foot age of the lot, building and unit age, structure type s, and distance to the nearest subway stop; and is a set of ZIP code area fixed eff ects that control for time invariant amenities and characteristi cs of the local neighborhood. a set of borough quarter year time fixed effects, is included to control for time varying economic trends that may differ by borough The dependent variable, is log per unit sales price of property in ZIP code in quarter The log transfor mation is commonly used in hedonic studies to reflect the non linearity of related characteristics (Biswas 2012) Lin, Rosenblatt, and Yao (2009) use d a standard hedonic price model as the p rice equation to examine the spillover effects of foreclosures. The equation wa s defined as: The dependent variable is the logged sales price. Structural characteristics include area in square feet, lot size and number of bathrooms. Age and the square o f age are included to control the nonlinear effect s of age on price s County and zip code

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25 dummies are two important variables to control for neighborhood characteristics, which represent the locational differences in demography, median income, and other fa ctors that might affect foreclosure rate s Quarterly dummies control for seasonal effects which have been found significant in property sale value s (Goodman 1993) The number of foreclosures in the neighborhood s delineated by distance and time is the last set of hedonic factors. This variable measures the spillover effect s of a foreclosure for a particular time and distance while controlling for foreclosures elsewhere. It is worth mentioning that the authors estim ated three models with different assumptions on time/distance effects: (A) time effect only; (B) distance effect only; (C) time in teractive with distance effects, where model (C) provi des the best fit to the data Biswas (2012) used a hedonic regression model to estimate the negative impact of foreclosures on non foreclosed housin g prices: The dependent variable is the natural log per unit sales prices of single family non foreclosed property in Primary Statistical Area (PSA) in year is a vector of structural characteristic s of the subject property including lot size, living area, age, the number of rooms etc. is a vector of the number of foreclosures within a given time and distance interval of the subject property a set of PSA year fixed effects, allows for housing price s varia tion over time at the PSA level and control s for local neighborhood characteristics. Quarterly dummies are also included in the regression to account for the seasonality of housing prices. Fi nally, is the error term. To measure the effects of foreclosures in differ ent submarkets, the author used a statistical clustering technique to identify housing s ubmarkets, where the submarket s were

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26 defined on the basis of structural and neig hborhood characteristics. The clustering approach wa s based on the principle of m inimizing the dissimilarities of property characteristics within a cluster, and maximiz ing the heterogeneity across cluster s. The clustering method resulted in three submarket s: high, medium, and low. Immergluck and Smith (2006) use d a hedonic regression model to estimate the impact of foreclosures on the value s of nearby sing le family properties and to control for other explanatory variables on property and location characteristics. They measure d the foreclosures counts within a radius of eighth of a mile and between a radius of eight of a mile and a quarter of a mile. The hed onic price model wa s defined as: is the natural log of the price of the property. is vector of property characteristics such as square footage, construction, etc. is vector of neighborhood characteristics includi ng population density, income, race, etc., as well as locational characteristics such as latitude and longitude. The remaining variables are used for measuring foreclosures. The definition of each foreclosure variable is listed below: is the foreclosure counts of conventional single family loans within an eighth of a mile from the property. is the foreclosure counts of conventional single family loans between an eighth and a quarter of a mile from the property. is t he foreclosure counts of government insured single family loans within an eighth of a mile from the property. is the foreclosure counts of government insured single family loans between an eighth and a quarter of a mile from the property. is the counts of other foreclosures (multifamily and commercial property) within an eighth of a mile from the property.

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27 is the counts of other foreclosures (multifamily and commercial property) between an eighth and a quarter of a mile from the property. The authors classified submarkets based on neighborhoods income level, given that low and moderate income neighborhoods experience a relatively higher level of foreclosures which may lead to m ore vacant or abandoned properties tha n high income neighborhoods. Thus, it is considered useful to test whether the effects of foreclosures in such neighborhoods differ from the effects of all transactions. To do so, the equation above is applied to only the property transactions in low and moderate income tracts

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28 CHA PTER 3 S URVEY OF STUDY AREA AND DATA Research Statement and Objective s This thesis is expected to illuminate how foreclosures affect housing prices after the bursting of the real estate bubble in the city of Orlando, Florida. As the real estate bubble busted, foreclosure rates increased dramatically in the U.S., which lead to a series of consequences such as high vacancy rate, and high crime rate. Additionally, foreclosures have lowered housing prices as both the supply of hous ing on the market has increased and as the rash of foreclosures has had a negative spillover effect on nearby property values. The state of Florida has one of the highest foreclosure rates as a percentage of all mortgaged homes in the United States. Thus, estimating the impacts of foreclosures in the state of Florida is necessary to better understand the implications of foreclosures on property valuation. This thesis focus es on evaluating the impact s of foreclosures on single family property value s and the spillover effects of foreclosures on nearby properties in Orlando, FL in 2011 and 2012. It also aims to estimate the effects of foreclosures using an interactive variables approach (Fik, Ling, and Mulligan 2003) That is to say, foreclosures may affect property value when they are associated with other factors such as property characteristics, locational characteris tics, or neighborhood characteristics. In addition, this thesis introduces a new variable, Foreclosure Intensive Index (FII), to measure the intensity of foreclosures, and to estimate the impact of foreclosure intensity on housing price s Moreover, this th esis intends to measure the effects of foreclosures on property values in the high end housing submarket (i.e., in

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29 neighborhoods with relatively high income levels), given that effects of foreclosure tend to differ across submarkets. This study has five m ajor objectives: To t est how foreclosures affect the prices of single family propert ies after controlling for property attributes, locational attributes, and neighborhood attributes To d etermine the spillover effects of foreclosures on nearby single famil y properties To e valuate how foreclosures affect housing price s when they are related to property characteristics, locational characteristics, and neighborhood characteristics using an interactive approach To estimate how foreclosure intensity affects sin gle family housing prices using the Foreclosure Intensive Index, which describes the intensity of foreclosures within the nearby vicinity To a ssess the impact s of foreclosure on housing price s in higher median income neighborhoods Study Area The study area of this thesis is Orlando, Florida. Orlando is located in Central Florida and is northeast of Tampa. According to 2010 US Census, the total population of Orlando is 238,300 of which 28.1 % is African American and 25.4 % is Hispanic. The perce ntage of population with b achelor's degree s or higher (percent of persons age 25 and over) is 31.9%. The number of total housing units in Orlando is 121,254 and the homeownership rate is 40.6%, which is about 18% lower than the state level. The median valu e of owner occupied housing unites is $199,600 and the median household income for Orlando is $42,755, which is slightly lower than the state level. The percentage of persons below poverty level is 17.3%, which is about 3% higher than the state level. The unemployment rate was 8.6% in March 2012, which is about 3% lower than 2010. The main highway through Orlando is I 4, which connects to downtown,

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30 various attractions, and suburbs in Orlando. Amtrak, an US rail service, provides rail connections from Orland o to many of the surrounding towns including Kissimmee, Sebring, Winter Haven, Sanford and Ocala. Orlando became a well known vacation spot due to the Disney World Theme Park. The Disney World Theme P ark which includes Magic Kingdom, Epcot, Hollywood S tudios, Animal Kingdom and Downtown Disney, is one of the most famous attractions of Orlando. Additionally, t here are a number of other attractions in Orlando such as Universal Studios and Sea World. As a famous holiday destination, Orlando receives about 52 million tourists ever year and is estimated as the fifth most popular US city for t ravelers visiting from overseas. It has more hotel rooms than any other US city except for Las Vegas. The a ccommodation industry stands for a major portion of the Orland o econo my and employs a large amount of the local population. It is not surprising that tourism helps to bring in the largest revenues for city of Orlando. In addition to tourism, hi tech industry such as electronics and aerospace are also main industries in Orlando as it is close to the NASA Kennedy Space Center. Central Florida Research Park is a large manufacturing facility for aeronautical crafts and related high tech research, which is home to over 120 companies and employs more than 8,500 people. This city is also home to the University of Central Florida the second largest university in Florida. A base map of Orlando is shown in Figure 3 1. According to the Orlando Regional Realtor Association the median housing price was $125,000 in 2012, an inc rease of about 7 % from 2011, which indicates a recovery period of Orlando housing market occurs from 2011 (Shanklin 2012b) According to the report from Clea r Capital Orlando was second in the country for home price increases

