Three Essays in House Foreclosures, Quasi-Experiment Model and Irrigation
by
Lina Cui
A dissertation submitted to the Graduate Faculty of
Auburn University
in partial fulfillment of the
requirements for the Degree of
Doctor of Philosophy
Auburn, Alabama
August 4, 2012
Keywords: housing sales price, foreclosure, REO, REO sales, irrigation, income inequality
Copyright 2012 by Lina Cui
Approved by
Diane Hite, Chair, Professor of Agricultural Economics and Rural Sociology
Joseph J. Molnar, Co-chair, Professor of Agricultural Economics and Rural Sociology
Valentina Hartarska, Associate Professor of Agricultural Economics and Rural Sociology
Denis Nadolnyak, Assistant Professor of Agricultural Economics and Rural Sociology
ii
Abstract
In recent years much governmental and public attention has been focused on house
foreclosures as they related to the recent recession. Housing spillovers can degrade neighborhood
quality and depress property tax revenues, which are an important source of funding of local
public goods such as public schools. It is my aim to study these spillover effects and to assess
how the timing and number of foreclosures affect surrounding house values, and ultimately erode
the tax base in Atlanta, Georgia.
Chapter 1 examines the effect of foreclosures on subsequent home sales prices by
employing a general spatial model and a generalized spatial two stage least squares (GS2SLS)
model. Potential endogeneity of foreclosures is also explored using spatial system models.
Chapter 2 employs difference-in-differences (DID) model and propensity score matching
(PSM) to study the effect of foreclosures on neighborhood property values in the city of Atlanta
from 2000-2010. A difference-in-differences model not only removes biases from comparisons
between the treatment and control group that could be the result from systematic differences, but
also removes biases from comparisons over time in the treatment group that could be the result
of trends. Like difference-in-differences method, propensity score matching (PSM) removes the
differences between treatment and control groups by matching treatment and control units based
on a set of covariates.
Chapter 3 examines the impact of irrigation adoption on farmers? cropping income and
the total profit of agricultural products sold. It also examines income inequality using agriculture
iii
products sales value. This paper is the first attempt to use U.S. county level data to examine the 9
Southeast states? irrigation impacts. Irrigation is often promoted as a technology that can increase
crop production, improve agriculture income and alleviate poverty. However, irrigation is a
relatively expensive technology for small-scale and poor farmers, which impedes their
opportunities to adopt irrigation technology. Income inequality may increase due to adoption
barriers.
ii
Acknowledgements
The year 2006 was my turning point in life. In the summer of that year I came to Auburn,
it is the place where I started my graduate study, where I met my husband, Tong Liu, where my
children, Lawrence and Caroline were born. I appreciate all of the great gifts from god and all of
the great people I have ever met. First, I would thank Dr. Joseph J. Molnar, Dr. Conner Bailey,
and Dr. Henry Thompson. Without them, I would not have had a chance to study at Auburn and
would not have had a chance to learn quantitative research methods. Without Dr. Molnar and Dr.
Thompson, I might have not even started my PhD study. Second, I would like to thank Dr. Diane
Hite, who provided me with many good research ideas and insightful comments. I also
appreciate the opportunities she gave to me for oral presentations at conferences and teaching at
Auburn. Without her, I could not bring my work to successful completion. I also like to thank my
committee members, Dr. Valentina Hartarska, Dr. Denis Nadolnyak, Dr. Henry Kinnucan (who
was initially on my committee) and Dr. Tannista Banerjee for their invaluable comments and all
the help they provided. Last but not least, I thank my great parents and mother-in-law for their
unconditional support and love during the past six years.
ii
Table of Contents
Abstract ........................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables .................................................................................................................................. ii
List of Figures ................................................................................................................................. ii
List of Abbreviations ...................................................................................................................... ii
CHAPTER 1 ................................................................................................................................... 1
CHAPTER 2 ................................................................................................................................... 3
The Effect of House Foreclosures on Neighborhood Property Value ............................................ 3
1. Introduction ............................................................................................................................. 3
2. Literature Review .................................................................................................................... 5
3. Data .......................................................................................................................................... 8
4. Model ..................................................................................................................................... 11
4.1. Hedonic Model................................................................................................................... 11
4.2. Spatial Hedonic Model ...................................................................................................... 12
4.3. Endogeneity Testing .......................................................................................................... 15
4.4. Zero-Inflated Negative Binomial (ZINB) Model .............................................................. 15
5. Results ................................................................................................................................... 19
5.1. Effects of Characteristics on Number of Foreclosures ...................................................... 28
5.2. Foreclosure Effects on House Values ................................................................................ 35
iii
6. Tax Loss Estimates ................................................................................................................ 43
7. Conclusion and Policy Implication........................................................................................ 45
CHAPTER 3 ................................................................................................................................. 48
The Contagion Effect of Foreclosures: A Quasi-Experiment Method ......................................... 48
1. Introduction ........................................................................................................................... 48
2. Literature Review .................................................................................................................. 50
3. Data ........................................................................................................................................ 53
4. Model ..................................................................................................................................... 55
4.1. Difference-In-Differences (DID) ....................................................................................... 55
4.2. Propensity Score Matching (PSM) .................................................................................... 57
5. Results ................................................................................................................................... 59
5.1. OLS Regression ................................................................................................................. 66
5.2. Difference-In-Differences .................................................................................................. 72
5.3. Propensity Score Matching ................................................................................................ 75
6. Conclusion ............................................................................................................................. 77
CHAPTER 4 ................................................................................................................................. 80
Irrigation and Income Inequality in the Southeast United States ................................................. 80
1. Introduction ........................................................................................................................... 80
2. Literature Review .................................................................................................................. 82
3. Data ........................................................................................................................................ 84
4. Model ..................................................................................................................................... 86
5. Results ................................................................................................................................... 89
6. Discussion and Conclusion .................................................................................................... 96
iv
CHAPTER 5 ................................................................................................................................. 97
REFERENCES ........................................................................................................................... 100
ii
List of Tables
Table 2.1 Descriptive Statistics for One to Four Unit Family Sales House Characteristics and
Neighborhood Characteristics, Atlanta, 2008 (N=10,121) ........................................................... 21
Table 2.2 Descriptive Statistics for Dummy Variables, Atlanta, 2008 (N=10,121) ..................... 25
Table 2.3 Zero-Inflated Negative Binomial Regression Analysis for Factors Affecting
Foreclosures .................................................................................................................................. 30
Table 2.4 Regression Coefficients for Heteroskedasticity ? Corrected OLS, Spatial
Autoregressive Model, General Spatial Model and GS2SLS Model ........................................... 39
Table 3.1 Descriptive Statistics for One to Four Unit Family Sales House Characteristics and
Neighborhood Characteristic, Atlanta, 2000-2010 (N=26,352) ................................................... 61
Table 3.2 Descriptive Statistics for Dummy Variables, Atlanta, 2000-2010 (N=26.352)............ 64
Table 3.3 The Effect of Foreclosures within Different Buffers, Regression Coefficients for
Heteroskedasticity ? Corrected OLS ............................................................................................ 68
Table 3.4 The Effect of REO and REO sales on Neighborhood Property Sales Value within
Different Buffers ........................................................................................................................... 71
Table 3.5 The Effect of Foreclosures within Different Buffers, Difference-In-Differences Model
....................................................................................................................................................... 73
Table 3.6 Baseline Characteristics (Treatment: DIS300>0) ......................................................... 76
Table 3.7 Propensity Score Matching, Caliper (1*E-4) method (Treatment: DIS300>0) ............. 77
Table 4.1 Descriptive Statistics for Study Variables, 9 Southeast States, 1997 and 2002 (N=568)
....................................................................................................................................................... 90
Table 4.2 First Stage OLS Regression Results for Irrigation ....................................................... 91
Table 4.3 OLS Regression Results for Determinants of Income .................................................. 93
Table 4.4 Two Stage Least Squares (2SLS) Regression Results for Determinants of Income .... 94
iii
Table 4.5 Regression Results for Farm Sale Value Inequality ..................................................... 95
ii
List of Figures
Figure 2.1 Historic Quarterly Home Sales Price Index, Atlanta MSA, Seasonally Adjusted (Data
Source: Federal Housing Finance Agency) .................................................................................. 17
Figure 2.2 Spatial Relationship between Foreclosures and Percent Black Residence, Atlanta,
2008............................................................................................................................................... 32
Figure 2.3 Spatial Relationship between Foreclosures and Per Capita Income, Atlanta, 2008 .... 33
Figure 2.4 Spatial Relationship between Foreclosures and Percent Owner Occupied Homes,
Atlanta, 2008 ................................................................................................................................. 34
Figure 2.5 Spatial Relationship between Property Tax Loss and Per Capita Income, Atlanta, 2008
....................................................................................................................................................... 44
Figure 3.1 Historic Quarterly Home Sales Price Index, Atlanta MSA, Seasonally Adjusted (Data
Source: Federal Housing Finance Agency) .................................................................................. 48
Figure 4.1 Lorenz Curve ............................................................................................................... 81
ii
List of Abbreviations
GS2SLS Generalized Spatial Two State Least Squares
OLS Ordinary Least Squares
GMM Generalized Method of Moments
ML Maximum Likelihood
MCMC Markov Chain Monte Carlo
HUD Housing and Urban Development
ZINB Zero-Inflated Negative Binomial
NB Negative Binomial
ZIP Zero-Inflated Poisson
HOPEA Home Ownership and Equity Protection Act
LTV Loan-To-Value
HMDA Home Mortgage Disclosure Act
HSI Hazardous Site Inventory
LTV Loan-To-Value
GEPD Georgia Environmental Protection Division
CBG Census Block Group
MSA Metropolitan Statistical Area
AIC Akaike Information Criterion
REO Real Estate Owned
iii
DID Difference-In-Differences
PSM Propensity Score Matching
CPI Consumer Price Index
2SLS Two Stage Least Squares
CCC Commodity Credit Corporation
USGS U.S. Geological Survey
LSDV Least Squares Dummy Variable
1
CHAPTER 1
In recent years much governmental and public attention has been focused on house foreclosures
as they related to the recent recession. Housing spillovers can degrade neighborhood quality and
depress property tax revenues, which are an important source of funding of local public goods
such as public schools. It is my aim to study these spillover effects and to assess how the timing
and number of foreclosures affect surrounding house values, and ultimately erode the tax base in
Atlanta, Georgia.
Chapter 2 and Chapter 3 employ the same dataset to study the effects of house
foreclosures. Chapter 2 uses cross-sectional data to examine the effect of foreclosures on
subsequent home sales prices in 2008 by employing spatial models. Chapter 3 uses panel data
(2000-2010) to study the effect of foreclosures on neighborhood property values in the city of
Atlanta. The quasi-experiment methods, difference-in-differences (DID) model and propensity
score matching (PSM) are employed and compared. A difference-in-differences model not only
removes biases from comparisons between the treatment and control groups that could be the
result from systematic differences, but also removes biases from comparisons over time in the
treatment group that could be the result of trends. Like difference-in-differences method,
propensity score matching removes the differences between treatment and control groups by
matching treatment and control units based on a set of covariates.
