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THE DYNAMIC INTERACTION BETWEEN RESIDENTIALMORTGAGE FORECLOSURE, NEIGHBORHOOD

CHARACTERISTICS, AND NEIGHBORHOOD CHANGE

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

By

Yanmei Li, M.A.

*****

The Ohio State University

2006

Dissertation Committee:

Professor Hazel Morrow-Jones, Adviser

Professor Donald R. Haurin

Professor Philip A. Viton

Approved by

AdviserGraduate Program in City and Regional

Planning



Copyright by

Yanmei Li

2006



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ABSTRACT

Many factors lead to mortgage default and foreclosure, and neighborhood

characteristics are among the most important (Quercia and Stegman, 1992). However,

few scholars have examined how neighborhood characteristics contribute to mortgage

foreclosure (Cotterman, 2001; Baxter and Lauria, 2000; Lauria, 1998) and none of the

previous studies have systematically addressed the mutual interaction between

foreclosure and neighborhood characteristics and change. This research uses multiple

datasets from Ohio’s two most populous counties to examine some of these previously

omitted or understudied aspects of the issue. Particular attention has been paid to each

neighborhood’s racial composition, economic level, housing prices and other housing

stock characteristics as well as to the changes over time in those variables.

The analysis starts with simple descriptive statistics, spatial autocorrelation analysis,

and comparison of different foreclosure patterns in the two counties. Then spatial

regression models, H-Robust models and Iterated Seemingly Unrelated Regression

(ITSUR) are used to explain the interaction between mortgage foreclosure and

neighborhood characteristics and change. The study finds that foreclosures cluster in low-

income minority neighborhoods and inner cities, although suburban areas have seen an

increase. Educational attainment, median household income, and average housing cost

burden contribute to foreclosures in both counties. As expected there are similarities and



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disparities in the interaction of foreclosure and neighborhoods between the two counties.

The use of panel data, Robust OLS, spatial lag models and SUR has solved some

problems related to spatial dependence, heteroskedasticity and mutual non-recursive

interaction between foreclosure and neighborhoods.

The research not only contributes to the literate and methodology in related topics,

but also contributes to our understanding of the relationship between foreclosure and

neighborhoods, and will assist in the creation of better policies to deal with the issue of

foreclosure. The policy recommendations include a strong focus on neighborhood

foreclosure prevention, not just policies aimed at individual homeowners. These policies

might focus on neighborhoods with low educational attainment, an increasing percentage

black population, or a high female headship rate. This project suggests that foreclosure

prevention programs not be the same in all places.



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Dedicated to my father and mother



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ACKNOWLEDGMENTS

I wish to thank my adviser, Professor Hazel Morrow-Jones, for her intellectual

support, encouragement, and enthusiasm that made this dissertation possible, and for her

patience in correcting my English, stylistic and scientific errors.

I thank Professor Jean M. Guldmann, Professor Phillip Viton, and Professor Donald

Haurin for their guidance which made the methodology more appealing.

I am grateful to Charlie Post from the Housing Research Center at Cleveland State

University to provide Cuyahoga County’s parcel data.

I wish to thank Katrin Anacker and Fang-Chi Hsu for their continuous

encouragement. I am indebted to Joe Gakenheimer for his support and suggestions in

writing and preparing this manuscript. I thank Eileen Frey, Cheryl Kaufman, and Donna

Fasnacht for their continuous prayers and love.

This research was supported by a grant from the Center for Urban and Regional

Analysis (CURA) at the Ohio State University. The financial support from the grant has

made this dissertation possible.



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VITA

December 25, 1975 ..……..…………...Born - Qujing, China

1998 ……………………….…………..B.S. Geography, East China Normal University,Shanghai, China

2001 …………………………………M.A. Regional Economics, Beijing Normal

University, Beijing, China

2001 – present ………………………Graduate Research Associate, The Ohio StateUniversity

PUBLICATIONS

1. Wu, Dianting, Yanmei Li, et. al. 2002. The Development of Intellectual Economy inChina. Economic Geography (Chinese). Vol. 22, No. 4

2. Wu, Dianting, Jie Tian, Yanmei Li, et. al. 2002. The Analysis of the Relationships

between Modernization, Industrialization, Urbanization, Intellectualization andEconomic Development in China. Systems Engineering – Theory and Practice

(Chinese). Vol.22. No. 11.3. Li, Yanmei, Dianting Wu, and Gang Zeng, 1999. The Characteristics and

Development Strategies of Hi-tech in Changjiang Delta, Areal Research and

Development (Chinese), Vol.18, No.34. Wu, Dianting, Shen Ji, and Yanmei Li. 1998. Dividing One Integrated Part to Three

Sections in Geographic Thoughts, Youth Geographer(Chinese), Vol.9, No.4

FIELDS OF STUDY

Major Field: City and Regional Planning



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TABLE OF CONTENTS

ABSTRACT ............................................................................................................. ii

ACKNOWLEDGMENTS................................................................................................ vVITA ................................................................................................................ vi

LIST OF TABLES........................................................................................................... ix

LIST OF FIGURES......................................................................................................... xi

CHAPTER 1 INTRODUCTION AND RESEARCH QUESTIONS......................... 1

Nature of the Problem..................................................................................................... 2Objective of the Research ............................................................................................... 3Research Questions......................................................................................................... 4Scope of the Research..................................................................................................... 6

CHAPTER 2 LITERATURE REVIEW...................................................................... 8Residential Mortgage Foreclosure .................................................................................. 8The Interaction between Neighborhood Characteristics, Neighborhood Change andResidential Mortgage Foreclosure ................................................................................ 31Major Problems in Neighborhood-Effects Research .................................................... 46Literature Summary and the Derivation of Research Questions .................................. 50

CHAPTER 3 RESEARCH METHODOLOGY........................................................ 52Hypotheses.................................................................................................................... 52Major Datasets Used in Foreclosure Research ............................................................. 56Summary of Datasets Used in this Research ................................................................ 62Variable Selection and Description .............................................................................. 64Research Methodology ................................................................................................. 73

CHAPTER 4 DESCRIPTIVE AND SPATIAL ANALYSIS .................................. 84Judicial Foreclosure Process and Sheriff’s Deed Transfer Data................................... 84Ohio’s Foreclosure Situation ........................................................................................ 87

Research Area and Geographic Definition of Neighborhood....................................... 91Data Description for Each County................................................................................ 99Conclusions................................................................................................................. 139

CHAPTER 5 THE INTERACTION BETWEEN RESIDENTIAL MORTGAGE

FORECLOSURE, NEIGHBORHOOD CHARACTERISTICS,

AND NEIGHBORHOOD CHANGE.............................................. 141Effects of Neighborhoods on Foreclosure .................................................................. 145



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Summary: Effects of Neighborhood Characteristics on Residential MortgageForeclosure.................................................................................................................. 167The Impact of Residential Mortgage Foreclosure on Neighborhood Change: ASeemingly Unrelated Regression (SUR) Approach.................................................... 173Conclusion: The Interaction between Residential Mortgage Foreclosure, Neighborhood

Characteristics, and Neighborhood Change................................................................ 188

CHAPTER 6 CONCLUSIONS, POLICY IMPLICATIONS AND FUTURE

RESEARCH DIRECTIONS............................................................ 192

APPENDIX A FORECLOSURE PROCEDURES ................................................. 207

APPENDIX B TOTAL SHERIFF’S DEEDS AT THE SCHOOL DISTRICTLEVEL IN FRANKLIN COUNTY................................................. 214

APPENDIX C SPATIAL AUTOCORRELATION OF SELECTED VARIABLES

............................................................................................................. 216

APPENDIX D SUR MODEL RESULTS................................................................. 229

APPENDIX E THE GEOGRAPHIC DISTRIBUTION OF SELECTED

NEIGHBORHOOD CHANGE INDICATORS AT THE BLOCK

GROUP LEVEL IN FRANKLIN AND CUYAHOGA COUNTIES............................................................................................................. 233

BIBLIOGRAPHY…………………………………………………………….……….239

NOTES ……………………………………………………………………………….252



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LIST OF TABLES

Table 3.1: List of Selected Variables................................................................................ 70

Table 4.1: Number of New Foreclosures Filed in 2004 by County (descending by thenumber of filings) ..................................................................................................... 89

Table 4.2: Selected Characteristics of the Two Counties ................................................. 96

Table 4.3: New Foreclosure Filings, Terminated Foreclosure Cases and Sheriff’s Deeds(1997–2004, Franklin County).................................................................................. 99

Table 4.4: The Total Single-family Sheriff’s Deeds (1997–2004, Franklin County)..... 100

Table 4.5: Change in Neighborhood Variables from 1990 to 2000 by Groups of Foreclosure Rate in Franklin County...................................................................... 116

Table 4.6: Sheriff’s Deeds as a Percentage of Total New Filings and Total ForeclosureCase Terminations in Cuyahoga County (1997–2004)........................................... 119

Table 4.7: Total Available Residential Sheriff’s Deeds in Cuyahoga County (1997–2004)................................................................................................................................. 120

Table 4.8: Change of Selected Neighborhood Variables by Groups of Foreclosure Rate inCuyahoga County (1990–2000) .............................................................................. 137

Table 5.1: Foreclosure Rate Characteristics for Franklin and Cuyahoga Counties........ 141

Table 5.2: Descriptive Analysis for Franklin County and Cuyahoga County ................ 143

Table 5.3: Comparison of OLS Regression and Spatial Regression of the Effect of

Neighborhood Characteristics (2000) and Change on Foreclosure Rate in FranklinCounty (Dependent Variable: Foreclosure Rate).................................................... 150

Table 5.4: Comparison of OLS Regression and Spatial Regression of the Effect of Neighborhood Characteristics (2000) and Change on Foreclosure Rate in CuyahogaCounty (Dependent Variable: Foreclosure Rate).................................................... 154

Table 5.5: Variables that are Significant in Each County.............................................. 169



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Table 5.6: Cross Model Covariance Matrix for Cuyahoga County ................................ 175

Table 5.7: ITSUR Estimate Results with “FORECLOSURE” (as an independent variable)Significant (System Weighted R-Square: 0.4104; System Weighted MSE: 1.0000)................................................................................................................................. 177

Table A.1: Legislation Requirement of Mortgage Foreclosure in Different States in theU.S. ......................................................................................................................... 208

Table B.1: Total Sheriff’s Deeds at the School District Level in Franklin County (1997-2004, Note: 11838 total cases and 6 cases can’t be identified at the school districtlevel) ....................................................................................................................... 215

Table D.1: ITSUR Estimate Results (where “FORECLOSURE” is not significant) ..... 230



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LIST OF FIGURES

Figure 2.1: The Interaction between Residential Mortgage Foreclosure, and Neighborhood Characteristics and Change............................................................... 30

Figure 3.1: Spatial Regression Decision Process (Anselin, 2005: 217) ........................... 78

Figure 4.1: Judicial Foreclosure Process .......................................................................... 86

Figure 4.2: New Foreclosure Filings in Ohio (1990–2005).............................................. 87

Figure 4.3: Change of Foreclosures Started in Ohio (1984–2003)................................... 89

Figure 4.4: Average Annual Growth Rate of New foreclosure Filings by County .......... 90

Figure 4.5 New Foreclosure Filings in Cuyahoga County and Franklin County (1990– 2004) ................................................................................................................................. 92

Figure 4.6: Research Area: Cuyahoga County and Franklin County, Ohio ..................... 95

Figure 4.7: Franklin County Foreclosure Rate Distribution at the Block Group Level(1997–2004)............................................................................................................ 101

Figure 4.8: Spatial Distribution of Sheriff’s Deeds in Franklin County (1997–2004) ... 103

Figure 4.9: Total Residential Sheriff’s Deeds in Franklin County (1997–2004) ........... 105

Figure 4.10: Comparison between the 1997 and 2004 of the Distribution of Sheriff’sDeeds in Franklin County ....................................................................................... 106

Figure 4.11: Foreclosure Rates by Block Groups in Franklin County (1997–2004)...... 107

Figure 4.12: Connectivity of Block Groups in Franklin County .................................... 109

Figure 4.13: Map of Foreclosure Rate Local Spatial Autocorrelation in Franklin County(1997–2004)............................................................................................................ 113

Figure 4.14: Total Sheriff’s Deeds in Cuyahoga County (1965–2004).......................... 118



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Figure 4.15: Cuyahoga County Foreclosure Rate Distribution at the Block Group Level(1997–2004)............................................................................................................ 121

Figure 4.16: Spatial Distribution of Sheriff’s Deeds in Cuyahoga County (1997–2004)................................................................................................................................. 122

Figure 4.17: Total Residential Sheriff’s Deeds in Cuyahoga County (1997–2004)....... 124

Figure 4.18: Comparison between the 1997 and 2004 Distribution of Sheriff’s DeedTransfer in Cuyahoga County................................................................................. 125

Figure 4.19: Foreclosure Rates by Block Groups in Cuyahoga County (1997–2004) ... 126

Figure 4.20: Foreclosure Rates by Block Groups in Cuyahoga County (1983-1989).... 128

Figure 4.21: Connectivity of Block Groups in Cuyahoga County.................................. 130

Figure 4.22: Map of Foreclosure Rate Local Spatial Autocorrelation in Cuyahoga County(1997–2004)............................................................................................................ 134

Figure 5.1: Summary of the Interaction between Residential Mortgage Foreclosure and Neighborhood Characteristics and Change............................................................. 191

Figure 6.1: Change in Female Headship Rate in Cuyahoga County (1990–2000, % points)................................................................................................................................. 201

Figure 6.2: Change in Percentage Population below the Poverty Line in CuyahogaCounty (% points) ................................................................................................... 202

Figure C.1: The Local Spatial Autocorrelation between Female Headship Rate in 2000and Foreclosure Rate (2001–2004) in Franklin County ......................................... 217

Figure C.2: The Local Autocorrelation between Median Household Income in 2000 andForeclosure Rate (2001–2004) in Franklin County ................................................ 218

Figure C.3: The Local Autocorrelation between Housing Cost Burden with a Mortgage in2000 and Foreclosure Rate (2001–2004) in Franklin County ................................ 219

Figure C.4: The Local Autocorrelation between Median Housing Value of Owner-Occupied Housing Units in 2000 and Foreclosure Rate (2001–2004) in FranklinCounty..................................................................................................................... 220

Figure C.5: The Local Autocorrelation between Housing Vacancy Rate in 2000 andForeclosure Rate (2001–2004) in Franklin County ................................................ 221



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Figure C.6: The Local Autocorrelation between Homeownership Rate in 2000 andForeclosure Rate (2001–2004) in Franklin County ................................................ 222

Figure C.7: The Local Spatial Autocorrelation between Female Headship Rate in 2000and Foreclosure Rate (2001–2004) in Cuyahoga County....................................... 223

Figure C.8: The Local Autocorrelation between Median Household Income in 2000 andForeclosure Rate (2001–2004) in Cuyahoga County.............................................. 224

Figure C.9: The Local Autocorrelation between Housing Cost Burden with a Mortgage in2000 and Foreclosure Rate (2001–2004) in Cuyahoga County.............................. 225

Figure C.10: The Local Autocorrelation between Median Housing Value of Owner-Occupied Housing Units in 2000 and Foreclosure Rate (2001–2004) in CuyahogaCounty..................................................................................................................... 226

Figure C.11: The Local Autocorrelation between Housing Vacancy Rate in 2000 andForeclosure Rate (2001–2004) in Cuyahoga County.............................................. 227

Figure C.12: The Local Autocorrelation between Homeownership Rate in 2000 andForeclosure Rate (2001–2004) in Cuyahoga County.............................................. 228

Figure E.1: Change in % Divorced Population in Cuyahoga County (1990–2000, % points) ..................................................................................................................... 234

Figure E.2: Change in % Population with College degrees or Higher in Cuyahoga County(1990–2000, % points)............................................................................................ 235

Figure E.3: Change in Homeownership Rate in Cuyahoga County (1990–2000, % points)................................................................................................................................. 236

Figure E.4: Change in Housing Vacancy Rate in Cuyahoga County (1990–2000, % points) ..................................................................................................................... 237

Figure E.5: Change in Median Housing Value in Cuyahoga County (1990–2000, % points) ..................................................................................................................... 238



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CHAPTER 1

INTRODUCTION AND RESEARCH QUESTIONS

Residential mortgage foreclosures are the processes that homeowners are legally

forced to foreclose on their properties because they default on their mortgage payment.

There are many factors contributing to foreclosures. Foreclosures have profound impacts

on individual homeowners, neighborhoods, mortgage lenders, and policies. As the first

step of a research agenda this project focuses on the interaction between residential

mortgage foreclosures, neighborhood characteristics, and neighborhood change.

Residential mortgage default and foreclosure issues did not attract much attention

until the mid 1970s, when the single-family foreclosure rate in the U.S. began to increase.

Most of the studies since then focused on the factors contributing to mortgage default and

foreclosure, with an emphasis on what financial institutions could do better to manage

their credit risk (Quercia and Stegman, 1992). Since 2000, there has been a dramatic rise

in foreclosure rates, especially in Ohio and Indiana, and it has caused great concern

among policy makers, citizen advocacy groups, fair housing agencies, and other

concerned individuals. This means that many more stakeholders are showing an interest

in mortgage default and foreclosure. The complexity of this issue has increased with the

advent of flexible financial products, along with increasing ethical and legal challenges



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facing the real estate profession (such as mortgage fraud, incomplete disclosure of costs

associated with mortgages, using a “teaser rate” to confuse loan borrowers, etc.).

Nature of the Problem

There is abundant literature on factors contributing to mortgage default and

foreclosure. Many of the previous studies focused on measuring default risk using

various factors and models, in order to provide more accurate mortgage pricing and risk

management for financial institutions. Neighborhood characteristics are one of the

important sets of factors that should be considered in these models of residential

mortgage foreclosure. But only a few scholars have paid attention to the impact of

neighborhood characteristics on residential mortgage foreclosure (Cotterman, 2001; von

Furstenburg and Green, 1974; Williams et. al., 1974; Sandor and Sosin, 1975).

Residential mortgage foreclosure is an issue in housing markets, and housing

markets are geographically bounded. So mortgage foreclosure, neighborhood

characteristics and neighborhood change are related to each other in complex ways. But,

just as with the studies of neighborhood effects on mortgage default and foreclosure, the

impacts of foreclosure on neighborhood characteristics and change have not been fully

explored, with the notable exception of the studies conducted by Lauria and Baxter in

New Orleans (Lauria, 1998; Lauria and Baxter, 1999; Baxter and Lauria, 2000), and

Immergluck and Smith in Chicago (Immergluck and Smith, 2005a, 2005b).

The interaction between mortgage foreclosure and neighborhood change is very

complicated and is related to many different aspects of housing market equilibrium,

economic development, lender and borrower decision theory and social transition, among



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other things. In order to examine the interactive relationships between residential

mortgage foreclosure and neighborhood characteristics and change, this study uses

Sheriff’s foreclosure sales data in the two most populous counties in Ohio, Cuyahoga

County and Franklin County, the central counties of the Cleveland and Columbus

metropolitan areas, respectively.

Ohio has one of the highest residential mortgage foreclosure rates in the United

States, where the foreclosure rate is defined as the number of mortgages in foreclosure as

a percentage of all mortgage loans outstanding (Krumkin, 2000). There has been a

tremendous increase in foreclosures in Ohio since 1995. These two Ohio counties provide

good case studies for testing hypotheses about the interaction between mortgage

foreclosure and neighborhood characteristics and change. The Sheriff’s Sales data are

combined with other datasets, such as census demographic data, housing and economic

data, and real property parcel data in each county, to develop a deeper understanding of

the complexities than has been previously available (Cotterman, 2001; Baxter and Lauria,

2000).

Objective of the Research

The objective of this research is to improve our understanding of the complex

relationship between neighborhood characteristics, foreclosure and neighborhood change.

In addition, I hope to make a significant contribution to housing and foreclosure policy in

Ohio. The findings will contribute to both theory and policy on foreclosure and

neighborhood change. The research results will also help target community-based

foreclosure prevention programs to the most at-risk neighborhoods. The document



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includes suggestions for ways to intervene to reduce foreclosure concentration and the

impacts of such concentration on neighborhoods. The research will contribute to the

limited academic literature on this topic by adding significant work over time and across

space, allowing an analysis of the context within which foreclosure occurs. The explicit

consideration of racial issues and the problem of house price depreciation incorporated in

this analysis will also enhance the existing models.

Research Questions

There are three primary questions that the research tries to answer. Following each

question are more detailed hypotheses.

1. Do neighborhood characteristics and changes affect residential mortgage default and

foreclosure? If so, how?

If the answer to this question is yes, several subsidiary questions need to be

addressed. For example, what neighborhood factors contribute to mortgage default risk

and rising mortgage foreclosure rates? What kinds of neighborhoods have seen the

highest increase in foreclosure sales? Why do different neighborhoods have different

foreclosure rates? Do the phenomena follow certain patterns over time in different

metropolitan areas?

2. Do mortgage foreclosures affect neighborhoods? How and under what circumstances?

According to previous research, mainly by Lauria and Baxter (1998, 1999, 2000),

the impact of mortgage foreclosure on neighborhoods in which foreclosed properties are



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located is very significant. They found important impacts on racial transition and general

economic condition of the neighborhoods.

Properties in some neighborhoods tend to have a lower price appreciation (Oliver

and Shapiro, 1995; Raffalovich, 2002; Shapiro, 2004), which in turn makes it more likely

that people will default on mortgages because a lower appreciation rate or depreciation

will decrease the property’s real value, and that leads to less equity. If values depreciate

enough, the property could end up with negative equity. Negative equity is one of the

leading reasons for people to default on mortgage payments (Case and Shiller, 1996;

Cunningham and Capone, 1990). Higher foreclosure rates in a neighborhood can

decrease housing values in the neighborhood and make price appreciation even lower,

thus making more people likely to default. On the other hand, the characteristics of the

residents of these neighborhoods must also be taken into account as those characteristics

could tend to lead to higher default rates. Thus, the complexity of the geographic

relationships, as well as the interdependencies of the people and the neighborhoods,

requires special attention. In this research, in addition to the racial composition and

general economic condition of neighborhoods, housing price appreciation and other

housing stock characteristics of neighborhoods are explored to find out whether and how

mortgage foreclosure affects housing price appreciation and neighborhood stability.

3. Can we model the cyclical nature of the relationship between neighborhood

characteristics, neighborhood change and foreclosure rates?

The first two research questions indicate that separating the impact of

neighborhoods on foreclosures from the impact of foreclosures on neighborhoods is a



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crucial methodological problem. Thus, in order to address these two substantive

questions, the research must address the methodological question that is relevant to many

neighborhood-effects studies. Neighborhood-effects research is a controversial area of

inquiry in social sciences (Dietz, 2002), although there is abundant literature addressing

research methods in the area. There are several difficult problems in this area of research,

including endogenous effects, omitted variables, and reflection problems (Dietz, 2002;

Manski, 2001). My research develops a model to deal with the nonrecursive nature of the

relationship between neighborhood characteristics, neighborhood change and foreclosure

rates, taking into account the possibility of endogenous effects, omitted independent

variables and the reflection problem (Dietz, 2002).

Scope of the Research

The major datasets used for this research are the Sheriff’s deed transfer data from

1997 to 2004 (in Cuyahoga County the data from 1983 to 1989 are also used), the census

block group data from 1990 and 2000, the census designated place data from 1990 and

2000, and real property parcel data from 2004 and 2005. These datasets were merged for

analysis purposes. More details in the data sets and the methodologies are included in the

relevant chapters. The second chapter of this dissertation provides the literature review

and conceptual models. Then I turn to the results.

The first section of results provides the descriptive and spatial analyses of the

foreclosure patterns in each county, and their relationships with selected neighborhood

characteristics.



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The second results section reports the outcomes of the advanced spatial analysis and

the spatial modeling. Spatial autocorrelation analysis measures how spatial

autocorrelation affects the regression results and how the spatial lag and error models

differ from the Ordinary Least Square (OLS) regression models when using

neighborhood variables to predict foreclosure rate.

The third section of the results formulates a Seemingly Unrelated Regression (SUR)

system to measure how foreclosure rates and other neighborhood and place-characteristic

variables affect each selected neighborhood change variable in each county.

In the final chapter of the dissertation, the major findings from the research will be

used as the basis for policy suggestions to help policy makers be aware of the spatial

patterns of foreclosure, the mutual impact of neighborhood variables and foreclosure, and

the specific neighborhood factors that are highly related to foreclosure. Targeted policies

can be created to manage neighborhood variables identified in this research in order to

break the cyclic nature of the relationship between neighborhood characteristics and

foreclosure. The establishment of spatial analysis and models and SUR models in the

research will provide an effective method for analyzing similar research questions, and

the combination of these spatial and quantitative models will contribute to foreclosure

research.



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CHAPTER 2

LITERATURE REVIEW

Residential Mortgage Foreclosure

Concepts of Mortgage Delinquency, Mortgage Default and Foreclosure

Mortgage foreclosure is “the process by which the mortgage originators claim legal

rights to the property by foreclosing the mortgage in the event of mortgage default”

(Frumkin, 2000). A mortgage delinquency, which usually means a mortgage repayment is

overdue 30 days or more, becomes a mortgage default when it is overdue by more than

90 days. When the mortgage is in default, the lender may choose to work with the

borrower to see if the loan can be modified or brought back to a normal balance. When

these efforts fail, the lender will usually file a foreclosure with the court to claim its legal

rights under the mortgage. Many studies treat “default” and “foreclosure” as synonymous

(Goering and Wienk, 1996), but in fact, default is incurred and affected by borrowers’

choices, while foreclosure is one of the options available to lenders to enforce the

repayment of a mortgage in default. This research will treat default and foreclosure as

two related but different processes.

A civic real estate sale is the final procedure in a judicial foreclosure process. The

property can be withdrawn from this process if the borrower(s) file for bankruptcy, bring



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the back payments up to date, sell the property, legally cancel the mortgage, or resume

the mortgage repayments.

In contemporary U.S. society, with its mature financial markets and innovative and

flexible financial products, buying a home has become much easier. Homebuyers do not

need to accumulate large amounts of savings in order to purchase a house. When certain

conditions are met, they can readily obtain a mortgage to finance a home purchase,

although different financial agencies might have different underwriting standards.

When a borrower obtains a mortgage to buy a house, a scheduled repayment of the

mortgage is incurred. This schedule is determined by the loan-to-value ratio, loan term,

mortgage interest rate, and interest compounding factors. But the mortgage performances

of borrowers differ greatly and are related to the differences in household characteristics,

such as income, family structure, and mobility decisions, and to the general economic

context, including recessions, interest rates and so forth. Mortgage performance includes

timely submission of mortgage payments, prepayment behavior, refinancing behavior,

mortgage delinquency and mortgage default. Of these behaviors only mortgage

delinquency and default are related to a possible change of homeownership status of

borrowers and the risk of borrowers losing their homes if they are not able to resume the

mortgage repayment.

Mortgage delinquency usually means missing one scheduled payment. At that time,

lenders cannot tell whether the payment will be continued or stopped in the next payment

cycle. But if several payments are missed, usually three (Quercia and Stegman, 1992),

lenders will consider borrowers to have defaulted. The criteria that determine a default

vary among financial institutions. When a loan defaults, lenders will choose either to use



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loss-mitigation techniques to work with the borrowers to resolve the issue and resume the

payments, or foreclose the mortgage by auctioning the mortgage property to cover the

loan balances of the borrowers (Capone Jr., et. al., 1996). Lenders choose the option that

costs the least to process. If the costs of working out the troubled loan are much larger

than foreclosure costs, lenders prefer to choose foreclosure. On the other hand, many

lenders are willing to work with borrowers first to find ways to resolve the issue. If this

cooperation fails, a foreclosure action will be filed in the local civic court or a non-

judicial trustee’s sales process will be initiated. A civic real estate sale is the final

procedure in a judicial foreclosure process.

The decision of whether to choose foreclosure depends greatly on state legislation

(Clauretie, 1987). In a non-judicial process, when loans are in default a notice of default

will be issued to the borrower. Then, if the borrower cannot repay the back payments, a

trustee’s sale will be initiated to sell the foreclosed properties. Thus a non-judicial

foreclosure does not need the involvement of courts and Sheriff’s Offices. But the

judicial process starts with foreclosure filings to the local court. Then, if the borrower

cannot walk out of the foreclosure process (e.g. cannot sell the property before the

auction, or get a bankruptcy), the court will order a Sheriff’s sale. Both judicial and non-

judicial processes have advantages and disadvantages. The biggest advantage of the

judicial process is the legal guarantee that helps the involved parties avoid disputes in

titles and other real estate claims. However, judicial foreclosure is much more expensive

in terms of legal fees and is more time consuming than non-judicial processes. Many

states in the U.S. allow both judicial and non-judicial processes, but Ohio only allows a

judicial process (see Appendix A for a detailed description of the process).



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According to research conducted by Clauretie (1987), states without judicial

foreclosure usually see a higher foreclosure rate because of the low foreclosure costs,

controlling the time span of the foreclosure process. This makes Ohio’s high foreclosure

rate even more surprising.

Previous Research on Mortgage Default and Foreclosure

Studies on residential mortgage default have changed over time. Many have focused

on lenders’ and financial institutions’ need to understand the mechanisms in mortgage

default. Using these studies, financial institutions have sought to minimize mortgage

default risk and losses associated with the risk. Only in recent years has research on the

social impacts of mortgage default and foreclosure begun to appear in the academic

literature. But the recent research still has not resolved some essential issues related to

foreclosure, such as whether there are racial differences in mortgage default decisions,

how and where the households move after they lose their homes due to the foreclosure

process, and how those changes affect the structure of a neighborhood.

Three Generation’s Research on Mortgage Default

In the early 1990s, Quercia and Stegman (1992) summarized the literature on

residential mortgage default. They provided a comprehensive analysis of mortgage

default risk from three different perspectives, that of lenders, borrowers, and institutions,

each of which is associated with a research generation. These perspectives have

contributed to the mortgage default literature either theoretically or empirically. Their

research also tried to seek different indicators to measure mortgage default risk, such as



12

default rates, expected mortgage losses, and interest rate premiums (Quercia and

Stegman, 1992).