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31 in 2011, with a 6.7 % increase in price. Dayton, Ohio enjoyed the largest gain with an 11.5 % increase in price (Shanklin 2012c) The result of this report is based on the combination of several factors, such as resales of the same properties, unemployment rates, and the number of foreclosures on the target market Alth ough the overall trend of August, 2012, from $126,000 in July, 2012, according to Orlando Regional Realtor Association (Shanklin 2012a) The decrease was caused by the large number of sales of both condo s and single family homes in lower price Although the median price of the Orlando market was increased by 5.1 % in August, 2012, co mpared to the year of 2011, it was the smallest increase in more than a year. There were two trends that continued to define the Orlando housing market during 2012: the supply of homes available for sale remained low, and distress sales (including foreclos ures sales) still dominated the market. The Orlando Regional Realtor Association also reported that of all the closings during 2012, foreclosures made up 23.2 % ; short sales constituted 28.8 % ; and normal sales accounted for the rest of 48 % (Shanklin 2012a) The Florida Realtors (2012) estimated that about 600,000 houses in Florida remain in a "shadow inventory which means that those properties are in the risk of default or foreclosure but not yet listed for sale The Metropolitan Statistical Area ( MSA) of Orlando Kissimmee has the 7 th highest foreclosures rate ( 14.9 % ) in 2012, in which the pr ime foreclosure rate is 10.9 % and the subprime foreclosure rate is 36.4 % (FloridaRealtors 2012) In May 2012, there were a total of 3,715 foreclosures fillings (including default notices, auction no tices and bank repossessions), reported in the Orlando MSA, which was an 80 % jump from May 2 011 (RealtyTrac 2012) RealtyTrac

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32 also reported that in March 2012, the average sale price of an Orlando home wa s $126,288 and the average sale price of a foreclosure home was $104,270, a $22,018 saving (Tisner 2012) When compared geographically, foreclosures in Orlando were 0.11% above the national statistics and 0 .04% below the Florida statistics in March 2012 (Tisner 2012) However, increased foreclosures do not indicate that residents who are experiencing financial difficulties will necessarily lose their home s because there are various ways to deal with foreclosures, such as loan modification or negotiating a short sale. Additionally, f oreclosures are time consuming, for it takes about 24 months at minimum from start to finish on a foreclosure sale Although fo reclosures have been a great concern to the Orlando housing market, few early studies on foreclosure s were conducted on Orlando. Based on the above mentioned background information on foreclosures in Orlando, this thesis has sufficient reasons for investig ating the effects of foreclosures on housing price s and nearby property value s in Orlando. Hypotheses A large number of researches indicated that foreclosures cause discount s on housing price s (Sumell,2009; Campbell, Giglio, and Pathak, 2009; Clauretie an d Daneshvary, 2009), which leads to the first hypothesis. Hypothesis 1: Foreclosures affect single family housing price s negatively. Previous researches suggest ed that the spillover effect of foreclosures on nearby property values is negative, which leads to the second hypothesis. Hypothesis 2: Foreclosure s lower the price s of nearby single family properties Although many studies focus ed on the negative effect s of foreclosures on housing price s and surrounding property values, few of them address ed the imp act s of

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33 foreclosures in an interactive approach. It is expected th at the impacts of foreclosures are different when they are associated with different property characteristics and neighborhood characteristics. For instance, a foreclosure with larger living area is expected to have a higher discount on the property price comparing to a foreclosure with smaller living area. This expectation leads to the third hypothesis. Hypothesis 3: Foreclosures affect single family housing price s in an interactive way. Fore closure Intensive Index, a newly introduced variable is utilized to estimate the impact s of foreclosure intensity on housing price s It is expected that higher foreclosure intensive index can significantly lower the single family housing price s Thus, the fourth hypot hesis is shown below : Hypothesis 4: Higher Foreclosure Intensive Index can lower single family housing price s significantly. A number of studies estimate d the effects of foreclosures in different submarkets. Many of them define d submarket base d on geographic limit (such as census tract and ZIP code) or neighborhood characteristics (such as income level). This thesis defines submarket s base d on median household income and focuses on the impact s of foreclosures on property value s in neighborhoods with higher median household income. The fifth hypoth esis is shown below : Hypothesis 5: The way that foreclosures affect housing price s is different in submarkets or neighborhoods with higher me dian household income, compared to the global market. Data S ource T he data for this thesis were collected from the following sources:

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34 Orange County Property Appraiser S ingle family housing unit transactions (including foreclosure sales) data for Orlando in 2011 and 2012 were obtained from the Orange County Propert y Appraiser T his dataset contains sale s price, property characteristics (living area, age, etc.), and X Y coordinates of each property location. United States Census Bureau All s ocioeconomic data and the majority of GIS layers are obtained from the Unit ed States Census Bureau S ocioeconomic data, which is based on block group, contain s median household income, poverty level, educational level, and etc. GIS layers contain major roads which is used in the calculation of the distance between a subject prop erty and the nearest major road. City of Orlando This dataset contains GIS layer s of Orlando downtown, which is used in the calculation of the distance between a subject property and downtown. University of Florida GeoPlan Center This dataset contains G IS layer s of public and private schools in Florida in 2012, which is used in the calculation of the distance between a subject property and the nearest school. Federal Railroad Administration This dataset contains the Florida subs et of the National Rail Network, which is used in the calculation of the distance between a subject property and the nearest railroad. Data and Variables The single family property sales data for the city of Orlando in 2011 and 2012 were obtained from the Orange County Property Apprais er, which provides information on real estate transactions for O range County (including Orlando) For each observed sale price in dollars (PRICE -the dependent variable), the Orange County Property Apprais er record s basic property characteristics such as living area (feet), number of floors, number of bedrooms and bathrooms, year built, construction material s as well as the { x, y } coordinates of each property. This dataset control s for property characteristics and absolute locations of propertie s. It also provides transaction information including the sale price of each transaction, sale date, name of the seller and the buyer, and whether or not it is a foreclosure sale. The sale s data are cleaned by

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35 removing transactions that have sale price s l ower than $ 1,000, which are not viewed as real sale s in this study. After cleaning the data, 1,543 single family property transactions are selected, among which 137 are foreclosure sales. Figure 3 2 shows the distribution of single family housing units in cluding both foreclosure and non foreclosure sales in 2011 and 2012. Figure 3 3 and Figure 3 4 display the housing density surface and foreclosure densi ty surface separately. Both density surfaces are calculated using Kernel Density function in ArcGIS. The se figures show that the trends of the two density surface are similar to some extent, which is not surprising because the possibility of the occurrence of foreclosures increases as the number of housing units increase. Foreclosures are measured by a dumm y variable FORECLOSURE which equals to 1 if the single family property is a foreclosure sale. In order to estimate the spillover effect s of foreclosures on nearby properties, a num ber of measures of foreclosure proximity are created based on the distance intervals. The number of foreclosures within 1000 feet, 2000 feet, and 3000 feet of each target sale were calculated in ArcGIS. Three variables ( F1000, F2000, and F3000 ) were added to each o bservation in the sales dataset, in which F1000 is the number of foreclosures within 1000 feet of the subject sale; F2000 is the number of foreclosures within 2000 feet of the subject sale; F3000 is the number of foreclosures within 3000 feet of the subject sale. It is important to notice that the number of foreclosur e s is measured by concentric circles extending from the subject sale but not measured by concentric rings. F or instance, the number of foreclosures within 3000 feet of a sale contains the number of foreclosures within 2000 feet of a sale. Figure 3 5 provides a schematic representation of the hedonic model for property values and nearby foreclosures. Three buffer areas