Compared to the cross-sectional data, panel data has an advantage to reduce omitted
variable problems by subtracting constant unobserved variables. However, using cross-sectional
2
data, spatial models also help avoid omitted variable problems by controlling spatially correlated
housing prices and spatially correlated errors.
Chapter 3 examines the impact of irrigation adoption on farmers? cropping income,
agricultural income and the total profit of agricultural products sold. It also examines income
inequality using agriculture products sales value. This paper is the first attempt to use U.S.
county level data to examine the 9 Southeast states? irrigation impacts. Irrigation is often
promoted as a technology that can increase crop production, improve agriculture income and
alleviate poverty. However, irrigation is a relatively expensive technology for small-scale and
poor farmers, which impedes their opportunities to adopt irrigation technology. Income
inequality may increase due to adoption barriers. Thus, irrigation is suspected endogenous to
farmers? cropping income.
In this dissertation, addressing endogeneity problem is one interest. The endogeneity
problems in Chapter 1 and Chapter 3 are caused by reverse causality. Because neighborhood
house values depreciated by foreclosures may lead to more foreclosures, foreclosures may thus
be endogenous to the sales price. Previous studies argue that it is hard to find an instrumental
variable which is correlated with foreclosures but not correlated with the residuals of the hedonic
price equation. The contributions of Chapter 1 include creating an innovative way to examine
endogeneity through accounting for foreclosure timing and it also addresses the endogeneity of
the spatially lagged dependent variable by using GS2SLS procedures. Chapter 3 deals with
endogeneity with 2SLS regression. Because irrigation is a relatively expensive technology for
small-scale farmers and poor farmers, it impedes their opportunities to adopt irrigation
technology. Thus, irrigation is potentially endogenous to agricultural sales income.
3
CHAPTER 2
The Effect of House Foreclosures on Neighborhood Property Value
1. Introduction
In recent years much governmental and public attention has been focused on house foreclosures
as they related to the recent recession. Housing price spillovers can degrade neighborhood
quality and depress property tax revenues, which are an important source of funding of local
public goods such as public schools. It is my aim to study these spillover effects and to assess
how the timing and number of foreclosures affect surrounding house values, and ultimately
erodes the tax base in Atlanta, Georgia.
Atlanta typifies the new South, and Georgia ranks as the state with the fourth highest
default rate in the nation (RealtyTrac, 2011). By the end of 2006, the state foreclosure rate
reached 2.7%, which was up from 1.1% in 2000. Foreclosures in Georgia are concentrated in
Fulton County and DeKalb County, which are part of the same metropolitan area as the capital
city, Atlanta. One of the reasons that the number of foreclosures is high in Georgia is that
Georgia law permits creditors to foreclose on homes more quickly than in other states1. Lenders
can declare a borrower in default and reclaim a house in as few as 60 days (Bajaj, 2007), thus
increasing the speed with which foreclosures occur. Individuals who file Chapter 13 bankruptcy
due to foreclosures in Georgia do so at three times the national rate (Uchoa, 2008).
Foreclosures not only deteriorate the appeal of neighborhood and lead to disordered
communities, they also depress their own and surrounding house values (Immergluck and Smith,
1 ?In New York State, the time between the filing of a lis pendens and the auction of the property is typically about
18 months? (Schuetz et al., 2008). In Ohio, it usually takes 150-180 days to foreclose a property (foreclosure.com).
4
2006; Leonard and Murdoch, 2009; Lin et al., 2009; Schuetz et al., 2008; Skogan, 1990; Towe
and Lawley, 2010). This is because house prices are usually set by comparables in the
neighborhood, so there is a direct price effect; further there may well be a negative spillover
effect if neighborhood is reduced by foreclosures. In addition, foreclosures increase housing
supply, so if demand remains the same or decreases, prices will be driven down further. Cities,
counties, and school districts thus may lose tax revenue due to foreclosed homes (Immergluck
and Smith, 2006). The fact that more foreclosures occur in poor neighborhoods exacerbates
inequality in school quality.
Since the housing market is idiosyncratic due to differences in socio-economic
characteristics and state laws on foreclosures, research on a specific market may provide
information that is more valuable to local policy makers. We examine foreclosure effects in
Atlanta by incorporating spatial effects and foreclosure timing. Generalized spatial two stage
least squares (GS2SLS) procedures are used to examine foreclosure effects. GS2SLS is an
advanced methodology that addresses endogeneity of a spatially lagged dependent variable when
the model contains both spatial lags in the endogenous variables and spatial autocorrelation in
the disturbances. Results from ordinary least square (OLS) regression, spatial autoregressive
regression and general spatial regression are also analyzed and compared.
In addition, neighborhood house values depreciated by foreclosures may lead to more
foreclosures, thus signaling potential endogeneity. We thus examine the endogeneity problem
with a systems approach that incorporates surrounding house characteristics, neighborhood
characteristics and loan characteristics. Because no evidence of endogeneity was found in the
systems model, we use parcel level data to explore how surrounding house characteristics,
neighborhood characteristics and loan characteristics affect foreclosures within a certain buffer
5
using a zero-inflated negative binomial (ZINB) regression, thus furthering the foreclosure
literature.
2. Literature Review
There are a few recent studies addressing foreclosure effects using the hedonic price model.
Nearly all the research focuses on metropolitan areas and uses publicly available datasets to
study foreclosure effects in some specific time period(s). However, most of the existing literature
is based on data from before the subprime crisis, and no empirical analysis using complete parcel
information has been conducted for Atlanta. As an African-American dominated city, Atlanta
ranks fairly low among southern cities in the dissimilarity measure of segregation; in addition,
Atlanta?s local government is well represented by African Americans. Since the housing market
is idiosyncratic due to differences in socio-economic characteristics and state laws on
foreclosures, research on a specific market may provide more valuable information for local
policy makers.
Immergluck and Smith (2006) combine foreclosure data from 1997 and 1998 with
neighborhood characteristics data and more than 9,600 single family property transactions in
Chicago in 1999. After controlling for 40 property and neighborhood characteristics, they find
that foreclosures of conventional single-family loans have a significant impact on nearby
property values. Each conventional foreclosure within 1/8 mile (about 660 feet) of a single-
family home results in a decline of 0.9% in value. Cumulatively, for the entire city of Chicago,
the 3,750 foreclosures that occurred in 1997 and 1998 are estimated to have reduced nearby
property values by more than $598 million. However, this study covers a relatively short period
of sales (one year) and foreclosures (two years), failing to address endogeneity in the model,
since it cannot control for previous years' sales prices. In order to minimize the reverse causation
6
problem, Immergluck and Smith (2006) add the median home value at the census tract level as a
control for sales price in the model. However, the census tract level control is much larger than
the targeted research distance interval, which is within 1/4 mile of foreclosures, so the estimation
may be biased due to measurement error. Thus, the endogeneity problem is not well addressed in
their study.
Lin et al. (2009) use 20% of mortgages made in the United States from 1990 to 2006 to
examine the spillover effects of foreclosures on neighborhood property values in Chicago
metropolitan area in 2003 and 2006. Besides analyzing house characteristics, quarterly dummies
are added to control for seasonal effects, and county and zip dummies are added to control for
community level characteristics. They also apply the Heckman (1979) two-step model to correct
for sample selection bias. The researchers use both loan characteristics and borrower
characteristics to examine foreclosure status. Though there is a statistically significant bias, the
effects on the hedonic model are quite small. The results show that spillover effects occur within
ten blocks and up to five years from the foreclosure date. The effect decreases as time passes and
as space between the foreclosure and the subject property increases. In addition, foreclosures
reduced the surrounding house values by half in the boom period in 2003 as much as those in the
downside market period in 2006. However, Lin et al. (2009) do not distinguish between the
direct foreclosures effects and the spatially dependent home prices.
Schuetz et al. (2008) use property sales and foreclosure filings data in New York City
from 2000 to 2005 to examine foreclosure effects on neighborhood property values. As it uses
panel data, this study has the advantage of addressing the effect of previous years' sales prices on
current year sales price. In New York City, the foreclosure process between the filing of a lis
pendens and the auction of the property usually takes eighteen months. Nine time and distance
7
intervals are created to measure the foreclosure effects according to the foreclosure filing
timeline. Regression results show that properties close to the foreclosures sell at a discount, and
the magnitude of price discount increases with the number of nearby foreclosures, but not in a
linear fashion.
Rogers and Winters (2009) apply a hedonic price model to study foreclosure impacts on
nearby property values in St. Louis County, Missouri, by using single-family sales data from
2000-2007 and foreclosure data from 1998-2007. They adjust the model to account for spatial
autocorrelation. This study supports the hypothesis that foreclosure impacts decrease as distance
and time between the house sale and foreclosure increase. The results show a similar magnitude
of foreclosure impact compared to Immergluck and Smith?s (2006) study, but a much smaller
foreclosure impact compared to Lin et al. (2009) study. Roger and Winter (2009) compared
marginal foreclosure impacts in two different periods: 2003-2005 and 2006-2007. Consistent
with Lin et al. (2009) study, both ordinary least squares (OLS) and generalized method of
moments (GMM) estimates suggest a greater impact in 2006-2007 than in 2003-2005. In 2006-
2007, a foreclosure that happened within the previous six months and within a 200 yard (600 feet)
radius reduces house sales price by 1.6%, but just 0.6% in 2003-2005.
Leonard and Murdoch (2009) use four models to examine foreclosure impacts on single-
family homes values in and around Dallas County, Texas, in 2006. OLS regression is used to
estimate the model without controlling for spatial dependency and neighborhood pricing trends.
Maximum likelihood (ML) procedures are used to examine the spatial autoregressive model and
the general spatial model, and GMM procedures are used to examine the general spatial model.
Regression results suggest that foreclosures within 250 feet, between 500 and 1000 feet and
between 1000 and 1500 feet of a sale depreciate sales prices.
8
One limitation of these studies is that most of them do not fully address the potential for
endogeneity of the foreclosure variable. Since depressed house prices may lead to more
foreclosures, foreclosures may thus be endogenous to the sales price. These studies argue that it
is hard to find an instrumental variable which is correlated with foreclosures but not correlated
with the residuals of the hedonic price equation. The contributions of this study include creating
an innovative way to examine endogeneity through accounting for foreclosure timing and by
using GS2SLS procedures to address the endogeneity of the spatially lagged dependent variable.
This study also employs zero-inflated negative binomial (ZINB) regression to explore the
reasons for foreclosures.
There exists a large literature that identifies the causes of foreclosures (Baxter and Lauria,
2000; Chan et al., 2010; Gerardi et al., 2007; Immergluck, 2009; Immergluck and Smith 2005).
These include borrower characteristics, loan characteristics and socio-economic characteristics.
People with subprime loans are more likely to default than those with government insured loans,
and subprime loans are concentrated in low-income and African-American neighborhoods
(Bunce et al., 2000; Calem et al., 2004). Shlay (2006) also finds that housing abandonment is
associated with poor neighborhoods.