The first generation’s studies were from the lender’s perspective. Minimizing credit

risk is one of the essential activities in the daily management of financial institutions

(Saunders and Cornett, 2003). The goal of lenders facing mortgage default by borrowers

is to lower the costs associated with defaults and foreclosures. This stream of research

found that mortgage default rates are correlated with loan characteristics, borrower

characteristics and property characteristics. For example, loans with higher loan-to-value

ratios, higher interest rates and longer terms are much more likely to be in default

compared to the reverse characteristics of those indicators. Higher initial payment-to-

income ratio, properties with poor conditions and unstable neighborhoods usually are also

associated with a higher default risk.

The second generation’s research was from the borrower’s perspective and probed

borrower payment models. The models are based on utility (net wealth) optimization in

consumer theories and option-based choices. The utility (net wealth) optimization

theories indicate that when borrowers make decisions (timely payment, prepay, refinance,

or default) in their mortgage performance they base those decisions on the maximization

of their net wealth. The option-based models view default as a put option, where the

borrowers can sell the property back to the lender for the value of the mortgage at the

beginning of each payment period. Those mortgage performance choices are determined

by many factors, such as transaction costs, family crises and mobility decisions.

The third generation’s research was from the perspective of large financial

institutional loan pools and financial regulators. The research explored, for example, how



13

default happens on an FHA-insured mortgage or on a fixed-interest mortgage, and how

some regulations (e.g., capital requirements) affect mortgage default. But the studies of

the third generations are more complete and have considered the roles of all three sectors:

lenders, large financial institutions and regulators, and borrowers.

Quercia and Stegman concluded that there are still some obstacles in the research.

One of the greatest is the lack of data about changes to borrower, lender and property

information over time, which limits the research to some extent. But the most difficult

problem is the lack of data about borrower’s issues and decisions. They also indicate that

mortgage default models need to incorporate not only the role of borrowers but also the

mobility decision of borrowers.

Mortgage Default and Foreclosure Factors

There are many factors determining the possible mortgage default risk of certain

loans, but loan-to-value ratio, payment-to-income ratio, householder’s occupation (with

or without volatile income), property and neighborhood condition, regional

unemployment rate, transaction costs, crisis events, and borrowers’ expectations are some

of the major elements contributing to the risk of mortgage default and foreclosure

(Quercia and Stegman, 1992; HUD, 1992; Vandell and Tribodeau, 1985).

Among those factors, the macro economic situation, housing price appreciation and

neighborhood characteristics are macro spatial factors that help determine borrower

characteristics in certain geographical areas and, therefore, the loan characteristics

associated with those borrowers. Using those factors, loan default risk in certain

geographical areas can be measured. According to previous literature on mortgage default



14

and foreclosure, the following is a list of major factors contributing to mortgage default

and foreclosure, although some of them are correlated to others:

• Macro economic situation

• Mortgage loan characteristics

• Types of Mortgage Products

• Borrower characteristics and default decisions

• Mortgage lending legislation and foreclosure legislation

• Lender decisions in mortgage foreclosure

•

Housing attributes

• Housing appraisal

• Housing price appreciation

• Mortgage fraud

• Neighborhood characteristics.

I will discuss each of these briefly, though they are not the focus of this dissertation.

Macro economic situation

Studies have found that mortgage default is largely related to macro economic

changes over time. The most obvious indexes that are associated with mortgage default

are the unemployment rate, interest rate, and housing price index.

Bellamy (2002) found that in Ohio between 1994 and 2001 the unemployment rate

fell consistently, with minor volatility, but foreclosure filings increased consistently.

Therefore, he suggested that the increasing foreclosure rates in Ohio are not solely

dependent on the economic situation. A report from Policy Matters Ohio in 2004 assumes



15

a weak economy since 2001 to be one of the leading reasons for the high foreclosure rate

in Ohio in recent years. Case and Shiller (1996) found that high mortgage default rates

“strongly follow” real estate price declines or interruptions of real estate price increases.

Also, mortgage delinquency and foreclosure rates themselves are important economic

indicators (Frumkin, 2000).

Mortgage Loan Characteristics

Loan characteristics are important factors that can affect mortgage defaults and

foreclosures. In early research there were many interesting findings, such as that the

interest risk is one indicator of mortgage risk, and a higher loan-to-value ratio means

more default risk.

Many studies found that the initial loan-to-value (LTV) ratio has a significant

influence on mortgage default (Von Fustenberg, 1969, 1970a, 1970b; Deng and Gabriel,

2002; Calhoun and Deng, 2002; Ambrose et. al. 2002). The LTV ratio directly

determines the equity position of a borrower (HUD, 2004), and HUD found that a high

LTV ratio is associated with a high default rate by examining FHA-insured and GSE

(Government Sponsored Enterprises: Fannie Mae, Freddie Mac and Ginnie Mae)-

purchased loans. Von Furstenberg (1969, 1970a, 1970b) thought that home equity at the

time of loan origination is highly related to mortgage default. When the LTV ratio is

raised by seven percentage points from 90% to 97%, default rates for new homes increase

by seven times. But research found that in subprime mortgages the LTV has little effect

on loan performance (OCC, 2003). Quercia et.al. found that LTV ratio does not affect



16

default in their panel data of rural low-income mortgage borrowers (Quercia et. al.,

1995).

Interest rates that financial institutions charge to loan borrowers reflect the

expectations from the lenders about default potential. The lenders are hedging possible

losses from credit risk, so interest rate should be related to mortgage default risk (Jung,

1962). The yield curve slope therefore should be a factor contributing to mortgage

defaults (HUD, 2004). This hypothesis was later proven by other research. For example,

Page (1964) found that default risk was related to property values; financial institutions

are willing to issue loans with a lower interest rate to borrowers purchasing a high-value

property. A borrower who caught a loan to buy an expensive house probably has good

credit so their interest rate is low. In a situation of burnout1, where borrowers passed up

some previous good opportunities to refinance the mortgage at a more favorable interest

rate, they have a higher tendency to default because of the high interest rates (HUD,

2004). Ambrose and Sanders (2003) also found that the change in yield curve has a direct

impact on the probability of mortgage default.

Besides interest rates and LTV ratios, loan term length and the age of the mortgage

are also important factors (von Furstenberg, 1969). Von Furstenberg (1969) found that

mortgage default risk positively correlates with the term of the mortgage and a mortgage

younger than 3 or 4 years is at higher risk as well.

HUD (2004) found that loan size is also a factor in determining loan default risk by

exploring both PMI (Private Mortgage Insurance) and FHA loan data. Smaller loan sizes

usually are associated with a higher default rate, which might be because that low-income

borrowers, low-liquid-asset borrowers, or borrowers with high income-volatility tend to



17

have smaller loans. That can also indicate high housing price volatility in low-priced

houses.

Quercia and Stegman (1992) stated that default patterns for adjustable rate

mortgages (ARMs) were not comprehensively studied before. With ARMs, payment

shock due to unexpected interest rate increase is one of the important reasons for people

to default. Early ARM payment accounts for the impact of the change of payment coupon

from an initial low rate (“teaser rate”) to an index-adjusted rate during the first year of the

loan. Therefore, early ARM payment can also explain some of the default risk.

Another factor contributing to mortgage delinquency and default is the presence and

holding status of junior or subordinate loans and liens (Herzog and Earley, 1970; LaCour-

Little, 2004). Their research found that junior or subordinate loans and liens might

increase the default risk of primary loans.

Types of Mortgage Products

Product types such as FHA-insured, VA-insured, Conventional, ARM, FRM (Fixed

Rate Mortgage), GRM (Graduated Rate Mortgage) and subprime loans affect mortgage

default risk due to their own characteristics. According to the Mortgage Bankers’

Association, FHA loans usually have a higher foreclosure rate than conventional

mortgages. But determinants of delinquency rates in different types of loans are different,

especially for some non-profit community lending organizations in which social

networks, business culture, funding sources, composition of the board and loan

committees, staff structure, loan intake, and collection tools are major organizational

factors affecting loan delinquency rate (Baku and Smith, 1998).



18

The role of subprime and predatory lending on increasing mortgage foreclosures is

addressed by many previous studies and in different states such as Ohio, Indiana and

Arizona (Realtors, 2003; Goldstein, 2004; Rhey and Posner, 2004; Stock et. al., 2001). A

study of the differences in mortgage default rates of prime and non prime mortgages

indicates that these mortgages are significantly different in many aspects, such as

different risk levels at the loan origination. They both default at elevated levels but

respond differently to incentives to prepay or default (Pennington-Cross, 2003). The

study also found that mortgage default is less responsive to the amount of equity when

credit scores are included in the analysis.

Borrower Characteristics and Default Decisions

Default decisions made by borrowers are determined by many factors. A default is

usually due to two situations: inability to pay and/or unwillingness to pay. Those two

scenarios should be separated when exploring mortgage default decisions. In addition to

factors described in the preceding section on borrower characteristics in mortgage default

risk models, certain events such as changes in borrower characteristics and life crises can

also make borrowers choose default. The most important such factors are job loss, family

structure change (such as divorce, children going to college, or decease of a financially

supportive adult), and moving.

When terminating a mortgage, the decision of a household to default is determined

by the borrower’s perception of the value of the mortgage versus the value of the home.

When the house is perceived to be less valuable than the outstanding mortgage balance, a

decision to default might be made and this is a voluntary default decision. Another



20

and credit history on mortgage default risk, recent research has begun to focus more on

comprehensive loan characteristics, borrower characteristics, and property and

neighborhood characteristics.

The payment-to-income ratio is thought to have a significant effect on mortgage

default, but empirical studies show mixed results. Therefore, we cannot say that its

impact on mortgage default is significant (HUD, 2004).

Earlier research found that the effect of income on mortgage default was ambiguous

(HUD, 1992). However, Van Order and Zorn (2002), in a recent study of competing risk

of mortgage termination, found that borrower income is an important determinant for

mortgage default decisions when the borrower’s property has negative equity. Although

few studies have focused on income levels, many have tried to examine the impact of

income variability (stability and growth) on mortgage default. Van Order and Zorn

(2002) also found a positive relationship between income variability and the mortgage

default rate. This means that high volatility of income is usually associated with a high

default rate. Borrowers with certain occupations, such as those who are self employed,

those whose major income depends on commissions, and those with low-skilled jobs

(HUD, 2004), have high income volatility. Research also found that borrowers with low

liquid assets have a higher mortgage default probability (HUD, 2004).

In several studies the impact of a borrower’s ethnic background was greatly reduced

by controlling other characteristics, such as down payment and credit history (Cotterman,

2002; Van Order and Zorn, 2002). Therefore, many researchers believe that loan default

differences among different racial groups can be better explained by down payment

amount and credit history of the borrowers. Some think that racial minorities are more



21

likely to become targets of predatory lending, but little attention has been paid to possible

racial disparities in mortgage performances, mortgage default and mortgage foreclosures.

Only in recent years have some scholars noticed this issue (Lauria and Baxter, 1999;

Lauria, 1998; Baxter and Lauria, 2000).

Mortgage default will affect the future credit worthiness of a borrower. But for some

borrowers, repeated mortgage decisions can be observed. Ambrose and Capone (2000)

found that borrowers with a first default have a greater risk for a second default, and the

risk is also greater when the time difference between two defaults is shorter than two

years. They also found that the economic factors affecting the first default have no effect

on the second default. The findings of this study imply that the ability of borrowers to

obtain another mortgage will be lower since they have been found to have a higher

default probability in their second mortgage.

Mortgage Lending Legislation and Foreclosure Legislation

The impact of mortgage lending legislation on foreclosure is under-investigated

because of the difficulty of evaluating how legislation contributes to foreclosure. But

recently, with the increasing awareness of mortgage foreclosure, some non-profit

organization and concerned citizen groups have begun to question whether loose

mortgage lending legislation and regulations are an important factor affecting

foreclosure. They might especially affect the geographically clustered distribution of

foreclosure in low-to-moderate-income neighborhoods. The major agenda that these

groups propose is to enact anti-predatory lending legislation and require real estate and

mortgage brokers/agents and real estate appraisers to pass stricter licensing exams. At the



23

State bankruptcy laws also have some impact on default decisions of borrowers. Lin

and White (2001) found that in states with higher bankruptcy exemptions borrowers have

greater tendencies to choose default.

Lender Decisions in Mortgage Foreclosure

There are three possible outcomes when a mortgage is defaulted: (1) resumption of

payments, (2) termination by prepayment, or (3) foreclosure (Phillips and VanderHoff,

2004). The value of termination options and local economic and housing market

conditions affect default resolution probabilities greatly. After a study of loan pools in a

large national savings and loan institution, Phillips and VanderHoff (2004) found that

judicial procedure and tenancy statutes decrease the probability of foreclosure by 25%,

due to the increasing costs of foreclosure. They also indicate a possibility of adjusting

mortgage rate premiums to compensate the added costs to lenders.

For FHA-insured loans, lenders do not bear many foreclosure costs when

foreclosures occur (Realtors, 2003). This could be one of the reasons why FHA loans

have a high foreclosure rate.

The amount of time between mortgage default and foreclosure is different

depending upon the mortgage interest rate and home equity values (Lauria, 2004). Lauria

found that lower interest rate loans and loans with an outstanding balance below the

median value of the home were given a longer time from default to foreclosure. Whether

there is racial discrimination in the foreclosure process is still unclear.



24

Housing Attributes

Housing attributes, such as the number of bedrooms in a dwelling and the units in

the building, building type, and building year, are important factors in loan approval in

the automated mortgage scoring system of many types of mortgages, and this is where

home equity values could go. Therefore they can be important indirect factors affecting

mortgage default risk (Sandor and Sosin, 1975).

Housing Appraisal

In research on the role of real estate appraisal on mortgage lending and performance

in Alaska’s housing market, Lacour-Little and Malpezzi (2003) found that if the appraisal

value of a property is higher than the estimated price from a hedonic model they

developed, the mortgage against this property is exposed to more default risk. In other

research, Lacour-Little and Green (1998) found that minorities and minority

neighborhoods are much more likely to get low appraisals, which increases the loan

application rejection rates of racial minorities. But they found that the low appraisal is

related to poor neighborhood and housing quality. Noordewier et. al. (2001) found that

properties with a higher appraisal value than similar recently sold properties are related to

higher default risk of the borrowers.

Housing Price Appreciation

Housing price change is a very important factor in determining mortgage default

probabilities. This is true, first, because of the possibility of negative equity, which

affects mortgage default decisions greatly (Quigley et. al., 1993), is largely determined by



25

housing price changes (Case and Shiller, 1996). Second, as housing prices fall, the loss

severity followed by a default increases, and loss severity increases non-linearly and

faster than the decline of housing prices (Case and Shiller, 1994). Third, the research on

housing price appreciation in low-income and/or minority neighborhoods should help

explain the disparities among mortgage foreclosure rates in different neighborhoods

(Raffalovich, 2002). By examining neighborhood effects on FHA-insured loans,

Cotterman (2001) found that low housing price appreciation in minority neighborhoods is

an important factor in the higher default rates in those neighborhoods.

Housing price appreciation is also an important motivation for people to move

(Kiel, 1993). When a moving decision is made, borrowers will choose either to sell or

default on the property in which they currently reside (Pavlov, 2001). When the equity

value is positive they will usually choose to sell the property and prepay the mortgage.

But when the equity value is negative and cannot offset default costs, they will choose to

default. However, only a small percentage of borrowers actually choose to default in this

situation. Their decision might be more determined by life crisis events because choosing

default is costly for borrowers in regard to its negative impact on borrower credit scores

(HUD, 1992; Foster and Van Order, 1985).

Mortgage Fraud

Foreclosure cases because of mortgage fraud are few and there is no literature

related to the relationship between mortgage fraud and foreclosures. Hence there are no

empirical studies conducted to see how mortgage fraud affects foreclosure. The definition

of mortgage fraud can be very broad, but here it means that lenders use some illegal



27

and other minorities are required to have a higher standard than Whites in loan approval,

and thus Blacks and other minorities have a lower tendency to default because those who

can have a loan are those who meet the higher standards; also “minorities tend to have

less attractive distributions of factors leading to default” compared to whites because of

the more strict underwriting standards (Cotterman, 2004). But a study by Berkovee et. al.

(1994, 1995) found that black homeowners have a higher default rate than white

homeowners, which contradictorily indicates that mortgage default has no relationship to

lending discrimination. Anderson and VanderHoff (1999) also found that Black

homeowners have a higher marginal default rate than white households, controlling for

borrower and property characteristics. Other scholars found that there are flaws with

using mortgage default to predict mortgage lending discrimination (Ross, 1996;

Anderson and VanderHoff, 1999). Controlling credit history reduces the effect of races

on mortgage default (Cotterman, 2002). Deng and Gabriel (2003) and Van Order and

Zorn (2001) found that minorities have higher default probabilities, but the losses from

their high default risk can be offset by their low tendency for prepayment. They

recommend that financial institutions should have similar pooling and risk-based

mortgage pricing for all borrowers, which will benefit more racial minorities and

therefore improve their homeownership rate. Cotterman (2004) concluded that Blacks

and Hispanic borrowers incur a larger loan loss rate than Whites in FHA-insured loans,

but he did not explore whether the loss could be offset by the lower prepayment tendency

of racial minorities.

Therefore, many researchers think that racial disparities in mortgage default can be

explained by other factors, such as down payment amount and credit history, which



3 0

Figure 2.1: The Interaction between Residential Mortgage Foreclosure, and Neighborhood Characteristi

Macro Economy

CreditHistory

Income Ethnicity …Housing

PriceChange

VacancyRate

TenureStatus

RacialComposition

… HousingAttributes

HousingAppraisal

HousingEquity

Default Foreclosure

(Lender Decisions)

Neighborhood Characteristics &

Changes Housing CharacteristicsBorrower Characteristics



31

The Interaction between Neighborhood Characteristics, Neighborhood

Change and Residential Mortgage Foreclosure

General Theories of Neighborhood Change

As mentioned earlier, the development of neighborhood change theory can be

summarized into three generations and research concentrations (Galster and Krall, 2003).

Because these generations have temporal overlap, it is not appropriate to conclude that

they have specific temporal orders. Many contemporary scholars still use the theories

formulated in the first generation in their research. Many conduct their research on

neighborhood change based on the theories of one of the three generations, or the

combined theories from two or three generations.

1. The first generation: descriptive, cartographic and causal analysis (1950 – )

Simple descriptive, cartographic and causal analysis dominates in this generation.

The major theoretical bases are the invasion-succession model that was proposed by the

Chicago School of Sociology (Park, 1952; Duncan and Duncan, 1957; Taeuber and

Taeuber, 1965), the life-cycle model (Hoover and Vernon, 1959), the

demographic/ecological model, the social-cultural/organizational model, the social

movement model (Downs, 1981; Bradbury, et. al., 1982; Schwirian, 1983), the stage

model, and the political-economy model. These theories have been followed by

Maclenna(1982), Taub, et. al. (1984), Grigsby, et. al. (1987), Rothenburg et. al. (1991),

Temkin and Rohe (1996), Lauria (1998), and Galster (2003). All these theories have

formed the fundamental basis of neighborhood change theory by describing how

neighborhoods change, the push and pull factors of the change, and what factors are

affected the most in the neighborhood-succession process. Some of the theories use



32

mechanisms in other disciplines, for example, ecology, to explain the dynamic processes

of a neighborhood. This research will use some of the aforementioned methodology, such

as cartography and descriptive and causal analysis to explain how foreclosures and

neighborhood characteristics and change interact with each other. Some of the terms

developed in this generation, such as racial transition and exogenous variables, will be

used extensively in this research.

2. The second generation: regression and predictive models (1970 – )

Regression and predictive models are used to explore how exogenous variables

affect neighborhood outcome indicators and estimate-related indicators (Galster and

Krall, 2003). Examples of those indicators are population density (Guest, 1972, 1973;

Fogarty, 1977), income or social class (Guest, 1974; Vandel, 1981; Coulson and Bond,

1990; Galster and Mincy, 1993; Galster et. al., 1997; Carter et. al., 1998),

homeownership rate (Baxter and Lauria, 2000), female headship rates (Krivo et. al.,

1998), and racial composition changes (Guest and Weed, 1976; Schwab and Marsh,

1980; Ottensmann et. al., 1990; Galster, 1990; Denton and Massey, 1991; Ottensmann

and Gleeson, 1992; Lauria and Baxter, 1999; Crowder, 2000; Ellen, 2000; Baxter and

Lauria, 2000). There are mixed findings in the studies, but all these indicators provided

the basis for this research when selecting variables. Similarly, when exploring the impact

of foreclosure on neighborhood characteristics and change, foreclosure rate is the

exogenous variable that affects the neighborhood indicators and their change. Only a few

scholars have paid attention to this matter. This research will also use regression and



33

predictive models to find out how foreclosures and neighborhood characteristics and

change interact.

3. The third generation: threshold effect, endogenous neighborhood theory,

neighborhood tipping and complexity models (1990 – )

In more recent neighborhood-change literature, Quercia and Galster (1997, 2000)

proposed a new theory that is called the “Threshold Effect”, which is defined as “a

dynamic process in which the magnitude of the response changes significantly as the

triggering stimulus exceeds some critical value” (Quercia and Galster, 1997: 409). They

advocate using non-linear regression models to predict threshold effects of the change in

neighborhood indicator outcomes caused by exogenous variables. Spline and quadratic

regressions are used in their studies of the threshold effects and neighborhood change.

Galster et. al. (2000) empirically tested the theory by analyzing some exogenous

variables on neighborhood quality-of-life indicators.

Many people have explored how exogenous variables affect the change in

neighborhood outcome indicators, but little has been done to explain how these indicators

change endogenously after the breakdown in stability by the exogenous variable.

Schelling (1971, 1972), and Galster and Krall (2003) are among several people who have

explored the endogenous dynamic change of the neighborhood outcome indicators

affected by exogenous variables. They named the model “Neighborhood Tipping”.

“Complexity Models” evolved from the neighborhood tipping theory.

This generation’s study of neighborhood change has created a new and interesting

scenario. The proposed methodology can be used to determine the extent that



34

foreclosures (the exogenous variable) affect the change in neighborhood indicators

(endogenous variables). Then, the threshold points at which the endogenous variables

change from stability to instability will be calculated. When foreclosures have

contributed to homogenous racial composition, the effect is very similar to the

“Neighborhood Tipping” theory.

Major Neighborhood Indicators Estimated

In these three generations of research, many neighborhood indicators have been

explored for their potential importance to the dynamics of neighborhood change:

• Income or Social Class (Guest, 1974; Vandel, 1981; Coulson and Bond, 1990;

Galster and Mincy, 1993; Galster et. al., 1997; Carter et. al., 1998)

• Homeownership Rate (Baxter and Lauria, 2000)

• Female Headship Rates (Krivo et. al., 1998)

• Racial Composition Changes (Guest and Weed, 1976; Schwab and Marsh,

1980; Ottensmann et. al., 1990; Galster, 1990; Denton and Massey, 1991;

Ottensmann and Gleeson, 1992; Lauria and Baxter, 1999; Crowder, 2000;

Ellen, 2000; Baxter and Lauria, 2000)

• Median Value of Homes (Galster and Krall, 2003)

• Property Delinquency Rate (Galster and Krall, 2003)

• Poverty Rates (Carter et. al., 1998; Galster and Mincy, 1993; Galster et. al.,

1997; Vandell, 1981; Krivo et. al. 1998)

These studies have found that some of the variables have complicated endodynamic

and exodynamic changes (e.g., poverty rate and change). Others are affected by



35

exogenous variables, such as foreclosures or metropolitan economic restructuring

affecting the racial transition of a neighborhood. This research will continue to test how

foreclosures and these selected variables interact with each other because all these

variables are very important indicators of neighborhood quality.

Theories of Neighborhood Change

Neighborhood change is an important focus in urban theory and social science. The

literature on neighborhood change, which is quite abundant, mainly focuses on social or

economic explanations of change.

The economic explanation of neighborhood change “focuses on residential

preferences and the interplay of supply-demand relationships in local housing markets”

(Baxter and Lauria, 2000). A simplified version of this idea says that many industries and

jobs moved out to the suburbs because of the development of the transportation network,

the universal use of automobiles, and land price differences inside and outside of the city

center. The process is followed by the out-movement of residents and workers who can

afford both the transit and housing costs in the suburbs and who prefer a less dense living

environment. Those residents who cannot afford those costs are left behind, and many of

them lose their jobs. With the decline of economic activities and household income in

those neighborhoods, housing demand decreased due to lack of housing appreciation,

safety, appropriate municipal service, and other factors that impact homebuyers’

preferences (Galster, et. al. 1997; Squires, 1994; Wilson, 1987, 1996). In the economic

explanation, neighborhood change starts as household segregation by income levels

(Grigsby et. al., 1987). The classical stage model explains in more detail the process of



36

neighborhood succession (Bradbury, et. al., 1982; Downs, 1981; Faris, 1967). With rapid

inner-city development, land becomes much scarcer and its value increases rapidly.

Overcrowding, deteriorating living environment, and increased crime rate then become

major issues facing inner-city neighborhoods. As a result, affluent people, who can afford

to choose more preferable housing situations, will move out to the less dense and more

livable suburb (Muth, 1969). The development of highway systems and the universal use

of automobiles stimulated the process. People who cannot afford to move stay in the

neighborhood. Many previously owner-occupied buildings are remodeled into cheap

multifamily rental units, and many people with low incomes move into the neighborhood

because it is close to work or school or because rents are low. The maintenance of these

old buildings is very poor, and landlords are not willing to invest to rehabilitate the

buildings because of low return potentials. The change from single family houses to

multifamily rental units made some neighborhoods more crowded than before, but in

some neighborhoods with low housing demand, the abandoned houses stayed empty most

of the time and finally became dilapidated, which negatively affects the city landscape.

Income, housing and neighborhood preferences of households determine the

establishment of the income-segregated housing submarkets (Grigsby et. al., 1987). This

segregation process is often called “filtering” in urban housing market theories (Grigsby

et. al., 1987).

Supplementing the stage theory, the neo-Marxist explains that economic change,

which causes household income change and neighborhood decline, is due to the industrial

shifts during the worldwide industrial revolution (Harvey, 1973; Logan and Molotch,

1987). Many neighborhoods lost manufacturing jobs, and the neighborhoods where



37

manufacturing workers lived began to decline due to unemployment of those workers

(Grant, 1995; Harrison and Bluestone, 1988). In recent years, especially after September

11th

, 2001, the U.S. economy experienced slower development, with increasing

unemployment and government budget deficit. Many large corporations continued and

even accelerated the movement of jobs overseas, which contributed to increasing

unemployment. Job loss and economic recession created good reasons for homeowners to

default on their mortgages, which further affected neighborhood-succession processes.

Economic change, especially when related to industrial sector shifts, affects more people

who lack skills and education because it is not easy for them to transfer to another job

sector (Jargowsky, 1997). Also, with the hiring of large numbers of cheaper, immigrant

laborers, local unemployed workers found it even more difficult to find a lower level

position. Wilson (1987, 1996) explains in more detail about how the dual labor market

contributes to the concentration of poverty in inner-city neighborhoods.

Major social theories are institutional and place-stratification theories. These argue

that racial and class identification and stereotypes affect “decisions in lending and

residential location made by residents, real estate agents, and bankers” (Lauria, 2000;

Farley et. al., 1994; Massey and Denton, 1993; South and Crowder, 1997). Many scholars

believe that racial segregation is an “institutional” process that causes poverty to

concentrate in certain neighborhoods and affects neighborhood change (Eggers and

Massey, 1992; Massey, 1990; Massey and Denton, 1993; Massey and Eggers, 1990).

These authors argue that racial composition or racial change is an important “proxy” for

neighborhood characteristics and change (Immergluck and Smith, 2003; Clark, 1992;

Ellen, 2000; Taub et. al., 1984). Some other scholars think that racial discrimination may



38

be occurring in housing markets because housing supply and demand is not sufficient to

explain neighborhood change without considering the effects of discrimination (Cook,

1988; Galster, 1990; Galster and Hill, 1992; Squires, 1994).

This research will test whether these theories are affecting neighborhood change in

Ohio’s two biggest counties. These theories are also part of the basis in variable selection,

such as median household income, unemployment rate, and occupational structure. The

change in the aforementioned indicators might affect foreclosures in a neighborhood.

Foreclosures might impact on the change in these indicators and thus contribute to

neighborhood decline. The research might also discover some social factors (e.g., racial

transition) underlying the relationship between foreclosures and neighborhood

characteristics and change.



39

The Interaction of Foreclosure and Neighborhood Characteristics and Change

Little literature contributes to our understanding of the relationship between

neighborhood change and residential mortgage foreclosure or the mechanics of that

relationship. Stone (1986) indirectly related neighborhood change with the mortgage

default rate. He found that higher default risk and foreclosure rates usually follow the

change from economic boom to economic downturn, especially for homeowners who

paid a high interest rate and an inflated housing price during the economic boom. Much

of the literature fails to address the geographical concentration of foreclosure and its

impact on neighborhood conditions (Cincotta et. al., 1998).

Lauria and Baxter (1998, 1999, 2000) are two of the few scholars who have tried to

explain how neighborhood change and characteristics affect mortgage foreclosure. Baxter

and Lauria (2000) think that mortgage foreclosure is one of the factors mediating the

effects of neighborhood economic situations and racial composition on neighborhood

tenure patterns, vacancy rates and racial composition changes. They believe that low

housing price appreciation and low incomes caused by economic downturns and

neighborhood succession are the main reasons for foreclosure, and foreclosure, in turn,

affects neighborhood changes in racial transition and income changes. But they do not

explain in detail whether or how mortgage foreclosure affects housing price appreciation

in a given neighborhood. Although negative equity is one reason leading to mortgage

default and housing abandonment (Case and Shiller, 1996; Cunningham and Capone,

1990), it might only affect a small portion of the total foreclosure cases. But mortgage

foreclosure might affect housing price change greatly in neighborhoods with concentrated

foreclosures.