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36 are drawn around each property (a radius of 1000 feet, 2000 feet, and 3000 feet). The number of foreclosure s within 3000 feet of the subject pr operty is eight in the example shown in Figure 3 5. FD_HD is a newly introduced variable called Foreclosure Intensive Index (FII), which is defined as following, (3 1) This index measures the intensity of foreclosures at any given location, taking into account housing unit density. It also indicates the possibility that units in the vicinity will be a foreclosure (as a probability density measure). The second dataset includes socioeconomic and demographic variables fo r Orl ando based on the 2010 U.S. Census, in which n eighborhood characteristics were controlled. For all block groups in the study ar ea, neighborhood characteristics were obtained such as population to work using public transportation (RTAN_PUB), median ho usehold income (MEDHHINC), number of households with public assistance income (HH_PUBA), civilian employed population 16 years and over (LABOR_CIV), percent of ercent of renter occupied housing that move d in 2005 or later (PCT_RENT5), and percent of the population below poverty (PCT_POV). Additionally, locational characteristics are also controlled For a subject property, distance to the nearest school (DIST_SCHOOL), to the nearest major road (DIST_MAJOR ROAD), to the nearest railroad (DIST_RAILROAD), and to downtown (DIST_DOWNTOWN) are calculated in ArcGIS. Note that a ll distance were measured in feet. Table 3 1 provides definitions and descriptive statistics of variables in the global market The mean s ale s price is approximately $152,872. T he minimum and maximum

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37 sale s price is $1,300 and $740,000, respectively Figure 3 6 displays the distribution of single family housing units along with sale s price which shows that housing price s are lower in northwe st Orlando and higher in southeast Orlando. Visually, housing units around downtown area are not necessarily associated with lower price s as it is expected. The mean living area of the sample housing is approximately 1,687 square feet. T he minimum and maxi mum living area is 480 and 4,631 square feet, respectively. The average median household income is $53,171; the mean percentage of population that has a b The average number of foreclosu res within 1000, 2000, and 3000 feet of a subject sale is 0.69, 1.71, and 3.05 respectively. The mean Foreclosure Intensive Index (FD_HD) is 0.088. Submarket s are defined based on median household income. This study uses the quantile method in ArcGIS and results three submarkets: low, medium, and high. Figure 3 7 displays the distribution of single family housing units in each submarket. T his thesis focuses on the high median household income submarkets which are mostly located in the central Orlando (ne ar downtown) and the southeast Orlando. Table 3 2 provides the descriptive statistics of variables in the high median household income submarket s In th at submarket s 710 si ngle family housing transactions are selected, among which 58 are foreclosure sales The mean sale s price is approximately $196,600. T he minimum and maximum sale s price is $1,600 and $740,000, respectively. The mean living area is approximately 1,894 square feet. The mean number of population to work using public transportation is 8. The average number of

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38 foreclosures within 1000, 2000, and 3000 feet of a subject sale is 0.68, 1.69, and 3.05 respectively. The mean Foreclosure Intensive Index (FD_HD) is 0.088.

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39 Table 3 1. Definition and d escriptive s tatistics of v ariables in the g lobal m arket Variable Name Definition Mean Std. Deviation Minimum Maximum Dependent Variable: PRICE Sale price of property 152871.920 90207.316 1300.000 740000.000 Property Attributes STYS Number of floors 1.170 0.374 1.000 2.000 AREA Living area (square feet) 1687.090 665.411 480.000 4631.000 POOL Number of pools 0.190 0.389 0.000 1.000 AGE Age of property 43.090 24.639 1.000 102.000 Locational Attributes X_COORD X coordinate of property location 540749.580 19821.529 494665.000 5804 43.000 Y_COORD Y coordinate of property location 1524622.090 15842.756 1483636.000 1554471.000 DIST_SCHOOL Distance to the nearest school (feet) 1939.188 1302.016 146.840 8437.366 DIST_MAJORROAD Distance to the nearest major road (feet) 2391.533 1939.72 4 57.880 9151.292 DIST_RAILROAD Distance to the nearest railroad (feet) 12321.750 9069.451 10.734 35183.445 DIST_DOWNTOWN Distance to downtown (feet) 17935.740 14824.252 0.000 60999.740 Neighborhood Attributes (Unit: block groups) TRAN_PUB Po pulation to work using public transportation 53.290 80.602 0.000 368.000 MEDHHINC Median household income 53171.230 21892.678 9440.000 153381.000 HH_PUBA Number of households with public assistance income 18.220 20.858 0.000 97.000 LABOR_CIV Civilian e mployed population 16 years and over 1787.850 1681.506 129.000 7449.000 PCT_BACHLR Percent of population that has 16.608 9.135 0.000 44.200

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40 Table 3 1 Continued. Variable Name Definition Mean Std. Deviation Minimum Maximum PCT_RENT 5 Percent of renter occupied housing that moved in 2005 or later 73.455 18.167 0.000 100.000 PCT_POV Percent below poverty 12.418 9.709 0.000 63.820 Foreclosure Attributes FORECLOSURE Dummy equal to 1 if the property is a foreclose sale 0.090 0.285 0.000 1.000 F1000 Number of foreclosures within 1000 feet of a subject sale 0.690 0.983 0.000 4.000 F2000 Number of foreclosures within 2000 feet of a subject sale 1.710 1.637 0.000 8.000 F3000 Number of foreclosures within 3000 feet of a subject sale 3.050 2.474 0.000 12.000 FD_HD Foreclosure Intensive Index 0.088 0.070 0.000 1.000

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41 Table 3 2. Descriptive s tatistics of v ariables in h igh m edian h ousehold i ncome s ubmarke ts Variable Name Definition Mean Std. Deviation Minimum Maximum Dependent Va riable: PRICE Sale price of property 196600.890 83575.628 1600.000 740000.000 Property Attributes STYS Number of floors 1.240 0.430 1.000 2.000 AREA Living area (square feet) 1893.700 739.868 612.000 4514.000 POOL Number of pools 0.200 0.399 0.000 1.000 AGE Age of property 43.390 28.978 2.000 98.000 Locational Attributes X_COORD X coordinate of property location 547137.370 20238.177 494665.000 580443.000 Y_COORD Y coordinate of property location 1520700.39 0 18746.571 1483636.000 1546816.000 DIST_SCHOOL Distance to the nearest school (feet) 2119.298 1574.547 205.314 8437.366 DIST_MAJORROAD Distance to the nearest major road (feet) 2310.093 2042.531 61.241 9151.292 DIST_RAILROAD Distance to the nearest railroad (feet) 12190.140 1 0868.388 10.734 35183.445 DIST_DOWNTOWN Distance to downtown (feet) 20519.670 19663.673 57.808 60999.740 Neighborhood Attributes (Unit: block groups) TRAN_PUB Population to work using public transportation 8.090 13.284 0.000 47.000 MEDHHINC M edian household income 71150.120 18743.012 53922.000 153381.000 HH_PUBA Number of households with public assistance income 10.030 12.401 0.000 34.000 LABOR_CIV Civilian employed population 16 years and over 2001.960 2070.516 193.000 6228.000 PCT_BACHLR Percent of population that has 22.692 7.506 7.950 44.200

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42 Table 3 2 Continued. Variable Name Definition Mean Std. Deviation Minimum Maximum PCT_RENT5 Percent of renter occupied housing that moved in 2005 or later 76.241 18.804 0.00 0 100.000 PCT_POV Percent below poverty 6.518 4.104 0.000 25.620 Foreclosure Attributes FORECLOSURE Dummy equal to 1 if the property is a foreclose sale 0.080 0.274 0.000 1.000 F1000 Number of foreclosures within 1000 feet of a subject sale 0 .680 1.025 0.000 4.000 F2000 Number of foreclosures within 2000 feet of a subject sale 1.690 1.587 0.000 8.000 F3000 Number of foreclosures within 3000 feet of a subject sale 3.050 2.362 0.000 11.000 FD_HD Foreclosure Intensive Index 0.088 0.070 0.000 1 .000