3. Data
The house transaction and foreclosure data were obtained from the Board of Tax Assessors in
Fulton County, Georgia. The dataset contains all parcel information for both sold and unsold
properties in city of Atlanta. It should be mentioned that a small part of Atlanta is situated in
DeKalb County. Because Fulton County and DeKalb County use different variables and codes to
record the sales properties, it is difficult to combine the dataset. Therefore, this study only
includes the Atlanta sales in Fulton County. The dataset includes property sales price, sales date,
9
address, property class, and the seller and buyer names, which help to identify foreclosures and
foreclosure sales.
Using transaction data from 2003 to 2008, a basic dataset that includes property
characteristics, sales price and sales date is constructed. Markov Chain Monte Carlo (MCMC) is
used to impute the missing data on small percentage of observations2. Before analyzing the data,
duplicate records are deleted. Only duplication of a parcel ID with identical date and sales price
are deleted, but houses with legitimate repeat sales are maintained. This dataset contains 64,613
one-to-four unit family sales from 2003 to 2008 and there are 10,121 one to four unit family
sales records in 2008 after cleaning the dataset. Properties sold directly to banks are coded as
foreclosures; however, the same property may sell at depressed prices subsequently, but the
subsequent sales are not considered to be foreclosures but are coded as foreclosure sales.3 The
dataset contains 7,209 foreclosures and foreclosure sales at Atlanta in 2008.
Each record is geocoded to get the longitude and latitude using ArcGIS according to the
property address and then intersected (overlaid) with census block group to identify which
census block group the property belongs to. In this study, census block group level data is used
to proxy for neighborhood characteristics. Thus, the sales records can be merged with
neighborhood characteristics according to the census block group ID number. The neighborhood
characteristics are sourced from the 1990 and 2000 Census data.
Since foreclosures are expected to have fewer impacts on sales at a greater distance,
buffer rings for each sales property are created by ArcGIS to measure the foreclosure effects on
2 The missing data in housing datasets is a common problem, usually a result of coding errors by different data entry
clerks. In this dataset, the maximum missing number of observations is for the variable number of bedrooms with
just 40 (about 0.4% of number of observations) or about. They are imputed by Markov Chain Monte Carlo (MCMC)
procedure. The regression is also tested with and without imputation, and the imputation impact on the results is
very small.
3 The Board of Tax Assessors records properties both sold to the bank and bank sales as foreclosures.
10
sales price. Schuetz et al. (2008) measure foreclosure effects by creating 0-250 feet, 250-500 feet
and 500-1000 feet intervals. Leonard and Murdoch (2009) find the foreclosure effects extend to
1500 feet in Dallas County. Immgergluck and Smith (2006) study foreclosures effects within ?
miles (about 1300 feet). Rogers and Winter (2009) examine foreclosures in St. Louis County and
find foreclosure effects take effect within 600 yards (1800 feet). Following the methods used in
the previous literature, five foreclosures intervals are created: DIS300 is the number of
foreclosures within 300 feet of sales, DIS600 is the number of foreclosures between 300 feet and
600 feet of sales, DIS1200 is the number of foreclosures between 600 and 1200 feet of sales,
DIS1500 is the number of foreclosures between 1200 feet and 1500 feet, and DIS2000 is the
number of foreclosures between 1500 feet and 2000 feet. According to the sales date and
foreclosure date of houses, the number of foreclosures within each buffer for each property is
calculated by using those foreclosures occurring before the sale of the house. For example, if a
house was sold on May 10th, 2008, we use foreclosures occurred before that date to calculate the
number of foreclosures within buffers.4
The percentage of subprime loans is a critical determinant affecting the number of
foreclosures within the buffers. The percentage of low-cost/high-leverage mortgage, high-
cost/low-leverage mortgage, and high-cost/high-leverage mortgage made 2004 to 2007 in census
tract are obtained from Home Mortgage Disclosure Act (HMDA) data of the U.S. Department of
Housing and Urban Development (HUD). The school district in which a property is located is
another important variable affecting house sales price, because parents usually would like to pay
4 Leonard and Murdoch (2009) used cumulative foreclosures in the whole year to calculate the number of
foreclosures within buffers, the endogeneity problem may occur. Also, in Immergluck and Smith (2006),
foreclosures occurred in 1997 and 1998 are used to test the foreclosure effects on sales price in 1999, foreclosures
occurred before 1997 (and not resolved in 1999) and occurred in 1999 are not considered, so foreclosure effects may
be overestimated in the study. Lin et al. (2009) combined foreclosures from a mortgage database with information
provided by outsider vendors, but the coverage of foreclosures is not very ideal.
11
more to be located in a good school zone. The school zone boundary for each elementary school
is established by the Atlanta Board of Education. There are 55 elementary school zones in city of
Atlanta, and houses in this study sample are located in 50 school zones. Thus, 50 dummies are
created not only to capture the school characteristics but also the fixed effects such as property
tax rate.
4. Model
4.1. Hedonic Model
The hedonic model is used to investigate the cross-sectional relationship between house sales
price and foreclosures. The hedonic price model is a commonly used method for studying
housing values (Hanna, 2007; Hite et al., 2001; Portney, 1981). The price of a house is usually
affected by its own physical characteristics and its location. Structural characteristics, socio-
demographic factors, and location to specific amenities or disamenities are included in the
hedonic model to capture positive or negative effects on house prices. Properties surrounded by
foreclosures suffer potential sales discount impacts. The hedonic sales price in equation (2.1) is
assumed to be a function of house, neighborhood, environmental, and foreclosure characteristics
(2.1)
where P is house sales price, H is a vector of the house characteristics, N is a vector of census
block group socio-demographic characteristics, E is a vector of the environmental disamenities,
and F is a vector of foreclosure counts within certain buffers.
The log-linear hedonic price model is given by
(2.2)
??? ),,,( FENHfP
081110
9876
54321008
__
_200015001200
600300ln
???
????
??????
???
????
??????
D u m m yS c h o o ld u m m yQ u a r t e r
d u m m yS a l e sD i sD i sD i s
D i sD i sE n v i r oN e i g h C h a r sH o u s e C h a r sP
12
where P08 is a vector of house sales price in 2008 expressed as the natural log form; HouseChars
is a vector of house characteristics; NeighChars is a vector of neighborhood characteristics at
census block group level; Enviro is a vector of the environmental disamenities; Dis300 is a
vector of number of foreclosures within 300 feet of sales house; Dis600 is a vector of number of
foreclosures between 300 feet and 600 feet of sales house; Dis1200 is a vector of number of
foreclosures between 600 feet and 1200 feet of sales house; Dis1500 is a vector of number of
foreclosures between 1200 feet and 1500 feet; Dis2000 is a vector of number of foreclosures
between 1500 feet and 2000 feet; Sales_dummy is a vector of dummies for sales type;
Quarter_dummy is a vector of dummies for quarters to control seasonality effects;
School_dummy is a vector of dummies for school zones.
4.2. Spatial Hedonic Model
The effect of potential spatial correlation on sales price needs to be addressed in the hedonic
analysis. Spillover effect theory (Lin et al., 2009; Vandell, 1991) states that the price of the
subject property is determined by selected comparable properties. Comparables are properties
that are recently sold and those that are close to the subject property by distance. The spatial
autoregressive model introduces surrounding average houses prices as a variable to explain their
effects on the subject property value.
(2.3)
where W08 is the spatial weight matrix for sales properties; ?1 is the parameter for spatial lag.
The spatial weight matrix can be constructed in various ways. Choosing a proper weight
structure is important for the model specification. A common method to construct the weight
matrix in the literature is to find the k nearest neighbor sales in terms of distance. Five to ten
08080811110
9876
54321008
__
_200015001200
600300
????
????
??????
????
????
??????
PWD u m m yS c h o o ld u m m yQ u a r t e r
d u m m yS a l e sD i sD i sD i s
D i sD i sE n v i r oN e i g h C h a r sH o u s e C h a r sP
13
nearest neighbors are the most frequent selection criterion in previous studies. This study
chooses eight nearest neighbors for each sales property to establish the spatial weight matrices,
where the eight nearest neighbors receive value 1 in the matrix, others receive value 0 in the
matrix. The matrix is row standardized, so that the spatial average price can be controlled for
each sales property5.
Using only one year?s price may be not sufficient to control for the sales trend. Following
Leonard and Murdoch?s (2009) work, sales prices from year 2003 to 2007 are added into the
regression,
(2.4)
where Pyy is a vector of sales price in year yy; Wyy is the spatial weight matrix establishing the
spatial relationship between sales in 2008 and sales in year yy. For matrices W07, W06 , W05, W04 ,
and W03, if there is a house sold in year 2007, 2006, 2005, 2004, or 2003 within 2000 feet to each
sale in 2008, then the matrices get a non-zero entry, otherwise, the entries get zero values.
In addition to the spatial lag, this paper also controls for spatially correlated errors to
control for unobserved heterogeneity. The lmsar function in MATLAB can be used to test for
spatial correlation in the residuals of a spatial autoregressive model. If the marginal probability is
less than 0.05, there is evidence that spatial dependence exists in the error structure. Thus, the
general spatial model that includes both a spatial lag and spatial error is appropriate for modeling
this type of dependence in the errors.
5 Five and ten nearest neighbors are also tested respectively, and the estimated coefficients differ less than 2%
compared to results in the paper, and the significance levels do not change.
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14
(2.5)
The generalized spatial two stage least squares (GS2SLS) procedure introduced by
Kelejian and Prucha (1998) is also used to examine the general spatial model in this paper.
GS2SLS procedures can correct for the endogeneity of spatial lag dependent variable and
produce consistent estimators when the model contains both spatial lags in the endogenous
variables and spatial autocorrelation in the disturbances. The spatially lagged price is
instrumented by spatially weighted lagged independent variables in the price equation.
GS2SLS procedures are developed based on the GMM procedures, and have several
advantages over the ML method which is often used to estimate spatial models. The ML
estimation produces consistent and efficient results only if two assumptions are met. First, the
disturbance should be normally distributed. Second, the variance of the disturbances should be
homoskedastic. ML procedures use a numerical hessian calculation, but in the presence of
outliers or non-constant variance the numerical hessian approach may not be valid because
normality and homodasticity in the disturbance generating process might be violated (Lesage,
1998). Further, ML estimation is often computationally challenging when the sample size is
large (Kelejian and Prucha, 1998). GS2SLS estimation relaxes the normality assumption and
allows for heteroskedatic errors in the spatial model, also it is computationally simple compared
to the ML estimation, especially for large sample sizes (Keijian and Prucha, 1998, 1999). In
addition, the GS2SLS estimation can be used to incorporate a high degree of flexibility in the
specification of the spatial weight matrix. In traditional spatial models, the selection of spatial
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15
weight matrix is usually an ad hoc process. Sometimes small changes in the spatial weight
matrix can result in changes to the model result. GS2SLS model can incorporate flexible spatial
weight matrix specification and get consistent results (Bucholtz, 2004).
4.3. Endogeneity Testing
We hypothesize that there are endogeneities in the sales equation. Surrounding sales prices
usually work as a signal that affect neighbors? decision to default. When the housing market falls,
the balance of the mortgage exceeds the house value, and borrowers may choose to default in
reaction. Therefore, depressed sales prices may induce more foreclosures. Most previous studies
just ignore the potential endogeneity problem, only a few put forward the endogeneity problem
as studying the foreclosure effects on house value (Immergluck and Smith, 2005; Rogers and
Winters, 2009), but no previous study has sufficiently controlled for endogeneity in cross-
sectional data.