40

Baxter and Lauria (2000) found that income decreases and housing price

depreciation, which are major factors contributing to mortgage foreclosure of borrowers,

are associated with economic changes and prior racial composition. The high foreclosure

rates in declining neighborhoods will affect neighborhood change in many aspects, such

as housing stock characteristics (vacancy rate, tenure status and housing price), racial

composition, and median income of the neighborhood. Also, the long-term impact of

housing foreclosures on the social-economic structure of a neighborhood depends on the

characteristics of the purchasers of those foreclosed properties (Lauria, 1998). Cotterman

(2001, 2003) found similar neighborhood effects on mortgage foreclosures and racial

disparities in mortgage foreclosure.

Neighborhood Effects in Mortgage Default and Foreclosure

In addition to the fact that loan defaults concentrate in neighborhoods with a high

percentage of poor-credit borrowers (Cotterman, 2003) and poor-quality neighborhoods,

no correlation between neighborhood characteristics and mortgage default has even been

found (HUD, 2004). Therefore, the impact of neighborhood characteristics on mortgage

default is still unclear, and very few studies have been done to explore the neighborhood

effects on mortgage default and foreclosure.

Cotterman (2001) examined how neighborhood and borrower characteristics affect

FHA default rates. His combination of neighborhood and borrower characteristics is to

separate the two effects (neighborhood and borrower effects) by controlling the effect of

each other. His study also includes credit history data for the individual borrowers. The

study found that higher default rates are associated with census tracts that have lower



41

median household incomes and higher concentrations of black homeowners, but

individual race or income is not related to default. The effect of neighborhood race and

income was reduced when lagged default, prepayment and neighborhood housing price

change were added to the regression model. In this situation, the effect of neighborhood

income on default is unchanged, but the effect of neighborhood racial composition on

default is not significant.

Williams et. al. (1974) found that neighborhoods with high unemployment rates

usually have higher mortgage default rates. Their finding is not surprising because often a

higher unemployment rate is associated with lower income and, thus, a higher default

rate. Sandor and Sosin (1975) found that neighborhood conditions are negatively related

to the mortgage interest rates that financial institutions of loan originators charge to the

borrowers. But they did not further explain whether those conditions are related to

mortgage discrimination, or whether they are caused by the aggregated individual

borrowers with poor credit scores who thus receive higher interest rates.

In terms of the spatial distributions of mortgage default and foreclosure, Von

Furstenburg and Green (1974) found that in the 1970s suburban areas had less default

risk than central-city locations, which might not be true in the contemporary setting.

Notice that many of the aforementioned studies focus on neighborhood effects on

mortgage default and default risk, while the relationship between neighborhood

characteristics and mortgage foreclosure is less investigated, except for a few scholars’

work.

Can (1998) states that when lending institutions make decisions about mortgage

underwriting or portfolio management, they consider many different factors at the



42

neighborhood level, such as recent house price movements, unemployment rates, vacancy

rates, and homeownership rates. Thus, the lending decision will cause mortgages with

similar characteristics to concentrate in certain areas. She claims that foreclosure is

spatially contagious. She states, “An abandoned property resulting from foreclosure in a

neighborhood acts as a catalyst by reducing the expected return on investment on

surrounding properties” (Can, 1998: 68). The direct consequence of this phenomenon is

lower quality housing, including those houses adjacent to the foreclosed ones; lower

demand for those houses, and thus lower prices of the houses; higher Loan-to-Value

(LTV) ratios; and increased risk of adjacent properties going through foreclosure and

abandonment. If the contagious chain keeps working, the final result is large-scale

neighborhood decline and increasing housing vacancy rates. Also, the large number of

foreclosed properties in a neighborhood will further reduce the housing prices in the

whole neighborhood because of the increased housing supply. This spillover effect of

foreclosure also contributes to the concentration of foreclosed properties in certain

neighborhoods.

Baxter and Lauria (2000) found that both economic change and prior racial

transition are associated with the reduction in median household income. Therefore racial

transition, unemployment and the reduction in household income causes the foreclosure

rate to increase rapidly. They concluded that racial transition and economic change

indirectly affect neighborhood decline through reduced income and increased foreclosure.

Neighborhood decline is associated with higher vacancy rate, higher percentage of a

black population, and higher percentage of renters.



43

The Impact of Mortgage Foreclosure on Neighborhood Restructuring

The impact of mortgage foreclosure is complicated, and many parties are affected.

For borrowers, losing their real property and titles and being driven out of their home is

the biggest direct loss; but the credit problem caused by foreclosure might be an obstacle

when they want to purchase homes again in the future. It would be very interesting to

explore the impact of mortgage foreclosure on borrowers and where they live after losing

their homes. For lenders, foreclosure brings about operational costs and income losses on

the mortgages. The impact of foreclosure on neighborhoods is also significant and many

neighborhood indicators are thought to be affected by mortgage foreclosure.

The Effect of Mortgage Foreclosure on Racial Composition and Transition

As mentioned before, minority neighborhoods usually have a higher foreclosure rate,

and in those neighborhoods housing price appreciation is much slower than in similar

white neighborhoods. The interaction between lower housing price appreciation and

higher foreclosure rates might cause the economic and housing situation in those

neighborhoods to deteriorate. Therefore, for foreclosure-mitigation purposes, more

intensive research needs to be done to confirm the existence of the relationship between

foreclosure and racial composition, economic condition and housing price appreciation.

Also, for foreclosed minority homeowners, the impaired credit quality will greatly affect

their future application for new loans and therefore can widen the existing

homeownership rate gap between white and minority homeowners; the result is more

housing hardship for those people.



45

have low to lowest income levels and a large proportion of and increasing Black

population.

The Impact of Mortgage Foreclosure on Neighborhood Property Value Change and

Other Housing Stock Characteristics

Hypothetically speaking, neighborhoods with a high foreclosure rate over time will

have depreciated housing prices, although the flipping of properties might happen with

those properties which were sold at a discount in the foreclosure process. Due to a lack of

literature and theory bases, research on this topic can be very challenging. It is well

known that foreclosed properties are usually sold at a discounted price compared to other

similar properties in the same or nearby neighborhoods (Carroll et. al., 1995; Forgey

et. al. 1994), although the discount is very different controlling for some factors, such as

location and common characteristics. However, there are controversies about whether

foreclosed properties will provide arbitrage in the real estate market (Carroll et. al.,

1995). Also, FHA properties and properties in their neighborhoods are usually sold at a

higher discount rate compared to properties with conventional loans because of the

adverse characteristics of those properties (Carroll et. al., 1995). Pennington-Cross (2003)

found that properties with loans that foreclose early in their life were sold at the highest

discount, and properties in a state requiring the judicial process of foreclosure are sold at

a higher discount than in states that do not require the legal process. He also found that a

more accurate appraisal of properties with low down payment loans leads to a lower

discount in foreclosure resale.

Recently, Immergluck and Smith (2005a, 2005b) conducted intensive research on

the impact of foreclosure on neighborhood characteristics in Chicago. They found that



47

Simultaneity or Reverse Causality

Reverse causality between individual and peer group behavior in neighborhood-

effects research is a common problem. Exploring the relationship between mortgage

foreclosure and neighborhood characteristics and change will inevitably have problems

with this issue because of the reverse causality between foreclosure and neighborhood

characteristics and change.

Reflection Problem and Inferring Causality

The reflection problem refers to the fact that the behavior of residents in a certain

neighborhood or cohort is reflective and that each person’s behavior affects everyone

else’s. Manski (2000) thinks that since the mean behavior of a group (neighborhood) is

determined by the behavior of the group members (neighborhood residents). It is hard to

tell whether the group behavior reveals the individual behavior or whether the group

behavior is the aggregation of individual behaviors. He describes the phenomenon as a

person’s movement and the movement of his image in a mirror, which is simultaneous.

Therefore he questions whether the group behavior is caused by the individual behavior

or is simply the reflection of individual behavior.

In neighborhood effects research inferring causality is an issue that we should be

aware of. We state that neighborhood indicators and their changes are interactive with

foreclosure rates, but it is difficult to tell whether the neighborhood indicators reveal

individual householders’ characteristics, or the aggregated householders’ characteristics.

Therefore it is difficult to tell whether it is the neighborhood or the individual

characteristics are related to foreclosures. At the same time the foreclosure rate is a ratio



49

having their characteristics affected by the neighborhood. However, they may self-select

into certain kinds of neighborhoods because of their characteristics and then also have

those characteristics affected by the neighborhood. In addition, the household’s choice of

neighborhood will be affected by the characteristics of the neighborhood and how they

interact with the characteristics of the household. Therefore, in this research if we claim

that foreclosures affect neighborhood change it means that an individual’s selection of a

neighborhood depends on foreclosures in that neighborhood, not only his or her own

characteristics, because neighborhood change is related to that individual’s

characteristics. So we have to assume that there are no selection problems in this research

and people’s behavior depends not only on their individual characteristics but also on

other exogenous factors, such as foreclosures.

Spatial Autocorrelation

Spatial autocorrelation is a universal problem when geographic data, either physical

or human, are involved in analysis. One significant example of spatial autocorrelation is

that housing price is highly related to location, and houses adjacent to each other usually

affect each other in terms of market price and value appreciation, assuming there are no

other non-spatial factors involved such as jurisdiction limitations, speculation or policy

issues. Each attribute correlates to not only the same attributes in a neighboring location

but also to different attributes in that location. Foreclosed properties will have a negative

impact on the property values of surrounding or adjacent properties (Immergluck, 2005a),

so spatial autocorrelation exists. Spatial autocorrelation also helps to reduce the



50

influence of omitted variables because spatial dependence among the errors is generally

due to omitted variables (Bell and Bockstael, 2000; Pace et. al. 1998).

Spatial regression is usually used instead of a general Ordinary Least Square (OLS)

regression model in studies where spatial autocorrelation might affect the research results

in a significant manner. This study will report results of a spatially lagged regression

model.

Literature Summary and the Derivation of Research Questions

Many factors lead to mortgage default and foreclosure, and neighborhood

characteristics are among the most important (Quercia and Stegman, 1992; Cotterman,

2002, 2003; HUD, 2004). However, few scholars have examined how neighborhood

characteristics contribute to mortgage foreclosure (Cotterman, 2001; Lauria, 1998; Lauria

and Baxter, 1999; Baxter and Lauria, 2000; Immergluck and Smith, 2005a, 2005b), and

even fewer have incorporated many neighborhood variables into mortgage default models

and foreclosure analysis. Some variables that theory tells us should be important (e.g.,

household income and mortgage payment amounts) have been found to have mixed

effects on mortgage default (Quercia and Stegman, 1992). In addition, the

methodological problems in neighborhood-effects research call for statistical models

which will resolve or reduce the effects of endogenous variables, omitted variables and

reflection problems (Dietz, 2002). The research uses foreclosure data from Ohio’s two

most populous counties to examine some of these previously omitted or understudied

variables. In addition, I try to incorporate spatial analysis, especially spatial trend

analysis, spatial autocorrelation and spatial regression models, into the OLS model. A



51

model correcting for heterscedasticity is also estimated. I then model the impact of

foreclosure on neighborhood change using SUR. I also pay particular attention to each

neighborhood’s racial composition, economic level, housing prices and other housing

stock characteristics as well as to the changes over time in those variables.



52

CHAPTER 3

RESEARCH METHODOLOGY

Based on the research questions and related literature review, the research will test

several hypotheses. These hypotheses will help answer some subsidiary questions

associated with the first two primary ones.

Hypotheses

Hypothesis 1: The effect of neighborhood characteristics on mortgage foreclosure does

not change randomly over time. For example, certain kinds of neighborhoods are likely to

have higher or lower foreclosure rates.

In his research on FHA loans, Cotterman (2000) found that mortgage foreclosure

concentration at the neighborhood level changes randomly over time. This means that the

geographical distribution of foreclosure rates is not fixed over time, which might mean

that neighborhood characteristics and changes are not associated with foreclosure-rate

changes. I will test that whether changing foreclosure rates over time are random because

I believe there is some neighborhood-based mechanism pushing the change according to

certain patterns. But if my hypothesis does not hold, further studies will be needed to

understand the relationship between foreclosure and neighborhoods.



53

Hypothesis 2a: Neighborhoods with slower housing value appreciation or negative

appreciation rates have higher mortgage foreclosure rates, holding neighborhood income

and racial composition constant.

Housing value change is a very important factor affecting mortgage default

tendencies. This is true because negative equity, which has a significant effect on

mortgage default decisions (Quigley et. al., 1993), is largely determined by housing value

changes in combination with loan characteristics (Case and Shiller, 1996). Research on

housing price appreciation in low-income and/or minority neighborhoods (Cotterman,

2001) found that low price appreciation in these neighborhoods is an important factor

leading to higher default rates. We do not yet know the causes of this correlation.

Hypothesis 2b: Neighborhoods with high foreclosure rates experience slower housing

value appreciation.

This hypothesis will test whether mortgage foreclosure rates cause slower housing

value appreciation or negative appreciation. If the hypothesis is supported, foreclosure

prevention efforts might focus on reducing foreclosure concentration in neighborhoods. If

it is not, the causal relationship between foreclosure rates and housing value appreciation

is recursive, instead of non-recursive, and the model discussed in the previous chapter

becomes simple.

Hypothesis 3a: Neighborhoods with minority concentrations and racially diverse

neighborhoods have higher mortgage foreclosure rates, holding income constant.

This study also will test whether racial composition of a neighborhood, holding

income and other indicators constant, affects mortgage foreclosure rate.



54

Hypothesis 3b: Neighborhoods with high foreclosure rates have large scale racial

transitions.

Lauria and Baxter’s research (1998, 1999, 2000) on New Orleans found that racial

transition occurs mostly in neighborhoods with high foreclosure rates. My study will test

whether this finding holds in two counties in Ohio as well. If it does, we need to consider

issues of causality and what processes link racial transition and foreclosure rates.

Hypothesis 4a: Low-moderate income neighborhoods have higher mortgage foreclosure

rates than middle-income and upper-income neighborhoods, holding racial composition

constant.

The purpose of this hypothesis is to test whether race is a key factor in mortgage

foreclosure, or if it is only a factor when associated with certain income categories of

neighborhoods. If controlling racial composition foreclosure rates correlate with income

level of a neighborhood, we can conclude that income has an independent effect.

Hypothesis 4b: Neighborhoods with high foreclosure rates also have declining median

incomes.

Lauria and Baxter’s research (1998, 1999, 2000) found that neighborhoods with

high foreclosure rates have declining median incomes. But, they could not explain

whether the declining median income is caused by rising foreclosure rates in those

neighborhoods. So this study will test whether the findings hold in the two counties in

Ohio and, if they do, explain how the mechanism works in this situation.



55

Hypothesis 5: Neighborhoods with high foreclosure rates have a greater housing supply

than housing demand with high vacancy rates and declining neighborhood quality.

This hypothesis will test the relationship between changes in vacancy rates in a

neighborhood and foreclosure rates.

Hypothesis 6: In counties with different macro economic situations, the interaction

between neighborhood characteristics, changes, and residential mortgage foreclosure has

different mechanics.

Macro economic situations affect macro mortgage repayment behavior and the real

estate market greatly. For two counties that have different economic situations and

growth rates, the relationship between neighborhood characteristics and foreclosure

should be different between them as well.



56

Major Datasets Used in Foreclosure Research

Because of the complexity of default and foreclosure processes, there are some

related datasets that have been used by scholars in default and foreclosure research. Each

of them has its own strengths and weaknesses, and oftentimes two or more have to be

combined in a study to help achieve research objectives.

1. The Mortgage Bankers Association Datasets

The U.S. Mortgage Bankers Association (MBA) provides quarterly aggregated case

counts for foreclosures (started) at the state level. The data usually start from the late

1970s and present the quarterly foreclosure rates for prime loans, sub prime loans, FHA-

insured loans, VA- insured loans, and other types of loans. The Association also provides

market-share data for different types of loans and the biggest vendors of those loans.

MBA datasets are appropriate to explore the foreclosure status and trends of the whole

U.S. When combined with other demographic, economic and legal data, the MBA

datasets can be used to compare foreclosure patterns for the 50 states. Since the datasets

also include longitude data, they can be used to run time-series analyses combined or not

combined with other datasets. Their major weakness is that they contain no data below

the state level.

2. Home Mortgage Disclosure Act Datasets

The Home Mortgage Disclosure Act (HMDA), enacted in 1975, requires major

financial institutions to provide data known as the HMDA data. HMDA data are

administered and monitored by the Federal Financial Institutions Examination Council



57

(FFIEC). FFIEC collects these data to assist in determining whether financial institutions

are serving the housing needs of their communities, assist public officials in allocating

public funds to attract private investment, and promote fair lending practices. HMDA

data includes mortgage applicants’ and borrowers’ characteristics at the origination of the

mortgage, including age, race, income, FICO credit score, and other information. If an

application for a mortgage is rejected, the reason for the rejection has to be documented.

HMDA data also include loan information at the origination of the mortgage. Some

people have combined HMDA data with foreclosure filing data, Mortgage Loan

Performance Data, and/or the HUD (U.S. Department of Housing and Urban

Development) sub-prime lenders list to map the distribution of sub-prime loans and the

distribution of foreclosure filings. These distributions are then compared to determine

whether the two phenomena are correlated. Some have tried to model how mortgage

default is related to borrower and loan characteristics.

HMDA data have some weaknesses and limitations. As far as foreclosure research

goes, since HMDA only captures borrower and loan characteristics at the origination of

the mortgage, not when default has happened, such research cannot accurately measure

what factors may be causing the default and foreclosure, except to predict the default risk

of the borrowers.

3. Mortgage Loan Performance Data

Mortgage loan performance data are mega datasets to trace the performance of

individual loans. The datasets capture most of the sub-prime loans and many prime loans

in the U.S. Loan Performance, Inc., a nationwide mortgage data provider, manages the



60

the cases. They usually do not include historical data, either. Commercial data can be

found from many carriers on the internet.

7. Sheriff’s Deed-Transfer Data

After a property is sold at the public auction most buyers will go to the Recorder’s

Office or the Auditor’s Office to record the deed transfer. (In some counties the recording

of the deed is voluntary while in others it’s mandatory. This depends solely on

requirements imposed by the local legislature. But many buyers will record the deed in

order to add security to the title). The biggest strength of using the deed-transfer data is

that there are enough historical data on record to do analysis over time. One weakness is

that if the recording process is not required by the county some buyers might not record

the transfer. In these cases, the deed-transfer data cannot cover all properties sold in the

Sheriff’s sales. Another limitation is that a small number of properties at Sheriff’s sales

are sold because of tax delinquency, mechanic’s liens and other obligations. But the

biggest drawback is the number of foreclosed properties that don’t get to Sheriff’s sales,

and it is not a random process because the best investments (probably in the best

neighborhoods) are purchased before this point.

The purpose of this research is to explore the relationship between mortgage

foreclosure and neighborhood characteristics and change. Selling a property at auction

finishes the foreclosure process (except that, in some states, the previous owner has

redemption rights within a certain time period after the transaction). We assume that

those foreclosed properties, instead of those filed for a foreclosure but then withdrawn

due to different reasons, might have a greater impact on neighborhoods. New foreclosure



62

Summary of Datasets Used in this Research

This study uses the following five sets of data:

• Cuyahoga County and Franklin County Sheriff’s Deed Transfer Data (1997–

2004)

• Cuyahoga County Sheriff’s Deed Transfer Data (1983–1989)

• Cuyahoga County and Franklin County Property Parcel Data (2004, 2005)

• Census Block Group Data (1990–2000)

• Census Designated Place Data (1990–2000)

• TIGER street line GIS data.

The Sheriff’s Deed transfer data are retrieved from the deed record index that is

managed by each county’s Recorder’s Office. The data in Cuyahoga County starts in

1983. Franklin County records can be traced back to the early 1990s, but records before

1997 only include legal descriptions of the property, which are not possible to geocode

when using the TIGER maps. Therefore, to compare data between the two counties,

those cases foreclosed between 1997 and 2004 are used.

The Sheriff’s Deed transfer data were merged with property parcel data using the

parcel ID number. Only sales of single-family homes were kept in the analysis because

multifamily homes are often classified as commercial instead of residential properties.

For Franklin County the parcel data is in GIS format; therefore, there was no need to

geocode the Sheriff’s Deeds data. But the property parcel data from Cuyahoga County

are not in the GIS format; therefore, the merged data had to be geocoded using TIGER as

base maps. In this process, any duplicated cases that might be caused by errors in

recording or multiple foreclosures were eliminated.



63

The geocoded datasets with GIS and parcel information were then spatially merged

with census block-group and designated-place boundary files. After the combination

there are a total of 11,844 cases in Franklin County and 12,353 cases in Cuyahoga

County. These cases were aggregated based on block groups. In Cuyahoga County there

are 1262 block groups and in Franklin County there are 883 block groups. The datasets

have block group ID numbers by which those datasets can be merged with the census

demographic, economic and housing data in 1990 and 2000. The census block group

boundary files and census data have been unified for the years of 1990 and 2000.

Therefore, there will not be any issues in terms of using the census data from two

different years because of the difference in boundaries for certain block groups or census

places.

Inevitably, during the process of merging, geocoding, and retrieving data some cases

are lost. Whether those lost cases will affect the research results remains unknown

without further investigations. Although it is possible to test the difference of the data

used in this research and the lost cases to see if they are different. But this research will

leave the test to the future and assume that there is no significant difference between the

lost cases and the cases used in this research. As stated before, the final datasets include

11,844 cases in Franklin County and 12,353 cases in Cuyahoga County.

To derive the foreclosure rate, all accumulated foreclosure cases between 1997 and

2004 in each block group are divided by the total owner-occupied housing units in the

same block group. Furthermore, in order to separate the neighborhood effects on

foreclosures from the impact of foreclosure on neighborhood indicators, two panels of

foreclosures are created. One is between 1983 and 1989 in Cuyahoga County, and the



67

Housing Characteristics

As the literature review section has indicated, housing characteristics and attributes

are important factors related to mortgage default and foreclosure. Housing tenure and

homeownership rate, average housing cost burden, change in owner-occupied housing

units, housing vacancy rate and the median value of owner-occupied homes are all very

important factors related to mortgage default and foreclosure. This study looks at the

relationship between foreclosure and single-family housing units; therefore,

homeownership rate in a neighborhood will be highly related to the foreclosures on those

properties because only owned homes can be foreclosed. In the succession of a

neighborhood some foreclosed single-family units will be transformed into rental

complexes with multiple housing units in each one.

A mortgage usually includes PITI — principle, interest, tax, and insurance

payments. In situations where there is a housing cost burden the borrowers may not have

considered some of the costs and the potential appreciation of those costs, for example,

the potential change in payment associated with ARM loans, GRM loans, loans with a

“tease” rate, 3-to-1 buy-downs, balloon payments, and other seemingly attractive

features. Many borrowers also are not aware that property taxation, utility payments, and

housing maintenance are also potential costs associated with being a homeowner. The

Bureau of Census defines the housing cost burden with a mortgage as follows:

A household has a "housing cost burden" if it spends 30 percent or moreof its income on housing costs. A household has a "severe housing cost burden" if it spends 50 percent or more of its income on housing. Owner housing costs consist of payments for mortgages, deeds of trust, contractsto purchase, or similar debts on the property; real estate taxes; fire, hazard,and flood insurance on the property; utilities; and fuels. Where applicable,owner costs also include monthly condominium fees. Household income is



68

the total pre-tax income of the householder and all other individuals atleast 15 years old in the household.

However, the housing cost burden in 1990 and 2000 is not comparable because the

two years recorded the burden in different ways. Median owner costs with a mortgage are

included in 1990, but median owner costs as a percentage of household income are

included in 2000. Therefore, the 1990 variable is a dollar value but the 2000 variable is a

percentage value. Simply dividing median household income by the median owner costs

is not the most accurate proxy for the housing cost burden measured in 1990. Therefore,

this research will only use the 2000 housing cost burden.

Housing vacancy rates should be closely related to mortgage foreclosure since

many foreclosed houses or houses going through foreclosure are vacant. In a

neighborhood with a high foreclosure rate, the vacancy rate is assumed to be high in part

because of the foreclosures. The high vacancy rate then decreases property values and

neighborhood quality, thus resulting in more severe foreclosure problems.

The change in the median value of owner-occupied housing units from 1990 to

2000 will provide the estimate for housing appreciation over the 10-year span. Housing

value appreciation is closely related to mortgage default risk because the borrower is

more likely to abandon the building and go through the foreclosure process when there is

negative equity. Therefore, housing value change is highly associated with mortgage

default and foreclosure, and high foreclosure rates will cause lower housing appreciation

not only to the foreclosed properties, but also the properties adjacent to them

(Immergluck and Smith, 2005a).

Some other variables, for example, the percentage of owner-occupied housing units

with a mortgage and the percentage of units with a second mortgage and/or home equity



70

VARIABLE DESCRIPTION

Neighborhood Characteristics and Change

Demographic

BLACK00 % black population in 2000

BLACK90 % black population in 1990

BLACK_D Change in % black population (1990–2000)

COLLEGEH00 % population (>25 years old) with college or higher education in 2000

COLLEGEH90 % population (>25 years old) with college or higher education in 1990

COLL_D Change in % population (>25 years old) with college or higher education (1990– 2000)

DIVORCE00 % divorced population (>16 years old) in 2000

DIVORCE90 % divorced population (>16 years old) in 1990

DIVOR_D Change in % divorced population (>16 years old) (1990–2000)

FEMALEKID00 % female-led households with children <18 years old in 2000

FEMALEKID90 % female-led households with children <18 years old in 1990

FEMALE_D Change in % female-led households with children (1990–2000)

TOTALHH00 Total households in 2000

TOTALHH90 Total households in 1990

HH_D % change of total households (1990–2000)

MINORITY00 % minority population in 2000

MINORITY90 % minority population in 1990

MINORITY_D Change in % minority population (1990–2000)

MALE1424_00 % male population between the age of 14 and 24 in 2000

Economic

INCOME00 Median household income in 2000

INCOME90 Median household income in 1990INCOME_D % change in median housing income (1990–2000)

UNEMPLOY00 Unemployment rate in 2000

UNEMPLOY90 Unemployment rate in 1990

UNEMPLOY_D Change in unemployment rate (1990–2000)

POVERTY00 % population below the poverty line in 2000

POVERTY90 % population below the poverty line in 1990

POVER_D Change in % population below the poverty line (1990–2000)

MNGMT00 % labor force working in management and executive occupation in 2000

MNGMT90 % labor force working in management and executive occupation in 1990

MNGMY_D Change in % labor force working in management and executive occupation (1990–

2000)

Continued

Table 3.1: List of Selected Variables



71

Table 3.1 continued

SERVICE00 % labor force working in service occupation in 2000

SERVICE90 % labor force working in service occupation in 1990

SERV_D Change in % labor force working in service occupation (1990–2000)

Housing

HCOSTM00 Median owner costs as a percentage of income for housing units with a mortgage in2000

TENURE00 Homeownership rate in 2000

TENURE90 Homeownership rate in 1990

TENURE_D Change in homeownership rate (1990–2000)

OWNER00 Total owner-occupied housing units in 2000

OWNER90 Total owner-occupied housing units in 1990

OWNER_D % change in total owner-occupied housing units (1990–2000)

VACANCY00 The vacancy rate among all housing units in 2000

VACANCY90 The vacancy rate among all housing units in 1990

VACAN_D Change in housing vacancy rate (1990–2000)

VALUE00 Median housing value in 2000

VALUE90 Median housing value in 1990

VALUE_D % change in median housing value (1990–2000)

MORTGAGE00 % owner-occupied housing units with a mortgage in 2000

MORTGAGE90 % owner-occupied housing units with a mortgage in 1990

SMORTGAGE00 % owner-occupied housing units with a second mortgage in 2000

YEARS00 Median years housing units built in 2000

YEARS90 Median years housing units built in 1990

Change in Census Place Characteristics

Demographic

PBLACK_D Change in % black population (1990–2000)

PCOLL_D Change in % population (>25 years old) with college or higher education (1990– 2000)

PDIVOR_D Change in % divorced population (>16 years old) (1990–2000)

PFEMALE_D Change in % female-led households with children (1990–2000)

PHH_D % change of total households (1990–2000)

Economic

PINC_D % change in median household income (1990–2000)

PUNEMPLOY_D Change in unemployment rate (1990–2000)PPOVER_D Change in % population below the poverty line (1990–2000)

PMNGMT_D Change in % labor force working in management and executive occupation (1990– 2000)

PSERV_D Change in % labor force working in service occupation (1990–2000)

Continued



73

Research Methodology

Simple Descriptive Analysis

After data cleansing, simple descriptive analysis will be used to measure how the

foreclosure cases are distributed over time and place in the two study counties.

Geocoding, geographic aggregation, frequency analysis, univariate analysis, bivariate

analysis, and t-tests will be used in this step of the research.

The geocoding process is the vehicle to find out how those foreclosure cases are

distributed. Each case is geocoded and layered with block groups, the census designated

place, and/or school districts, which provided a convenient way to compare the spatial

pattern in those eight years. All the cases in the eight years are then aggregated at the

block-group level and divided by the total number of owner-occupied housing units in the

same block group to determine a measure of the foreclosure rate. The foreclosure rate

will be classified into five categories and then displayed in a thematic map.

Spatial Analysis

When a variable’s spatial distribution is not random and the values of a variable at a

set of locations depend on values of the same variable at other locations, spatial

autocorrelation exists and will affect the OLS regression results (Odland, 1988). A typical

example in housing market research is housing price. The housing price in a

neighborhood will affect or be affected by the housing price in adjacent neighborhoods.

When we run hedonic housing price models, spatial autocorrelation should be tested. If

the autocorrelation is significant, the spatial lag or error terms should be added to the

hedonic housing price model (Basu and Tribodeau, 1998).



75

The Global Moran’s I can be expressed as:

∑∑∑

∑∑ −

−−×=

2)(

))((

x x

x x x xW

W

N I

i

jiij

ij

In a row-standardized format of the weighting matrix the term∑∑ ijW

N equals 1.

The Local Moran’s I can be expressed as:

∑= N

j

jijii zW z I

where zi and z j are standardized values of attributes in the region of i and j where

they are neighbors defined according to the weight matrix.

When exploring the spatial autocorrelation of one attribute, univariate Moran’s I is

used, but when exploring the spatial autocorrelation relationship between multiple

variables, for example, to explore whether the foreclosure rate in one neighborhood

would affect the housing price in adjacent neighborhoods, multivariate Moran’s I will be

used.