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43 Figure 3 1. Orlando b ase m ap Figure 3 2. Distribution of s ingle family h ousing u nits

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44 Figure 3 3. Housing d ensity s urface Figure 3 4. Foreclosure d ensity s urface

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45 Figure 3 5. Modeling the i mpact of f oreclosure on p roperty v alue Figu re 3 6. Distribution of s ingle family h ousing u nits a long with s ale p rice

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46 Figure 3 7. Single family h ousing s ubmarkets

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47 CHA PTER 4 METHODOLOGY Summary of Modeling Strategies A series of hedonic regression models are used to estimate the effects of forec losures on market value. The models are applied to both the global market and high median household income submarket s This thesis intends to estimate the impact s of foreclosures on single family housing prices by comparing the performance of two models: F oreclosures Contagion Model and Foreclosure Intensive Index Model. Figure 4 1 shows the flowchart of models for both the global market and high income submarket s P roperty attributes, locational attributes, and neighborhood attributes are all included as c ontrol variables in both models The differences of the two models lie in the selection of foreclosure attributes. In the foreclosure contagion model, foreclosure dummy (FORECLOSRUE), the number of foreclosures within 1000 feet (F1000), 2000 feet (F2000), and 3000 feet (F3000) are included. It is worth mentioning that the foreclosure contagion model contains two different approaches: non interactive approach and interactive approach. The non interactive approach intend s to test the impact s of foreclosure s a nd contagion effect s of foreclosure s while the interactive approach intend s to estimate the impact s of foreclosures when they are associated with property, locational, or neighborhood characteristics. In the foreclosure intensive index model, only the For eclosure Intensive Index (FD_HD) is included to represent foreclosure attributes This index indicates the possibility that units in the vicinity will be a foreclosure. The two models that contain three approaches are expected to provide a relatively compr ehensive understanding of the effects of foreclosures on single family housing prices.

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48 Foreclosure Contagion Model To identify the effect s of foreclosures on single family housing price s and nearby property values, a hedonic regression analysis was first conducted with a non interactive approach, controlling for propert y, locational, and neighborhood characteristics. The regression equation is defined as follows: (4 1) is the sale s price of a s ubject property. is a vector of property characteristics of a subject property, including the number of floors, living area, number of pools, and age of the property. is a vector of locational characteristics of a subject proper ty, including { x, y } coordinate s the 2 nd order polynomial expansion of { x, y } coordinate s distance to the nearest school, dis tance to the nearest major road distance to the nearest railroad, and distance to downtown. is a vector of neighbo rhood characteristics, including population to work using public transportation, median household income, c ivilian employed population 16 years and over p ercent of p ercent of renter occupied housing that moved in 2 005 or later and p ercent below poverty is a foreclosure dummy, which equals to 1 if the subject property is a foreclosure. is a vector of the count s of foreclosures within a given distance interval of a subject property The d istance interval s were defined as 1000 feet, 2000 feet, and 3000 feet. As it is mentioned in the previous chapter, t he number of foreclosure s is measured by concentric circles extending from the subject sale but not measured by concentric rings. Thus, for instance, the number of foreclosures within 3000 feet of a sale contains the number of foreclosures within 2000 feet of a sale. is an error term It is worth mention ing that a 2 nd order polynomial

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49 expansion of { x, y } coordinate s produce s 5 lo cational variables ( x, y, x 2 y 2 x*y ). The inclusion of the absolute location { x, y } enables us to obtain the geographic slope of housing price in the vicinity of some { x, y } locations (Fik, Ling, and Mulligan 2003) The interactive approach is an expansion of the non interactive approach. the foreclosure dummy and all ot her independent variables. Instead of considering the for eclosure attributes alone, the interaction is superior to estimate the impact s of foreclosures when they are associated with other variables such as property, location, or neighborhood characteristi cs. The regressio n equation is defined in the following : (4 2) is the sale s price of a subject property. , and are defined exactly the same as the non interact ive model. is the interaction or production of the foreclosure dummy and a vector of property characteristics of a subject property. is the interaction or production of the foreclosure dummy and the vector of locational characte ristics of a subject property. is the interaction or production of the foreclosure dummy and the vector of neighborhood characteristics of a subject property. is the interaction or production of the foreclosure dummy and the vec tor of the count s of foreclosures within a given distance interval of a subject property The foreclosure contagion model is expected to explain the effect s of foreclosures on housing price s and nearby property value s in both non interactive and interactiv e way s Foreclosure Intensive Index Model Forec losure Intensive Index (FII) measures the intensity of foreclosures at any given location, taking into account housing unit density. It also indicates the possibility

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50 that units in the vicinity will be a for eclosure (as a probability density measure). FII is defined below (4 3) This model uses only FII to represent foreclosure attributes, and also controls for property, locational, and neighborhoods characteristics. The hedonic regression equation is defined as follows: (4 4) is the sale s price of a subject property. is a vector of property characteristics of a subject property, including number of floors, living area, number of pools, and age of the property. is a vector of locational characteristics of a subject property, including { x, y } coordinate s the 2 nd order polynomial expansion of { x, y } coordinate s, distance to the nearest school, distance to the nearest major roads, distance to the nearest railroad, and distance to downtown. is a vector of neighborhood characteristics, including population to work using public transportation, median household income, c ivilian employed population 16 years and over p ercent of p ercent of renter occupied housing that moved in 2005 or later and p ercent below poverty is the Foreclosure Intensive I ndex. i s an error term. The foreclosure intensive index model is expected to estimate the impact of the FII on single family housing price s given that FII is an in dicator of foreclosure density.

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51 Figure 4 1. Flowchart of m odels for b oth g lobal m arket and h igh i ncome s ubmarket

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52 CHAPTER 5 RESULTS AND INTERPRETATION Global market This chapter presents the results of the previous hypotheses. Table 5 1 reports the regression results of the foreclosure contagion model with a non interactive approach in the global m arket. The R square of this model is 0.689, which indicates that 68 .9% of the single family housing price s can be explained by t he independent variables in this model. All significant variables are displayed with expected signs and reasonable magnitudes in this table. For the property attributes, a larger living area and the inclusion of a pool increase the housing price, given that the coefficients are posit ive. One square foot increase in the living area leads to an $85 increase in the property value and the additional of a pool raises the housing price by $24,568. For the locational attributes, an increase in the distance to the nearest major roads, to the nearest railroad, to downtown, and an increase in y coordinate lower the property value, given that the coefficients are all negative. A n increase in the distance to the nearest major road by one thousand feet is associated with a $2,941 decrease of the housing price. The result satisfies our expectation because properties near major roads may suffer fr om noise and lower air quality, which then leads to lower property value s A thousand feet increase in the distance to the nearest railroad is associated with a $1,393 decrease of housing price. The vicinity of the railroad usually represents an older indu strial area, where abandoned properties and lower quality communities are always located Thus, it is not surprising that properties near railroad s have lower sale price s A n increase in the distance to downtown by one thousand feet is associated with a $7 19 decrease of the housing price. There are several reasons that the downtown

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53 area is associated with lower housing price s For instance, m any renter occupied housing units are located near downtown, which increase s the mobility of the population. This l ar ge mobility would cause many social problems such as high crime rate, which then leads to lower housing price s The negative coefficient of y coordinate indicates that property value s decreases as houses move north. However, since the coefficient is small we can say that the impact of direction on sale price s is small yet significant. Two neighborhood attributes are shown to be significant in the table: the p ercent and the p ercent of the population below poverty With a p ositive co efficient, 1% increase of the degree raises the sale price by $2,917. The increase of the housing price is expected because a a high er education level and a higher income level People with high er income level s tend to be able to afford properties with higher price s Figure 5 1 shows the relationship between the sale price s of housing units and the percent of population that has a bach degree. Visually higher percent of population with a with higher sale price s With a negative coefficient, 1% increase in the p ercent of the population below poverty lowers the sale price by $1,092. The resu lt makes sense because people with low er price s Figure 5 2 shows the relationship between the sale price s of housing units and the percent of the population below poverty. Visually, a higher percent below poverty is associated with a lower sale price.