4.4. Zero-Inflated Negative Binomial (ZINB) Model
After testing for endogeniety, a zero-inflated negative binomial (ZINB) regression can be used to
examine what factors affect foreclosures. A ZINB model is a modified Poisson regression which
accommodates both overdispersion (distribution variance is larger than its mean) and excess
zeros found in count data. Because the distribution of foreclosures is generally skewed to the
right and contains a considerable proportion of zeros in five buffers (17% within 300 feet, 28%
between 300 feet and 600 feet, 15% between 600 feet and 1200 feet, 19% between 1200 feet and
1500 feet, and 13% between 1500 feet and 2000 feet), zero-inflated negative binomial (ZINB)
regression model is proper to be used. Overdispersion is often caused by unobservable individual
heterogeneity and/or excess zeros of the data (Sheu et al., 2004). Vuong test is used to test excess
zeros and compare zero-inflated model and non-zero-inflated model.
16
We hypothesize that for each sales property, the number of foreclosures within each
buffer is influenced by its surrounding house characteristics, neighborhood characteristics and
loan characteristics in the buffer. The advantage of this dataset is that it includes all properties in
city of Atlanta, both sold and unsold. Thus, it is possible to measure surrounding house
conditions for each sales house. The average dwelling condition, number of rooms, living area,
and house age in the buffer are chosen to explain the number of foreclosures. Percentage of black
residents, average household size, percentage of home ownership, per capital income, percentage
of people over 65 years old are added as neighborhood characteristics to explain the number of
foreclosures.
Loan characteristics at census tract level are critical to explain foreclosures, since
increasing foreclosures are the direct results of growing subprime loans. Subprime loans in this
paper are defined as high-cost loans, as their fees and interest rates are usually significantly
above those charged to typical borrowers (Gerardi et al., 2008). The Home Ownership and
Equity Protection Act (HOEPA) defines high-cost loans as loans with interest rates more than
eight percentage point of loan balance. High cost loans are usually high leverage loans, which is
related to the high loan-to-value (LTV) concept, i.e. borrowers have smaller down payments than
typical borrowers. High-cost, high-leverage loan borrowers usually carry a higher risk of default
than low-cost, low leverage loan borrowers. The census tract percentages of low-cost/high-
leverage loans, high-cost/low-leverage loans, and high-cost/high-leverage loans from HMDA
made from 2004 to 2007 are used to explain the number of foreclosures.
Figure 2.1 shows the historical quarterly home sales price index for Atlanta-Sandy
Springs-Marietta area and the national average. From the third quarter of 2003, the housing price
index in Atlanta is lower than the national average, and the price difference became larger in the
17
following years. In the Atlanta area, the home sales price peaked historically in mid-2007, then
decreased dramatically since the second quarter of 2007 (Federal Housing Finance Agency). The
sales prices in 2008 are much lower than those in 2007, so the change of sales prices between
2007 and 2008 in the buffer for each sales property in 2008 is also added as an explanatory
variable (?price) since the depreciating market is hypothesized to induce more foreclosures.
Figure 2.1 Historic Quarterly Home Sales Price Index, Atlanta MSA, Seasonally Adjusted (Data
Source: Federal Housing Finance Agency)
Equations (2.6)-(2.10) are used to explain the number of foreclosures within 300 feet,
between 300 feet and 600 feet, between 600 feet and 1200 feet, between 1200 feet and 1500 feet,
and between 1500 and 2000 feet as functions of house characteristics, neighborhood
characteristics, loan characteristics and sales price change in the buffer.
(2.6)
(2.7)
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18
(2.8)
(2.9)
(2.10)
where Cdu300 is the average house condition within 300 feet for each sales property; Lvarea300
is the average living area within 300 feet for each property; Age300 is the average house age
within 300 feet for each property; Rmbed300 is the average number of rooms within 300 feet for
each property; Pct_lchl is the percentage of HMDA mortgage made 2004 to 2007 that are low-
cost/high-leverage in the census tract; Pct_hcll is the percentage of HMDA mortgage made 2004
to 2007 that are high-cost/low-leverage in the census tract; Pct_hchl is the percentage of HMDA
mortgage made 2004 to 2007 that are high-cost/high-leverage in the census tract; ?price300 is
the average sales price change between year 2007 and 2008 within 300 feet; Cdu600 is the
average house condition between 300 feet and 600 feet for each sales property; Cdu1200 is the
average house condition between 600 feet and 1200 feet for each sales property; Cdu1500 is the
average house condition between 1200 feet and 1500 feet; Cdu2000 is the average house
condition between 1500 feet and 2000 feet. It is tested that Pct_hchl is correlated with number of
foreclosures, but not correlated with the residuals of price equations, which indicates that it is a
valid instrumental variable. A Hausman test is used to test equations (2.2) and (2.6)-(2.10)
systematically for endogeneity.
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19
5. Results
Table 2.1 reports the descriptive statistics for the variables in the model. The house
characteristics include lot size in square feet, living area in square feet, number of stories, age of
the house, number of bedrooms, number of full and half bathrooms, basement and attic condition,
heat type, overall dwelling condition, and the street condition in the parcel. The squares of
number of rooms and age are added because these variables may influence a house value in a
nonlinear way. In addition to structural house characteristics, many neighborhood characteristics
may affect house value. Percentage of black residents, average household size, percentage of
home ownership, and percentage of people over 65 years old in each census block group (CBG)
are chosen to represent neighborhood quality.
Environmental economic theory suggests that house value depends on environmental
quality. Since DeKalb County is located next to Fulton County, the properties located in the east
Atlanta may be affected by the hazard sites in DeKalb County. Thus, the location of point-
specific Hazardous Site Inventory (HSI) in Fulton County and DeKalb County are used to
calculate their effects on sales price. Data on polluting sites is obtained from the Georgia
Environmental Protection Division (GEPD). Using ArcGIS, the distance of sales property to the
nearest HSI can be calculated. It is hypothesized that holding other variables constant, the greater
the distance to the nearest HSI, the higher the house price is expected to be. The mean distance to
the nearest HSI in the sample is 2.14 kilometers. The minimum is 16 meters, and the maximum
is 6,281 meters.
The count of foreclosures within a specific distance for each property is the focus of this
section. Five distance intervals are created for each property. If the number of foreclosures is
statistically significant for DIS1500 but not for DIS2000, then spillovers from a foreclosure
20
affect other properties within 1500 feet of a sale, but not beyond 1500 feet. The average number
of foreclosures is 2.59 within 300 feet of sales house, 4.12 between 300 and 600 feet of sales
house, 12.74 between 600 feet and 1200 feet of sales house, 8.49 between 1200 feet and 1500
feet of sales house, and 16.62 between 1500 feet and 2000 feet of sales house in 2008.
21
Table 2.1 Descriptive Statistics for One to Four Unit Family Sales House Characteristics and
Neighborhood Characteristics, Atlanta, 2008 (N=10,121)
Variable Description Mean SD Minimum Maximum
Price Sales price (*$1,000) 185.87 351.11 10 10058.52
Street1 The street condition in the parcel is "paved" 0.99 0.09 0 1
Street2 The street condition is ?semi-improved? 0.003 0.05 0 1
Street3 The street condition is ?dirt? 0.004 0.06 0 1
Lotarea Lot area sqft (*1000) 11.44 11.69 0.52 588.06
Lvarea Living area sqft (*1000) 1.64 0.94 0.22 14.80
Stories Number of stories 1.20 0.40 1 3
Rmbed Number of bedrooms 3.01 0.93 1 12
Fixbath Number of full bathrooms 1.70 0.88 1 9
Fixhalf Number of half bathrooms 0.26 0.49 0 8
Bsmt1 No basement 0.08 0.28 0 1
Bsmt2 Crawl basement 0.57 0.50 0 1
Bsmt3 Part basement 0.16 0.37 0 1
Bsmt4 Full basement 0.18 0.39 0 1
Heat1 No heat 0.02 0.15 0 1
Heat2 Central heat 0.07 0.25 0 1
Heat3 Central air condition 0.21 0.40 0 1
Heat4 Heat pump 0.70 0.46 0 1
Attic1 No attic 0.87 0.33 0 1
Attic2 Unfinished attic 0.05 0.21 0 1
Attic3 Part finished attic 0.04 0.19 0 1
Attic4 Fully finished attic 0.03 0.18 0 1
Attic5 Fully finished/wall height attic 0.01 0.09 0 1
Age Age of sales house 50.01 29.95 0 138
Cdu1 Dwelling condition is excellent 0.09 0.29 0 1
Cdu2 Dwelling condition is very good 0.19 0.39 0 1
Cdu3 Dwelling condition is good 0.20 0.40 0 1
Cdu4 Dwelling condition is average 0.40 0.49 0 1
Cdu5 Dwelling condition is fair 0.07 0.25 0 1
Cdu6 Dwelling condition is unsound 0.03 0.16 0 1
Cdu7 Dwelling condition is poor 0.02 0.12 0 1
Cdu8 Dwelling condition is very poor 0.01 0.08 0 1
Black Percentage of black residents in CBG 0.79 0.32 0 1
Hsize Average household size in CBG 2.68 0.45 1.34 4.19
Own Percentage of residents own the house in
CBG 0.49 0.21 0 0.98
Income Per capital income in 1999 in CBG (*$1,000) 19.40 18 2.76 120.93
Old Percentage of people over 65 years old in
CBG 0.11 0.06 0 0.51
HSI The distance from sales house to the nearest
hazardous site inventory (*1000m) 2.14 1.23 0.02 6.28
DIS300 Number of foreclosures within 300 feet of
sales house 2.59 2.48 0 19
22
DIS600 Number of foreclosures between 300 and
600 yards of sales house 4.12 5.13 0 35
DIS1200 Number of foreclosures between 600 and
1200 yards of sales house 12.74 14.46 0 121
Dis1500 Number of foreclosures between 1200 and
1500 yards of sales house 8.49 10 0 76
Dis2000 Number of foreclosures between 1500 and
2000 yards of sales house 16.62 18.23 0 123
W07P07 Spatial lag of 2007 log sales price within
2000 feet of 2008 price 11.83 0.89 0 14.95
W06P06 Spatial lag of 2006 log sales price within
2000 feet of 2008 price 11.91 0.86 0 14.98
W05P05 Spatial lag of 2005 log sales price within
2000 feet of 2008 price 11.83 0.94 0 14.90
W04P04 Spatial lag of 2004 log sales price within