Spatial Regression

Spatial dependency is a very common phenomenon for geographically distributed

attributes such as those in housing markets. Therefore, when we are dealing with spatial

datasets we initially assume that there is spatial autocorrelation of certain attributes or a

dependency of some attributes on others in neighboring areas. These assumptions make

the research much more complicated, but due to the development of geostatistic

methodology and software incorporating spatial autocorrelation into traditional OLS

regression, the research is feasible.



76

The format of a general OLS model can be expressed as:

ξ β += X Y

where Y is the matrix of values for the dependent variable(s), X is the matrix of

values for the independent variables, and ξ is the error-term matrix.

When considering the effect of spatial autocorrelation, the formula can be revised to:

ν ξ ρ β ++= W X Y

where we notice that ρ is the spatial autoregressive coefficient, W is the spatial

weighting matrix, ξ is the spatial error term and υ is another error term. This transformed

OLS regression, which contains a spatial autocorrelation error term, is usually called

spatial error regression.

Another format of spatial regression is based on spatial lags and is called spatial lag

regression. The general format of the spatial lag regression model is:

ν ρ β ++= Wy X Y

where the Wy is a spatially lagged variable of the dependent variable Y

When considering which regression models are appropriate to measure the datasets

(OLS, spatial error, or spatial lag), there are some rules that a researcher can follow,

although these rules are not absolute when making a decision (Anselin, 2005). The

process starts with the Lagrange Multiplier (LM)-error 2 and LM-lag test3. If none of the

tests are significant, then one can choose the OLS model. If only one is significant, then

we should use spatial lag or error models to do further tests. If the LM-error test is

significant, then we should choose the spatial error model; otherwise, we should choose

the spatial lag model.



77

If both the LM-error and LM-lag tests are significant, then we look at the Robust

LM-error diagnostic. If the Robust LM-error 4 is significant, we choose the spatial error

model, and if the Robust LM-lag is significant, we choose the spatial lag model. See

Figure 3.1 for a summary of this process.

In this research, when measuring neighborhood effects on foreclosures the OLS

model will be tested in both counties. If the effect is different in each county, then the

analysis will run the OLS models separately for the counties. If the OLS spatial

dependence diagnosis finds significant spatial dependence spatial error or lag models will

be used to estimate the neighborhood effects. The rule of choosing between the error or

lag models is based on the chart in Figure 3.1.



78

Figure 3.1: Spatial Regression Decision Process (Anselin, 2005: 217)



79

Seemingly Unrelated Regression (SUR)

Seemingly unrelated regression (SUR), also called joint generalized least squares

(JGLS) or Zellner estimation, is a system of OLS for multiple equations. Like OLS, SUR

assumes that all the regressors are independent variables, but SUR uses the correlations

among the errors in different equations to improve the regression estimates. The SUR

method requires an initial OLS regression to compute residuals. The OLS residuals are

used to estimate the cross-equation covariance matrix.

SUR may improve the efficiency of parameter estimates when there is

contemporaneous correlation of errors across equations. Under two sets of circumstances,

SUR parameter estimates are the same as those produced by OLS: when there is no

contemporaneous correlation of errors across equations (the estimate of contemporaneous

correlation matrix is diagonal); and when the independent variables are the same across

equations.

Theoretically, SUR parameter estimates will always be at least as efficient as OLS

in large samples, provided that the equations are correctly specified. However, in small

samples the need to estimate the covariance matrix from the OLS residuals increases the

sampling variability of the SUR estimates, and this effect can cause SUR to be less

efficient than OLS. If the sample size is small and the across-model correlations are

small, then OLS is preferred to SUR. The consequences of specification error are also

more serious with SUR than with OLS.



80

SUR Parameter Estimation Procedure

In a SUR regression system, all parameters in the equations are estimated

simultaneously by applying Aitken’s Generalized Least Squares (GLS) to the whole

system of equations (Zellner, 1962). The derivation of the cross-model covariance is

based on the residuals from the equation-by-equation OLS.

The general format of the equation system can be expressed as:

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

+

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

=

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

M M M M e

e

e

e

X

X

X

X

y

y

y

y

MM

L

LLLLL

L

L

L

M

3

2

1

3

2

1

3

2

1

3

2

1

000

000

000

000

β

β

β

β

or simply as

e X y += β

In this research, y is a 14×1 matrix of all the change variables (endogenous

variables) at the neighborhood level in Cuyahoga County. In each equation there are

different independent variables (exogenous variables) that consist of the neighborhood

characteristics variables in 1990 and the change variables in census place characteristics.

The selection of the independent variables in each equation is based on the level of

correlation coefficients between those variables and foreclosure rates. In some equations

the independent variables are the subset of the independent variables in several other

equations. However, in using the SUR this research will make the following assumptions:

1. The spatial effects are ignored although there might be spatial autocorrelation

between the variables in neighboring block groups.



81

2. The covariance of the error terms between and within the equations is not equal to

zero.

Then the variance-covariance matrix is assumed to be of the form

∑ Φ=⊗=⊗

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

=′T T

MM M M M

I I ee E

σ σ σ σ

α σ σ σ

σ σ σ σ

σ σ σ σ

L

LLLLL

L

L

L

321

35333231

25232221

15131211

)(

where IT is a unit matrix of order T×T and σμμ΄=E(eμteμ΄t) for t = 1, 2, …, T, and μ, μ΄

= 1, 2, …, M. In temporal cross-section regressions, t represents time. In the original

equation, system variances and covariances are constant from period to period and there

is an absence of any auto or serial correlation of the error terms. In a regression related to

geographic problems, t stands for the t’th geographic region. The original equation

system is the form such that there is correlation between error terms or dependent

variables relating to a particular region but not to different regions. Error variances and

covariances are assumed to be constant from region to region (the error terms of each

equation have a zero mean).

Then the GLS estimator is given by

[ ] y I X X I X y X X X T T )()()(ˆ 111111 ⊗′⊗′=Φ′Φ′= ∑∑ −−−−−− β

In constructing the estimator we need the inverse of Φ-1

, which is given by:

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=Φ −

I I I

I I I

I I I

MM M M

M

M

σ σ σ

σ σ σ

σ σ σ

...

............

...

...

21

22221

11211

1



82

Therefore the parameter estimator of the coefficient vector is as follows:

⎥⎥⎥⎥⎥⎥⎥

⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢

⎢⎢

⎣

⎡

′

′

′

×

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

′′′

′′′

′′′

=

⎥⎥⎥⎥

⎥

⎦

⎤

⎢⎢⎢⎢

⎢

⎣

⎡

=

∑

∑

∑

=

=

=−

∧

∧

∧

∧

M

M

M

M

M

M M

MM

M

M

M

M

M M

M

M

M y X

y X

y X

X X X X X X

X X X X X X

X X X X X X

1

1

2

2

1

1

1

1

2

2

1

1

22

2222

1221

11

2112

1111

2

1

...

...

............

...

...

...

μ

μ

μ

μ

μ

μ

μ

μ

μ

σ

σ

σ

σ σ σ

σ σ σ

σ σ σ

β

β

β

β

Then, the variance-covariance matrix of the estimator is:

System Weighted R 2

and System Weighted Mean Square Error

In SUR the goodness of fit of the system of equations is measured by the System

Weighted R2

and System Weighted Mean Square Error (MSE). The System Weighted R2

is computed as follows:

R2 = Y' W R (X'X)-1 R' W Y / Y' W Y

In this equation the matrix X'X is R'W R, and W is the projection matrix of theinstruments:

Z Z Z Z SW ′′⊗= −− 11 )(

The matrix Z is the instrument set, R is the regressor set, and S is the estimated

cross-model covariance matrix.

1

2

2

1

1

2

1

22

22

12

21

1

1

21

12

11

11

...

...............

...

)(

−

∧

⎥⎥⎥⎥

⎥

⎦

⎤

⎢⎢⎢⎢

⎢

⎣

⎡

′′′

′′′

′′′

=

M M

MM

M

M

M

M

M

M

M

M

X X X X X X

X X X X X X

X X X X X X

V

σ σ σ

σ σ σ

σ σ σ

β



83

The system weighted MSE is computed as follows:

MSE = [1/tdf ] ( Y' W Y - Y' W R (X'X)-1

R' W Y )

In this equation, tdf is the sum of the error degrees of freedom for the equations in

the system.



84

CHAPTER 4

DESCRIPTIVE AND SPATIAL ANALYSIS

Judicial Foreclosure Process and Sheriff’s Deed Transfer Data

When borrowers are in default, usually defined as being three months behind in

mortgage repayments, lenders in some states can file a foreclosure lawsuit in the local

civic court and initiate a judicial foreclosure process. In other states, lenders file a notice

of default and initiate a trustee’s sale of the properties without going through the judicial

system. This is called non-judicial foreclosure. In some states both judicial and non-

judicial systems exist. In Ohio, only judicial foreclosure is allowed.

There are several critical procedures in the process of judicial foreclosure. When the

mortgage lender detects a loan default, they will notify the loan borrower about the

default and possible plans to restore a normal status. If the borrower does not respond, or

the work-out plans or loan modification plans fail, the lender will file a civil lawsuit

against the borrower (mortgagor) to claim the payoff of the loan balance. This is the start

of the foreclosure process. If at this stage the borrower repays the loan, sells the property

and pays off the loan, or files for bankruptcy the case will be finalized. If none of these

occur, the property, as collateral for the mortgage, will be put into a civic sale. These

sales are usually conducted by the Sheriff’s Office of each county, which holds an



85

auction on a regular basis. If the loan balance is fully paid, or the borrower announces

bankruptcy, or the property is bought by private investors prior the auction, the property

will be withdrawn from the sales process. Otherwise, the property will go to the Sheriff’s

Sale. If no bid is made on a property the lenders will usually take the title of the property

to sell in the future; this kind of property is called a Real Estate Owned (REO) property.

Properties sold at the Sheriff’s sales will be recorded at the county Recorders’ Office as

Sheriff’s Deeds. The process of judicial foreclosure and the handling of the properties

before and after foreclosure is summarized in Figure 4.1.

Because of the complex nature of the process when conducting foreclosure

research, it is difficult to decide what datasets to use. The choice largely depends on the

objectives of the research. If we want to know what factors lead to foreclosures,

foreclosure filing datasets or mortgage loan performance datasets can be used. But if we

want to explore the impact of the foreclosure process (from foreclosure start to finish) on

neighborhood characteristics and change, it is appropriate to use the Sheriff’s Deeds

transfer data since those properties are foreclosed and probably have the biggest impact

on neighborhoods.



86

Figure 4.1: Judicial Foreclosure Process

Lenders file lawsuit to the

court (Foreclosure Started)

Court

Trial

Borrower

Loses

Order of civic

sales

Public

Auction

Sheriff’s Deeds

(Foreclosure

Finished)

Re-

auction

Mortgage

Default

Workout, payment

brought back to

current

Some properties

withdrawn

Foreclosure

Terminated

Withdrawn (borrower

bankruptcy, properties sold

prior to auction, etc.)

REO

No Yes



87

Ohio’s Foreclosure Situation

The U.S. residential mortgage foreclosure rate has slightly increased over time (see

Figure 4.2). But in Ohio, the residential mortgage foreclosure rate has increased rapidly

since 1995. Since 1999, the rate has been above the U.S. average and became the highest

in all states in 2003, with a rate of 2.9%, compared to 1.2% nationally (MBA, 2004).

According to the annual court summary of the Supreme Court of Ohio, from 2001 to

2002 new foreclosure filings increased by 27.3%, and they increased another 3.3% from

2002 to 2003 (Supreme Court of Ohio, 2002, 2003). Foreclosure filings in 2003 were

double the number in 1998.

Figure 4.2: New Foreclosure Filings in Ohio (1990–2005)

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Year

N e w

F o r e c

l o s u r e F i l i n g s



88

After mapping rate increases for new foreclosure filings, I found that the increased

rates are unevenly distributed among the 88 counties in Ohio. Economic patterns may

explain the foreclosure distribution pattern, but the topic needs further research. Previous

studies found that a weak economy and predatory lending significantly contributed to the

high foreclosure rate in Ohio (Schiller et. al., 2004; Bellamy, 2002). Those studies

recommended that the State take measures to enact anti-predatory lending legislations.

But none of the studies could identify the exact reasons for the high foreclosure rate

because of small samples, third-party surveys (surveys to Sheriff’s Offices), and lack of

advanced statistical analysis.

The geographic distribution of the growth rate of foreclosure filings does not show

strong patterns among the counties, and the majority of counties have an annual increase

of more than 10% (see Figure 4.4). In the period between 1996 and 2004, there were

more counties with a high annual rate increase in foreclosure filings. Among the 88

counties, Cuyahoga County and Franklin County have the highest number of new

foreclosure filings in 2004 (see Table 4.1).



89

Figure 4.3: Change of Foreclosures Started in Ohio (1984–2003)

Source: Mortgage Bankers Association of America, 2004

Table 4.1: Number of New Foreclosures Filed in 2004 by County (descending by thenumber of filings)

County Number of ForeclosuresFiled

Cuyahoga 9,751

Franklin 5,940

Hamilton 4,528

Montgomery 4,002

Lucas 2,766

Summit 3,358

Stark 2,129

Butler 1,952

Lorain 1,510

Mahoning 1,367



90

Ross

Stark

Wood

Knox

Darke

Pike

Licking

Scioto

Allen

Adams

Huron

Lorain

Gallia

Wayne

Butler

Brown

Perry

Clark

LoganUnion

Trumbull

Seneca

Ashtabula

Athens

Henry

Hardin

Erie

Meigs

Noble

Miami

Mercer

Franklin

Fulton

Belmont

Portage

Preble

Vinton

Putnam

Fairfield

Highland

Hancock

Carroll

Shelby

Monroe

Lucas

Marion

Medina

Muskingum

Clinton

Richland

Holmes

Summit

GreeneMorgan

Morrow

Madison

Guernsey

Fayette

Warren

Pickaway

Ashland

Coshocton

Washington

Geauga

Hocking

Williams

JacksonClermont

Lake

Paulding

HarrisonDelaware

Tuscarawas

Defiance

Auglaize

Lawrence

Cuyahoga

Wyandot

Hamilton

ColumbianaCrawford

Jefferson

Mahoning

Van Wert

Sandusky

Ottawa

Champaign

Montgomery

Growth Rate from 1996 to 2004, %0 - 100

100 - 300300 - 500500 - 800

800 - 1100

Ross

Stark

Wood

Knox

Darke

Pike

Licking

Scioto

Allen

Adams

Huron

Lorain

Gallia

Wayne

Butler

Brown

Perry

Clark

LoganUnion

Trumbull

Seneca

Ashtabula

Athens

Henry

Hardin

Erie

Meigs

Noble

Miami

Mercer

Franklin

Fulton

Belmont

Portage

Preble

Vinton

Putnam

Fairfield

Highland

Hancock

Carroll

Shelby

Monroe

Lucas

Marion

Medina

Muskingum

Clinton

Richland

Holmes

Summit

GreeneMorgan

Morrow

Madison

Guernsey

Fayette

Warren

Pickaway

Ashland

Coshocton

Washington

Geauga

Hocking

Williams

JacksonClermont

Lake

Paulding

HarrisonDelaware

Tuscarawas

Defiance

Auglaize

Lawrence

Cuyahoga

Wyandot

Hamilton

ColumbianaCrawford

Jefferson

Mahoning

Van Wert

Sandusky

Ottawa

Champaign

Montgomery

Growth Rate from 1990 to 1996, %-70 - -35

-35 - 00 - 100

100 - 200200 - 360

Figure 4.4: Average Annual Growth Rate of New foreclosure Filings by County



91

Research Area and Geographic Definition of Neighborhood

Research Area

Initially, I hoped to explore how mortgage foreclosure and neighborhood

characteristics interact in the three biggest counties in Ohio: Cuyahoga, Franklin and

Hamilton. However, because of data availability issues, Hamilton County had to be

excluded from the research. The other two major counties should provide a good study

comparison because they have quite different characteristics (see Table 4.2).

New Foreclosure Filings of the Research Area

In Franklin County new foreclosure filings have risen continuously since 1990, but

the rise has been steep and rapid since 1995 (see Figure 4.5). In 1990 there were 2,533

filings and by 2004 this had risen to 5,940. New filings doubled during those 15 years.

New foreclosure filing data prior to 1990 was not aggregated by the Supreme Court of

Ohio, therefore a longer term trend cannot be analyzed. The potential reasons for the

rapid increases in new filings since 1995 are not clearly known, although Schiller (2003,

2004) stated that the unemployment rate cannot explain the trend because between 1995

and 2000 the economy was booming. It is encouraging to see that since 2002 the filings

have begun to drop slightly. It will be very interesting to follow the trend in future years

to try to connect macro economic trends to new foreclosure filings.

Foreclosure has been a serious issue in Cuyahoga County since 1990. In 1990 new

foreclosure filings numbered 5,595, and in 2004 the new filings jumped to 9,751. Over

those 15 years there were 84,672 total filings. Franklin County has seen slight decreases

in the last three years, but in Cuyahoga County foreclosure filings dropped slightly in



92

2003 but rose again in 2004. Therefore, Cuyahoga is a county with serious foreclosure

problems.

0

2,000

4,000

6,000

8,000

10,000

12,000

1 9 9 0

1 9 9 1

1 9 9 2

1 9 9 3

1 9 9 4

1 9 9 5

1 9 9 6

1 9 9 7

1 9 9 8

1 9 9 9

2 0 0 0

2 0 0 1

2 0 0 2

2 0 0 3

2 0 0 4

Year

N e w

F o r e c l o s u r e F i l i n g s

Cuyahoga

Franklin

Figure 4.5 New Foreclosure Filings in Cuyahoga County and Franklin County (1990– 2004)

General and Social Characteristics of the Research Area in 2000

Franklin County is in Central Ohio and the capital city of the state, Columbus,

makes up the major part of the county (see Figure 4.6). The total population in Franklin

County is about 1.07 million and 75.5% are Whites. Compared to Cuyahoga County, the

population in Franklin County is much younger with more education. In Franklin County

the median age of the population is 32.5 and in Cuyahoga County the median age is 37.3.



93

In Franklin County 85.7% of the population are high school graduates (or higher),

compared to 81.6% in Cuyahoga County.

Cuyahoga County is located at the northeastern corner of the state and Cleveland

makes up the major part of the county (see Figure 4.6). The total population in Cuyahoga

County is 1.39 million and 67.4% are Whites. Therefore, Cuyahoga County has a larger

percentage of a minority population.

There are 438,778 total housing units in Franklin County and 56.9% are owner-

occupied. Female headship rate, defined as the percentage of female householders

without a husband present, is 13.0%.

There are 616,903 total housing units in Cuyahoga County and 63.2% are owner-

occupied. Female headship rate is 15.7%.

Economic Characteristics of the Research Area

In Franklin County 70.7% of the population over 16 are in the labor force, and in

Cuyahoga County the ratio is 62.5%. In 2000 the unemployment rate was 3.0% in

Franklin County and 3.9% in Cuyahoga County. The 2005 unemployment rate was 6.1%

for Cuyahoga County and 5.3% for Franklin County. This increase in unemployment may

have contributed to overall increases in foreclosures.

Median household income in Franklin County is $42,734 (in 2004 inflation-adjusted

dollars); in Cuyahoga County it is $39,168. And, in Franklin County there are fewer

families that are under the poverty line (8.2%, compared to 10.2% in Cuyahoga County).

Generally speaking, the economic situation in Franklin County is better than that in

Cuyahoga County with a higher household income, lower poverty rate, and a lower



94

unemployment rate. This probably relates to the occupational structure of the two

counties. In Cuyahoga County manufacturing is one of the dominant industries, and

industry structural change in recent years has forced a significant amount of population

into unemployment or underemployment.

Housing Characteristics of the Research Area

Although the two counties have different numbers of the total housing units, their

housing characteristics are very similar. The median value of owner-occupied housing

units in Franklin County is $116,200, compared to $113,800 in Cuyahoga County. The

median housing costs for owners with a mortgage is $1,077 in Franklin County compared

to $1,057 in Cuyahoga County. Also, there is a larger percentage of rental units in

Franklin County (43.1%) than in Cuyahoga County (36.8%).

Summary

Compared to Franklin County, Cuyahoga County has an older population with less

education. The county’s population has a larger percentage of minorities, a lower median

household income, a higher unemployment rate, and a higher percentage population

below the poverty line. There are more owner-occupied housing units in Cuyahoga

County. Manufacturing is one of the major sectors in Cuyahoga County, while in

Franklin County financial sectors, public administration, and information technology

hold important positions in the economy. These characteristics would lead us to expect a

worse foreclosure problem in Cuyahoga County than in Franklin County.



95

Columbus

Cleveland[

[

Figure 4.6: Research Area: Cuyahoga County and Franklin County, Ohio



96

2000

Franklin County Cuyahoga County

Number % Number %

General Characteristics

Total population 1,068,978 - 1,393,978 -

Median age (years) 32.5 - 37.3 -

White 806,851 75.50 938,863 67.40

Black or African American 191,196 17.90 382,634 27.40

American Indian and Alaska Native 2,899 0.30 2,529 0.20

Asian 32,784 3.10 25,245 1.80

Native Hawaiian and Other Pacific Islander 466 0.00 338 0.00

Some other race 10,992 1.00 20,962 1.50

Two or more races 23,790 2.20 23,407 1.70

Average household size 2.39 - 2.39 -

Female householder, no husband present 57,195 13.00 89,793 15.70

With own children under 18 years old 36,260 8.30 51,100 8.90

Average family size 3.03 - 3.06 -

Total housing units 438,778 93.20 616,903 -

Owner-occupied housing units 249,633 56.90 360,980 63.20

Renter-occupied housing units 189,145 43.10 210,477 36.80

Vacant housing units 32,238 6.80 45,446 7.40

Social Characteristics

High school graduate or higher 579,896 85.70 764,186 81.60

Bachelor's degree or higher 215,180 31.80 235,413 25.10

Male, Now married, except separated (>= 15 years old) 201,802 50.10 261,433 51.40Female, Now married, except separated (>=15 yearsold) 201,354 45.90 261,741 44.10

Economic Characteristics

In labor force (>= 16 years old) 584,391 70.70 676,874 62.50

Unemployed 24,594 3.00 41,778 3.90

Median household income (in 2004 inflation-adjusteddollars) 42,734 - 39,168 -

Median family income (in 2004 inflation-adjusteddollars) 53,905 - 49,559 -

Per capita income (in 2004 inflation-adjusted dollars) 23,059 - 22,272 -

Families below poverty level 21,742 8.20 36,535 10.30

Female householder, no husband present 13,787 24.30 - -Female householder, no husband present, with

children < 18 12,421 30.30 - -

Individuals below poverty level 121,843 11.60 179,372 13.10

Continued

Table 4.2: Selected Characteristics of the Two Counties



97

Table 4.2 Continued

Sales and office occupations 167,418 29.90 181,884 28.70

Farming, fishing, and forestry occupations 472 0.10 606 0.10

Construction, extraction, and maintenanceoccupations 36,533 6.50 42,211 6.70

Production, transportation, and material-movingoccupations 66,742 11.90 94,237 14.90

Industry

Agriculture, forestry, fishing and hunting, and mining 1,229 0.20 901 0.10

Construction 28,664 5.10 28,952 4.60

Manufacturing 51,907 9.30 102,279 16.10

Wholesale trade 21,861 3.90 24,570 3.90

Retail trade 74,001 13.20 68,699 10.80

Transportation and warehousing, and utilities 29,537 5.30 30,779 4.90

Information 22,167 4.00 17,821 2.80

Finance, insurance, real estate, and rental and leasing 57,468 10.30 54,773 8.60

Professional, scientific, management, administrative,and waste management services 61,573 11.00 64,340 10.10

Educational, health and social services 107,669 19.30 137,562 21.70

Arts, entertainment, recreation, accommodation andfood services 46,648 8.30 48,796 7.70

Other services (except public administration) 23,888 4.30 28,090 4.40

Public administration 32,517 5.80 26,857 4.20

Class of Worker

Private wage and salary workers 445,498 79.70 523,380 82.50

Government workers 86,647 15.50 81,138 12.80

Self-employed workers in own non-incorporated business 26,072 4.70 28,671 4.50

Unpaid family workers 912 0.20 1,230 0.20


Median value of owner-occupied homes ($) 116,200 - 113,800 -

Median owner costs with a mortgage ($) 1,077 - 1,057 -

Median owner costs without mortgages ($) 326 - 346 -

Source: www.census.gov



99

Data Description for Each County

Franklin County, Ohio

General Description

As mentioned previously, only about one third of the new foreclosure filings end up

with being sold at the Sheriff’s sales. Table 4.3 indicates the number of cases of new

foreclosure filings, the number of terminated foreclosure cases, total Sheriff’s Deeds

(SD) and the percentage of those Sheriff’s Deeds among new filings and terminated cases

in each year (please refer to Figure 4.1 for the judicial foreclosure process). We notice

that Sheriff’s Deeds account for about 37% of the total new filings or terminated cases

from 1997 to 2004, and in recent years the percentage has increased greatly. The reason

why the increase becomes rapid since 2003 needs further investigation. Therefore the

sudden jump of the percentage Sheriff’s Deeds as new filings might make the estimation

results of the regression models (Chapter 5) biased due to omitted variables. It would be

difficult to fix the problem due to the unknown or immeasurable omitted variables.

Year 1997 1998 1999 2000 2001 2002 2003 2004All

Years

New Foreclosure Filings 2,533 2,992 3,468 3,832 5,077 6,104 6,072 5,940 36,018

Terminated Cases (TC) 2,529 2,994 3,404 3,896 4,837 6,014 6,628 6,871 37,173

Total SD 626 1,092 1,115 1,505 1,660 2,106 2,546 3,228 13,878

SD as % of New Filings 24.71 36.50 32.15 39.27 32.70 34.50 41.93 54.34 38.53

SD as % TC 24.75 36.47 32.76 38.63 34.32 35.02 38.41 46.98 37.33

Table 4.3: New Foreclosure Filings, Terminated Foreclosure Cases and Sheriff’s Deeds(1997–2004, Franklin County)



100

Among the total of 13,878 Sheriff’s Deeds over the time period, there are 476

duplicated records that were either foreclosed multiple times or recorded by mistakes.

These duplicated cases are eliminated from the research.

After eliminating duplicated cases, the Sheriff’s Deed data are merged with property

parcel data. There are 11,844 single-family properties in the final dataset (see Table 4.4).

Year 1997 1998 1999 2000 2001 2002 2003 2004 Total

Cases 551 989 966 1,308 1,423 1,771 2,140 2,696 11,844

Table 4.4: The Total Single-family Sheriff’s Deeds (1997–2004, Franklin County)

Those 11,844 parcels are aggregated at the block group level, then the aggregated

cases are divided by total owner-occupied housing units to derive a foreclosure rate in

each block group. There are 883 block groups in Franklin County and 137 block groups

have missing values. The foreclosure rate in most of the block groups is lower than

15.00%. But more than 100 block groups have a foreclosure rate higher than 15%.

Some block groups (137) have a foreclosure rate of 0, which means that either the

block groups don’t have foreclosed properties or the foreclosed properties are not

included in the research due to data collecting and processing errors. Excluding missing

values, the average foreclosure rate measured by the accumulated Sheriff’s Deeds during

1997 to 2004 is 7.64% in 746 block groups, with a standard deviation of 10.55% (the

lowest value is 0.18%; the highest value is 73.08%). Most of the block groups have a

foreclosure rate between 0 and 15.00% in the eight years studied (see Figure 4.7).



101

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

>0 – 1% >1–5% >5–15% >15 – 25% >25 – 50% >50 – 75%

Foreclosure Rate (1997-2004)

% B

l o c k G r o u p s

Figure 4.7: Franklin County Foreclosure Rate Distribution at the Block Group Level(1997–2004)

We notice that 58.44% block groups have a foreclosure rate lower than 5.00%, and

85.38% have a foreclosure rate lower than 15.00%. The rest, 14.62%, are the block

groups with a high foreclosure rate ranging from 15.00% to 75.00%. When considering

median household income in a neighborhood we found that foreclosures in this dataset

generally concentrate in neighborhoods with low to moderate income. High income

neighborhoods have very low foreclosure rates. The lower the median income, the higher

the foreclosure rate will be.

Looking at detailed spatial patterns of the deed transfers, we found that the cases are

highly clustered in certain areas, especially distressed inner-city areas, and the clustering

does not change over time, although in recent years those cases began to scatter to



102

wealthier suburbs (see Figure 4.8). This pattern is not surprising since households in

these distressed inner-city areas suffer more during economic downturns. On the other

hand, those houses might be less attractive to pre-foreclosure investors and, therefore,

many of them have to go to the Sheriff’s sales auction. So the dataset may over represent

them.



103

Sheriff’s Deeds (1997) Sheriff’s Deeds (1998)


Continued

Figure 4.8: Spatial Distribution of Sheriff’s Deeds in Franklin County (1997–2004)



104

Figure 4.8 Continued





105

Figure 4.9: Total Residential Sheriff’s Deeds in Franklin County (1997–2004)



106

Sheriff's Sales Deed Transfer Records in 1997

Sheriff's Sales Deed Transfer Records in 2004

blockgroup

Figure 4.10: Comparison between the 1997 and 2004 of the Distribution of Sheriff’sDeeds in Franklin County



1 0 7

Foreclos

0

0

0

0

0

0

Figure 4.11: Foreclosure Rates by Block Groups in Franklin County (1997–20



108

Spatial Autocorrelation Analysis

Univariate and multivariate spatial autocorrelation are the two most common

analysis methods in spatial autocorrelation analysis. Univariate spatial autocorrelation

explores whether a variable is spatially autocorrelated with the same variable in adjacent

neighborhoods. Multivariate analysis explores the autocorrelation between two or more

different variables in adjacent neighborhoods. In this analysis, several sets of variables

are considered to explore whether spatial autocorrelation exists. The first set is the

univariate autocorrelation of foreclosure rates; the other sets are the bivariate

autocorrelation between foreclosure rate and median housing value of owner-occupied

housing units, percentage minority population, housing vacancy rate, and homeownership

rate. Among those bivariate autocorrelation analyses, the mutual relationship between

foreclosure rate and the selected neighborhood indicators are explored. For example, the

autocorrelation test starts with the effect of the median housing value in 2000 on

foreclosure rate between 2001 and 2004, and then the effect of foreclosure rate between

1997 and 2000 on median housing value in 2000. All the variables in this section are

standardized. All the univariate and bivariate autocorrelation tests are based on global

and local autocorrelation analyses. The significance of using local autocorrelation

analysis is to determine the relationship at the individual block group level; thus, local

autocorrelation analysis maps can be generated to illustrate those relationships.