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54 The two foreclosure attributes shown in the table are what we must give the most attention. The coefficient of the foreclosure dummy (FRECLOSURE) is 49,105, which indicates that a foreclosure property lowers the sa le price by $49,105 compared to a non foreclosure property holding other control variables constant. The result satisfies our expectation of hypothesis1 that foreclosures aff ect single family housing prices negatively. To estimate the effects of foreclosu res on nearby property value s the number of foreclosures within 1000 feet, 2000 feet, and 3000 feet of a subject property are added into the model, in which t he number of foreclosures within 2000 feet (F2000) is a significant variable. The coefficient of F2000 is 2,571, which indicates that each additional foreclosure within 2000 feet of a subject sale is associated with a $2,571 decrease in the sale price holding other control variables constant. The result satisfies our expectation of hypothesis2 that foreclosures lower the price s of nearby single family properties. Table 5 2 presents the results of the foreclosure contagion model with an interactive approach in the foreclosure dummy and all other independent variables. Instead of considering the for eclosure attributes alone, the interaction enable s us to estimate the impact of foreclosures when they are associated with other variables such as property, location, and neighborhood characteris tics. The R square of this model is 0.690, which indicates that 69.0% of the single family housing price s can be explained by the independent variables in this model. All significant variables are displayed with expected signs and reasonable magnitudes in this table. Significant variables of property attributes, locational attributes, and neighborhood attributes are exactly the same as the previous

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55 model with a non interactive approach. The coefficients change slightly while keeping the same sign s The numb er of foreclosures within 2000 feet (F2000 ) shows a significant variable with a negative sign. Two new interactive variables demonstrate significant variables in this model: F_AREA and F_PCT_BACHLR. The interpretation of interactive variables is more diff icult because the influence of one variable is dependent on the other variable that is interacted. F_AREA is the interaction of the foreclosure dummy and the living area, of which the coefficient is 20. Given that the coefficient of AREA is 86, we can cal culate that for a non foreclosed property, one square foot increase in living area leads to an $86 increase in the sale price, holding other control variables constant. However, for a foreclosed property, one square foot increase in living area only leads to a $66 ($66 = $86 1*$20) increase in the sale price, holding other control variables constant. The negative coefficient of F_AREA indicates that foreclosures with larger living area s decrease the sale price more when compared to foreclosures with small er living area s holding other control variables constant. That is also to say, foreclosures with larger living area s have a larger negative effect on housing price s than those with smaller living area s holding other control variables constant. However, i foreclosures with larger living area s have lower price s than those with smaller living area s because the living area (AREA) still has a positive coefficient whose absolute value is larger than that of F_AREA. For instance, the sale pric e of a 1000 square feet foreclosed property is $66,000 ($66,000 = $86*1,000 $20*1,000), and the sale price of a 2000 square feet foreclosed property is $132,000 ($132,000 = $86*2,000 $20*2,000). From this example although the sale price of a 2000 sqft foreclosed

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56 property is higher than that of a 1000 sqft foreclosed property, the foreclosure discount of the 2000 sqft property ($40,000 = $20*2,000) is larger than that of the 1000 sqft property ($20,000 = $20*1,000). This result makes sense because prope rties with larger living area s tend to have higher sale price s as previously discussed. If such a property is a foreclosure, the foreclosure discount is expected to be larger than that of properties with lower price s or properties with smaller living area s Another inter active variable is F_PCT_BACHLR -the interaction of the foreclosure dummy and the interpretation of this variable is similar to that of F_AREA. Given that the coefficient of PCT_BACHLR is 2,986 and the coefficient of F_PCT_BACHLR is 1,089, we can calculate that for a non foreclosed pro perty, 1% increase in the population with b in the sale price, holding other control variables constant. However, for a foreclosed pro perty, 1% increase in the population with b $1,089) increase in the sale price, holding other control variables constant. The negative coefficient of F_PCT_BACH LR indicates that forec losures with population that has a higher percent of b when compared to those with a lo wer percent of b say, foreclosu res with population that has a higher percent of b a larger negative effect on housing price s than those with a lower percent of b that foreclosures wi th population that has a higher percent of b price s than those with a lower percent of b

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57 has a positive coefficient whose absolute value is larger than that of F_PCT_BACHLR. For instance, the sale price of a foreclos ed property with population that has 10 % of b $1,089*10), and the sale price of a foreclosed property with population that has 20 % of b ( $37,940 = $2,986*20 $1,089*20). From this example although the sale price of a foreclosed property with 20 % of b h igher than those with 10 % of b 20 % of b ($21,780 = $1,089*20) is larger than those with 10 % of b degree ($10,890 = $1,089*10). This result makes sense because properties with a hig her percent of population degree tend to have hig her sale price s as previously discussed. If such a property is a foreclosure, the foreclosure discount is expected to be larger than that of properties with lower price s or properties with a lower percent of population that has a b ults of the interactive approach satisfy our expectation of hypothesis3 that foreclosures affect single family housing price s in an interactive way. In particular, foreclosures affect housing price s significantly when they are associated with the living ar ea and the pe rcent of population that has a b The foreclosure intensive index m odel use s only the Foreclosure Intensive I ndex (FII) to represent foreclosure attribute s and also controls for property, locational, and neighborhoods characte ristics. The definition of the Foreclosure Intensive I ndex has been previously discussed in methodology. Table 5 3 reports the regression results of the foreclosure i ntensive index model in the global market. The R square of this model is 0.696, which indi cates that 69.6% of the single family housing price s can be

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58 explained by the independent variables in this model. Notice that the foreclosure intensive index model performs better than the foreclosure contagion model for it has a higher R square. Most of t he significant variables of property, locational, and neighborhood attributes are the same as the results of the foreclosure contagion models. The sign s and magnitude s of the coefficients are also similar to the results of the foreclosure contagion model s and are shown as expected. The age of the property (AGE) is one of the new significant variable s in this model. The coefficient of AGE is 607, which indicates that one year increase in the property age decrease s the housing price by $607. This result sat isfies our expectation because older prop erties tend to sell at lower price s For the neighborhood attributes, two new variables are shown to be significant: the median household income (MEDHHINC) and the number of households with public assistance income (HH_PUBA). The coefficient of the median household income (MEDHHINC) is 0.657, which indicates that a $1,000 increase in the median household income raises the housing price by $657. The result makes sense because people with a high median household income tend to be able to afford properties with higher price s Figure 5 3 shows the relationship between the sale price s of housing units and the median household income. Visually, higher median household income is associated with higher sale price s The coeffi cient of the number of households with public assistance income (HH_PUBA) is 2 92, which indicates that an increase in the number of households with public assistance income lowers the housing price by $292. The result satisfies our expectation because a l arger number of households with public assistance income indicate a higher poverty level, which then leads to lower housing price s as previously

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59 discussed. Figure 5 4 shows the relationship between the sale price s of housing units and the number of househo lds with public assistance income. Visually, a larger number of households with public assistance income is associated with a lower sale price. The Foreclosure Intensive I ndex (FII), shown as FD_HD in the table, has a large negative coefficient ( 156,299 ) which indicates that a unit increase in the FII lowers the housing price by $156,299. Since the range of the FII is from 0 to 1, it is more appropriated to say that a 0.01 unit increase in the FII lowers the housing price by $1,563. The results satisfy o ur expectation of hypothesis4 that a higher Foreclosure Intensive I ndex lowers single family housing price s significantly. It also proves that this index is a good indicator of foreclosure intensity and performs very well in predicting the single family pr operty value s High Median Household Income Submarket s After estimating the impact of foreclosure s in the global market, this thesis also intends to measure the effects of foreclosures on single family property value s in the high end housing submarket (i.e ., in neighborhoods with relatively high income levels), given that effects of foreclosure tend to differ across submarkets. As it is previously mentioned, this thesis defines three submarket s (low, medium, and high) based on the median household income, a nd focus es on the impact of foreclosures on property value s in submarkets with high median household income. Table 5 4 reports the regression results of the foreclosure contagion model with a non interactive approach in the submarkets with high median hou sehold income The R square of this model is 0.591, which indicates that 59.1% of the single family housing price s can be explained by the independent variables in this model. All significant variables are displayed with expected signs and reasonable magni tudes in this table.