2000 feet of 2008 price 11.63 1.23 0 14.90
W03P03 Spatial lag of 2003 log sales price within
2000 feet of 2008 price 11.55 1.11 0 14.93
Pct_lchl Percentage of HMDA mortgage made 2004
to 2007 that are low-cost and high-leverage
in census tract
0.08 0.04 0.01 0.22
Pct_hcll Percentage of HMDA mortgage made 2004
to 2007 that are high-cost and low-leverage
in census tract
0.27 0.12 0.02 0.49
Pct_hchl Percentage of HMDA mortgage made 2004
to 2007 that are high-cost and high-leverage
in census tract
0.16 0.07 0.01 0.28
Cdu300 Average house condition within 300 feet 3.34 1.08 1 8
Lvarea300 Average living area (*1000) within 300 feet 1.64 0.86 0.30 14.80
Age300 Average house age within 300 feet 50.04 23.41 0 132
Rmbed300 Average number of room within 300 feet 3.01 0.70 1 10
?Price300 Average sales price change (*10,000 dollar)
between 2007 and 2008 within 300 feet -6.69 21.57 -429.11 262.50
Cdu600 Average house condition between 300 feet
and 600 feet 3.34 1.03 1 8
Lvarea600 Average living area (*1000) between 300
feet and 600 feet 1.63 0.83 0.32 14.80
Age600 Average house age between 300 feet and 600
feet 50.26 21.26 0 123
Rmbed600 Average number of room between 300 feet
and 600 feet 3.00 0.63 1 8
?Price600 Average sales price change (*10,000 dollar)
between 2007 and 2008 between 300 feet
and 600 feet
-7.77 18.61 -431.20 152.50
Cdu1200 Average house condition between 600 feet
and 1200 feet 3.54 0.84 1 7
23
Lvarea1200 Average living area (*1000) between 600
feet and 1200 feet 1.54 0.58 0.91 7.80
Age1200 Average house age between 600 feet and
1200 feet 55.72 12.65 1 98
Rmbed1200 Average number of room between 600 feet
and 1200 feet 2.91 0.33 1.84 5.24
?Price1200 Average sales price change (*10,000 dollar)
between 2007 and 2008 between 600 feet
and 1200 feet
-9.32 19.76 -413.02 375.27
Cdu1500 Average house condition between 1200 feet
and 1500 feet 3.54 0.83 1 8
Lvarea1500 Average living area (*1000) between 1200
feet and 1500 feet 1.54 0.58 0.60 8.72
Age1500 Average house age between 1200 feet and
1500 feet 56.01 12.10 2.85 108
Rmbed1500 Average number of room between 1200 feet
and 1500 feet 2.90 0.38 2 6
?Price1500 Average sales price change (*10,000 dollar)
between 2007 and 2008 between 1200 feet
and 1500 feet
-8.54 19.06 -413.02 352.97
Cdu2000 Average house condition between 1500 feet
and 2000 feet 3.54 0.81 1 6.12
Lvarea2000 Average living area (*1000) between 1500
feet and 2000 feet 1.55 0.57 0.73 6.44
Age2000 Average house age between 1500 feet and
2000 feet 56.06 11.28 4 118
Rmbed2000 Average number of room between 1500 feet
and 2000 feet 2.90 0.31 2 5
?Price2000 Average sales price change (*10,000 dollar)
between 2007 and 2008 between 1500 feet
and 2000 feet
-9.67 20.36 -431.20 648.26
24
Table 2.2 reports the descriptive statistics for dummy variables. Quarterly dummies are
created to control for seasonal sales effects. It is hypothesized that houses are usually sold at a
lower price in the winter than in the summer. The types of sale vary in the sample, and the sales
types affect the sales price directly, so sales type dummies are included in the regression. Sale1
represents a valid sale, Sale2 is the sale to or from an exempt or utility, Sale3 represents
properties remodeled or changed after sale, Sale4 represents sales between individual and
corporation, Sale5 represents a liquidation or foreclosure sale, Sale6 represents a land contract or
unusual financing sale, and Sale7 is a sale that includes additional interest. School district
dummies are also important variables that affect house sales price. There are 55 elementary
school zones in city of Atlanta, and houses in this sample are located in 50 school zones.
A Hausman test is used to test for endogeneity by estimating equations (2.2) and (2.6)-
(2.10) simultaneously, but there is no evidence that endogeneity exists in the model, thus the
OLS model is consistent. The reason is that maybe we use foreclosures occurred before sales to
calculate the number of foreclosures within a buffer so that controls the potential endogeneity
problem. However, equations (2.6)-(2.10) can be used to unravel the factors affect foreclosure in
each buffer. Table 2.3 reports that the Vuong statistic values are larger than 1.96 and statistically
significant, indicating that the ZINB model is superior to the negative binomial (NB) model. In
this study, unobservable heterogeneity is likely to be another problem. The number of
foreclosures varies with house characteristics, socio-demographic factors and loan characteristics.
However, it also may be affected by social interaction and personal moral issues (Towe and
Lawley, 2010). A ZINB model is preferred to a zero-inflated Poisson (ZIP) model because the
results show the estimated alphas are statistically significant, indicating that heterogeneity also
causes overdispersion even after the excess zero issue is addressed.
25
Table 2.2 Descriptive Statistics for Dummy Variables, Atlanta, 2008 (N=10,121)
Variable Description Mean SD Minimum Maximum
Sale1 Valid sale 0.13 0.34 0 1
Sale2 To/from exempt or utility 0.02 0.12 0 1
Sale3 Remodeled/Changed after sale 0.01 0.12 0 1
Sale4 Related individuals or corporation 0.06 0.24 0 1
Sale5 Liquidation/Foreclosure 0.72 0.45 0 1
Sale6 Land contract/Unusual financing 0.01 0.09 0 1
Sale7 Includes Additional interest 0.00 0.02 0 1
Quarter1 Sold in the first quarter 0.25 0.43 0 1
Quarter2 Sold in the second quarter 0.26 0.44 0 1
Quarter3 Sold in the third quarter 0.26 0.44 0 1
Quarter4 Sold in the fourth quarter 0.22 0.42 0 1
SD1 Adamsville Elementary School 0.00 0.08 0 1
SD2 Benteen Elementary School 0.01 0.11 0 1
SD3 Mary Mcleod Bethune Elementary School 0.02 0.14 0 1
SD5 Capitol View Elementary School 0.02 0.13 0 1
SD6 Cascade Elementary School 0.00 0.06 0 1
SD7 Cleveland Avenue Elementary School 0.01 0.11 0 1
SD8 William M. Boyd Elementary School 0.02 0.14 0 1
SD9 Warren T. Jackson Elementary School 0.02 0.13 0 1
SD10 Morris Brandon Elementary School 0.02 0.14 0 1
SD11 Garden Hills Elementary School 0.01 0.11 0 1
SD12 E. Rivers Elementary School 0.02 0.13 0 1
SD13 Bolton Academy 0.01 0.12 0 1
SD14 Beecher Hills Elementary School 0.01 0.12 0 1
SD15 Daniel H. Stanton Elementary School 0.03 0.17 0 1
26
SD16 John Wesley Dobbs Elementary School 0.05 0.21 0 1
SD17 Hill-Hope Elementary School 0.01 0.10 0 1
SD18 Connally Elementary School 0.05 0.22 0 1
SD19 Centennial Place Elementary School 0.01 0.07 0 1
SD20 Continental Colony Elementary School 0.00 0.08 0 1
SD21 Ed S. Cook Elementary School 0.03 0.18 0 1
SD22 Deerwood Academy 0.02 0.15 0 1
SD23 Paul L. Dunbar Elementary School 0.01 0.11 0 1
SD25 Margaret Fain Elementary School 0.01 0.09 0 1
SD26 Fickett Elementary School 0.01 0.12 0 1
SD27 William Finch Elementary School 0.06 0.25 0 1
SD28 Charles L. Gideons Elementary School 0.06 0.24 0 1
SD29 Grove Park Elementary School 0.03 0.16 0 1
SD30 Heritage Academy Elementary School 0.01 0.12 0 1
SD31 Alonzo F. Herndon Elementary School 0.02 0.15 0 1
SD32 Joseph Humphries Elementary School 0.01 0.09 0 1
SD33 Emma Hutchinson Elementary School 0.01 0.11 0 1
SD34 M. Agnes Jones Elementary School 0.06 0.23 0 1
SD35 L. O. Kimberly Elementary School 0.01 0.10 0 1
SD36 Mary Lin Elementary School 0.00 0.06 0 1
SD37 Leonora P. Miles Elementary School 0.01 0.11 0 1
SD38 Morningside Elementary School 0.02 0.14 0 1
SD39 Parkside Elementary School 0.02 0.15 0 1
SD40 Perkerson Elementary School 0.03 0.16 0 1
27
SD41 Peyton Forest Elementary School 0.01 0.07 0 1
SD42 William J Scott Elementary School 0.02 0.13 0 1
SD43 Thomas Heathe Slater Elementary School 0.03 0.18 0 1
SD44 Sarah Rawson Smith Elementary School 0.02 0.13 0 1
SD45 Springdale Park Elementary School 0.02 0.13 0 1
SD46 F. L. Stanton Elementary School 0.03 0.16 0 1
SD47 Thomasville Heights Elementary School 0.01 0.10 0 1
SD49 George A. Towns Elementary School 0.02 0.12 0 1
SD50 Bazoline Usher Elem School 0.02 0.14 0 1
SD52 West Manor Elementary School 0.00 0.06 0 1
SD53 Walter F. White Elementary School 0.02 0.15 0 1
SD55 Carter G. Woodson Elementary School 0.01 0.11 0 1
28
5.1. Effects of Characteristics on Number of Foreclosures
Table 2.3 presents the results of the ZINB regression. Between 300 feet and 2000 feet, a property
surrounded by houses in good condition houses has a greater number of foreclosures within the
buffer than a property surrounded by fair or poor condition houses. The reason is potentially that
houses in good condition have higher values and usually suffer more than those moderate
condition houses if the housing market drops. So the loan balance of good condition house is
more likely to exceed the house value, and it is more likely to be foreclosed. The number of
foreclosures within a buffer increases if the average living area of surrounding houses decreases.
The number of foreclosures within a buffer increases if the average age of surrounding houses is
older. Between 300 feet and 600 feet, and between 1200 feet and 1500 feet, the number of
foreclosures increases if the average number of rooms decreases.
The percentage of black residents has a statistically significant effect on the number of
foreclosures. Every 10% increase in the percentage black residents increases the number of
foreclosures by 0.05 within 300 feet, increases the number of foreclosures by 0.04 between 600
feet and 1200 feet, 0.05 between 1200 feet and 1500 feet, and 0.04 between 1500 feet and 2000
feet. Larger household size decreases the risk of foreclosures. Every additional household
member decreases the number of foreclosures by 0.08 between 300 feet and 600 feet, by 0.1
between 600 feet and 1200 feet, by 0.11 between 1200 feet and 1500 feet, and by 0.11 between
1500 feet and 2000 feet. A higher percentage of home ownership reduces the number of
foreclosures within 300 feet, but increases that number beyond 600 feet. On average, every10%
increase in home ownership decreases the number of foreclosures by 0.05 within 300 feet, and
by 0.02 between 300 feet and 600 feet. But between 1500 feet and 2000 feet, every 10% increase
in home homeownership increases foreclosures by 0.02. Income has a significant effect on
29
foreclosures in all buffers. Every 1000 dollar increase in per capital income in a CBG leads to a
decrease in the number of foreclosures by 0.02 unit within 300 feet, 0.04 between 300 feet and
600 feet, 0.03 between 600 feet and 1200 feet, 0.04 between 1200 feet and 1500 feet, and 0.04
between 1500 feet and 2000 feet. The percentage of people over 65 years old has no consistent
effect on foreclosures within different buffers.