Connectivity of Block Groups in Franklin County

Connectivity of different block groups is the basis of calculating weighting matrixes.

The connectivity of block groups is illustrated in Figure 4.12. From left to right, the



109

different colors in the histogram denote the number of neighboring block groups for any

given block group. For example, dark blue means that a block group is neighboring with

one block group, lighter blue means that a block group is neighboring with two other

block groups, and so forth. Combining these numbers with the number associated with

each column in the histogram, we found that there is only one block group that has one

neighboring block group and 14 block groups with two neighboring block groups. There

are 234 block groups that are adjacent to five block groups. Most of the block groups

have three to eight neighboring block groups.

Figure 4.12: Connectivity of Block Groups in Franklin County



111

Foreclosure Rate and Percentage Minority Households

Contrary to the relatioonship between foreclosure and median housing value, the

relationship between foreclosure rate and racial composition of neighboring block groups

are positive, both for the correlation between racial composition and foreclosure rate

(2001– 2004) (Moran’s I = 0.4603) and the correlation between foreclosure rate (1997–

2000) on racial composition (Moran’s I = 0.4440). This means that racial composition in

2000 does are auto correlated with foreclosure rate between 2001 and 2004 in

neighboring block groups, and foreclosure rate between 1997 and 2000 is autocorrelated

with racial composition in 2000 in neighboring block groups.

Foreclosure Rate and Housing Vacancy Rate

When looking at both the correlation between housing vacancy rate and foreclosure

rate between 2001 and 2004 (Moran’s I = 0.4078) and the correlation between

foreclosure (1997–2000) and housing vacancy rate (Moran’s I = 0.4103), we found that

the relationships are positive.

Foreclosure Rate and Homeownership Rate

The autocorrelation between foreclosure rate and homeownership rate is not very

significant because Moran’s I is only slightly close to -0.15, for both the correlation

between homeownership rate in 2000 and foreclosure rate (2001–2004) and the

correlation between foreclosure rate (1997–2000) and homeownership rate in 2000.



112

Local Spatial Autocorrelation

Local spatial autocorrelation explores the relationship between block groups in

terms of a certain variable (univariate), such as foreclosure rate, or the relations between

two variables (bivariate).

The analysis of local spatial autocorrelation of foreclosure rate in Franklin County

shows that a high-high spatial autocorrelation exists in the inner-city neighborhoods with

low income and a large percentage of minority population, for example, Franklinton,

Olde Town East, and Weinland Park. Those neighborhoods have high foreclosure rates

and are surrounded by ones with a high foreclosure rate. Some higher-end neighborhoods

that are adjacent to the low income ones, such as Victorian Village, Italian Village, and

German Village, present a low-high autocorrelation. Most of the northern part of the

county has a low-low autocorrelation. The autocorrelation is not significant in southern

areas. The only high-low autocorrelation in the northern suburbs of metropolitan

Columbus appears in two block groups in Dublin and Worthington. Therefore, the

neighborhoods with a significant local Moran’s I are either located in inner-city low

income neighborhoods (high-high autocorrelation), inner-city median to high income

neighborhoods (low-high autocorrelation), or suburban median to high income

neighborhoods (low-low autocorrelation). The southern part of the County has mixed-

income neighborhoods and the local Moran’s I is not significant.



113

Figure 4.13: Map of Foreclosure Rate Local Spatial Autocorrelation in Franklin County(1997–2004)



114

Temporal Change in Selected Neighborhood Variables

From 1990 to 2000, Franklin County experienced rapid changes in some

neighborhood characteristics. Generally speaking, the most significant changes were an

increased percentage of minority population and thus a decreased percentage of white

population, increased housing cost burden, and increased total housing units. The

countywide change and the change by different levels of foreclosure rate are summarized

in the following sections.

Demographic Characteristics

For the entire county, the black population increased by 3.92% points and the

minority population in general increased by 8.03% points. Thus, the white population

decreased by 8.00% points. The county is more diverse than ever, and the outflow of the

white population continues to be a significant phenomenon. However, the most

significant increase of minority population and decrease of white population is

concentrated in the neighborhoods with a foreclosure rate of 5–15% (see Table 4.5).


It is interesting to note that from 1990 to 2000 the unemployment rate decreased by

0.90% points, and for those block groups with observations the decrease is even bigger

(1.30% points). The population employed in management occupations increased

dramatically by 20.22% points, and the biggest increase is in those neighborhoods with

the lowest foreclosure rate. The population employed in service occupations increased by

2.55% points, but there is no obvious patterns observed at different levels of foreclosure



115

rate. The change in occupation structure and unemployment rate and their relationship

with the foreclosure rate indicates that, although the unemployment rate decreased

between 1990 and 2000, neighborhoods with lower-paid populations see a higher

foreclosure rate. However, the relationship between service occupation and foreclosure

rate is important, unlike that between management occupation and foreclosure rate.

The median household income increased by $2,400 (2000 constant value), and the

housing value of owner occupied housing units increased by $14,930 (2000 constant

value). The most significant increase in the median household income and housing value

is in those neighborhoods with a low foreclosure rate (lower than 1%) (see Table 4.5).


The countywide housing vacancy rate increased by 0.60% points from 1990 to 2000.

The vacancy rate is closely related to the foreclosure rate, the higher the foreclosure rate

the higher the vacancy rate. In neighborhoods with the lowest foreclosure rate the

vacancy rate decreased by 0.80% points. Generally speaking, the homeownership rate in

the county increased by 2.81% points and the percentage of housing units with a

mortgage increased by 3.56% points. There is no significant pattern observed at different

levels of foreclosure rate for these two indicators (see Table 4.5).



1 1 6

Variable Missing or 0 >0 – 1% >1 – 5% >5 – 15% >15 – 25% >25 – 50% >50 – 7


Total Number of Households

+23.33% +517.66% +53.00%* +63.16%+ -7.50%*** -5.00%* +2.31%

Percentage Black Population

+2.49%** +1.47%*** +4.32%*** +6.54%*** +3.09%* +3.48%+ +4.02%

PercentageMinorityPopulation

+8.85%*** +4.92%*** +7.50%*** +11.03%*** +7.23%*** 7.79%*** +5.31%

Percentage WhitePopulation

-8.80%*** -4.90%*** -7.50%*** -11.00%*** -7.20%*** -7.80%*** -5.30%

PercentagePopulationDivorced (>16)

+0.14% +1.88%*** +2.29%*** +2.22%*** +1.84%+ +0.30% -4.00%

PercentagePopulation withCollege andHigher Education

+1.75% +4.89%*** +5.86%*** +5.99%*** +3.20%* +5.04%** +7.31%


UnemploymentRate

-0.50% +0.06% -0.60%** -1.60%*** -2.20%* -2.10% -1.20%

PercentagePopulation inServiceOccupation

+2.79%*** +1.87%*** +3.12%*** +3.30%*** -0.90% +2.27% -1.50%

PercentagePopulation inManagement

Occupation

+25.02%*** +32.79%*** +20.43%*** +12.15%*** +10.94%*** +10.62%*** +7.79%*

Table 4.5: Change in Neighborhood Variables from 1990 to 2000 by Groups of Foreclosure Rate in Franklin Co



1 1 7

Table 4.5 Continued

PercentagePopulation under the Poverty Level

+2.35%* +0.90%** +0.83%* -0.30% -3.00%+ -4.60%* -10.50%

MedianHouseholdIncome5

-$281.4 +$5,727.1*** +$2,483.2*** +$1,598.3** +$1,468.6+ +$2,289.9* +$5,834


Vacancy Rate -0.60% -0.80%* +0.20% +1.59%*** +3.00%*** +3.33%*** +3.95%

HomeownershipRate

+0.02% +6.21%*** +2.43%*** +3.23%*** +3.12%* +1.29% -6.50%

PercentageHousing Units

with a Mortgage

+15.79%*** +2.55%+ +2.95%* +2.33% -4.90%+ +0.11% +2.05%

Median HousingValue (owner-occupied)6

+$14,726* +$24,105*** +$15,500*** +$9,092.1*** +$8,968.6*** +$12,777*** +$15,153

*** 0.001 significant level ** 0.01 significant level * 0.05 significant level + + 0.10 significant level



118

Cuyahoga County, Ohio

General Description

In terms of Sheriff’s Deeds, the Cuyahoga County Recorder’s Office provides the

most complete records of Sheriff’s sales data among those counties I examined. There are

more than 30 years of historical data on Sheriff’s Deeds. Since 1983 parcel IDs have been

incorporated into the index file, which can be used as a 21-year time series record of

Sheriff’s sales data.

Sheriff's Deeds

0

500

1000

1500

2000

2500

3000

1 9 6 5

1 9 6 7

1 9 6 9

1 9 7 1

1 9 7 3

1 9 7 5

1 9 7 7

1 9 7 9

1 9 8 1

1 9 8 3

1 9 8 5

1 9 8 7

1 9 8 9

1 9 9 1

1 9 9 3

1 9 9 5

1 9 9 7

1 9 9 9

2 0 0 1

2 0 0 3

Year

Figure 4.14: Total Sheriff’s Deeds in Cuyahoga County (1965–2004)

The 21-year span is shown in Figure 4.14 for reference. However, to compare the

two counties this research uses those sales between 1997 and 2004. Over those eight

years, there were 54,584 total new foreclosure filings and 16,705 recorded Sheriff’s



119

Deeds in Cuyahoga County. The percentage of Sheriff’s Deeds to new filings and

terminations is relatively low at 27.48%. In Franklin County this percentage is slightly

higher. The percentage has been decreasing since 1997. Further investigations need to be

done to explore why the recorded deeds account for such a low percentage of either total

cases of new filings or terminations.

Year 1997 1998 1999 2000 2001 2002 2003 2004All

Years

New ForeclosureFilings

3,989 4,925 5,387 5,900 6,959 8,987 8,686 9,751 54,584

Terminated Cases(TC)

4,092 5,287 5,597 6,217 7,857 10,001 10,185 11,550 60,786

Total SD 1,503 1,904 1,929 1,995 2,201 2,180 2,327 2,666 16,705

SD as % NewFilings

37.68 38.66 35.81 33.81 31.63 24.26 26.79 27.34 30.60

SD as % TC 36.73 36.01 34.46 32.09 28.01 21.80 22.85 23.08 27.48

Table 4.6: Sheriff’s Deeds as a Percentage of Total New Filings and Total ForeclosureCase Terminations in Cuyahoga County (1997–2004)

There were 16,705 Sheriff’s Deeds between 1997 and 2004. After eliminating cases

with multiple records (619) there are 16,086 cases left. After merging the extracted

dataset with the County parcel data in 2004, there are 13,894 residential properties in the

dataset. The number of properties in each year is shown in Table 4.7. As with Franklin

County, Sheriff’s Deeds records in Cuyahoga County capture only part of the foreclosure

data.



120

Year 1997 1998 1999 2000 2001 2002 2003 2004 Total

Cases 1,164 1,480 1,512 1,672 1,866 1,860 2,011 2,329 13,894

Table 4.7: Total Available Residential Sheriff’s Deeds in Cuyahoga County (1997–2004)

When geocoding the 13,864 total residential Sheriff’s Deeds, 13,096 (94.46%) of

them can be matched to their physical street address on the map with a score higher than

80. There are 683 cases that can be matched with a score between 0 and 80, and the other

85 cases could not be geocoded at all due to missing housing parcel IDs, addresses, or the

timing lag of the reference base maps from TIGER. Franklin County already has a

countywide geocoded parcel map from the Auditor’s Office, so the geocoding process is

not needed for that county.

The next step is to merge the observations with the census data at the block group

level in 1990 and 2000 using the uniformed block group boundary shape files. There are

1,262 block groups in Cuyahoga County in 2000, among which 1,156 have Sheriff’s

Deeds observations. Excluding missing values, the average foreclosure rate measured by

the accumulated Sheriff’s Deeds during 1997 to 2004 is 6.49% in 1,149 block groups,

with a standard deviation of 7.93% (the lowest value is 0.13%; the highest value is

66.67%).

Among 1262 block groups, 113 have missing values or a 0 percent foreclosure rate.

The majority of the block groups (87.30% of the 1149 block groups with observations)

have a foreclosure rate lower than 15%. Only a slight percentage (3.22%) has a

foreclosure rate higher than 25% (see Figure 4.15). When considering the effect of



121

median household income on the distribution of foreclosure rates among those different

block groups, I found that higher income neighborhoods have lower foreclosure rates.

Most of the foreclosures are in neighborhoods with an income range of $20,000–$80,000.

This pattern is very similar to that in Franklin County.

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

>0–1% >1–5% >5–15% >15–25% >25–50% >50–75%

Foreclosure Rate (1997-2004)

% B

l o c k G r o u p s

Figure 4.15: Cuyahoga County Foreclosure Rate Distribution at the Block Group Level(1997–2004)



1 2 2



Figure 4.16: Spatial Distribution of Sheriff’s Deeds in Cuyahoga County (1997–2004)



1 2 3

Figure 4.16 Continued





1 2 4

Figure 4.17: Total Residential Sheriff’s Deeds in Cuyahoga County (1997–2004)



1 2 6

Foreclosure Rate

0.0013 - 0.0100

0.0101 - 0.0500

0.0501 - 0.1500

0.1501 - 0.2500

0.2501 - 0.5000

0.5001 - 0.7500

Figure 4.19: Foreclosure Rates by Block Groups in Cuyahoga County (1997–2



127

Foreclosures in 1983–1989

In order to create necessary time lags when exploring how foreclosures are related

to neighborhood change, Sheriff’s Deeds in 1983–1989 in Cuyahoga County are used.

There were 9,185 Sheriff’s Deeds during this time period. After getting rid of the

duplicated cases there are 8,900 deeds left. Those properties were then merged with 1988

Cuyahoga County parcel data7. There are 7,872 residential properties. After geocoding

there left 7,412 valid cases (460 addresses could not be geocoded, thus could not merge

with the census data). Then those cases are aggregated at block group level, merged with

the census block and place data, and divided by 1990 owner-occupied housing units in a

block group to derive foreclosure rates. The aggregated foreclosure rate in 1983–1989 in

Cuyahoga County is 3.67% (including those block groups with 0 foreclosures) based on

1221 block groups. Forty-one block groups have missing values. The highest foreclosure

rate is 59.02%. The standard deviation of foreclosure rates is 5.50%. Figure 4.20 is the

detailed map of the distribution of foreclosure rates at the block group level in Cuyahoga

County in the time period.



1 2 8

Foreclosure Rate

0.0000 - 0.0100

0.0101 - 0.0500

0.0501 - 0.1500

0.1501 - 0.2500

0.2501 - 0.5000

0.5001 - 0.7500

Figure 4.20: Foreclosure Rates by Block Groups in Cuyahoga County (1983-1



129

Spatial Autocorrelation Analysis

As with Franklin County, both global and local spatial autocorrelation are analyzed

for Cuyahoga County. Univariate and multivariate autocorrelation are analyzed to

explore whether the foreclosure rate in one neighborhood is autocorrelated with the

foreclosure rate in adjacent neighborhoods, and whether the foreclosure rate in one

neighborhood is autocorrelated with other neighborhood indicators in adjacent

neighborhoods.

Connectivity of Block Groups in Cuyahoga County

In Cuyahoga County, most of the block groups are neighboring three to eight other

block groups. There are 350 block groups that have five neighboring block groups. Only

a few block groups have only one and two adjacent block groups; and only a few have

more than 10 adjacent block groups. The weighting matrix is derived based on the

connectivity of block groups and thus is used as the basis for further spatial regression

analysis.



130

Figure 4.21: Connectivity of Block Groups in Cuyahoga County

Global Spatial Autocorrelation

Foreclosure Rate

The spatial autocorrelation of foreclosure rates in different neighborhoods is

significant with a Moran’s I of 0.5588. This means that foreclosure rate is autocorrelated

with the foreclosure rate in neighboring block groups. Therefore, the spatial distribution

of foreclosure rates is not random but is highly clustered. Similar patterns show in the

foreclosure rates between 1997 and 2000 (Moran’s I = 0.4339), and between 2001 and

2004 (Moran’s I = 0.5647). This means that foreclosures become more clustered after

2000.

Foreclosure Rate and Median Housing Value

Looking at the relationship between median housing value in 1990 and foreclosure

rate between 1997 and 2004, we found that these variables are negatively autocorrelated



131

(Moran’s I = -0.3714). This means that median housing value in one neighborhood is

negatively autocorrelated with foreclosure rate in neighboring block groups. Similar

patterns exist when investigating how foreclosure rate between 1997 and 2004 is

autocorrelated with median housing value in 2000 (Moran’s I = -0.3068). These simple

relationships between foreclosure rate and median housing value indicate that those two

variables are not only closely related to each other but also have a spatial relationship.

Median housing value is autocorrelated with foreclosure rate and vise versa; foreclosure

rate is autocorrelated with median housing value in adjacent neighborhoods.

Foreclosure Rate and Percentage Minority Population

Contrary to the relationships between foreclosure and median housing value, the

relationships between foreclosure rate and racial composition of neighboring block

groups are positive, both for the correlation between racial composition and foreclosure

rate (2001–2004) (Moran’s I = 0.5752) and the correlation between foreclosure rate

(1997–2000) and racial composition (Moran’s I = 0.5211). This means that the 1990

racial transition is autocorrelated with foreclosure rate between 1997 and 2004 in

neighboring block groups, and foreclosure rate between 1997 and 2004 is autocorrelated

with racial composition in 2000 in neighboring block groups.

Foreclosure Rate and Housing Vacancy Rate

When looking at both the correlation between housing vacancy rate and foreclosure

rate between 2001 and 2004 (Moran’s I = 0.4170) and the correlation between



132

foreclosure rate (1997–2000) on housing vacancy rate (Moran’s I = 0.3621), we found

that the relationships are positive.

Foreclosure Rate and Homeownership Rate

Unlike in Franklin County, the autocorrelation between foreclosure rate and

homeownership rate is negatively significant, looking either at the correlation between

homeownership rate in 2000 and foreclosure rate (2001–2004) (Moran’s I = -0.3570) or

the correlation between foreclosure rate (1997–2000) and homeownership rate in 2000

(Moran’s I = 0.3024).

Local Spatial Autocorrelation

Local spatial autocorrelation explores the relationship between block groups in

terms of a specific variable, such as foreclosure rate. The analysis of local spatial

autocorrelation of foreclosure rate in Cuyahoga County shows that high-high spatial

autocorrelation exists in the inner-city neighborhoods with low income and a large

percentage minority population (see Figure 4.22). Those neighborhoods have high

foreclosure values and are surrounded by those with high foreclosure rate. Some higher

end neighborhoods that are adjacent to the low income ones present a low-high

autocorrelation. Most of the outlying suburban sections of the county have a low-low

autocorrelation. There are only a few scattered block groups with a high-low

autocorrelation. The autocorrelation is not significant in the inner suburban areas.

Therefore, the neighborhoods with a significant local Moran’s I are located in inner-city,

low income neighborhoods (high-high autocorrelation), inner median to high income



133

neighborhoods (low-high autocorrelation), or outlying suburban median to high income

neighborhoods (low-low autocorrelation).



1 3 4

Figure 4.22: Map of Foreclosure Rate Local Spatial Autocorrelation in Cuyahoga County (



135

Temporal Change in Selected Neighborhood Variables

Generally speaking, the pattern of change in neighborhood variables in Cuyahoga

County is very similar to that in Franklin County. The percentage of black and minority

population is increasing and the percentage of white population is decreasing. The

population is more educated than in 1990. The housing vacancy rate has increased and

the housing cost burden for those with a mortgage has increased dramatically (see Table

4.8).


Unlike Franklin County, the total number of households decreased slightly (0.42%)

for Cuyahoga County. The higher the neighborhood foreclosure rate the higher the

decrease in household formations. Neighborhoods with increased number of total

households are those with a relatively low foreclosure rate (less than 1%). All other types

of neighborhoods experienced loss of households.

There is more increase in black population and less increase in overall percentage

minority population than in Franklin County. This might mean that the County is not as

attractive to all minorities as Franklin County, while it is more attractive to black

population. The biggest increase of black and minority populations concentrate on those

neighborhoods with a foreclosure rate of 5–15%. The percentage of white population has

decreased by 8.30% points, which is similar to the decrease in Franklin County. The

biggest decrease in white population is observed in the neighborhoods with foreclosure

rates of 5–50%.



136

Compared to 1990, the divorced population increased by 1.18% points, but there is

no significant pattern observed at different levels of foreclosure rate. In most block

groups the education level of the residents has increased; for the entire county the

increase is 7.05% points for the population with college and higher education. The only

neighborhoods with a decrease in educational attainment are those several with the

highest foreclosure rate (50–75%).


The unemployment rate in Cuyahoga County decreased by 1.90% points compared

to that in 1990. As with Franklin County, the percentage population employed in a

management occupation is highly related to foreclosure rate, where the higher the

foreclosure rate the lower the increase in percentage population in management

occupations. The percentage population employed in service occupations increased

slightly (by 1.77% points). The median household income increased $1,545.6 (2000

constant value), which is much lower than the increase in Franklin County.


The increase in the median value of owner-occupied housing units is about $16,363.

The housing vacancy rate increased by 0.66% points, and the pattern is not significant

when considering its relationship with foreclosure rate. The homeownership rate

increased by 2.36% points. Compared to Franklin County, the percentage increase of

housing units with a mortgage is much larger at 8.36% points (It is 3.56% points for

Franklin County).



1

3 7

Variable Missing or 0 >0 – 1% >1 – 5% >5 – 15% >15 – 25% >25 – 50%


Total number of Households

-15.40% +6.46%*** -0.9% -4.90%** -11.80%*** -17.10%** -3

Percentage Black Population

-1.60% 0.74%*** +3.89%*** +6.99%*** +4.51%*** -2.50% -4

Percentage MinorityPopulation

0.11%** +2.62%*** +6.94%*** +10.13%*** +6.56%*** -0.20%* -2

Percentage WhitePopulation

-6.70%** -3.70%*** -8.80%*** -13.60%*** -13.40%*** -10.20%* -2

Percentage PopulationDivorced (>16 yearsold)

-2.60% +1.99%*** +1.66%*** 0** 0* -2.80% -9

Percentage Population

with College or Higher Education

+3.45%*** +7.05%*** +6.99%*** +6.44%*** +5.64%*** +2.30%** -1


Unemployment Rate -2.90% -0.80%** -1.40%*** -4.10%*** -3.10% -10.70% -7

Percentage Populationin Service Occupation

+0.45%** +1.15%*** +1.26%*** +1.71%*** -0.80% +0.03%** -3

Percentage Populationin ManagementOccupation

23.63%*** +24.68%*** +20.66%*** +12.77%*** +9.67%*** +9.11%*** -3

Table 4.8: Change of Selected Neighborhood Variables by Groups of Foreclosure Rate in Cuyahoga



1

3 8

Table 4.8 Continued

Percentage Populationunder the PovertyLevel

-4.50% +0.12%** +0.59%*** -2.70%** -4.00% -7.60% -1

Median HouseholdIncome8

-$428.80* +$543.40** +$1,656.1*** +$1,147.50*** +$133.39** -$1,073 -$


Vacancy Rate -2.70% +0.29%*** +0.10%** +0.49%*** +1.55%*** +1.24%** -1

Homeownership Rate -2.40% 1.67%*** +1.87%*** +4.12%*** -0.30%* -5.20% -2

Percentage HousingUnits with a Mortgage

+1.39%** +2.95%*** +8.38%*** +9.85%*** +8.03%*** -7.90% -4

Median Housing Value

(owner-occupied)9

-$4,472 +$15,940*** +$17,166*** +$15,356*** +$14.147*** +$18,858 -$

*** 0.001 significant level ** 0.01 significant level * 0.05 significant level + 0.10 significant level



139

Conclusions

Simple statistical and spatial analysis shows that in both counties new foreclosure

filings and the foreclosure rate measured in this study have increased dramatically since

the mid 1990s. Foreclosures have concentrated in low to moderate income and inner-city

neighborhoods, although suburban areas have seen some increases in recent years. In

both counties foreclosures become a broader county-wide issue over time. Among the

new foreclosure filings there are only about a quarter to a third that finished the

foreclosure process. The reason why many of those new filings did not terminate as

foreclosed properties remains unknown.

The economic situation of the two counties is different with Franklin County having

a stronger economy and a younger and better educated population. Cuyahoga County has

a higher homeownership rate than in Franklin County. When aggregating data and

considering all block groups, the residential foreclosure rate from 1997 to 2004 in

Franklin County is 6.47% and in Cuyahoga County is 6.49%. Block groups with

foreclosures are usually those with a median income between $20,000 and $80,000. The

study found that the educational levels of the population in both counties have increased

greatly, but those neighborhoods with the highest foreclosure rate (50–75%) show

decreases in educational attainment.

One of the significant findings is that in both counties the percentage of the white

population decreased by about 8% between 1990 and 2000. The biggest increase of

minority population occured in those neighborhoods with foreclosure rates in the range of

5–15%. When considering the effect of occupation on foreclosures, the research found

that the percentage population in management and executive jobs negatively relates to the



140

foreclosure rate. The neighborhood unemployment rate is not significantly related to

foreclosures in either county.

The housing vacancy rate has been found to be positively related to the foreclosure

rate. The pace of real income increase does not match the increase in housing value,

leaving more people vulnerable to foreclosures.

Spatial autocorrelation shows that foreclosures clustered in both counties.

Foreclosures in one block group are autocorrelated with foreclosures in neighboring

block groups. Foreclosures are positively autocorrelated with percentage minority

population and housing vacancy rate in neighboring block groups. Foreclosures are

negatively autocorrelated with median housing value of owner-occupied housing units in

neighboring block groups. In Franklin County foreclosures and homeownership rate is

not autocorrelated, but in Cuyahoga County the two are significantly autocorrelated. All

those findings indicate that foreclosures and some neighborhood characteristics are

autocorrelated with each other in neighboring block groups.



142

In Franklin County in 2001–2004 the aggregated average foreclosure rate is 4.27%,

with a maximum of 57.14% and a standard deviation of 6.72% (see Table 5.1).

Cuyahoga County’s foreclosure rate is lower in this time period, with an aggregated

average foreclosure rate of 3.38%. The highest rate is 37.50%. In 1983–1989 the

aggregated average foreclosure rate is 3.67% and the highest rate is 59.02%. Since the

two time periods do not have the same length, those numbers are not precisely

comparable.. Compared to Franklin County, the distribution of foreclosures in Cuyahoga

County is more even, especially in the data period (both 1983–1989 and 2001–2004)

according to the standard deviation. There is a larger percent of block groups that have

been affected by foreclosures (see Table 5.1).

Table 5.2 includes the basic descriptive statistics for all the variables used in this

analysis of the interactive mutual relationships between foreclosures, neighborhood

characteristics, and neighborhood change for Franklin County and Cuyahoga County. We

notice that most of the variables are significantly different between the two counties

(including the foreclosure rates in 2001–2004). In the next sections, we will undertake:

1. Several different regressions to estimate the neighborhood effects on foreclosures

using the 2001–2004 foreclosure rates in each county. These models include OLS, spatial

models and using H-Robust OLS for heteroskedasticity correction. We will run the

models separately for each county. 2. Seemingly Unrelated Regression (SUR) to estimate

the impact of foreclosures on neighborhood change using the 1983–1989 foreclosure

rates in Cuyahoga County only since the comparable data in Franklin County are not

available.