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60 For the property attributes, a larger living area and the inclusion of a pool increase the housing price in the high end submarkets, given that the coeffi cients are positive. One square foot increase in living area leads to a $90 incre ase in the property value and the addition of a pool raises the housing price by $16,896. For the locational attributes, a thousand feet increase in the distance to downtown is associated with a $1,504 decrease in the housing price, of which the rationale has been previously discussed. Compared to the global market, DIST_MAJORROAD and DIST_RAILROAD are not shown in Table 5 4 That is to say, distance to the nearest major road and to the nearest railroad do not affect the housing price s significantly in the high end submarkets. The variable XY represents the product of x coordinate and y coordinate of each subject property. The coefficient of XY is negative but very small, which indicates that the property value decreases slightly as houses mov e northeast in the submarkets with high median household income This variable highlights the interactive nature of the absolute location to explain the variations in sale price s and indicates the presence of the non linear relationship between the sale prices and { x, y } coordinates (Fik, Ling, and Mulligan 2003) Three neighborhood attributes demonstrate significant variables: th e population to work using public transportation (TRAN_PUB), the median household income (MEDHHINC), and the (PCT_BACHLR). With a negative coefficient, an additional person using public transportation to w ork decreases the sale price by $ 534 in the high end submarkets. The result is expected because a larger population using public transportation to work indicates a lower income level. People with low er income can only afford housing units

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61 with lower price s The coefficient of the median household income is 0.815, which indicates that a $1,000 increase in the median household income raises the sale price by $815 in the high end submarkets. This result makes sense because people with higher income tend to be able to afford properties with higher prices With a positive coefficient, 1% increase in the raises the sale price by $810, of which the rationale has been previously discussed. Two significant foreclosure attributes are shown in the table, which are the same two attributes as that in the global market. The coefficient of the foreclosure dummy (FRECLOSURE) is 60,363, which indicates that in the high end submarkets, a foreclosure property lowers the sale p rice by $60,363 compared to a non foreclosure property holding other control variables constant. The coefficient of the number of foreclosures within 2000 feet (F2000) is 3,280, which indicates that in the high end submarkets, each additional foreclosur e within 2000 feet of a subject sale is associated with a $3,280 decrease in the sale price holding other control variables constant Compared to the global market, both the foreclosure dummy and the number of foreclosures within 2000 feet have larger neg ative effects on property values in the submarkets with high median household income given that the absolute value of the negative coefficients are larger. Table 5 5 presents the results of the foreclosure contagion model with an interactive approach in the submarkets with high median household income The R square of this model is 0.591, which indicates that 59.1% of the single family housing price s can be explained by the independent variables in this model. All significant variables are displayed with expected signs and reasonable magnitudes in this table.

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62 Significant variables of property attributes, locational attributes, and neighborhood attributes are exactly the same as the results in the previous model with a non interactive approach. The coeffici ents change slightly while keeping the same signs The number of foreclosures within 2000 feet (F2000) still show s a significant variable with a negative sign. As the only one significant interactive variable in this model, F_AREA, the interaction of the foreclosure dummy and the living area, has a n egative coefficient of 29. As it is previously mentioned, the interpretation of interactive variables is more difficult because the influence of one variable is dependent on the other variable that is interact ed. However, the rationale of this interactive term in the high end submarkets is exactly the same as that in the global market. Given that the coefficient of AREA is 92, we can calculate that for a non foreclosed property, one square foot increase in the living area leads to a $92 increase in the sale price, holding other control variables constant. However, for a foreclosed property, one square foot increase in the living area only leads to a $63 ($63 = $92 1*$29) increase in the sale price, holding oth er control variables constant. The negative coefficient of F_AREA indicates that foreclosures with larger living area s decrea se the sale price more when compared to those with smaller living area s holding other control variables constant. That is also to say, foreclosures with larger living area s have a larger negative effect on housing price s than those with smaller living area s that foreclosures with larger living area s have lower price s than those with smaller living area s because the living area (AREA) still has a positive coefficient whose absolute value is larger than that of F_AREA. For instance, the sale price of a 1000 square feet

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63 foreclosed property is $63,000 ($63,000 = $92*1,000 $29*1,000), and the sale price of a 2000 square feet foreclosed property is $126,000 ($126,000 = $92*2,000 $29*2,000). From this example although the sale price of a 2000 sqft foreclosed property is higher than that of a 1000 sqft foreclosed propert y, the foreclosure discount of the 2000 sqft property ($58,000 = $29*2,000) is larger than that of the 1000 sqft property ($29,000 = $29*1,000). This result makes sense because properties with larger living area s tend to have higher sale price s as previous ly discussed. If such a property is a foreclosure, the foreclosure discount is expected to be larger than properties with lower price s or properties with smaller living area s Compared to the global market, F_PCT_BACHLR is not shown in the table. That is t o say, the interaction of the foreclosure dummy and the p does not affect the housing price s significantly in the submarkets with high median household income In addition, both the number of foreclosures wi thin 2000 feet and the interaction of the foreclosure dummy and the living area have larger negative effects on property values in the high end submarkets, given that the absolute value of the negative coefficients are larger. For the submarkets with high median household income the foreclosure i ntensive index m odel, a model that use s only the F or eclosure Intensive I ndex (FII) to represent foreclosure attribute s and controls for property, locational, and neighborhoods characteristics, is also c onducted Table 5 6 r eports the results of the foreclosure intensive index model in the high end submarkets. The R square of this model is 0.569, which indicates that 56.9% of the single family housing price s can be explained by the independent variables in this mo del. Notice that the foreclosure

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64 contagion models perform better than the foreclosure intensive index model in the submarkets for they have higher R squares (0.591 for both the non interactive approach and the interactive approach). Most of the significant variables of property, locational, and neighborhood attributes are the same as the results in the foreclosure contagion models. The sign s and magnitude s of the coefficients are also similar to the results in the foreclosure contagion model s and are s hown as expected. The rationales of most significant variables have been previously discussed. The foreclosure intensive index (FII), shown as FD_HD in the table, has a large negative coefficient ( 233,010 ) which indicates that a unit increase of the FII lower s the housing price by $233,010. Since the range of the FII is from 0 to 1, it is more appropriated to say that a 0.01 unit increase of the FII lowers the housing price by $2,330. It also proves that this index is a good indicator of the foreclosure intens ity and performs very well in predicting the single family property value s Compared to the global market, the Foreclosure Intensive I ndex has a larger impact on single family housing price s in the submarkets with high median household income given that t he absolute va lue of the negative coefficient is larger. After comparing the results in both the global market and the high end submarkets, it can be concluded that the results satisfy our expectation of hypothesis5 that the way that foreclosures affect s ingle family housing price s is different in submarkets or neighborhoods with higher me dian household income, compared to the global market.