Percentage of subprime loans made from 2004 to 2007 significantly affects foreclosures
in all buffers. Within 300 feet, every 10% increase in low-cost and high-leverage loans increases
foreclosures by 0.15, a 10% high-cost and low-leverage loans increase foreclosures by 0.16, and
10% more of high-cost and high-leverage loans increase foreclosures by 0.28. The price
difference between 2007 and 2008 (?Price) also affects foreclosures as a feedback effect. Since
most of the sales prices in 2008 are lower than those in 2007, a large proportion of price change
values are negative. Thus, every 10,000 dollars increase in price difference in absolute value
increases foreclosures by 0.04 units within 300 feet, 0.01 between 300 feet and 600, 0.008
between 600 feet and 1200 feet, 0.008 between 1200 feet and 1500 feet, and 0.006 between 1500
feet and 2000 feet. Figure 2.2 shows the spatial relationship between foreclosures and percentage
of black residents in the Atlanta area in 2008. Foreclosures are concentrated in the central city
and areas with a high percentage of African American residents, especially concentrated in the
CBGs where African American residents are more than 80%. Figure 2.3 shows the spatial
relationship between foreclosures and per capita income. Foreclosures are more likely to be
concentrated in low income districts. In Figure 2.4, the relationship between foreclosures and
percentage of home ownership suggests that foreclosures are concentrated in areas with
relatively moderate percentage of home ownership, not in the lowest home ownership area as
expected.
30
Table 2.3 Zero-Inflated Negative Binomial Regression Analysis for Factors Affecting
Foreclosures
Variable Coefficient
(t statistics)
Dis300 Dis600 Dis1200 Dis1500 Dis2000
Intercept 0.07
(0.40)
0.50**
(2.19)
0.88***
(4.35)
0.25
(1.13)
0.56***
(2.59)
Cdu300 -0.02
(-1.19)
Lvarea300 -0.14***
(-3.29)
Age300 0.0025***
(4.24)
Rmbed300 0.04
(1.35)
?Price300 -0.04***
(-6.74)
Cdu600
-0.07***
(-2.77)
Lvarea600
-0.01
(-0.14)
Age600
0.01***
(7.12)
Rmbed600
-0.08*
(-1.79)
?Price600
-0.01***
(-11.73)
Cdu1200
-0.18***
(-6.58)
Lvarea1200 -0.23***
(-6.58)
Age1200 0.02***
(17.05)
Rmbed1200 -0.06
(-1.27)
?Price1200 -0.01***
(-9.08)
Cdu1500 -0.23***
(-7.58)
Lvarea1500 0.02
(0.25)
Age1500 0.02***
(17.15)
Rmbed1500 -0.15***
(-2.76)
?Price1500 -0.01***
31
(-8.66)
Cdu2000 -0.24***
(-8.13)
Lvarea2000 -0.04
(-0.65)
Age2000 0.03***
(25.13)
Rmbed2000 -0.09
(-1.50)
?Price2000 -0.01***
(-6.60)
Black 0.54***
(6.77)
0.14
(1.45)
0.38***
(4.61)
0.50***
(5.34)
0.39***
(4.75)
Hsize -0.04
(-1.48)
-0.08**
(-2.04)
-0.10***
(-3.04)
-0.11***
(-3.25)
-0.11***
(-3.58)
Own -0.48***
(-8.57)
-0.20***
(-2.85)
0.01
(0.19)
0.00
(0.07)
0.24***
(4.09)
Income -0.02***
(-9.04)
-0.04***
(-15.12)
-0.03***
(-16.06)
-0.04***
(-16.29)
-0.04***
(-18.57)
Old -0.03
(-0.17)
0.08
(0.35)
-0.59***
(-2.89)
0.12
(0.61)
-0.25
(-1.38)
Pct_lchl 1.50***
(2.64)
2.67***
(3.82)
2.82***
(5.05)
3.32***
(5.16)
1.90***
(3.40)
Pct_hcll 1.61***
(7.29)
2.27***
(8.04)
3.16***
(13.58)
3.42***
(13.35)
2.88***
(12.64)
Pct_hchl 2.84***
(12.27)
5.38***
(18.47)
6.47***
(25.81)
6.41***
(23.72)
6.87***
(27.95)
Alpha
0.18***
(22.61)
0.59***
(43.44)
0.60***
(56.75)
0.62***
(52.24)
0.61***
(58.72)
Vuong test of
ZINB versus
NB
5.09*** 6.17*** 7.50*** 5.47** 6.22***
Note: ***Statistically significant at 1%; **Statistically significant at 5%; *Statistically
significant at 10%.
The asymptotic t statistics are in parentheses.
32
Figure 2.1 Spatial Relationship between Foreclosures and Percent Black Residence, Atlanta,
2008
33
Figure 2.2 Spatial Relationship between Foreclosures and Per Capita Income, Atlanta, 2008
34
Figure 2.3 Spatial Relationship between Foreclosures and Percent Owner Occupied Homes,
Atlanta, 2008
35
5.2. Foreclosure Effects on House Values
Foreclosure effects are the focus of this study. Table 2.4 reports the estimation results for
different regression models. Two models are estimated by OLS, the spatial autoregressive
regression, the general spatial regression, and the GS2SLS regression respectively. The reported
coefficients of OLS regression are corrected for heterogeneity, and the reported t-statistics are
based on robust errors. Model 1 serves as the base for all models, including the foreclosure
variables DIS300, DIS600, DIS1200 and DIS1500 as well as house characteristics and
neighborhood characteristics variables. It is found that foreclosures take effects within 1500 feet,
so another interval between 1500 and 2000 feet, DIS2000 is added in Model 2.
In Model 1, OLS regression suggests that foreclosure spillover effects extend to 1500 feet.
One more foreclosure within 300 feet of a property depresses its sales price by 1.59%, one more
foreclosure between 300 feet and 600 feet decreases sales price by 0.66%, one more foreclosure
between 600 feet and 1200 feet decreases sales price by 0.35%, and one more foreclosure
between 1200 feet and 1500 feet decreases sales price by 0.41%. The spatial autoregressive
regression, general spatial regression and the GS2SLS regression also indicate that foreclosure
effects extend to 1500 feet, although suggesting a slightly smaller effect than the OLS regression,
which makes sense after controlling for the spatial dependency and correlation between spatial
errors. The GS2SLS regression presents that one more foreclosure within 300 feet depresses the
sales price by 1.57%, one more foreclosure between 300 feet and 600 feet depresses the sales
price by 0.54%, one more foreclosure between 600 feet and 1200 feet reduces the sales price by
0.3%, and one more foreclosure between 1200 feet and 1500 feet decreases the sales price by
0.37%.
36
Model 2 reports smaller coefficient estimates than Model 1 after including 1500 feet to
2000 feet buffer. Both the OLS and GS2SLS regressions find that the foreclosure effect extends
only to 1200 feet, which suggests a relatively local spillover effects when compared to the
estimates from Model 1. The GS2SLS regression shows sales price decreases by 1.54% when
there is one more foreclosure within 300 feet, 0.53% if there is one more foreclosure between
300 feet and 600 feet, and 0.25% if there is one more foreclosure between 600 feet and 1200 feet.
Foreclosures do not take effect beyond 1200 feet. The spatial autoregressive regression and
general spatial regression report that foreclosures take effect within 2000 feet, but not between
1200 feet and 1500 feet.
In general, the spatial models control for spatial lags and spatial autocorrelation, which
produce a better fit than OLS regression, as indicated by higher adjusted R2 value. Comparing the
three spatial models, the spatial autoregressive regression deals with spatial dependency, and the
positive spatial lag coefficient ? indicates that a higher sales price in neighboring buffers exerts a
positive influence on average selling price across the entire Atlanta one to four unit family
neighborhood sample. The general spatial regression not only controls for spatial dependency,
but also deals with spatial autocorrelation in the residuals. The spatial autocorrelation coefficient
is statistically significant, which indicates there may be unobserved heterogeneity, which the
spatial error can mitigate. In addition, the general spatial regression produces a better fit to the
sample data indicated by higher log likelihood values and lower Akaike Information Criterion
(AIC). Thus, the general spatial model outperforms the OLS model and the spatial autoregressive
model. The GS2SLS estimates are obtained by introducing a set of instruments in a two stage
least squares (2SLS) procedure (Keijian and Prucha, 1998). Although the coefficient differences
between the general spatial regression estimated by the ML method and GS2SLS method are not
37
considerable, the GS2SLS procedures can address endogenous spatially lagged dependent
variable and produce consistent coefficients by relaxing normality assumption and homogeneity
assumption, so GS2SLS regression is preferred to the other spatial regressions.
All the structural variables and neighborhood variables have their expected signs based
on previous findings. For example, the GS2SLS results suggest that one more bedroom results in
a 10.58% (13.23%-2*1.33%) house price increase. An additional year of house age decreases the
sales price by 1.7% (-1.73%+2*0.014%), but when the house is older than 62 years, the age
begins to have positive effect on the sales price. The Cdu is the general dwelling condition for
each property. Eight dummy variables are created to distinguish different dwelling conditions,
with Cdu1 being the excellent condition and Cdu8 being the very poor condition. The
coefficients reflect a nonlinear relationship between the dwelling conditions and the sales price.
In the regression, the dummy Cdu8 is deleted, so the coefficients of Cdu1 to Cdu7 should be
interpreted as the differences from the coefficient of Cdu8. In the GS2SLS Model 1, a property
in excellent condition (Cdu1) on average sells for 22% higher price than one in very poor
condition (Cdu8) and the difference is statistically significant. A property in very good condition
(Cdu2) on average sells for 14.5% more than one in very poor condition (Cdu8), a property in
good condition (Cdu3) on average sells for 11.3% more than one in very poor condition (Cdu8),
the property with average condition (Cdu4) sells 4.83% higher price and the property with fair
condition (Cdu5) sells 0.61% higher price than that with very poor condition (Cdu8) although the
statistics values are not significant. However, the property with unsound (Cdu6) and poor
condition (Cdu7) sells less than that with very poor condition (Cdu8). As the neighborhood
characteristics, all the regression models report significant effects of percentage of black
residents, household size and per capita income on house sales price. The GS2SLS regression
38
results report that every 1% increase in the percentage of black residents in a CBG reduces sales
prices by 0.16%; one more household member one average in a CBG reduces sales price by
7.8%; and every $1000 increase in per capital income increases house sales price by 0.29%.
One interesting point is that the coefficient for percentage of home ownership is
statistically significant in the OLS model, but not in any of the spatial models. Increasing
homeownership does not necessarily increase house price. From the early 1990s to 2005,
national homeownership rate increased dramatically due to emergence of innovative subprime
loans. Subprime loans helped borrowers who were not qualified for prime mortgages to afford a
house. However, due to a subprime loan borrower?s low income, impaired credit scores, and a
subprime loan?s high interest, most borrowers choose to default. Defaults increase the housing
supply and decreases house price as a result. Thus, higher homeownership rates within a census
block group do not lead to a significantly higher house price.