143

Franklin County Cuyahoga CountyVariable

N Mean N MeanDifference

FORECLOSURE (83 –89) 1221 0.0367 0.0011

FORECLOSURE (01 – 04) 880 0.0427 1262 0.0338 -0.0089***

Neighborhood Characteristics and Change

Demographic

BLACK00 882 0.2053 1243 0.3240 0.1187***

BLACK90 880 0.1665 1244 0.2760 0.1095***

MINORITY00 882 0.2707 1243 0.3794 0.3041***

MINORITY90 880 0.1907 1244 0.3041 0.1134***

MALE142400 882 0.0839 1243 0.0653 -0.0190***

FEMALEKID00 880 0.0897 1241 0.1030 0.0132**

FEMALEKID90 880 0.0783 1242 0.0894 0.0111**

DIVORCE00 882 0.1225 1243 0.1166 -0.0060*

DIVORCE90 879 0.1059 1244 0.1012 -0.0050+

COLLEGEH00 881 0.5522 1243 0.4865 -0.0660***

COLLEGEH90 879 0.5029 1244 0.4099 -0.0930***BLACK_D 880 0.0392 1238 0.0481 0.0090+

COLL_D 878 0.0490 1238 0.0755 0.0266***

DIVOR_D 879 0.0167 1238 0.0152 -0.0020

FEMALE_D 878 0.0116 1238 0.0128 0.0012

HH_D 880 1.2630 1242 0.0275 -1.2350+

Economic

INCOME00 ($, 2000) 883 44,177.0793 1262 41,029.2710 -3,148**

INCOME90 ($, 2000) 883 41,777.0000 1262 38,951.0000 -2,826**

UNEMPLOY00 880 0.0508 1240 0.0778 0.0270***

UNEMPLOY90 880 0.0594 1242 0.0936 0.0341***

SERVICE00 880 0.1566 1240 0.1695 0.0129**

SERVICE90 880 0.1312 1240 0.1469 0.0156***

MNGMT00 880 0.3398 1240 0.3149 -0.0250**

MNGMT90 880 0.1371 1240 0.1134 -0.0240***

POVERTY00 880 0.1386 1241 0.1519 0.0133*

POVERTY90 880 0.1368 1244 0.1529 0.0161*

INCOME_D 880 0.4212 1242 0.4376 0.0163

UNEMPLOY_D 878 -0.0088 1236 -0.0145 -0.0060*

POVER_D 878 0.0018 1238 -0.0005 -0.0020

MNGMT_D 878 0.2022 1236 0.2006 -0.0020

SERV_D 878 0.0255 1236 0.0228 -0.0030

Housing

YEARS00 883 1956.6365 1262 1919.7472 -36.89***YEARS90 883 1952.0000 1262 1916.0000 -35.91***

VALUE00 ($, 2000) 883 110,213.7010 1262 105559.0333 -4,655

Continued

Table 5.2: Descriptive Analysis for Franklin County and Cuyahoga County



144

Table 5.2 Continued

VALUE90 ($, 2000) 883 95,283.0000 1262 87,465.0000 -7,818**

HCOSTM00 883 0.2098 1262 0.2275 0.0177***

VACANCY00 880 0.0671 1242 0.0750 0.0079**

VACANCY90 880 0.0612 1242 0.0647 0.0034

TENURE00 880 0.5820 1241 0.6394 0.0574***

TENURE90 880 0.5535 1242 0.6105 0.0571***

MORTGAGE00 846 0.7529 1219 0.6721 -0.0810***

MORTGAGE90 864 0.7100 1231 0.5707 -0.1390***

SMORTGAGE00 846 0.0964 1219 0.0812 -0.0150***

TENURE_D 878 0.0281 1238 0.0286 0.0005

OWNER_D 864 0.8762 1231 0.0309 -0.8450*

VACAN_D 878 0.0060 1239 0.0101 0.0041

VALUE_D 850 0.6380 1218 0.7220 0.0839


Demographic

PBLACK_D 597 0.0214 993 0.0435 0.0221***

PCOLL_D 597 0.0720 993 0.0781 0.0061**

PDIVOR_D 597 0.0145 993 0.0146 0.0001

PFEMALE_D 597 0.0098 993 0.0141 0.0043

PHH_D 598 0.1408 993 -0.0069 -0.1480***

Economic

PINC_D 598 0.3428 993 0.3174 -0.0250***

PUNEMPLOY_D 597 -0.0088 993 -0.0160 -0.0070***

PPOVER_D 597 -0.0107 993 -0.0005 0.0102***

PMNGMT_D 597 0.2236 993 0.1978 -0.0260***

PSERV_D 597 0.0192 993 0.0244 0.0053***

Housing

PTENURE_D 597 0.0355 993 0.0427 0.0072+

POWNER_D 598 0.1644 993 0.0002 -0.1640***

PVACAN_D 597 -0.9937 993 -0.9920 0.0017

PVALUE_D 598 0.5402 993 0.6910 0.1508***

Note: Difference = Mean (Cuyahoga’s) – Mean (Franklin’s)

*** 0.001 significant level

** 0.01 significant level* 0.05 significant level+ 0.10 significant level



145

The following sections will present the research results for the research questions

about the effect of neighborhoods on foreclosures and the impact of foreclosures on

neighborhoods separately. We begin with the comparison between OLS regressions,

spatial regressions and heterscedasticity-corrected regressions to compare the model

results and develop the best prediction of the effect of neighborhoods characteristics on

foreclosure rates. SUR is used to explore how foreclosures contribute to neighborhood

change.

Effects of Neighborhoods on Foreclosure

In order to explore how neighborhood indicators and change affect foreclosure rates

in different block groups the three sets of variables (demographic, economic and housing

characteristics) and the changes in the variables are used in the analysis. For the

foreclosure panel data from 2001 to 2004 static neighborhood characteristics are

measured by the 2000 census block group values. In these models only the variables at

the neighborhood level are used. The demographic characteristics and changes include

indicators of racial composition, family structure, educational attainment, and percentage

divorced population. The effects of percent black population and percent minority

population are separately considered in the model because the two effects might be

different. The economic characteristics and changes include variables related to median

household income, unemployment rate, occupational structure and percentage population

below the poverty line. Housing characteristics and change variables include the median

years that the housing units were built (e.g., 19xx), median housing value of owner-

occupied housing units, average housing cost burden, housing vacancy rate,



146

homeownership rate, and the mortgage status of owner-occupied housing units. Please

refer to the previous section and Chapter 3 for detailed narratives and statistical

descriptions of the selected variables.

As mentioned in Chapter 3, since spatial data are used in the research the effect of

spatial autocorrelation should be tested as the first step of the analysis. Ordinary Least

Square (OLS) methods were used in Geoda to test the spatial dependency of the model.

Then if there are significant spatial autocorrelation effects in the OLS model, spatial lag

or error models have to be tested to see how the spatial autocorrelation has affected the

model results. Originally only the neighborhood change variables are included in the

OLS model to predict foreclosure rates and the effect of spatial autocorrelation. This

research finds that spatial models have significantly improved the model fit by increasing

the log likelihood. When static neighborhood characteristics are added into the model the

effect of spatial autocorrelation still exists, but the effects have been reduced

significantly. This means that spatial autocorrelation is more significant when not

controlling for static neighborhood characteristics.

When combining the two counties and adding a dummy variable standing for each

county I found that the two counties are significantly different in terms of neighborhood

effects on foreclosures. Therefore each county will be separated to run the regression

models to see how the effects differ.

After running the OLS model I found that heteroskedasticity is significant for both

counties. In this situation I used White’s robust standard errors to correct for

heteroskedasticity. Then I compared the results with the spatial models. Thus I will report

results from an OLS model, a model corrected for spatial autocorrelation and a model



147

corrected for heteroskedasticity. At present I am unable to correct for both spatial

autocorrelation and heterscedasticity in the same model. Thus I will focus my analysis

only on the variables that are significant in both the Heteroskedasticity-Robust OLS11

models and the Spatial Models. There is no difference in the coefficients, but there is a

difference in the levels of significance. Thus to make the analysis as conservative as

possible, the significance levels of the variables analyzed are determined based on

whichever model has the lower significance level for a specific variable . In this way I

hope to correct for both heteroskedasticity and spatial autocorrelation even though I am

unable to run a statistical model that simultaneously corrects both. Since this method

corrects for both heteroskedasticity and spatial autocorrelation separately, future work

should formally correct for heteroskedasticity and spatial autocorrelation at the same

time.

Summary of OLS and Spatial Regression Models

The OLS results of Franklin County’s neighborhood effects on foreclosures indicate

that the R 2 is 0.55, which means a relatively good fit. The Jarque-Bera test is used to

check for multicollinearity and the result is significant, which indicates that the R-square

might improve if multicollinearity is reduced. However, the variables were included for

sound theoretical reasons so the research will continue to use them all. Heteroskedasticity

is significant for the model too, which means that the error term does not have a constant

variance and thus the significant heteroskedasticity has violated one assumption of OLS..

Also the variance of the coefficient distribution increases. However, it is usually difficult

to determine the nature of the bias. But the significant heteroskedasticity will not yield



148

biased coefficient estimates. The issue of significant heteroskedasticity can be corrected

or alleviated by redefining the variables, using Weighted Least Squares to re-estimate the

equation, or when working with large samples, using heteroskedasticity-corrected errors

to make the standard errors more accurate (though still biased).

All the indicators of spatial dependence are significant. In this situation both spatial

error and spatial lag models are used to see which one will have the best fit. We notice

(see Table 5.3) that both models have improved the R-square slightly12. The spatial lag

model turns out to have the higher R 2

(0.60) and the spatial lag term is highly significant,

although heteroskedasticity still exists. Among the three models (OLS, spatial error and

spatial lag) the spatial lag model also has the highest log likelihood. Because it is the

strongest model the parameter estimates from the spatial lag model will be analyzed to

see what neighborhood characteristics and changes significantly affect the foreclosure

rates in Franklin County.

Notice that the R 2

is 0.54 for the OLS model for Cuyahoga County (see Table 5.4).

Muticollinearity and Heteroskedasticity are apparent in this regression as they were for

Franklin County. Like Franklin County, when only the change variables are included the

effect of spatial autocorrelation on the foreclosure rates is large. But when the static

neighborhood characteristic variables are added the spatial effect is greatly reduced. All

the indicators for diagnosing spatial dependence (LM lag, Robust LM lag, LM error, and

Robust LM error) are significant. Again, both the spatial error and lag models are tested

to see which one has the best fit. The spatial lag model has the highest log likelihood in

Cuyahoga County as it did in Franklin County. Therefore the spatial lag model is the best

fit, although the three models (OLS, spatial error and spatial lag) yield very similar



149

results (as they did in Franklin County). Notice that change in the homeownership rate

does not affect foreclosure rates in the OLS model while it does in the Spatial Lag Model

(see Table 5.4). This is important because when the spatial autocorrelation of foreclosures

is controlled the homeownership rate is significantly related to foreclosure rates. Thus

considering spatial effects can improve the model results by changing our view of the

neighborhood effects on foreclosures. But the most important reason for using the spatial

models is because they can yield more efficient estimates by accounting for spatial

effects and the effects of related omitted variables.



1

5 0

OLS Model H-Robust OLS Spatial Error Model

CoefficientStd-

Error

t-

ValueCoefficient

Std-

Error

t-

valueCoefficient Std-Error

Constant 0.0082 0.0191 0.43 0.0082 0.0073 1.12 0.0136 0.0188

Demographic Characteristics and Change

BLACK00 0.0276 0.0283 0.98 0.0276 0.0345 0.80 0.0277 0.0293

MINORITY00 -0.0010 0.0265 -0.04 -0.001 0.0290 -0.03 0.0090 0.0271

MALE142400 -0.0390 0.0338 -1.15 -0.039 0.0511 -0.76 -0.0305 0.0341

FEMALEKID00 0.0677+ 0.0350 1.93 0.0677 0.0646 1.05 0.0502 0.0350

DIVORCE00 -0.0617 0.0525 -1.17 -0.0617 0.0708 -0.87 -0.0920+ 0.0516

COLLEGEH00 -0.0664*** 0.0183 -3.64 -0.0664** 0.0201 -3.31 -0.0740*** 0.0182

BLACK_D 0.0047 0.0197 0.24 0.0047 0.0282 0.17 -0.0009 0.0197

COLL_D 0.0357* 0.0180 1.99 0.0357+ 0.0187 1.91 0.0435* 0.0176

DIVOR_D -0.0284 0.0433 -0.65 -0.0284 0.0505 -0.56 -0.0307 0.0418

FEMALE_D 0.0209 0.0357 0.58 0.0209 0.0503 0.42 0.0155 0.0342

HH_D -0.0003 0.0002 -1.23 -0.0003+ 0.0002 -1.91 -0.0003 0.0002

Table 5.3: Comparison of OLS Regression and Spatial Regression of the Effect of Neighborhood Charon Foreclosure Rate in Franklin County (Dependent Variable: Foreclosure Rate)



1

5 1

Table 5.3 Continued

Economic Characteristics and Change

INCOME00 5.09E-007* 2.02E-007 2.525.09E-

07**

1.7759E-07

2.874.01E-

007*2.01E-007

UNEMPLOY00 0.1408** 0.0519 2.71 0.1408 0.1242 1.13 0.1307** 0.0495

SERVICE00 0.1229** 0.0412 2.98 0.1229+ 0.0663 1.85 0.0956* 0.0396

MNGMT00 -0.0038 0.0353 -0.11 -0.0038 0.0340 -0.11 -0.0170 0.0341

POVERTY00 0.0604* 0.0235 2.57 0.0604+ 0.0310 1.95 0.0262 0.0235

INCOME_D 0.0080 0.0049 1.63 0.008 0.0070 1.14 0.0104* 0.0046

UNEMPLOY_D -0.0221 0.0393 -0.56 -0.0221 0.0751 -0.29 -0.0063 0.0373

POVER_D -0.0655** 0.0252 -2.60 -0.0655* 0.0282 -2.32 -0.0372 0.0241

MNGMT_D 0.0053 0.0302 0.18 0.0053 0.0291 0.18 -0.0023 0.0294

SERV_D -0.0438 0.0331 -1.32 -0.0438 0.0469 -0.93 -0.0304 0.0314

Housing Characteristics and Change

YEARS00 -2.77E-005* 1.30E-005 -2.12 -2.77E-05*

1.1963E-

05 -2.32 -1.26E-005 1.29E-005

VALUE00 -1.02E-007* 5.10E-008 -2.00 -1.02E-07*4.9611E-

08-2.06 -5.18E-008 0



1

5 2

Table 5.3 Continued

HCOSTM00 0.0011*** 0.0003 3.79 0.0011* 0.0004 2.47 0.0011*** 0.0003

VACANCY00 0.3375*** 0.0474 7.13 0.3375*** 0.0710 4.75 0.2708*** 0.0474

TENURE00 0.0286* 0.0124 2.31 0.0286* 0.0139 2.05 0.0182 0.0122

MORTGAGE00 0.0188+ 0.0097 1.95 0.0188 0.0148 1.27 0.0114 0.0094

SMORTGAGE000.0315+ 0.0190 1.65 0.0315 0.0310 1.02 0.0357* 0.0178

TENURE_D -0.0944*** 0.0166 -5.69 -0.0944** 0.0297 -3.18 -0.0894*** 0.0158

OWNER_D 0.0005 0.0005 0.95 0.0005 0.0003 1.44 0.0006 0.0005

VACAN_D -0.2132*** 0.0465 -4.58 -0.2132*** 0.0623 -3.42 -0.1610*** 0.0458

VALUE_D -0.0019* 0.0008 -2.56 -0.0019** 0.0007 -2.60 -0.0013+ 0.0007

LAMDA 0.3522*** 0.0457

W-FORECLOSURE

# of Observations 883 883

R-Square 0.55 0.58



1

5 3

Table 5.3 Continued

Log Likelihood13 1486.72 1507.47

Diagnostics for Multicollinearity

Jarque-Bera14 13666.25***

Diagnostics for Heteroskedasticity

Breusch-Pagantest15

2960.84*** 2962.92***

White test16 853.01***

Diagnostics for Spatial Dependence

LM (lag) 79.62***

Robust LM (lag) 53.69***

LM (error) 36.57***

Robust LM(error)

10.64**

Likelihood Ratiotest

41.49***

*** 0.001 significant level** 0.01 significant level* 0.05 significant level+ 0.10 significant level



1

5 4

OLS Model H-Robust OLS Spatial Error Model

CoefficientStd-

Error

t-

valueCoefficient

Std-

Error

t-

valueCoefficient Std-Error

Constant 0.0005 0.0067 0.07 0.0005 0.0011 0.44 0.0123+ 0.0071


BLACK00 0.0289* 0.0130 2.22 0.0289+ 0.0152 1.91 0.0212 0.0143

MINORITY00 0.0067 0.0139 0.48 0.0067 0.0160 0.42 0.0203 0.0150

MALE142400 0.0281 0.0250 1.13 0.0281 0.0425 0.66 0.0246 0.0237

FEMALEKID00 0.0456** 0.0170 2.69 0.0456 0.0392 1.16 0.0392* 0.0164

DIVORCE00 -0.0200 0.0253 -0.79 -0.02 0.0344 -0.58 -0.0110 0.0243

COLLEGEH00 -0.0267* 0.0115 -2.32 -0.0267* 0.0114 -2.34 -0.0281* 0.0115

BLACK_D 0.0203* 0.0085 2.40 0.0203* 0.0103 1.97 0.0182+ 0.0095

COLL_D 0.0079 0.0109 0.73 0.0079 0.0132 0.60 0.0101 0.0105

DIVOR_D 0.0204 0.0200 1.02 0.0204 0.0262 0.78 0.0097 0.0191

FEMALE_D -0.0054 0.0175 -0.31 -0.0054 0.0345 -0.16 -0.0040 0.0167

HH_D 0.0013 0.0019 0.68 0.0013 0.0026 0.49 0.0006 0.0018

Table 5.4: Comparison of OLS Regression and Spatial Regression of the Effect of Neighborhood Charon Foreclosure Rate in Cuyahoga County (Dependent Variable: Foreclosure Rate)



1

5 5

Table 5.4 Continued


INCOME00 3.52E-7*** 1.06E-7 3.313.52E-

07***

9.9591E-08

3.53 2.92E-7** 1.04E-7

UNEMPLOY00 -0.0176 0.0249 -0.71 -0.0176 0.0421 -0.42 -0.0126 0.0237

SERVICE00 -0.0108 0.0177 -0.61 -0.0108 0.0293 -0.37 -0.0228 0.0172

MNGMT00 -0.0305 0.0207 -1.47 -0.0305 0.0251 -1.22 -0.0204 0.0199

POVERTY00 0.0557*** 0.0154 3.61 0.0557* 0.0263 2.11 0.0374* 0.0152

INCOME_D -0.0016 0.0023 -0.69 -0.0016 0.0033 -0.49 -0.0020 0.0022

UNEMPLOY_D 0.0183 0.0167 1.09 0.0183 0.0346 0.53 0.0216 0.0159

POVER_D -0.0368** 0.0139 -2.64 -0.0368+ 0.0211 -1.74 -0.0341* 0.0135

MNGMT_D 0.0023 0.0171 0.14 0.0023 0.0191 0.12 -0.0066 0.0165

SERV_D -0.0115 0.0137 -0.84 -0.0115 0.0209 -0.55 -0.0039 0.0132


YEARS00 2.31E-006 5.82E-006 0.39 2.31E-06

8.6598E-

06 0.27 2.05E-006 5.95E-006

VALUE00 -4.16E-008 2.77E-008 -1.50 -4.16E-082.7978E-

08-1.49 -3.68E-008 0



1

5 6

Table 5.4 Continued

HCOSTM00 0.0587*** 0.0132 4.44 0.0587* 0.0243 2.42 0.0381** 0.0128

VACANCY00 0.0466* 0.0228 2.05 0.0466 0.0482 0.97 0.0546* 0.0221

TENURE00 -0.0163* 0.0068 -2.40 -0.0163+ 0.0086 -1.88 -0.0117+ 0.0068

MORTGAGE000.0181** 0.0063 2.89 0.0181+ 0.0107 1.70 0.0057 0.0061

SMORTGAGE00-0.0129 0.0124 -1.04 -0.0129 0.0158 -0.82 -0.0001 0.0120

TENURE_D -0.0211 0.0131 -1.62 -0.0211 0.0162 -1.30 -0.0343** 0.0123

OWNER_D -0.0117*** 0.0033 -3.54 -0.0117+ 0.0066 -1.77 -0.0086** 0.0031

VACAN_D 0.0287 0.0200 1.43 0.0287 0.0430 0.67 0.0163 0.0195

VALUE_D 0.0033* 0.0013 2.48 0.0033 0.0028 1.18 0.0036** 0.0013

LAMDA 0.3535*** 0.0384

W-FORECLOSURE

# of Observations 1262 1262

R-Square 0.54 0.58

Log Likelihood17 2657.05 2686.90



1

5 7

Table 5.4 Continued

Diagnostics for Multicollinearity

Jarque-Bera18 18287.26***

Diagnostics for Heteroskedasticity

Breusch-Pagantest19

1974.68*** 1667.17***

White test20 1157.61***

Diagnostics for Spatial Dependence

LM (lag) 107.72***

Robust LM (lag) 72.70***

LM (error) 51.25***

Robust LM(error)

16.23***

Likelihood Ratiotest

59.71***

*** 0.001 significant level** 0.01 significant level* 0.05 significant level+ 0.10 significant level



158

The following narrative will report the variables that are significant for both the

Heteroskedasticity-Robust OLS and Spatial Lag Model, so that we can make the results

more efficient by correcting for each problem separately but considering their results

together.

In Franklin County, when the Robust OLS model and the spatial lag models are both

used none of the change variables among the demographic characteristics are significant.

However, one static neighborhood characteristic is significant in both models and thus is

related to foreclosures (see Table 5.3).

In addition racial composition in 2000, which was assumed to affect foreclosures, is

not significant for Franklin County, when controlling for other factors. The percentage

divorced population does not affect foreclosures either. The only one demographic

variable that is significant is percentage population with college degree or higher and the

relationship, as expected, is negative.

None of the economic change variables have an effect on foreclosure rates in

Franklin County. Median household income in 2000 has a positive relationship with

foreclosures in both models. This seems strange since higher income should be associated

with lower foreclosure rates. Perhaps this is the remaining effect of median income once

educational level is accounted for. Poverty level and percentage population employed in

executive or management occupation do not have a significant impact on foreclosure

rates. However, percentage population employed in service occupations is significant and

positive in both of the models we are considering.

More changes in housing characteristics have significant impacts on foreclosure

rates in neighborhoods in Franklin County than any other variable type. Change in the



159

homeownership rate, change in the housing vacancy rate, and change in housing value

are all negatively related to foreclosure rates. This means that high turnover and change

in housing characteristics in 1990–2000 are related to foreclosure rates in 2001–2004 in

Franklin County. The change in housing vacancy rates is negatively related to

foreclosures and this might mean that more dynamic housing markets have fewer

foreclosures. Among static housing characteristics in 2000 the average housing cost

burden and the housing vacancy rate are significant factors affecting foreclosure rates and

both are positive in both the H-Robust model and the spatial lag model. Thus

neighborhoods where residents pay significant portions of their income, and those with

high vacancy rates are at more risk of foreclosures in Franklin County.

In Cuyahoga County, the effect of neighborhood demographic characteristics and

change on foreclosure is very similar to that in Franklin County. Educational attainment

in 2000 is the one of the major factors in both equations, with the expected negative sign.

But in Cuyahoga County percentage black population and its change are also significant

(and positive) in affecting foreclosures, although the significance level is 0.10. The

potential reason for this difference between the two counties might be the difference in

racial composition between the two counties. Cuyahoga County has a larger percent of

black population than Franklin County. So either the larger proportion or more

segregation could cause this effect. The standard deviation of percentage black

population is larger than that in Franklin County too, which means that black population

in Cuyahoga County is more clustered than in Franklin County. Percentage minority

population does not have a significant effect on foreclosures in either county.



160

Cuyahoga County’s results for the impact of economic neighborhood characteristics

on foreclosure rates are slightly different from those for Franklin County. The percentage

population below the poverty line (positive) and its change (negative) has significant

impacts on foreclosures in Cuyahoga County unlike Franklin County. Similar to Franklin

County, median household income has a positive relationship with foreclosures. The

change in percentage population below the poverty line is negatively related to

foreclosures and this might be because of collinearity, omitted variables, and some other

reasons. While in Franklin County, occupational structure (percentage population

employed in service occupations) also affects foreclosures, but this has no significant

impact in Cuyahoga County.

Average housing cost burden, Homeownership rate, change in number of owner-

occupied housing units, and percentage of housing units with a mortgage are all

significant factors affecting foreclosures in Cuyahoga County in both models. This is

very different from Franklin County. In fact, average housing cost burden is the only

common factor for both counties regarding the effect of housing characteristics and

change on foreclosures.

Common Neighborhood Characteristics Affecting Foreclosures in Both Counties

Educational Attainment

The importance of residents’ educational attainment in determining neighborhood

quality has been stated in previous chapters. Educational attainment is highly related to

median household income, housing value, and other neighborhood or personal indicators.

But when controlling for most of those indicators educational attainment still remains



161

significant. This might mean that educational attainment is more important than other

factors in contributing to foreclosure. Another explanation of why educational attainment

in a neighborhood is related to foreclosure might be because the higher the educational

attainment of a household the more possible it is for them to have a prime loan. They will

also be less likely to become the victims of predatory lending. The models found that the

higher the educational attainment of the residents in a neighborhood the lower the

foreclosure rate in that neighborhood for both counties, controlling for other factors.

Median Household Income

We expected that median household income will be negatively related to

foreclosures. But this research finds that in both counties median household income is

positively related to foreclosure rates. This seems strange. But this might be because

although a neighborhood has a higher income the housing cost burden can be high, thus

contributing to more foreclosures. As suggested above, this may be a residual effect after

educational attainment is taken into account. Another explanation is that householders

with lower income in a higher income neighborhood might be more likely to default on

their properties and thus increased the foreclosure rates in that neighborhood. However,

those explanations should not ignore the possible effect of omitted variables and other

factors in the regression.

Average Housing Cost Burden

The average housing cost burden is measured by the ratio of housing expenses (with

a mortgage) to monthly income in the neighborhood. The results indicate that a higher



162

housing cost burden has a positive effect on foreclosure rates. In the spatial lag model in

Franklin County a one percentage point increase (or decrease) in the average housing cost

burden is related to a foreclosure rate increase (or decrease) of 0.0011% points. This

effect is not very large but it is significant at the 0.001 level (see Table 5.3). In Cuyahoga

County when the housing cost burden is 1% point higher (or lower) foreclosure rates

increase (or decrease) by 0.0391% points (see Table 5.4). This result is consistent with

that in Franklin County. A large housing cost burden seems to be a consistent indicator

of neighborhoods with higher foreclosure risks.

Difference in the Effect of Neighborhood Characteristics on Foreclosures in Each

County

Franklin County

Percentage Labor Force Employed in Service Occupation

The use of the variable percentage labor force employed in service occupations in

this research is intended to explore how service occupations, which are often associated

with lower paying and/or unstable jobs, affect foreclosure rates in neighborhoods. The

results in Franklin County indicate that service employment is positively related to

foreclosure rates. The variable was not significant in Cuyahoga County.

Housing Vacancy Rate

The housing vacancy rate is one of the most important indicators of the health of

housing markets. Higher housing vacancy rates usually mean a surplus of housing supply

compared to housing demand. Housing vacancy is necessary in the housing market since

in many situations the housing absorption rate cannot be as high as 100% (if it were



163

mobility would be severely restricted). A moderate to low housing vacancy rate usually

will not have a significant negative effect on the housing markets (thought it might drive

up prices). But a high housing vacancy rate indicates a housing market that is unhealthy.

This research found that the housing vacancy rate has a significant positive impact on the

foreclosure rate in one of the two study areas, but not the other. This means that

neighborhoods with a weak housing market will have much higher foreclosure rates.

Change in Homeownership Rate

The homeownership rate in a neighborhood should be related to foreclosures

because previous research found that homeownership is highly related to neighborhood

quality and stability. We find that the change in the homeownership rate is negatively

related to foreclosures in Franklin County only (see Table 5.3). This means that increases

in the homeownership rate will lower the foreclosure rate in a neighborhood in that

county.

Change in Housing Vacancy Rate

The research results indicate that the higher the change in housing vacancy rate in a

neighborhood, the lower the foreclosure rates in Franklin County. This means that the

increases in vacancy rates in a neighborhood are negatively related to foreclosure rates.

This seems counter intuitive, but might indicate that the more dynamic a neighborhood

housing market is the less likely the neighborhood will have a high foreclosure rate.



164

Change in Median Housing Value

In the literature housing value is found to have a strong impact on foreclosures,

especially for individual homeowners. Many scholars believe that negative home equity

is one of the factors leading to mortgage default decisions of the borrowers and it can

arise from negative appreciation and low house values. However this study examines

neighborhoods, not individual owners and found that the median housing value does not

have an effect on foreclosure rates at the neighborhood level, holding other things

constant. However, in Franklin County the change in median housing value has a

negative relationship with foreclosure rate. When the average housing values increase

foreclosure rates decrease. This argues that change in value is more critical than the

average value in a neighborhood in Franklin County and an upward trajectory is

important as we would expect.

Cuyahoga County

Percentage Black Population and Change

We found that racial composition and turnover (especially percentage black

population and change in percentage black population) have a significant positive impact

on foreclosures in Cuyahoga County. The more racial turnover (i.e. increase in

percentage black population), the higher the foreclosure rate, which means that racially

stable neighborhoods will have a relatively lower foreclosure rate, though stable white

neighborhoods have lower foreclosure rates than stable black neighborhoods.



165

Percentage Population below the Poverty Line and Change

Percentage population below the poverty line has a positive impact on foreclosures

in Cuyahoga County. Therefore, poorer neighborhoods are often associated with higher

foreclosure rates in Cuyahoga County, but not in Franklin County.

A higher poverty level in 2000 is associated with higher foreclosure rates in the later

time period, but the more the poverty rate increased between 1990 and 2000, the smaller

that effect. This might mean that when there is more population below the poverty line,

housing affordability will decrease thus fewer people will have mortgages. Foreclosure

rates will decrease with the decrease in affordability.

Homeownership Rate and Change in Percentage Owner-occupied Housing Units

The homeownership rate has a negative relationship with foreclosure rates. This

result is different from that in Franklin County because in Franklin County the

homeownership rate itself does not have an impact on foreclosures. The change in

percentage owner-occupied housing units is also found to be positively related to

foreclosure rates in Cuyahoga County. The effect is similar to that of the change in

homeownership rate.

Percentage Housing Units with a Mortgage

This indicator has a significant effect on foreclosures in Cuyahoga County but not in

Franklin County. The larger the percentage owner-occupied housing units with a

mortgage, the higher the foreclosure rates (see Table 5.4). In contemporary U.S. society

the majority of owner households hold a mortgage. In Franklin County the



167

Summary: Effects of Neighborhood Characteristics on Residential Mortgage

Foreclosure

Comparing the research results from the two counties we found that the same

economic characteristics and change variables do not significantly affect foreclosures

(see Table 5.5). This is consistent with our initial expectations because the economic

situations of the two counties are different, thus the neighborhood effects on foreclosures

might be different. However, the counties have some similarities in terms of the effect of

neighborhood characteristics and change, although some of the variables have different

impact on foreclosures in the two counties.

For both counties percentage population with college degrees or higher has a

negative impact on foreclosures. Therefore, educational attainment at the neighborhood

level is the common and important factor contributing to neighborhood foreclosures.

Educational attainment is related to many other factors. More education leads to higher

and more stable income, and the higher the educational attainment the less likely that the

residents will be the victims of mortgage fraud and/or predatory lending, thus lowering

the foreclosure rates.

For both counties median household income has a positive impact on foreclosures,

which is difficult to explain. However, this might be related to the increasing housing

cost burden. The increase in housing value and costs associated with owning a home is

much larger than the increase in median household income, thus the increase in median

household income is positively related to foreclosures. At the same time the increase in

median household income will also make it easier for homeowners to get mortgages in



168

these neighborhoods, and people may stretch to purchase the most expensive houses thus

using mortgage types invented in the late 1990s which are more risky.

For both counties housing cost burdens has a positive impact on foreclosures. This is

consistent with our expectations and detailed narratives of the rationales can be found in

previous sections.