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65 Table 5 1. Regression r esults of f oreclosure c ontagion m odel with n on interactive a pproach for the g lobal m arket I ndependent Variables Coef. Std. Error t Sig. VIF (Constant) 742802.284 204302.697 3.636 0.000 AREA 84.735 2.485 34.100 0.000 1.654 POOL 24658.311 3475.339 7.095 0.000 1.107 DIST_MAJORROAD 2.941 0.842 3.492 0.000 1.614 DIST_RAILROAD 1.393 0.225 6. 199 0.000 2.513 DIST_DOWNTOWN 0.719 0.192 3.743 0.000 4.901 Y_COORD 0.477 0.132 3.603 0.000 2.656 PCT_BACHLR 2917.004 204.344 14.275 0.000 2.108 PCT_POV 1092.629 180.014 6.070 0.000 1.848 FORECLOSURE 49105.149 4630.614 10.604 0.000 1.050 F200 0 2571.236 842.032 3.054 0.002 1.149 Table 5 2. Regression r esults of f oreclosure c ontagion m odel with i nteractive a pproach for the g lobal m arket Independent Variables Coef. Std. Error t Sig. VIF (Constant) 744775.042 204114.543 3.649 0.000 AREA 86.054 2.512 34.251 0.000 1.699 POOL 24633.832 3467.307 7.105 0.000 1.107 DIST_MAJORROAD 3.013 0.841 3.585 0.000 1.616 DIST_RAILROAD 1.336 0.224 5.962 0.000 2.510 DIST_DOWNTOWN 0.726 0.191 3.793 0.000 4.899 Y_COORD 0.480 0.132 3.636 0.000 2.6 64 PCT_BACHLR 2986.239 206.259 14.478 0.000 2.158 PCT_POV 1103.396 179.589 6.144 0.000 1.848 F2000 2416.170 842.588 2.868 0.004 1.156 F_AREA 19.718 4.592 4.294 0.000 3.325 F_PCT_BACHLR 1088.669 499.991 2.177 0.030 3.307

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66 Table 5 3. Regres sion r esults of f oreclosure i ntensive i ndex m odel for the g lobal m arket Independent Variables Coef. Std. Error t Sig. VIF (Constant) 548906.185 204435.953 2.685 0.007 AREA 80.584 2.535 31.794 0.000 1.756 POOL 21945.231 3444.125 6.372 0.000 1.110 AGE 607.198 94.582 6.420 0.000 3.353 DIST_MAJORROAD 3.445 0.844 4.081 0.000 1.656 DIST_RAILROAD 1.383 0.234 5.909 0.000 2.783 DIST_DOWNTOWN 1.290 0.220 5.859 0.000 6.580 Y_COORD 0.332 0.132 2.507 0.012 2.710 MEDHHINC 0.657 0.094 6.989 0.000 2.617 HH_PUBA 291.535 80.330 3.629 0.000 1.733 PCT_BACHLR 2037.220 237.114 8.592 0.000 2.897 PCT_POV 464.435 195.645 2.374 0.018 2.228 FD_HD 156298.936 20298.090 7.700 0.000 1.239 Table 5 4. Regression r esults of f oreclosure c ontagion m odel with n on interactive a pproach for the h igh i ncome s ubmarkets Independent Variables Coef. Std. Error t Sig. VIF (Constant) 284103.506 76964.289 3.691 0.000 AREA 89.853 3.728 24.103 0.000 1.864 POOL 16896.079 5443.525 3.104 0.002 1.157 DIST_DOWNTOWN 1.504 0.160 9.411 0.000 2.422 XY 3.507E 07 0.000 3.721 0.000 1.335 TRAN_PUB 533.971 163.294 3.270 0.001 1.153 MEDHHINC 0.815 0.130 6.248 0.000 1.464 PCT_BACHLR 810.441 354.900 2.284 0.023 1.739 FORECLOSURE 60363.624 7638.766 7.902 0.000 1.074 F2000 3280.143 1440.679 2.277 0.023 1.280

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67 Table 5 5. Regression r esults of f oreclosure c ontagion m odel with i nteractive a pproach for the h igh i ncome s ubmarkets Independent Variables Coef. Std. Error t Sig. VIF (Constant) 275624.247 76927.267 3.583 0.0 00 AREA 92.419 3.723 24.822 0.000 1.859 POOL 16526.386 5444.383 3.035 0.002 1.157 DIST_DOWNTOWN 1.539 0.160 9.648 0.000 2.411 XY 3.452E 07 0.000 3.663 0.000 1.334 TRAN_PUB 521.732 163.315 3.195 0.001 1.153 MEDHHINC 0.824 0.130 6.315 0.000 1.46 5 PCT_BACHLR 759.209 355.400 2.136 0.033 1.744 F2000 3341.150 1439.480 2.321 0.021 1.278 F_AREA 28.805 3.650 7.891 0.000 1.085 Table 5 6. Regression r esults of f oreclosure i ntensive i ndex m odel for the h igh i ncome s ubmarkets Independent Varia bles Coef. Std. Error t Sig. VIF (Constant) 362433.648 78165.702 4.637 0.000 AREA 90.822 3.809 23.843 0.000 1.851 POOL 15539.591 5535.232 2.807 0.005 1.138 DIST_DOWNTOWN 1.418 0.162 8.751 0.000 2.365 XY 4.193E 07 0.000 4.429 0.000 1.281 TRAN_PUB 540.066 165.778 3.258 0.001 1.130 MEDHHINC 0.866 0.121 7.165 0.000 1.196 FD_HD 233010.214 34779.798 6.700 0.000 1.413

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68 Figur e 5 1. Percent of p opulation with b d egree Figure 5 2. Percent below p overty

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69 Figure 5 3. Median h ouseho ld i ncome Figure 5 4. N umber of h ouseholds with p ublic a ssistance i ncome

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70 CHAPTER 6 CONCLUSION AND DISCUSSION Conclusion This thesis uses a series of hedonic regression models to estimate the impacts of foreclosures on single family property values and the spillover effects of foreclosures on nearby properties in Orlando, FL in 2011 and 2012. It also aims to estimate the effects of foreclosures using an interactive variables approach (Fik, Ling, and Mulligan 2003) B o th the foreclosure contagion model s and the foreclosure intensive index model show expected results in the global market and the submarket s with high m edian household income The i mpacts of foreclosures on housing price s and nearby property value s are estimated in the global market A foreclosed property lowers the sale price by $49,105. The contagion effects of foreclosure s indicate that each additiona l foreclosure within 2000 feet of a subject sale is associated with an approximately $2,500 decrease in the sale price ($2,571 for the non interactive approach and $2,416 for the interactive approach). Moreover, foreclosures affect property value s when the y are associated with other factors. For instance, f oreclosures with larger living area s have larger negative effect s on housing price s than those with smaller living area s and f oreclosures with ve larger negative effect s on housing price s than those with a lower per Foreclosure Intensive I ndex has a significant negative effect on housing price s as expected, for a 0.01 unit increase of the F oreclosur e Intensive I ndex lowers the housing price by $1,563. It also proves that this index is a good indicator of the foreclosure intensity and performs very well in predicting the single family property value s

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71 In the submarkets with high median household income the effects of foreclosures on single family property value s are s lightly different. A foreclosed property lowers the sale price by $60,363. The contagion effects of foreclosure indicate that each additional foreclosure within 2000 feet of a subject sale is associated w ith an approximately $3,300 decrease in the sale price ($3,280 for the non interactive approach and $3,341 for the interactive approach). The interactive approach estimates that foreclosures with larger living area s have larger negative effect s on housing price s than those with smaller living area s The Foreclosure Intensive I ndex has a significant negative effect on housing price s as expected, for a 0.01 unit increase of the Foreclosure Intensive I ndex lowers the housing price by $2,330. Based on these con clusions, in the city of Orlando, the negative effects of foreclosures on single family housing price s are larger in the high end submarkets than in the global market. Limitation and Future Research There are several lim itations in this thesis. First, hed onic regressions like ly suffer from omitted variable problems because it is impossible to observe all property, location and neighborhood characteristics (Frame 2010; Harding, Rosenblatt, and Yao 2009) Second, the reasons for the foreclosure discounts ca nnot be estimate d due to data limitation. This thesis only measures the quantitative negative effects of foreclosures but not other qualitative effects of the foreclosures process, such as stics before and after foreclosure. Third, the hedonic regressions in this thesis do not control for temporal variables. It is widely believed that the effects of foreclosure s on sale price s vary in different stages of the foreclosure process. Fo u rth, as F lorida has one of the highest foreclosure rate s in the country the estimation of the impact s of foreclosure s in Orlando