39
Table 2.4 Regression Coefficients for Heteroskedasticity ? Corrected OLS, Spatial Autoregressive Model, General Spatial Model and
GS2SLS Modela
Variable OLS Spatial Autoregressive
Model
General Spatial Model GS2SLS Model
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Intercept 10.50***
(40.08)
10.50***
(40.09)
8.86***
(26.12)
8.88***
(26.15)
9.40***
(61.15)
9.38***
(27.95)
8.54***
(18.02)
8.5394***
(18.03)
DIS300 -0.0159***
(-2.88)
-0.0156***
(-2.82)
-0.0159***
(-3.12)
-0.0156***
(-3.06)
-0.0161***
(-3.06)
-0.0157***
(-2.99)
-0.0157***
(-3.15)
-0.0154***
(-3.10)
DIS600 -0.0066**
(-1.99)
-0.0065**
(-1.96)
-0.0058*
(-1.93)
-0.0057*
(-1.90)
-0.006*
(-1.94)
-0.0059*
(-1.89)
-0.0054*
(-1.84)
-0.0053*
(-1.82)
DIS1200 -0.0035**
(-2.36)
-0.0029*
(-1.93)
-0.0033**
(-2.38)
-0.0028**
(-1.96)
-0.0037**
(-2.56)
-0.0031**
(-2.09)
-0.003**
(-2.20)
-0.0025*
(-1.82)
DIS1500 -0.0041**
(-2.02)
-0.0024
(-1.14)
-0.0038**
(-2.14)
-0.0023
(-1.12)
-0.0038**
(-2.06)
-0.0022
(-1.06)
-0.0037**
(-2.16)
-0.0023
(-1.15)
DIS2000 -0.002
(-1.47)
-0.0019*
(-1.70)
-0.002*
(-1.76)
-0.0018
(-1.62)
Street2 -0.34*
(-1.70)
-0.34*
(-1.69)
-0.34*
(-1.96)
-0.34**
(-1.96)
-0.33*
(-1.92)
-0.33*
(-1.92)
-0.34**
(-1.97)
-0.34**
(-1.97)
Street3 -0.23*
(-1.90)
-0.23*
(-1.91)
-0.20
(-1.45)
-0.20
(-1.46)
-0.20
(-1.44)
-0.20
(-1.45)
-0.19
(-1.39)
-0.02
(-1.40)
Lotarea 0.002**
(2.30)
0.002**
(2.29)
0.002**
(2.41)
0.002**
(2.40)
0.002**
(2.38)
0.002**
(2.36)
0.002**
(2.45)
0.002**
(2.44)
Lvarea 0.13***
(8.03)
0.14***
(8.06)
0.13***
(6.94)
0.13***
(6.97)
0.13***
(6.94)
0.13***
(6.94)
0.13***
(6.83)
0.13***
(6.85)
Stories 0.13***
(4.43)
0.13***
(4.37)
0.13***
(3.53)
0.12***
(3.49)
0.13***
(3.89)
0.13***
(3.64)
0.12***
(3.48)
0.12***
(3.44)
Age -0.02***
(-13.21)
-0.02***
(-13.25)
-0.02***
(-8.75)
-0.02***
(-8.79)
-0.02***
(-16.90)
-0.02***
(-9.44)
-0.02***
(-11.34)
-0.02***
(-11.39)
Age2 0.00015***
(10.34)
0.00015***
(10.40)
0.00014***
(6.69)
0.00014***
(6.73)
0.00014***
(12.73)
0.00014***
(7.20)
0.00014***
(8.66)
0.00014***
(8.72)
Rmbed 0.15***
(3.56)
0.15***
(3.55)
0.13***
(3.48)
0.13***
(3.48)
0.13***
(3.54)
0.13***
(3.44)
0.13***
(3.47)
0.13***
(3.47)
40
Rmbed2 -0.015***
(-2.76)
-0.015***
(-2.76)
-0.014***
(-2.64)
-0.014***
(-2.64)
-0.014***
(-2.69)
-0.014***
(-2.61)
-0.014***
(-2.62)
-0.013***
(-2.62)
Fixbath 0.06***
(3.34)
0.06***
(3.36)
0.05***
(2.72)
0.05***
(2.74)
0.05***
(2.79)
0.05***
(2.74)
0.05***
(2.70)
0.05***
(2.72)
Fixhalf 0.04*
(1.71)
0.04*
(1.70)
0.03
(1.33)
0.03
(1.32)
0.03
(1.30)
0.03
(1.29)
0.03
(1.31)
0.03
(1.30)
Bsmt2 0.05
(1.37)
0.05
(1.41)
0.06
(1.62)
0.06*
(1.66)
0.05
(1.41)
0.05
(1.45)
0.06*
(1.85)
0.06*
(1.89)
Bsmt3 0.13***
(3.16)
0.13***
(3.17)
0.12***
(3.05)
0.12***
(3.07)
0.11***
(2.80)
0.12***
(2.83)
0.13***
(3.22)
0.13***
(3.24)
Bsmt4 0.14***
(3.68)
0.14***
(3.70)
0.14***
(3.50)
0.14***
(3.53)
0.13***
(3.28)
0.13***
(3.30)
0.14***
(3.66)
0.14***
(3.68)
Heat2 -0.08
(-0.97)
-0.08
(-0.95)
-0.09
(-1.35)
-0.09
(-1.33)
-0.09
(-1.41)
-0.09
(-1.38)
-0.09
(-1.29)
-0.08
(-1.27)
Heat3 -0.03
(-0.36)
-0.03
(-0.34)
-0.03
(-0.56)
-0.03
(-0.54)
-0.04
(-0.62)
-0.04
(-0.60)
-0.03
(-0.48)
-0.03
(-0.47)
Heat4 0.05
(0.64)
0.05
(0.66)
0.04
(0.61)
0.04
(0.63)
0.03
(0.56)
0.03
(0.58)
0.04
(0.65)
0.04
(0.67)
Attic2 0.08**
(2.03)
0.08**
(2.06)
0.08*
(1.84)
0.08*
(1.86)
0.08*
(1.86)
0.08*
(1.88)
0.08*
(1.80)
0.08*
(1.82)
Attic3 0.01
(0.27)
0.01
(0.27)
0.01
(0.26)
0.01
(0.26)
0.02
(0.36)
0.02
(0.35)
0.01
(0.18)
0.01
(0.18)
Attic4 0.07
(1.51)
0.07
(1.49)
0.07
(1.45)
0.07
(1.43)
0.07
(1.49)
0.07
(1.46)
0.07
(1.40)
0.07
(1.38)
Attic5 0.22***
(3.60)
0.22***
(3.55)
0.20**
(2.14)
0.20**
(2.11)
0.21**
(2.18)
0.21**
(2.14)
0.19**
(2.05)
0.19**
(2.01)
Cdu1
0.28**
(2.33)
0.28**
(2.34)
0.23**
(2.07)
0.23**
(2.08)
0.23**
(2.21)
0.23**
(2.12)
0.22**
(2.05)
0.22**
(2.06)
Cdu2 0.20*
(1.78)
0.20*
(1.80)
0.15
(1.46)
0.15
(1.47)
0.16
(1.58)
0.16
(1.52)
0.14
(1.43)
0.15
(1.44)
Cdu3 0.16
(1.45)
0.16
(1.45)
0.12
(1.14)
0.19
(1.14)
0.12
(1.23)
0.12
(1.17)
0.11
(1.12)
0.11
(1.12)
Cdu4 0.09 0.09 0.05 0.05 0.05 0.05 0.05 0.05
41
(0.78) (0.81) (0.50) (0.51) (0.54) (0.54) (0.49) (0.50)
Cdu5 0.05
(0.42)
0.05
(0.43)
0.01
(0.09)
0.01
(0.10)
0.01
(0.12)
0.01
(0.12)
0.01
(0.06)
0.01
(0.07)
Cdu6 -0.25**
(-2.03)
-0.25**
(-2.03)
-0.29**
(-2.54)
-0.29**
(-2.54)
-0.29***
(-2.69)
-0.29**
(-2.57)
-0.29***
(-2.58)
-0.29***
(-2.58)
Cdu7 -0.07
(-0.50)
-0.07*
(-0.49)
-0.11
(-0.89)
-0.11
(-0.89)
-0.11
(-0.94)
-0.11
(-0.89)
-0.11
(-0.92)
-0.11
(-0.91)
Black -0.25**
(-2.50)
-0.24**
(-2.43)
-0.18*
(-1.95)
-0.18*
(-1.89)
-0.19**
(-2.01)
-0.19*
(-1.91)
-0.16*
(-1.75)
-0.15*
(-1.69)
Hsize -0.10***
(-2.79)
-0.10***
(-2.77)
-0.08**
(-2.25)
-0.08**
(-2.24)
-0.09**
(-2.33)
-0.09**
(-2.19)
-0.08**
(-2.25)
-0.08**
(-2.23)
Own 0.10*
(1.72)
0.11*
(1.76)
0.09
(1.32)
0.09
(1.36)
0.09
(1.35)
0.10
(1.38)
0.08
(1.21)
0.08
(1.25)
Income 0.0046***
(3.98)
0.0046***
(3.92)
0.0035**
(2.41)
0.0035**
(2.37)
0.0039**
(2.55)
0.0038**
(2.49)
0.0029**
(2.07)
0.0029**
(2.02)
Old -0.21
(-1.06)
-0.21
(-1.06)
-0.18
(-0.85)
-0.18
(-0.86)
-0.20
(-0.91)
-0.20
(-0.91)
-0.15
(-0.77)
-0.15
(-0.77)
HSI 0.003
(0.24)
0.002
(0.15)
-0.0004
(-0.03)
-0.001
(-0.07)
-0.0005
(-0.04)
-0.0016
(-0.11)
-0.0004
(-0.03)
-0.0014
(-0.10)
W07P07 0.009
(0.49)
0.009
(0.48)
0.003
(0.16)
0.003
(0.16)
0.006
(0.31)
0.005
(0.29)
-0.0005
(-0.03)
-0.0006
(-0.03)
W06P06 0.01
(0.58)
0.01
(0.62)
0.01
(0.46)
0.01
(0.49)
0.01
(0.53)
0.01
(0.55)
0.01
(0.38)
0.01
(0.41)
W05P05 0.03*
(1.68)
0.03*
(1.67)
0.03
(1.26)
0.03
(1.25)
0.03
(1.24)
0.03
(1.22)
0.02
(1.14)
0.02
(1.13)
W04P04 0.008
(1.15)
0.008
(1.13)
0.01
(1.30)
0.01
(1.28)
0.01
(1.09)
0.01
(1.08)
0.016
(1.60)
0.016
(1.58)
W03P03 -0.01
(-1.35)
-0.001
(-1.35)
-0.003
(-0.26)
-0.003
(-0.26)
-0.006
(-0.46)
-0.006
(-0.45)
0.001
(0.08)
0.001
(0.08)
Sale1 0.24***
(4.79)
0.24***
(4.81)
0.23***
(4.43)
0.23***
(4.45)
0.23***
(4.56)
0.23***
(4.54)
0.22***
(4.30)
0.22***
(4.32)
Sale2 -0.82***
(-4.11)
-0.82***
(-4.09)
-0.83***
(-10.11)
-0.83***
(-10.08)
-0.84***
(-10.16)
-0.83***
(-10.10)
-0.83***
(-10.06)
-0.83***
(-10.03)
42
Sale3 0.03
(0.45)
0.03
(0.48)
0.01
(0.14)
0.015
(0.16)
0.016
(0.17)
0.018
(0.20)
0.006
(0.06)
0.008
(0.08)
Sale4 -0.52***
(-8.19)
-0.52***
(-8.19)
-0.52***
(-8.98)
-0.52***
(-8.96)
-0.52***
(-8.94)
-0.52***
(-8.91)
-0.52***
(-9.00)
-0.52***
(-8.98)
Sale5 -0.30***
(-5.73)
-0.30***
(-5.72)
-0.30***
(-6.23)
-0.30***
(-6.23)
-0.30***
(-6.16)
-0.30***
(-6.15)
-0.30***
(-6.23)
-0.30***
(-6.22)
Sale6 -0.24
(-1.60)
-0.24
(-1.60)
-0.24**
(-2.22)
-0.24**
(-2.22)
-0.24**
(-2.22)
-0.24**
(-2.32)
-0.24**
(-2.21)
-0.24**
(-2.21)
Sale7 0.48*
(1.85)
0.46*
(1.89)
0.49
(0.95)
0.47
(0.93)
0.52
(1.01)
0.51
(0.99)
0.46
(0.90)
0.45
(0.87)
Quarter1 0.32***
(10.34)
0.31***
(9.61)
0.33***
(10.86)
0.32***
(10.18)
0.33**
(10.56)
0.31***
(9.88)
0.34***
(11.10)
0.32***
(10.43)
Quarter2 0.19***
(7.07)
0.18***
(6.68)
0.19***
(7.17)
0.19***
(6.82)
0.19***
(7.05)
0.19***
(6.68)
0.20***
(7.28)
0.19***
(6.94)
Quarter3 0.19***
(7.21)
0.18***
(7.04)
0.19***
(7.39)
0.18***
(7.22)
0.19***
(7.38)
0.18***
(7.20)
0.19***
(7.39)
0.19***
(7.23)
? 0.15*** 0.15*** 0.10*** 0.11*** 0.23*** 0.22***
? 0.08*** 0.07*** -0.07** -0.06**
Adj. R2 0.58 0.58 0.58 0.58 0.58 0.58 0.58 0.58
Log-
likelihood
-9537.99 -9536.53 -9534.76 -9533.18
AIC 19287.98 19287.06 19281.52 19280.36
a School district dummy variable parameter estimates not reported here for sake of space.