169

Note: + (0.05) = sign (significant level)

Table 5.5: Variables that are Significant in Each County

Franklin

County

Cuyahoga

County


BLACK00 + (0.10)

COLLEGEH00 - (0.01) - (0.10)

BLACK_D + (0.10)


INCOME00 + (0.05) + (0.01)

SERVICE00 + (0.01)

POVERTY00 + (0.05)

POVER_D - (0.10)


HCOSTM00 + (0.05) + (0.05)

VACANCY00 + (0.001)TENURE00 - (0.10)

MORTGAGE00 + (0.10)

TENURE_D - (0.01)

OWNER_D - (0.10)

VACAN_D - (0.001)

VALUE_D - (0.05)

Spatial Lagged Foreclosure Rate

W-FORECLOSURE + (0.001) + (0.001)



170

There are some differences in neighborhood effects on foreclosures between the

two counties (see Table 5.5).

Percentage black population is related to foreclosures in Cuyahoga County but not

in Franklin County. This might be because the two counties have different racial

characteristics. In Cuyahoga County there is a larger percent of black population and the

distribution of the black population in different block groups is more segregated. Change

in percentage black population is the only change variable in demographic characteristics

that is significant for Cuyahoga County, although not for Franklin County.

Economic effects are quite different for the two counties. In Franklin County the

occupational structure has impacts on foreclosures, while in Cuyahoga County the

poverty rate (percentage population below the poverty line) and its change play dominant

roles in affecting foreclosures. It is not clear why the percentage population below the

poverty line affects foreclosures, especially when controlling for unemployment rate,

occupational structure and median household income. Probably it is because high poverty

in the neighborhood may make it unlikely that houses there will be worth anything thus

householders are more likely to walk away. The change in this percentage has a negative

relationship with foreclosure rates. This might mean that when there is a higher decrease

in percentage population below the poverty line there will be a lower foreclosure rate.

Among the housing characteristics and change variables (except average housing

cost burden), all significant variables are different between the counties. In Franklin

County, housing vacancy rates have a significant positive impact on foreclosures. This is

not surprising since higher vacancy rates are usually associated with neighborhoods with

decreased life and housing quality. The decreased housing quality will lower the housing



171

values and thus might force the homeowners to default on the property. On the other

hand, neighborhoods with higher vacancy rates will have more people with lower income

these people may more easily become victims of foreclosures. Change in homeownership

rate, change in housing vacancy rate, and change in median housing value are all

negatively related to foreclosures in Franklin County. In Cuyahoga County foreclosure is

negatively related to the homeownership rate, although at a 0.10 significance level.

Neighborhoods with more renters are usually associated with lower incomes and this

becomes a factor contributing to neighborhood foreclosures. The percentage of housing

units with a mortgage also has a positive significant effect on foreclosures. Similar to the

effect of homeownership rate on foreclosures change in owner-occupied housing units

has a negative impact on foreclosures.

The effect of the change in housing value on foreclosures has mixed results in the

two counties. In Franklin County it has the expected negative impact on foreclosures,

which means that the larger the increase in housing value, the lower the foreclosure rate,

and the higher the decline in housing value the higher the foreclosure rate. In Cuyahoga

County median housing value does not have a relationship with foreclosures.

Neighborhood effects on foreclosures share some common factors between the two

counties, but each county has also shown some different issues. The effect of racial

composition and turnover only exists in Cuyahoga County and is very important to

explore further. Change in housing value has a different effect on foreclosures in the two

counties. The importance of educational attainment, median household income, and

housing cost burden to foreclosures is consistent between the two counties. Although we

cannot conclude that those factors affect foreclosures equally or universally in other



172

counties in or out of Ohio, this research serves as a good first step in understanding the

neighborhood factors contributing to foreclosures in a neighborhood. A detailed

conclusion and policy implications based on these research results will be presented in

Chapter 6.

So far I have analyzed the effect of neighborhood characteristics and change on

foreclosure rates. In the next section I turn to the impact of foreclosure rates on

neighborhood change.



173

The Impact of Residential Mortgage Foreclosure on Neighborhood Change:

A Seemingly Unrelated Regression (SUR) Approach

The impact of residential mortgage foreclosure on neighborhood change is

measured by predicting the effect of foreclosures in 1983–1989 in Cuyahoga County on

neighborhood change in 1990–2000. Since Sheriff’s Deeds in 1983–1989 in Franklin

County are not available or not geographically identifiable, only Cuyahoga County’s data

will be used to test whether mortgage foreclosure has an impact on neighborhood

changes. The results of the previous section indicate significant differences between the

two counties, so it is unfortunate that we cannot study Franklin County. However, the

research methodology can be duplicated in the future when testing the impact in other

geographic areas.

As mentioned in the research methodology section SUR systems can estimate

regression coefficients more efficiently than single-equation OLS regressions (Zellner,

1962) when certain assumptions are met. This research compares the results of OLS and

SUR, and finds that SUR can better predict the equation systems than OLS due to the

correlated cross-model errors (see Table 5.6 for the Cross Model Covariance Matrix in

Cuyahoga County) since it considers the correlation between error terms of the equations.

In SUR procedures all coefficients are estimated simultaneously by using Aitken’s

general least squares (GLS). The goodness of fit of the SUR system is generally based on

the weighted system R-square and weighted mean square error (MSE) of the equation

systems. In order to minimize the determinant of the error covariance matrix Iterated

SUR 21 (ITSUR) was used in this research. All fourteen neighborhood change variables

are used as dependent variables in the SUR system. The independent variables are chosen

based on the correlation coefficients of those variables with the change variables. The



174

ones with a coefficient lower than 0.02 are omitted from the equation system. Although

independent variables in all other equations are the subsets of the ones in the equation of

median housing value (as the dependent variable), ITSUR will still be used to include the

effects of correlated error terms between equations. In ITSUR estimation, other factors,

such as spatial effects, might be omitted variables, and thus can bias the error structure.

The OLS and ITSUR estimations yield similar parameter estimates. Due to

correlated residuals between the equations, ITSUR is more efficient than OLS and

produces more accurate estimates than OLS. When running the ITSUR system, the

weighted R-square is 0.4104 and the Weighted MSE is 1.0000, with 11,276 degrees of

freedom. The model fits relatively well with the data. The results indicate that foreclosure

rates have a relationship with educational attainment, change in percentage divorced

population, change in female headship rate, change in percentage population below the

poverty line, change in homeownership rate, change in housing vacancy rate, and change

in median housing values (see Table 5.7 for details). All of the signs are consistent with

our expectations expect for the negative relationship of foreclosure rates with change in

housing vacancies and the positive relationship of foreclosures with change in property

values. Future investigations should focus on understanding these results.



1 7 5

BLACK_D COLL_D DIVOR_D FEMALE_D HH_D INCOME_D

BLACK_D 0.010146 -.000391 0.000006 0.002190 -.000408 -.001299

COLL_D -.000391 0.005776 -.000051 -.000115 0.002255 0.002930

DIVOR_D 0.000006 -.000051 0.001581 0.000203 -.000645 -.000591

FEMALE_D 0.002190 -.000115 0.000203 0.003671 -.000524 -.003213

HH_D -.000408 0.002255 -.000645 -.000524 0.314843 -.000363

INCOME_D -.001299 0.002930 -.000591 -.003213 -.000363 0.071090

UNEMPLOY_D 0.000709 -.000532 -.000089 0.000598 -.001453 -.003270

POVER_D 0.001388 -.001132 0.000253 0.001468 -.001785 -.009223

MNGMT_D -.000498 0.002719 -.000080 -.000399 0.000210 0.001749

SERV_D 0.000815 -.000959 -.000016 0.000462 0.000513 -.001802

TENURE_D -.000696 0.000299 -.000344 -.001127 -.003801 0.005347

OWNER_D -.002565 0.002221 -.001085 -.001868 0.087901 0.011377

VACAN_D 0.000421 -.000193 0.000037 0.000089 -.000245 0.000171

VALUE_D -.002666 0.001252 -.001221 -.000930 0.007705 0.010164

POVER_D MNGMT_D SERV_D TENURE_D OWNER_D VACAN

BLACK_D 0.001388 -.000498 0.000815 -.000696 -.002565 0.0004

COLL_D -.001132 0.002719 -.000959 0.000299 0.002221 -.0001

DIVOR_D 0.000253 -.000080 -.000016 -.000344 -.001085 0.0000

FEMALE_D 0.001468 -.000399 0.000462 -.001127 -.001868 0.0000

Table 5.6: Cross Model Covariance Matrix for Cuyahoga County



1 7 6

Table 5.6 Continued

HH_D -.001785 0.000210 0.000513 -.003801 0.087901 -.0002

INCOME_D -.009223 0.001749 -.001802 0.005347 0.011377 0.0001

UNEMPLOY 0.001122 -.000062 -.000009 -.000524 -.002604 0.0000

POVER_D 0.005798 -.000254 0.000436 -.001630 -.003185 0.0002

MNGMT_D -.000254 0.006618 -.001707 0.000159 0.001453 -.0003

SERV_D 0.000436 -.001707 0.004211 -.000422 -.001630 0.0002

TENURE_D -.001630 0.000159 -.000422 0.006603 0.013371 -.0000

OWNER_D -.003185 0.001453 -.001630 0.013371 0.128129 -.0013

VACAN_D 0.000291 -.000350 0.000237 -.000073 -.001351 0.0015

VALUE_D -.000401 0.002442 0.000757 0.001457 0.055470 0.0005



1 7 7

Dependent Variable COLL_D DIVOR_D FEMALE_D POVER_D TENURE_D V

ROOT MSE (σ) 0.0760 0.0398 0.0606 0.0761 0.0813

INTERCEPT 0.1190 0.3627 -0.0492 0.7368 0.0506

FORECLOSURE(83–89) -0.1682* -0.0699+ 0.2563*** 0.1459* 0.1670*

Neighborhood Characteristics

Demographic

BLACK90 0.0373 0.0673* 0.1262** -0.1254* -0.0164

MINORITY90 -0.0403 -0.0596* -0.1266** 0.1338* 0.0268

FEMALEKID90 -0.1401** -0.0796** -0.5158*** 0.0904+ 0.0513 0

DIVORCE90 -0.0766 -0.8414*** 0.0623 0.0669 -0.0350

COLLEGEH90 -0.4443*** -0.0858*** -0.1225*** -0.0234

Economic

INCOME90 1.53e-7 -2.46e-7 1.48e-7 1.23e-6***

UNEMPLOY90 -0.0279 0.1699*** 0.2247*** -0.0764

SERVICE90 0.0807+ 0.0181 -0.0163 0.0169 0.0386

MNGMT90 0.1115 -0.0677* -0.0131 -0.0541

POVERTY90 -0.0340+ -0.0486+ -0.7100*** -0.0195 0

Table 5.7: ITSUR Estimate Results with “FORECLOSURE” (as an independent variable) SignifR-Square: 0.4104; System Weighted MSE: 1.0000)

22



1 7 8

Table 5.7 Continued

Housing

YEARS90 -0.0001 0.0001+ 0.0001

VALUE90 4.17e-7*** -1.06e-7+ -8.74e-8 -4.51e-8

VACANCY90 -0.0473 0.1398* 0.1104+ -0

TENURE90 0.0008 -0.0688*** -0.0265* -0.1036*** -0.1371*** -0

MORTGAGE90 0.0121 0.0141+ -0.0225 -0


Demographic

PBLACK_D 0.2796* 0.0389 0.1285 0.0352

PCOLL_D 0.1782 -0.1029 -0.3364+ -0.0587 0.2108

PDIVOR_D 0.2181 0.6753** 0.0637 0.3289 -0.1913

PFEMALE_D -1.1866* -0.0614 0.5197 -0.5063

PHH_D -0.3208 0.1022 0.4887 0.0604

Economic

PINC_D -0.0160 -0.0536 0.0227 0.0640 0.1259

PUNEMPLOY_D -0.4558 -0.8730 0.1899



1 7 9

Table 5.7 Continued

PPOVER_D -0.4454 -0.0308 -0.2043 0.4194 -0.0795

PMNGMT_D 0.5262*** 0.1040* 0.1523* -0.0689

PSERV_D -0.4456 -0.2809 0.0390 0.1722

Housing

PTENURE_D 0.3850 0.4911 0.2745

POWNER_D 0.2524 -0.1190 -0.0005 -0.5103

PVALUE_D 0.0225 0.0517 -0.0200 -0.0562 -0.1090

PVACAN_D -0.2136 0.3548 -0.1501 0.7613

*** 0.001 significant level ** 0.01 significant level* 0.05 significant level + 0.10 significant level



180

Change Variables Associated with Foreclosures

As mentioned before there are seven change variables that are significantly related

to foreclosures: change in educational attainment (negative), change in percentage

divorced population (negative), change in female headship rate (positive), change in

percentage population below the poverty line (positive), change in homeownership rate

(positive), change in housing vacancy rate (negative), and change in median housing

value (positive). The foreclosure rate has different levels of significance in those

regression equations. In all other equations where the dependent variables are the other

change variables, foreclosure rate is not significant. The estimate results of those latter

equations are in Appendix D (Table D.1).

Change in Percentage Population with College or Higher Education (>25 years old)

The importance of residents’ educational attainment to the quality and stability of a

neighborhood has been stated in previous sections. In this analysis the research found that

foreclosure rates are negatively related to the change in percentage population with

college degrees or higher. This means that higher foreclosure rates in the previous time

period are associated with a lower increase (or a larger decrease) in the percentage

population with college degrees or higher. This is consistent with our expectations

because educational attainment is highly related to other household characteristics, and

thus can be related to foreclosures. Neighborhoods with higher foreclosures are usually

associated with poor neighborhood quality. Neighborhood decline associated with

foreclosures will be less attractive to populations with higher education attainment.

It is also possible that people with higher educational attainment move out of



181

neighborhoods because of the expected impact of the rising foreclosure rates on housing

values, crime and other neighborhood indicators. On the other hand, the lower increase or

larger decrease in educational attainment associated with higher foreclosure rates might

be because the neighborhoods with higher foreclosure rates have characteristics that

attract people with lower educational attainment, thus the in-movement of those people

will lower the general educational attainments of the residents in those neighborhoods.

Change in Percentage Divorced Population (>16 years old)

It is interesting to see that foreclosures are associated with the change in percentage

divorced population in a negative way. This means that higher foreclosure rates in the

previous time period are related to lower increase (or higher decrease) in the percentage

divorced population in a neighborhood. There are several potential explanations for this

phenomenon.

The first one is that more divorced people had their homes foreclosed and moved

out of the neighborhoods. Foreclosures can be caused by the financial shock of a divorce

and divorced householders may be at more risk of other financial problems. On the other

hand, it is also possible that the foreclosed homes are attractive to singles or married

couples, especially those first-time homebuyers who can only afford the discounted price

of those foreclosed houses. Another possibility would be gentrification pioneers (those

who are not divorced) purchasing the foreclosed properties from which divorced

households move out.



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Change in Female Headship Rate

The female headship rate is widely used to measure neighborhood quality (Galster

and Krall, 2003) and how welfare policy and benefits affect family structure (especially

the formation of female-led households) (Moffitt, 2000). Female-led households are

vulnerable to various financial and housing hardships, and are often the victims of

predatory lending (CRL, 2004). According to the Center for Responsible Lending (CRL),

female-led households also account for a larger share of subprime loans than of prime

loans. Given all these entire issues associated with female-led households, it is not

surprising to see that foreclosures have a positive relationship with the change in female

headship rate. This means that higher foreclosure rates in the previous time period are

associated with faster increases (or slower decreases) in the percentage of female-led

households.

Higher foreclosures usually happen in neighborhoods with higher percentage

female-led households. And many of those households are minority, especially black

households. Because those female-led households are in a more vulnerable financial

situation on average they are more likely to become victims of foreclosure. Even if many

of those householders are renters, the clustering of this type of households is often

associated with poor neighborhood quality. In such a neighborhood if a homeowner gets

in trouble there is less reason to try to work out the problem and it is more likely that the

homeowners will give up the property to foreclosure and move away. When the higher

foreclosure rates and the concentration of female-led households are highly associated

with each other, creates an even more vulnerable environment for female-led households.

Policy makers might want to pay more attention to neighborhoods with concentrations of



183

female-led households. It might also be a good idea to initiate programs to help those

households in dealing with the stress and impacts of foreclosures.

Change in Percentage Population below the Poverty Line

The research found that foreclosures have a positive relationship with the change in

percentage population below the poverty line. This means that higher foreclosures

between 1983 and 1989 are associated with a larger increase (or a slower decrease) in the

percentage population below the poverty line during the subsequent decade. Thus higher

foreclosures are related to the neighborhoods that are more likely to have a concentration

of population below the poverty line.

Higher foreclosures can make a neighborhood less attractive to people with higher

income because of the decreased quality of houses and neighborhoods. On the one hand

higher foreclosures will increase housing vacancy rates when homeowners are forced to

move out of the neighborhood because of foreclosures or simply because of the

deteriorated neighborhood and housing quality. The loss of homeowners can increase the

percentage population below the poverty line in the neighborhoods. On the other hand,

even if the foreclosed houses are occupied again, the new owners or renters might be

those with lower income, even those below the poverty line, since the home is likely to be

sold at a discount. This can contribute to the increase in percentage population below the

poverty line. Homeowners who have not been foreclosed are also likely to leave

neighborhoods with high foreclosure rates and those homes may be subdivided for rental

to lower income groups.



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Change in Homeownership Rate

The importance of homeownership has been stated by many scholars in their

research, and the interaction between the homeownership rate and foreclosures is very

important and can have significant policy implications. This research has very important

findings in terms of the relationships between the two. Both the homeownership rate and

the change in the rate have been found to affect foreclosure rates in the preceding

analysis of neighborhood effects on foreclosures. In this section the research found that

foreclosures in a previous time period are associated with the later change in the

homeownership rate in a positive manner. This means that an increase in foreclosure rates

is associated with an increase or slower decrease in the homeownership rate. This seems

to be the opposite of what we expected. This might mean that higher foreclosures in some

neighborhoods will cause housing values to drop, thus attracting lower income people

moving into those neighborhoods to become homeowners, even to occupy the previously

vacant properties or rental properties. This is a very important possibility given how often

we try to get more owners in a neighborhood by getting poorer people (more likely to

have a foreclosure) into owner-occupied houses in those neighborhoods.

When foreclosure rates rise in a neighborhood there will be more vacant housing

units. Many of those houses (even those who are not foreclosed in the research time

period) are purchased by people. Therefore, the neighborhood may gain some owners

because of the foreclosure. Another possible reason is that gentrification can happen in

those neighborhoods with higher foreclosures (and lower values), and thus the process of

gentrification can improve the homeownership rate in those neighborhoods.



185

On the other hand, if a neighborhood is experiencing decreasing homeownership

rate, increasing foreclosure rates will slow down the decreasing process.

Foreclosures being positively associated with increases in homeownership rates in

Cuyahoga County, is difficult to explain. It may be accurate with the some possible

explanations listed above or there may be omitted variables that were not controlled.

Further investigation can be helpful if there are data available in other geographic areas,

or if there are other important variables to include.

Change in Housing Vacancy Rate

The housing vacancy rate has been found to positively contribute to foreclosures in

previous neighborhood effects research. In this analysis of the impact of foreclosures on

neighborhood change the results indicate that foreclosures have a negative relationship

with housing vacancy rates. This means that higher foreclosures in a previous time period

are associated with slower increases (or faster decreases) of housing vacancy rates in the

subsequent decade. This is also an unexpected result, similar to the relationship between

foreclsoures and the change in the homeownership rate. It might be because higher

foreclosures will bring investors to those foreclosed properties (including the ones

foreclosed beyond the research time period) and those properties are converted to rental

properties (with higher value than they had as owner occupied properties – especially if

subdivided). Another explanation could be the effect of redevelopment and/or

gentrifications. These processes will reduce vacant housing units in those neighborhoods.

Concentrations of foreclosures (higher foreclosure rates) might attract various kinds of

investors to inexpensive properties. If these investors are gentrifiers, in particular, the



186

time gap between the two sets of data used in this analysis could allow for improvements

in the units, decreases in vacancy rates and increases in property values.

Change in Median Housing Values

As mentioned in the previous section of neighborhood effects research, the

relationship between the change in median housing value and foreclosures has different

results in the two counties. In Franklin County, change in median housing value is

negatively related to subsequent foreclosure rates, but in Cuyahoga County change in

median housing value is positively related to subsequent foreclosure rates. In this analysis

of the impact of foreclosures on later housing values we found that foreclosures have a

positive relationship with change in median housing value in Cuyahoga County. This

seems odd and deserves more research. Although we argue that urban redevelopment

policies and gentrification might cause this effect, it is also possible that omitted

variables, time differences between the data sets and data collection errors might also

contribute to this unexpected phenomenon.

The results indicate that higher foreclosures are associated with a larger increase (or

a slower decrease) in median housing values. In this situation, when two neighborhoods

both have an increased median housing value, the one with a higher foreclosure rate will

be associated with a larger increase in the value (might be associated with subsequent

revitalization or gentrification processes). When two neighborhoods both have a

decreased median housing value a higher foreclosure rate is associated with a slower

decrease in median housing value (might be associated with increased housing values due

to renovated foreclosed properties). If one neighborhood has an increased median



187

housing value and the other one has a decreased median housing value, the neighborhood

with a higher foreclosure rate is the one with an increased median housing value.



188

Conclusion: The Interaction between Residential Mortgage Foreclosure,

Neighborhood Characteristics, and Neighborhood Change

The interaction between residential mortgage foreclosure and neighborhood

characteristics and change is very complicated and there are many factors related to the

issue. However, this research finds some very interesting phenomena among the

relationship. The two elements interact with each other in terms of specified

neighborhood indicators and their changes. The research results do not support all of the

initial hypotheses, and so contradict some previous research on the topic.

First of all, neighborhood characteristics and change in the immediately proceeding

time period affects foreclosure rates. Many factors are involved, although some of the

effects are different for the two study counties. Common factors affecting foreclosures

for the two counties are percentage population with college degrees or higher, median

household income, and average housing cost burden. In Cuyahoga County percentage

black population and the change in this percentage has a positive relationship with

foreclosures. However, foreclosure does not affect the change in percentage black

population in Cuyahoga County. This finding is very different from what Baxter and

Lauria (2000) found in the relationship between foreclosures and racial turnover in New

Orleans. It is difficult to determine which finding is more appropriate because there are

many differences in social-economic characteristics in different places. If some of the

characteristics are not controlled there will be inconsistent results from the analyses. The

results may also indicate the importance of local context to the impact of foreclosure.

There are also some unique attributes in the neighborhood effects of each county.

When looking at demographic characteristics and change we found that percentage black



189

population and the change in percentage black population has an impact on foreclosures

in Cuyahoga County, which is not the case in Franklin County. From the point of view of

economic characteristics and change, percentage labor force employed in service

occupation affects foreclosures in Franklin County, while percentage population below

the poverty line and the change in the percentage affect foreclosures in Cuyahoga

County. Besides the common housing factor change in homeownership rate, change in

housing vacancy rate and change in median housing value are all negatively affect

foreclosures in Franklin County, while homeownership rate, percentage housing units

with a mortgage, and change in owner-occupied housing units affect foreclosures in

Cuyahoga County. The reasons for these differences between counties need further

exploration.

When we examine whether and how foreclosures affect neighborhood change in

Cuyahoga County using the 1983-1989 foreclosure data we found that there are seven

change variables from 1990 to 2000 that are significantly related to those earlier

foreclosures: change in educational attainment (negatively), change in percentage

divorced population (negatively), change in female headship rate (positively), change in

percentage population below the poverty line (positively), change in homeownership rate

(positively), change in housing vacancy rate (negatively), and change in median housing

value (positively). Among those relationships the ones between change in

homeownership, change in housing vacancy rate, and change in median housing value are

different from what we expected. We argued that perhaps neighborhood revitalization,

renovation of the foreclosed properties, and/or gentrification might contribute to those

relationships. Also data collection errors, omitted variables, the correlation between the



190

change variables at the neighborhood level, the relatively long time span between the

beginning of one data set and the end of the other and spatial effects might also make the

estimated results somewhat difficult to interpret. Thus using simultaneous spatial models

might resolve some of those issues. But it is difficult to find an instrument variable to

help identify the simultaneous equations. Also incorporating spatial effects into

simultaneous equation models might be very challenging. These would be fruitful areas

for future research.

If we draw a diagram to see how neighborhood characteristics and change interact

with foreclosures (see Figure 5.1) we find that change in percentage population below the

poverty line is the only factors with mutually interactive relationship with foreclosures in

Cuyahoga County. Other neighborhood characteristics and change variables only interact

with foreclosures in a one way direction. However, we notice that the change in median

housing value negatively affects foreclosures in Franklin County, but in Cuyahoga

County it is not related to foreclosures. This is very interesting and deserves further

investigation. In Cuyahoga County the change in percentage population below the

poverty line yields opposite signs in the neighborhood effects analysis versus the impact

analysis of foreclosures. It is negatively related to foreclosures when we examine them as

precursors to the foreclosure rate. When foreclosures are used to explain neighborhood

change variables (foreclosure rates in an earlier time period affect neighborhood change

in a later time period) the effect of foreclosure rates is positive. Please refer to the

previous detailed narratives for potential reasons explaining this phenomenon.



1 9 1

Note:

[1] The red arrows mean that the interaction works for both counties.[2] The thick arrows mean that the variables are mutual interactive with foreclosures in Cuyahoga C

BLACK

COLLEGEH

BLACK_D

COLL_D

DIVOR_D

POVER_DPOVERTYSERVICE

Foreclosure

FEMALE_D

INCOME

Figure 5.1: Summary of the Interaction between Residential Mortgage Foreclosure and Neighborhood C



192

CHAPTER 6

CONCLUSIONS, POLICY IMPLICATIONS AND

FUTURE RESEARCH DIRECTIONS

As mentioned in previous chapters the relationship between residential mortgage

foreclosure and neighborhood characteristics and change is very complicated and is

related to many aspects of the issue. However, this research has helped us understand

more about the relationship. The research methodology and results can provide useful

insights to both theory and policy in foreclosure research. The research can also serve as

a pilot study for larger future research projects. At the beginning the research proposed

three research questions and several sets of hypotheses. The findings answered most of

the questions, although not all hypotheses were supported.

The first research question is whether and how neighborhood characteristics and

change affect foreclosures. Our results indicate that neighborhood characteristics and

change affect foreclosures in a profound manner. First we examined the spatial patterns

of foreclosure rates and found that foreclosures rates are spatially autocorrelated across

neighboring block groups. This indicates that foreclosures may be spatially contagious.

We would also expect that foreclosures in one time period will be spatially autocorrelated

with neighborhood indicators in neighboring block groups in the following time period.

We also found strong heteroskedasticity in the data sets of both counties. The



193

neighborhood effects in the two study counties share some common attributes, although

there are disparities between the results for the two counties as well.

The independent variables were divided into three categories of neighborhood

characteristics and change, demographic, economic and housing. These three categories

all affect foreclosures in a similar way in both counties through the variables educational

attainment (demographic), median household income (economic) and average housing

cost burden (housing). There are some differences in the neighborhood effects on

foreclosures between the two counties. The most important difference that is related to

our hypothesis is that racial composition and turnover affect foreclosures only in

Cuyahoga County. Change in median housing value has a positive effect on foreclosures

in Franklin County but there is on effect in Cuyahoga County. The detailed explanation

can be found in Chapter 5.

The second question is whether and how foreclosures affect neighborhood change.

Our results found that foreclosures in a previous time period do affect some

neighborhood change indicators for the subsequent decade. Higher foreclosure rates are

related to increases in the less educated population, female-led households and the poor

population in neighborhoods. Foreclosure rates are negatively related to the percentage

divorced population.

The relationships between foreclosure rates and the change in homeownership rate,

the change in housing vacancy rate, and the change in median housing value in Cuyahoga

County have results that are not consistent with our expectations. We expected that

foreclosures would hinder the increase in homeownership rates, but the research results

indicate that foreclosure rates are positively related to the change in homeownership



194

rates. We also expected that foreclosures would aggravate housing vacancy issues in a

neighborhood, but the results indicate that foreclosure rates are negatively related to the

change in housing vacancy rate. We expected that foreclosures would decrease housing

value appreciation or speed up depreciation, but the results indicate that foreclosure rates

are positively related to the change in median housing values. While data issues may be a

problem, revitalization and gentrification of declining neighborhoods and investor

behavior (subdivision, renovation or flipping of the foreclosed properties) might help

explain the research results as well. In the future when data from more geographic areas

are available more research can be done to test whether the effects happen in other places

and what contextual variables affect the relationships. At the same time a spatial

simultaneous equation model might be constructed and estimated with the same dataset.

However, it will be very challenging to identify the estimation techniques related to the

spatial simultaneous equation models. Resolving identification problems associated with

simultaneous equations becomes more difficult when there are a large number of related

variables and non-recursive causal relationships in the model.

The third question asked at the beginning of the research is related to developing a

model that can separate the two effects. The use of panel data made it possible to separate

the effect of neighborhood characteristics on foreclosures from the effect of foreclosures

on neighborhoods, and the use of spatial analysis, spatial regression, heteroskedasticity

correction and Seemingly Unrelated Regression (SUR) contributed significantly to the

research methodology on this topic. However, even if we use panel data (foreclosures in

1983-1989 affect neighborhood change from 1990 to 2000; and then neighborhood

characteristics in 2000 and change from 1990 to 2000 affect foreclosures in 2001-2004)



195

other factors, such as policy change, omitted variables, and collinearity between the

variables, will affect the estimation results. As indicated above, a goal for future research

should be to combine the separate effects in a simultaneous equation model.

When looking at the sets of hypotheses in detail we found that the findings

supported some of the initial expectations but not others.

The research finds that foreclosures concentrate in certain neighborhoods over time

and strong spatial autocorrelation in foreclosure rates exists. These findings support our

expectations.

In terms of the interaction between foreclosures and change in housing value the

research did not support the idea that housing value depreciation contributes to

foreclosures, although change in housing value does negatively affect foreclosures in

Franklin County. This means that the drops in housing value in the earlier time period are

associated with increases in the foreclosure rates as we would expect. The SUR found

that foreclosure rates in the earlier time period significantly and positively contribute to

the change in median housing value in a neighborhood in Cuyahoga County. This is very

different from what we expected. Therefore, the interaction between housing value and

foreclosures at a neighborhood level is more complex than that the literature indicates

and needs further investigation.