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72 may not be applied to other areas with lower foreclosure rate s Fifth, this thesis does not consider geographic submarkets. Foreclosure s in different geographic submarkets may have different effects on housing prices. Finally, error terms are not examined in this thesis. The sample housing units are expected to have spatial autocorrelations given their spatial distribution. In the futur e research, it would be helpful to control for occupancy status, the impacts of foreclosure s on housing price s For the interactive approach, it is possible to add more interactive va riables into the hedonic regressions to estimate the non linear relationship between the sale price s and other factors. For instance, the relationship between the sale price s and age may not be linear. In addition, i t would be helpful to consider better da ta that related to foreclosures. For instance, the crime rate s may be a good neighborhood variable, for the vicinity of foreclosures tend to have higher crime rate s It is also possible to co nsider adding locational dummy variables to examine the foreclosu re effects in different geographic submarkets. For example, the effects of foreclosures in northwest Orlando may be different from that in southeast Orlando. Updated foreclosure notices and sales documents with more detailed transaction information are nee ded in future research. Moreover, more application s of GIS should be considered in the future real e state research, as GIS has beco me an increasing powerful tool in the recent years.

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73 LIST OF REFERENCES Ambr ose, B., and C. Capone. 1998. Modeling the conditional probability of foreclosure in the context of single family mortgage default resolutions. Real Estate Economics 26 (3):391 429. Biswas, A. 2012. Housing submarkets and the impacts of foreclosures on pro perty prices. Journal of Housing Economics 21 (3):235 245. Calomiris, C., S. Longhofer, and W. Miles. 2008. The foreclosure house price nexus: lessons from the 2007 2008 housing turmoil. National Bureau of Economic Research Working Paper 14294:1 55. http:/ /www.nber.org/papers/w14294 (last accessed 14 February 2013). Campbell, J., S. Giglio, and P. Pathak. 2009. Forced sales and house prices. National Bureau of Economic Research Working Paper 14866:1 26. http://www.nber.org/papers/w14866 (last accessed 14 Fe bruary 2013). Clauretie, T. M., and N. Daneshvary. 2009. Estimating the House Foreclosure Discount Corrected for Spatial Price Interdependence and Endogeneity of Marketing Time. Real Estate Economics 37 (1):43 67. Cutts, A., and R. Green. 2004. Innovative servicing technology: Smart enough to keep people in their houses? Freddie Mac Working Paper 04 03:1 25. http://freddiemac.com/news/pdf/fmwp_0403_servicing.pdf (last accessed 13 February 2013). Ellen, I., M. Schill, S. Susin, and A. Schwartz. 2002. Buildin g homes, reviving neighborhoods: Spillovers from subsidized construction of owner occupied housing in New York City. Journal of Housing Research 12 (2):185 216. Fik, T., D. Ling, and G. Mulligan. 2003. Modeling spatial variation in housing prices: a variab le interaction approach. Real Estate Economics 31 (4):623 646. FloridaRealtors. 2012. The Distressed Property Market and Shadow Inventory in Flor Florida Realtors http://www.floridarealtors.org/Research/upload/The Distressed Property Market in Florida 2.pdf. Foote, C. L., K. Gerardi, and P. S. Willen. 2008. Negative equity and foreclosure: Theory and evidence. Journal of Urban Economics 64 (2):234 245. Frame, W. 2010. Estimating the effect of mortgage foreclosures on nearby property values: A critical review of the literature. Federal Reserve Bank of Atlanta Economic Review 95 ( 3):1 9. http://www.econstor.eu/handle/10419/57661 (last accessed 13 February 2013).

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74 Goodman, J. L. 1993. A Housing Market Matching Model of the Seasonality in Geographic Mobility. Journal of Real Estate Research 8 (1):117 137. Grover, M., L. Smith, and R. M. Todd. 2008. Targeting foreclosure interventions: An analysis of neighborhood characteristics associated with high foreclosure rates in two Minnesota counties. Journal of Economics and Business 60 (1 2):91 109. Harding, J. P., E. Rosenblatt, and V. W. Ya o. 2009. The contagion effect of foreclosed properties. Journal of Urban Economics 66 (3):164 178. Hartley, D. 2010. The impact of foreclosures on the housing market. Federal Reserve Bank of Cleveland Economic Commentary 2010 15. http://www.clevelandfed.or g/research/commentary/2010/2010 15.pdf?WT.oss=the magnitude and impact of negative equity&WT.oss_r=245 (last accessed 14 February 2013). Humphries, S. 2012. U.S. Home Values Post Largest Monthly Gain Since 2006; Majority of Markets Forecasted To Hit Bottom by Late 2012. Zillow Real Estate Research http://www.zillowblog.com/research/2012/04/24/u s home values post largest monthly gain since 2006 majority of markets forecasted to hit bottom by late 2012/. Immergluck, D., and G. Smith. 2006. The External Cost Single Family Mortgage. Housing Policy Debate 17 (1):57 79. LaCour Little, M. 2000. The evolving role of technology in mortgage finance. Journal of Housing Research 11 (2):173 205. Leonard, T., and J. C. Murdoch. 2009. The neighborhood effects of foreclosure. Journal of Geographical Systems 11 (4):317 332. Lin, Z., E. Rosenblatt, and V. W. Yao. 2009. Spillover Effects of Foreclosures on Neighborhood Property Values. The Journal of Real Estate Finance and Economics 38 (4):387 407. Mikelbank, B. A. 2008. Spatial analysis of the impact of vacant, abandoned, and foreclosed properties. Federal Reserve Bank of Cleveland Office of Community Affairs Working Paper :1 29. National and Twelfth District Developments. 2007. The Subprime M ortgage Market. Federal Reserve Bank of San Francisco 2007 Annual Report :6 17. RealtyTrac. 2012. RealtyTrac http://www.realtytrac.com/. Rogers, W., and W. Winter. 2009. The impact of foreclosures on neighboring housing sales. Journal of Real Estate Resea rch 31 (4):455 479.

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75 Schuetz, J., V. Been, and I. G. Ellen. 2008. Neighborhood effects of concentrated mortgage foreclosures. Journal of Housing Economics 17 (4):306 319. Shanklin, M. 2012a. Orlando home prices dip in tightening market. Orlando Sentinel ht tp://articles.orlandosentinel.com/2012 09 17/business/os home prices august 20120917_1_home prices house prices core orlando market. 2012b. Orlando home prices rise even as sales slacken. Orlando Sentinel http://articles.orlandosentinel.com/2012 10 1 5/business/os orlando home sales 20121015_1_core orlando market midpoint price orlando area. 2012c. Report: Orlando to lead nation in 2012 house price gains. Orlando Sentinel http://articles.orlandosentinel.com/2012 01 10/business/os orlando house pr ice 2012 20120109_1_house price gains home price gains clear capital. Inner City Cleveland. Journal of Housing Research 18 (1):45 61. Tisner. 2012. Orlando FL Foreclosure Tr ends March 2012. Orlando FL Real Estate Kissimmee and Celebration http://blog.orlandoavenue.com/orlando fl foreclosure trends march 2012/. Voicu, I., and V. Been. 2008. The Effect of Community Gardens on Neighboring Property Values. Real Estate Economic s 36 (2):241 283.

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76 BIOGRAPHICAL SKETCH Ms. Yibin Xia is from Shenzhen, China concentration on economic geography at University of Cincinnati in 2010. She received her master s degree from the University of Florida in the spring of 2013.