When exploring the effect of racial composition on foreclosure rates the research

found that percentage black population and its change affect foreclosures only in

Cuyahoga County. In Franklin County, racial composition does not directly contribute to

foreclosure rates. On the other hand, the SUR found that foreclosures do not affect the

change in racial composition of a neighborhood in Cuyahoga County. Therefore, the



196

research does not support our initial hypothesis that racial factors would contribute to

foreclosures in both counties (it is not true in Franklin County), nor do the findings

support our expectations that foreclosures affect racial turnover of a neighborhood. So the

findings of the relationship between racial composition and foreclosures are not

consistent with research by Baxter and Lauria (2000) in New Orleans. This might

because some place-related characteristics are not controlled in either or both of the

studies, thus the results are different. For example, New Orleans might also be more

racially segregated than Cuyahoga County, and thus foreclosure will acerbate the

segregation. It is important that future research consider multiple geographic areas and

their racial contexts in order to shed more light on this issue.

For both counties median household income is positively related to foreclosures.

This does not support our initial hypothesis that median household income would be

negatively related to foreclosures. Perhaps the increase in housing cost burden has offset

the benefits of gaining income, thus higher income will be related to higher foreclosure

rates. On the other hand, foreclosure rates do not affect the change in income in our

Cuyahoga County analysis.

Housing vacancy rates in an earlier time period are significantly related to

foreclosure rates in Franklin County in the later time period in a positive way, as we

expected. But foreclosure rates in the earlier period are negatively associated with the

change in the housing vacancy rate in Cuyahoga County during the subsequent decade.

The two counties are different in terms of demographic, economic and housing

characteristics and change. Therefore the interaction between residential mortgage

foreclosure and neighborhood characteristics and change is different, although there are



197

some common findings in both counties. In particular, the economic and racial context of

the two counties underlies some of the differences that we have discovered.

The research has answered most of the research questions, and some results indicate

important directions for future research.



198

Policy Implications

Concern about foreclosure and its impact on homeowners and neighborhoods has

promoted research and policy innovations in recent years. One of the purposes of this

project is to provide information to policy makers to help in understanding the

relationship between residential mortgage foreclosure and neighborhood characteristics

and change. Therefore, the research findings have significance in helping address

foreclosure issues.

For both counties foreclosures have concentrated in certain neighborhoods. These

are usually inner city areas, although there are some scattered cases stretching to the

suburbs, especially in later years. Therefore policy makers should pay particular attention

to those neighborhoods with clustered foreclosure cases. However, since there are many

factors affecting foreclosures and the factors vary somewhat between counties, each of

those neighborhoods should have tailored programs for foreclosure prevention. For both

counties, educational attainment is one of the demographic factors related to foreclosure

rates. Policy makers can try to promote educational attainment and it can be incorporated

into community development policies. If the effect of educational attainment on

foreclosures lies in the fact that more educated people do not easily become the victims

of predatory lending, then financial education to the residents might help prevent

foreclosures, and all neighborhood residents would benefit, especially in those

neighborhoods with low educational attainment.

The research found that decreasing housing cost burden in a neighborhood is related

to decreasing foreclosure rates. Policy makers might initiate funds to help alleviate

housing cost burdens, especially for highly cost burdened homeowners in high cost



199

burden neighborhoods. Decreasing housing vacancy rates in a neighborhood is also

related to decreasing foreclosure rates and also helps diminish the clustering of foreclosed

homes. Policy can encourage redevelopment for areas with high vacancy rates, or

encourage real estate investors to purchase vacant properties. This latter policy would

have to guard against flippers and predatory lenders who could simply make the problem

worse. The goals of all those policies cannot be achieved in a short time and there might

be many obstacles in implementing the policies. However, the research provides a

foundation for policy makers to refer to when making policy changes and it argues for

policies aimed at neighborhoods, not just policies aimed at individual homeowners. This

is particularly important because of the clear spatial clustering and probable contagion

effect in foreclosures.

As the impact of foreclosure on neighborhood change variables indicates, change in

female headship rate and change in homeownership rate are positively related to

foreclosures in Cuyahoga County. These relationships could lead to increased involuntary

income segregation or concentration of the low income population in neighborhoods with

high foreclosures (and thus weakens housing appreciation and other market indicators).

Policy makers can focus on the neighborhoods with an increasing percentage of female

headship rate and/or an increase in percentage population below the poverty line with

pre-foreclosure prevention and post foreclosure remedial programs (see Figure 6.1 and

Figure 6.2 for the change in female headship rate and the change in percentage

population below the poverty line). However, this does not mean that we should not put

any efforts into other neighborhoods.



200

Generally speaking foreclosure prevention should not be the same in all places. Each

neighborhood has unique characteristics and patterns of change and each county has a

unique economic and demographic context. Therefore, when we seriously work to

prevent foreclosures we need to have an individualized program for each neighborhood

that is vulnerable to foreclosures. The research has provided some specified findings in

terms of what neighborhood characteristics and change variables will affect foreclosure

rates, and what neighborhood change variables are affected by foreclosures. Hopefully

the significance of the research for policy making can be recognized in this way.



2 0 1

Change in Femaile Headship Rate (% points)

-30.59% - 0.0000

0.01% - 10.00%

10.01% - 20.00%

20.01% - 30.00%

30.01% - 50.00%

Figure 6.1: Change in Female Headship Rate in Cuyahoga County (1990–2000, %



2 0 2

Change in Percentage Population Below the Poverty Line (% points)

-0.4590 - 0.0000

0.0001 - 0.1000

0.1001 - 0.2000

0.2001 - 0.3000

0.3001 - 0.5000

Figure 6.2: Change in Percentage Population below the Poverty Line in Cuyahoga Coun



203

Future Research Directions

Since this research explores the mutual interaction between residential mortgage

foreclosure and neighborhood characteristics and change, the research results can be used

as the foundation for a Structural Equation Model that incorporates the separate effects

into one model. Thus the methodology can be changed to see how consistent the results

will be. Ideally spatial effects would be considered although this makes the

methodological task even more challenging. This should be the first step in future

research on the topic. However, when exploring the impact of foreclosures on

neighborhood change, a spatial simultaneous model can be separately constructed, if

feasible, thus resolving some issues in the current SUR model (such as the omitted

variables, non-recursive relationships, and others).

Another path to use in studying the impact of foreclosures on neighborhood change

could focus on using spline regression models to capture the “threshold effects”. These

possible effects suggest that the relationships are not continuous, but rather that there is

little effect from a particular variable until a “threshold” is reached and then the effect is

relatively large. For example, it would be valuable to find out at what point foreclosures

will contribute to neighborhood change positively, or negatively, or at what point

neighborhood characteristics become important to foreclosures.

As far as other methodological issues go, future research should try to find a viable

approach for running the spatial regression models controlling for both heteroskedasticity

and spatial autocorrelation at the same time, and then compare the results with those in

this research. The purpose of the comparison is to see if separately controlling for

heteroskedasticity and spatial autocorrelation yields similar results as running the model



204

by controlling the two problems simultaneously. Ideally we should also pay attention to

other issues associated with the spatial regression models, such as omitted variables, data

improvements, and multicollinearity.

Many of the research findings are very interesting, but need further investigation.

One of the future research directions is to find out why some neighborhood

characteristics and changes contribute to foreclosures and others do not. And on the other

hand, why certain neighborhood change variables are related to foreclosures but others

are not. When we want to know the relationship of an individual neighborhood factor and

foreclosures we can only focus on one factor, such as the relationship between racial

turnover and foreclosure, the relationship between the female headship rate and

foreclosure, or the relationship between the homeownership rate and foreclosure.

The biggest problem in this research lies in data and time limitations. Sheriff’s real

estate sales data and court records can be easily accessed through many approaches, but

they usually do not have the data format that an academic researcher needs. To rebuild

the data consumes time. Therefore, given time and budget, future research can use those

data and merge them with multiple years’ Home Mortgage Disclosure Act (HMDA) data

and county property transaction data to find out how loan and borrowers’ characteristics,

as well as housing attributes at the loan’s origination can affect foreclosure. This will be

much more accurate than simulations of default probability using HMDA data at the loan

origination such as those performed by Ambrose and Sanders (2002). The research can

expand to using data from other states in the U.S. to find out the relations between real

mortgage default and loan and borrowers’ characteristics at the loan origination. The

result will provide more reliable underwriting standards for lenders. In addition,



205

research should examine the relationship between foreclosure filings and sheriff sale

properties. What happens to the properties that are foreclosed but do not go to sheriff’s

sales and do they have different relationships with neighborhood variables?

Another aspect of the future research is to track the addresses of the borrowers

whose properties were foreclosed and conduct a survey to explore the reasons why they

defaulted, the impact of the default on them and where they moved. This will be very

helpful to learn how borrower characteristics affect default decisions and foreclosure

risks.

The tracking of addresses includes both tracking where the borrowers go after

foreclosure and civic real estate sales, and also who buys the foreclosed properties and

what they do with them. The negative impact of foreclosure on a homeowner is very

significant. After losing their homes borrowers will have different tenure and moving

selections. Whether they choose to rent or buy again (and over what time period) and

where they move will affect neighborhood changes greatly, in both their previous

neighborhoods and their future neighborhoods. There are a variety of other questions

about those who have defaulted and lost their homes to foreclosure. There is no

assistance program helping them recover from the financial and emotional stress from

foreclosure. The other interesting set of questions refers to those who bought the

foreclosed properties. What are the buyers’ characteristics and what impact do they have

on the neighborhoods in which the properties are located? Other questions, such as how

foreclosure affects children’s school outcomes and individual development can also be

explored. All those questions are very challenging and interesting but no literature has

addressed them.



206

Another direction for the future research is to find out why lenders choose

foreclosure, not other alternatives to resolve a troubled mortgage. Default decisions are

usually made by borrowers. But it is up to lenders to choose ways to resolve a troubled

loan. Is the decision based on cost effectiveness, or other reasons? Is there any racial or

geographic bias when lenders choose whose mortgage will be foreclosed, whose

mortgage will be modified, when there will be foreclosure sales, or when other

alternatives will be preferred?

Since the research in foreclosure and its impact on borrowers, lenders and the

neighborhoods is not mature, there are many directions for research related to

foreclosures. The research reported here provides a first step and thus contributes to the

scanty literature in this field.



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APPENDIX A

FORECLOSURE PROCEDURES



2 0 8

StateSecurity

InstrumentJudicial Non-Judicial Initial Step

ProcessPeriod (Days)

SalePublication

(Days)

RedemptionPeriod(Days)

Sale

Alabama Mortgage • • Publication 49-74 21 365 Trustee

Alaska Trust Deed • • Notice of Default 105 65 365* Trustee

Arizona Trust Deed • • Notice of Sale 102 41 None Trustee

Arkansas Mortgage • • Complaint 70 30 365* Trustee

California Trust Deed • • Notice of Default 117 21 365* Trustee

Colorado Trust Deed • • Notice of Default 91 14 75 Trustee

Connecticut Mortgage • Complaint 62 NA Court Decides Court

Delaware Mortgage • Complaint 170-210 60-90 None Sheriff District of Columbia Trust Deed •

Notice of Default 47 18 None Trustee

Florida Mortgage • Complaint 135 NA None Court

GeorgiaSecurityDeed • • Publication 37 32 None Trustee

Hawaii Mortgage • • Publication 220 60 None Trustee

Idaho Trust Deed • • Notice of Default 150 45 365 Trustee

Illinois Mortgage • Complaint 300 NA 90 Court

Table A.1: Legislation Requirement of Mortgage Foreclosure in Different States in the U.S.



2 0 9

Table A.1 Continued

Indiana Mortgage • Complaint 261 120 None Sheriff

Iowa Mortgage • • Petition 160 30 20 Sheriff

Kansas Mortgage • Complaint 130 21 365 Sheriff

Kentucky Mortgage • Complaint 147 NA 365 Court

Louisiana Mortgage • Petition 180 NA None Sheriff

Maine Mortgage • Complaint 240 30 90 Court

Maryland Trust Deed • Notice 46 30 Court Decides Court

Massachusetts Mortgage • • Complaint 75 41 None Court

Michigan Mortgage • Publication 60 30 30-365 Sheriff

Minnesota Mortgage • • Publication 90-100 7 1825 Sheriff

Mississippi Trust Deed • • Publication 90 30 None Trustee

Missouri Trust Deed • • Publication 60 10 365 Trustee

Montana Trust Deed • • Notice 150 50 None Trustee

Nebraska Mortgage • Petition 142 NA None S

Nevada Trust Deed • •

Notice of

Default 116 80 None Trustee



2 1 0

Table A.1 Continued

NewHampshire Mortgage •

Notice of Sale 59 24 None Trustee

New Jersey Mortgage • Complaint 270 NA 10 Sh

New Mexico Mortgage • Complaint 180 NA 30-270 Co

New York Mortgage • Complaint 445 NA None C

NorthCarolina Trust Deed • •

NoticeHearing 110 25 None Sheriff

North Dakota Mortgage • Complaint 150 NA 180-365 She

Ohio Mortgage • Complaint 217 NA None Sheriff

Oklahoma Mortgage • • Complaint 186 NA None Sheriff

Oregon Trust Deed • • Notice of Default 150 30 180 Trustee

Pennsylvania Mortgage • Complaint 270 NA None Sheriff

Rhode Island Mortgage • • Publication 62 21 None Trustee

SouthCarolina Mortgage • Complaint 150 NA None Court

South Dakota Mortgage • • Complaint 150 23 30-365 Sheriff

Tennessee Trust Deed • Publication 40-45 20-25 730 Trustee

Texas Trust Deed • • Publication 27 NA None Trustee



2 1 1

Table A.1 Continued

Utah Trust Deed • Notice of Default 142 NA Court Decides Trustee

Vermont Mortgage • Complaint 95 NA 180-365 Court

Virginia Trust Deed • • Publication 45 14-28 None Trustee

Washington Trust Deed • • Notice of Default 135 90 None Trustee

West Virginia Trust Deed • Publication 60-90 30-60 None Trustee

Wisconsin Mortgage • • Complaint 290 NA 365 Sheriff

Wyoming Mortgage • • Publication 60 25 90-365 Sheriff

Major Source: www.realtytrac.com



212

Foreclosure Procedure in Ohio

Judicial Foreclosure Available: Yes

Non-judicial Foreclosure Available: No

The Ohio standard mortgage provides for a conditional transfer of title to the lender.If the borrower pays the principal and interest; performs the obligations of the mortgage,including payment of taxes, assessments and hazard insurance and does not commitwaste, then the borrower will obtain full title at the end of the mortgage term. Ohiomortgages must be foreclosed by court action.

LawsuitThe lender must sue the borrower in the county where the property is located. The

lender must ask the court to foreclose the mortgage and order a sale of the property.

Sale ProceduresWhen land is to be sold under a foreclosure order, the officer conducting the sale

shall call upon three disinterested freeholders of the county to give an estimate of thevalue of the property. A copy of the appraised value must be left with the court clerk. The property must forthwith be offered for sale at a price of not less than two-thirds of theappraisement.

Advertising

The land will not be sold until the officer handling the foreclosure gives publicnotice of the sale by advertising the time and place of the sale at least 30 days in advanceof the sale. The advertisements will be sufficient if they are published once a week for three consecutive weeks before the day of the sale, with each ad on the same day of theweek.

Method of SaleThe sheriff handles foreclosure sales in Ohio . The officer will sell to the highest

bidder at the time and place indicated in the advertised notice. The sale must take place atthe courthouse. If the bidder fails to pay the price, the court "shall punish as for contemptany purchaser of real property who fails to pay the purchase money therefore." If there is

no sale for lack of bidders, then the court may order a new appraisement and order thesale for one-third in cash and the balance later.

ConfirmationThe sheriff returns the writ of execution indicating that a sale was made to the

court, which upon examination of the sale proceedings to make sure they were inconformity with the law and with the court orders, enters into its records a confirmation



213

of the legality of the sale and directs the officer who made the sale to create and deliver the purchaser a deed for the property.

Special ProceduresIf the property is in danger of being damaged the court may appoint a receiver to

take charge of it.

DeficiencyA deficiency judgment may be lender along with the order commanding a

foreclosure sale. The deficiency is void two years after the foreclosure sale is confirmed.However, the enforcement may continue if the debtor signs an agreement to postpone theenforcement past two years.

RedemptionThe debtor can redeem by paying the amount of the judgment plus costs and

interests up until the confirmation of sale, but not afterward.

Source: http://www.defaultresearch.com



214

APPENDIX B

TOTAL SHERIFF’S DEEDS AT THE SCHOOL DISTRICT LEVEL INFRANKLIN COUNTY



2

1 5

SCHOOL DISTRICT NAME23

Code 1997 1998 1999 2000 2001 2002 2003 2

BEXLEY CSD 2501 2 5 5 2 3 5 2

CANAL WINCHESTER LSD 2502 - - 4 10 11 25 21

COLUMBUS CSD 2503 379 699 648 892 936 1192 1402 1

DUBLIN CSD 2513 8 15 10 13 16 25 30

GAHANNA-JEFFERSON CSD 2506 15 10 23 18 28 29 43

GRANDVIEW HEIGHTS CSD 2504 - 1 - 2 - - 2

GROVEPORT MADISON LSD 2507 21 33 31 50 68 80 111

HAMILTON LSD 2505 4 11 16 33 28 32 39

HILLIARD CSD 2510 15 21 22 42 41 48 68

PLAIN LSD 2508 1 5 3 5 3 5 9

REYNOLDSBURG CSD 2509 7 14 10 14 19 19 31

SOUTH-WESTERN CSD 2511 48 111 128 143 178 203 243

UPPER ARLINGTON CSD 2512 5 2 7 3 3 7 13

WESTERVILLE CSD 2514 18 29 29 34 42 49 56

WHITEHALL CSD 2515 11 18 17 27 18 27 25 WORTHINGTON CSD 2516 17 15 11 12 22 15 24

PICKERINGTON LSD 2307 - - - - - - 3

LICKING HEIGHTS LSD 4505 - - 2 3 6 10 18

JONATHAN ALDER LSD 4902 - - - - - - -

MADISON-PLAINS LSD 4904 - - - - - - -

NEW ALBANY-PLAINS LSD 2508 - - - - - - -

OLENGANTY LSD 2104 - - - - - - -

TEAYS VALLEY LSD 6503 - - - - - - -

Table B.1: Total Sheriff’s Deeds at the School District Level in Franklin County (1997-2004, Note: can’t be identified at the school district level)



216

APPENDIX C

SPATIAL AUTOCORRELATION OF SELECTED VARIABLES



217

Figure C.1: The Local Spatial Autocorrelation between Female Headship Rate in 2000and Foreclosure Rate (2001–2004) in Franklin County



218

Figure C.2: The Local Autocorrelation between Median Household Income in 2000 andForeclosure Rate (2001–2004) in Franklin County



219

Figure C.3: The Local Autocorrelation between Housing Cost Burden with a Mortgage in2000 and Foreclosure Rate (2001–2004) in Franklin County



221

Figure C.5: The Local Autocorrelation between Housing Vacancy Rate in 2000 andForeclosure Rate (2001–2004) in Franklin County



222

Figure C.6: The Local Autocorrelation between Homeownership Rate in 2000 andForeclosure Rate (2001–2004) in Franklin County



2 2 3

Figure C.7: The Local Spatial Autocorrelation between Female Headship Rate in 2000 and Foreclosure RCuyahoga County



2 2 4

Figure C.8: The Local Autocorrelation between Median Household Income in 2000 and Foreclosure RatCounty



2 2 5

Figure C.9: The Local Autocorrelation between Housing Cost Burden with a Mortgage in 2000 and ForeCuyahoga County



2 2 6

Figure C.10: The Local Autocorrelation between Median Housing Value of Owner-Occupied Housing URate (2001–2004) in Cuyahoga County



2 2 7

Figure C.11: The Local Autocorrelation between Housing Vacancy Rate in 2000 and Foreclosure Rate (2County



2 2 8

Figure C.12: The Local Autocorrelation between Homeownership Rate in 2000 and Foreclosure Rate (20County



229

APPENDIX D

SUR MODEL RESULTS



2 3 0

Dependent Variable BLACK_D HH_D INCOME_D UNEMPLOY_D MNGMT_D SE

ROOT MSE (σ) 0.1007 0.5611 0.2666 0.0502 0.0814

INTERCEPT 0.1556* -5.9269 1.3293 -0.0969 0.5746

FORECLOSURE (83–89) 0.1245 0.1368 -0.1131 0.0586 -0.0787

Neighborhood Characteristics

Demographic

BLACK90 0.0707 -0.1498 0.1717 0.0126 -0.0084

MINORITY90 -0.1830* 0.0753 -0.2557 0.0151 0.0122

FEMALEKID90 -0.0247 -0.0641 0.1207 0.1147** -0.1989***

DIVORCE90 0.0956 1.0426* -0.3568 -0.0742 -0.2018**

COLLEGEH90 -0.1392*** 0.4006+ 0.4419*** -0.0910*** 0.3399***

Economic

INCOME90 2.07e-7 -1.72e-6 -0.00001*** 3.63e-7+ 1.10e-7

UNEMPLOY90 -0.0374 -0.3259 -0.3225+ -0.9598*** -0.0183

SERVICE90 -0.0478 0.3180 -0.0091 0.0343 0.0356 -

MNGMT90 0.0325 -0.7804+ 0.5247** 0.0339 -0.8970***

POVERTY90 -0.0940+ -0.1742 1.1051*** 0.1195*** 0.1123**

Table D.1: ITSUR Estimate Results (where “FORECLOSURE” is not significant)



2 3 1

Table D.1 Continued

Housing

YEARS90 0.0007 -0.0018*** 0.00009+ -0.0001

VALUE90 -2.04e-7 -1.11e-6 1.53e-6*** -9.35e-8 6.75e-7***

VACANCY90 0.2106** 0.2779 -0.0880 0.0967* -0.0002

TENURE90 -0.0666* 0.4421*** -0.0225 0.0016

MORTGAGE90 -0.0284 0.1602 -0.0477 0.0164 -0.0378*


Demographic

PBLACK_D 1.1124*** -0.2587 -0.0021 0.1003

PCOLL_D -0.4186 1.2331 -0.2124 0.4316

PDIVOR_D -0.4131 0.0930 0.3211 -0.2409

PFEMALE_D -0.2475 0.0448 -0.9977 -0.1725 -0.4667

PHH_D 0.3125 1.6735 0.7695 0.0163 0.0239

Economic

PINC_D -0.0058 -0.3259 -0.0526 0.0936 -0.0136

PUNEMPLOY_D -0.0880 -4.0054 -0.8215 -0.1036 -0.2433



2 3 2

Table D.1 Continued

PPOVER_D -0.4732 2.8284 0.1346 -0.0142 0.2956

PMNGMT_D 0.1819* -0.2665 0.2160 -0.0011 0.5423***

PSERV_D 0.1597 -2.1254 -0.3965 0.1749 0.1827

Housing

PTENURE_D 0.4972 1.4083 0.0389 0.2790

POWNER_D -0.3872 1.2556 -0.8065 -0.0338 -0.0899

PVALUE_D 0.0241 0.2966 0.0762 -0.0814 0.0071

PVACAN_D -4.5057 -1.9122 0.3614

*** 0.001 significant level ** 0.01 significant level* 0.05 significant level + 0.10 significant level



233

APPENDIX E

THE GEOGRAPHIC DISTRIBUTION OF SELECTED NEIGHBORHOODCHANGE INDICATORS AT THE BLOCK GROUP LEVEL IN FRANKLIN AND

CUYAHOGA COUNTIES



2 3 4

Change in % Divorced Population (% points)

-20.75% - -0.01%

0.0000 - 5.00%

5.01% - 10.00%

10.01% - 15.00%

15.01% - 30.00%

Figure E.1: Change in % Divorced Population in Cuyahoga County (1990–2000, %



2 3 5

Change in % Population with College or Higher Degrees (% points)

-27.14% - -0.01%

0.0000 - 5.00%

5.01% - 15.00%

15.01% - 25.00%

25.01% - 52.00%

Figure E.2: Change in % Population with College degrees or Higher in Cuyahoga County (1990–2000, %



2 3 6

Change in Homeownership Rate (% points)

-100.00% - -65.00%

-64.99% - -35.00%

-34.99% - 0.0000

0.01% - 25.00%

25.01% - 55.00%

Figure E.3: Change in Homeownership Rate in Cuyahoga County (1990–2000, %



2 3 7

Change in Housing Vacancy Rate (% points)

-35.53% - -15.00%-14.99% - 0.0000

0.01% - 33.00%

33.01% - 66.00%

66.01% - 100.00%

Figure E.4: Change in Housing Vacancy Rate in Cuyahoga County (1990–2000, %



2 3 8

Change in Median Housing Value (% points)

-100.00% - -50.00%

-49.99% - 0.0000

0.01% - 400.00%

400.01% - 800.00%

800.01% - 1200.00%

Figure E.5: Change in Median Housing Value in Cuyahoga County (1990–2000, %



239

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NOTES

1 Burnout means the tendency of mortgage pools in mortgage-related securities to become less sensitive tointerest rate as they tend to maturity. The older the pool, the more burnt out is the sensitivity to interest ratechanges. In a burnout refinancing the mortgage will not bring more benefits than before the burnout, thusthe borrowers missed previous good opportunities to refinance and have a higher tendency to default.

2 As suggested by Burridge (1980), the Largange Multiplier principle can be applied for the test for spatialerror dependence can be based on. The test is:

)(/)( 22

'

W W W tr W e

error LM e +′=− σ

where tr represents the matrix trace operator, σ2 is a maximum likelihood estimate for the error variance

(i.e., σ2=e’e/N). The LM-error follows an asymptotic χ2(1) distribution under the null hypothesis of no

spatial dependence (H0: λ=0).

3 To test for the substantive spatial dependence, Anselin (1988) suggested an alternative LargrangeMultiplier test:

⎥⎦

⎤⎢⎣

⎡ +′+′′

=− )()(

/ 2

22W W W tr

MWXbWXbWyelag LM

σ σ

where Wy is the spatial lag, b is vector of the OLS estimators for parameters β, M is a projection matrix,

M=I-X(X’X)-1X’. The LM-lag also follows an asymptotic χ2(1) distribution under the null hypothesis of

no spatial dependence (H0: ρ=0).

4 The robust LM tests are robust to misspecification of the other source of spatial dependence—i.e. therobust LM error test is robust to any spatial lag dependence that may be present and vice versa (i.e. tests for error dependence in presence of missing lag), the robust LM lag test is robust to any spatial error dependence that may be present (tests for lag dependence in presence of missing error).

5 The 1990 Median Household Income has been converted to the 2000 constant dollar values based on thedeflator.

6 The Median Housing Value of owner-occupied housing units has been converted to the 2000 constantdollar values based on the deflator.

7

Among the available parcel datasets (1988, 1994, 1997, 2000, 2004), 1988 Cuyahoga Parcel data are theclosest to capture most of the 9,185 Sheriff’s Deeds in 1983–1989.

8 The Median Household Income has been converted to the 2000 constant dollar values based on thedeflator.

9 The Median Housing Value of owner-occupied housing units has been converted to the 2000 constantdollar values based on the deflator.

10 The foreclosure data in this time period are not available in Franklin County.



253

11 Heteroskedasticity-Robust OLS adjustment is used to correct for heteroskedasticity in the OLSregression, base on White’s standard errors. Heteroskedasticity-robust (H-Robust) standard errors are very popular in applied econometrics, and in practice they are more often used to deal with heteroskedasticitythan Weighted Least Squares. Please refer to textbooks in econometrics for detailed procedures of

conducting the adjustments.12 R 2 in a spatial model is called Pseudo R-square, which is defined as the ratio of the variance of the predicted values over the variance of the observed values for the dependent variable. Therefore it is notdirectly comparable with the results from the OLS regression. Log Likelihood is appropriately comparable between spatial models and OLS models.

13 Please refer to Spatial Econometrics: Methods and Models (Luc Anselin, 1989) for the detailed statisticdescription of the log likelihood functions in spatial models.

14 The Jarque-Bera test is a goodness-of-fit measure of departure from normality, based on the samplekurtosis and skewness. The test statistic JB is defined as

)4

)3((6

)( 22 −+−= K Sk n JB

where S is the skewness, K is the kurtosis, n is the number of observations, and k is the number of estimatedcoefficients used to create the series. The statistic has an asymptotic chi-squared distribution with twodegrees of freedom and can be used to test the null hypothesis that the data are from a normal distribution;since samples from a normal distribution have an expected skewness of 0 and an expected kurtosis of 3. Asthe equation shows, any deviation from this increases the JB statistic.

15 The first step of Breusch-Pagan test is to obtain the residuals of the estimated regression equation. Thenuse the squared residuals as the dependent variable in a secondary equation that includes all theindependent variables suspected of being related to the error term:

i pi pii Z Z e μ α α α ++++= L1102)( .

Afterwards use a Chi-square test to test that all the coefficients in this equation are zero.

16 As in a Breusch-Pagan test the first step of White test is to obtain the residuals of the estimatedregression equation. Then use the squared residuals as the dependent variable in a secondary equation thatincludes all the independent variables from the original regression equation:

i pi pii X X e μ α α α ++++= L1102)(

Afterwards use a Chi-square test to test that all the coefficients in this equation are zero.

17 Please refer to Spatial Econometrics: Methods and Models (Luc Anselin, 1989) for the detailed statisticdescription of the log likelihood functions in spatial models.

18 The Jarque-Bera test is a goodness-of-fit measure of departure from normality, based on the samplekurtosis and skewness. The test statistic JB is defined as

)4

)3((

6

)( 22 −+

−=

K S

k n JB

where S is the skewness, K is the kurtosis, n is the number of observations, and k is the number of estimatedcoefficients used to create the series. The statistic has an asymptotic chi-squared distribution with two



degrees of freedom and can be used to test the null hypothesis that the data are from a normal distribution;since samples from a normal distribution have an expected skewness of 0 and an expected kurtosis of 3. Asthe equation shows, any deviation from this increases the JB statistic.

spatial lag regression

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