Note: ***Statistically significant at 1%; **Statistically significant at 5%; *Statistically significant at 10%.
The asymptotic t statistics are in parentheses.
43
6. Tax Loss Estimates
One of the direct effects of foreclosures is a reduction of house values depresses local tax
revenues. Because property taxes fund local public goods, property taxes losses would depreciate
welfare of local economy. This section conducts an analysis of how tax collections will be
impacted by changes in marginal implicit prices. Property taxes for Atlanta are calculated by
subtracting the homestead exemption from 40% of the purchase price of the home, dividing that
by 1000 and multiplying that amount by the county millage rate. The homestead exemption in
Fulton county is $15,000, so the property tax is calculated as follows,
Property Tax = * 50.91 (2.11)
The tax loss is thus calculated as
Property Tax Loss= * 50.91 (2.12)
The marginal effect of foreclosures on surrounding house prices is computed using the
parameters of the GS2SLS regression Model 1from Table 2.4.
= ?4*sales price + ?5*sales price + ?6*sales price + ?7*sales price (2.13)
The estimated property tax loss for 10,121 one-to-four unit family houses in Atlanta is
about $2.2 million in 2008. It is hypothesized that the biggest losers will likely be in the poorest
neighborhoods where foreclosures tend to be concentrated. Figure 2.5 shows the spatial
relationship between property tax loss and per capita income, confirming that the biggest tax
losers are those census block groups with lower per capita income.
However, this analysis underestimates the foreclosure effects significantly, as it only
includes one to four unit family houses but does not include larger multi-family houses,
commercial or industrial properties. In addition, relocation costs in the event of a foreclosure and
44
other transaction costs are not considered. The benefits from foreclosure reduction would be
expected to be higher if a full sample of spectrum is included.
Figure 2.5 Spatial Relationship between Property Tax Loss and Per Capita Income, Atlanta, 2008
45
7. Conclusion and Policy Implication
By using a unique dataset, this study examines foreclosure impacts on neighborhood property
values in Atlanta using one to four unit family houses. The spatial distribution of the foreclosure
pattern is also analyzed. The OLS results, spatial autoregressive regression, general spatial
regression and GS2SLS regression are analyzed and compared. The general spatial model
controls for spatial lags and controls for spatial error to avoid omitted variable problem. The
GS2SLS regression is more appealing when the residuals are heteroskedatic and when the finite
samples do not meet the normality requirement. The foreclosure effects extend up to 1500 feet of
a property. The results present a slight larger spillover effects when compared to other studies.
The marginal foreclosure impact is -1.57% within 300 feet, - 0.54% between 300 feet and 600
feet, -0.3% between 600 and 1200 feet, and -0.37% between 1200 feet and 1500 feet.
Immergluck and Smith (2006) find that the marginal foreclosure impact in Chicago City in 1999
is -1.14% within 1/8 mile (about 600 feet) and -0.33% between 1/8 and 1/4 mile (about between
600 feet and 1200 feet). Roger and Wither (2009) find that the marginal foreclosure impact on
single-family sales in Louis County, Missouri, is about -1% of sales price within 200 yards
(about 600 feet) from 2000-2007. Leonard and Murdoch (2009) find that foreclosures within 250
feet of a sale depreciate selling price by 0.5%, and foreclosures between 250 feet and 500 feet
decrease sales price by 0.1% in Dallas County, Texas, in year 2006. The larger effect of
foreclosures may be due to the faster speed of foreclosures in Atlanta caused the higher
percentage of foreclosures.
Although endogeneity problems are not found in this study after testing our equations
systems with the Hausman test, the zero-inflated negative binomial regression results explain the
46
reasons for number of foreclosures and ArcGIS program presents the foreclosure patterns
through maps of foreclosure activity.
Foreclosures are more likely to be concentrated in low-income and minority districts,
which is consistent with results of previous studies finding low-income and minority borrowers
are more likely to have subprime loans. A sales property surrounded by houses in good condition
has a greater number of foreclosures within the buffer than a sales property surround by houses
in fair or poor condition. The number of foreclosures within a buffer increases if the surrounding
houses? living area decreases and if the average age of surrounding houses is greater. Larger
household size decreases the risk of foreclosures. Higher percentage of home ownership
decreases the number of foreclosures within 300 feet, but increases the number of foreclosure
beyond 600 feet. Towe and Lawley (2010) also found that an increase in the percentage of
owner-occupied units in a neighborhood has a positive impact on the hazard rate of foreclosure.
The reason is maybe because there are more houses have potential to be foreclosed on with
higher home ownership while renters probably do not foreclose that fast.
The price difference between 2007 and 2008 also affects the number of foreclosures.
Falling house prices between 2007 and 2008 caused by previous foreclosures creates a climate in
which even more foreclosures occur, since subprime loans borrowers may lose confidence to the
market and choose to default.
The percentage of subprime loans made from 2004 to 2007 significantly increases
foreclosures in all buffers. High-cost and high-leverage loans have the greatest effects on the
number of foreclosures. The sales price regressions show that higher home ownership in a
neighborhood does not significantly improve the house price. Thus, the increasing home
ownership resulting from access to subprime loans in recent years has a deleterious effect on
47
sustainability in the housing market. On the contrary, a portion of houses were foreclosed on as a
result of excess speculation in the housing market. From the results, policy makers should
consider programs to make home ownership consistent with a borrower?s economic situation and
credit history, as well as program to quickly resolve the large inventory of foreclosed houses on
the market. Banks should be more careful about the subprime loans lending standards, but should
also avoid refusing responsible borrowers in low-income and high-minority neighborhoods.
Hartarska and Gonzalez-Vega (2006) find that the credit counseling program, which helps low-
income borrowers to estimate the amount of debt they will be able to service, can help decrease
mortgage loan default rate.
The Georgia law increases foreclosures quickly, which speeds up the crisis. Although
foreclosures can be put back on the market sooner than in other states, most foreclosures are sold
at a greatly discounted price. It may also reduce surrounding house sales prices and thus affect
property tax collection. Because property taxes fund local public goods, losses in the property
taxes revenues would have a multiplier impact in degrading provision of local public goods. The
estimated property tax loss for 10,121 one-to-four unit family houses at Atlanta is about $2.2
million in 2008. If the full spectrum of houses types and foreclosures were considered, reducing
foreclosures would result in an even higher social benefit. The result also confirms that the
biggest tax losers are those census block groups with lower per capita income where more
foreclosures are likely to occur.
48
CHAPTER 3
The Contagion Effect of Foreclosures: A Quasi-Experiment Method
1. Introduction
The financial crisis caused by subprime loans started in 2006 and became apparent in 2007. The
number of foreclosures in 2007 was 79% higher than in previous years nation-wide (Veiga 2008;
Rogers and Winter 2009). Defaults depress housing market, and foreclosures reduce surrounding
house values (Immergluck and Smith 2006; Schuetz, Been and Ellen 2008; Lin, Rosenblatt and
Yao 2009; Leonard and Murdoch 2009). Figure 2.1 shows the historical quarterly home sales
price index for Atlanta-Sandy Springs-Marietta area versus the national average. From the third
quarter of 2003, the housing price index in Atlanta is lower than the national average, and the
price difference became larger in the following years. In the Atlanta area, the home sales price
peaked historically in mid-2007, then decreased dramatically since the second quarter of 2007
(Federal Housing Finance Agency).
Figure 3.1 Historic Quarterly Home Sales Price Index, Atlanta MSA, Seasonally Adjusted (Data
Source: Federal Housing Finance Agency)
49
Previous studies indicate that foreclosures depreciate neighborhood house sales price.
However, there is difficulty over time in establishing how much of the depreciation is caused by
foreclosures, and how much is caused by macroeconomic or regional trends. The problem is that
there could be a trend over time that the prices of all the houses in a neighborhood, not only
houses in foreclosure infestation areas, are declining due to external economic conditions. The
question is that whether the foreclosures cause a decline in sales price through spillover effects
or all the houses in a neighborhood experience price decline due to external economic conditions.
Thus, it is important to identify whether the foreclosures or the time trend caused depreciation in
housing sales price.
Difference-in-differences model can not only remove biases from comparisons between
the treatment and control group that could be the result from permanent differences, but also can
remove biases from comparisons over time in the treatment group that could be the result of
trends (Wooldridge 2007). Like the difference-in-differences method, propensity score matching
(PSM) can also eliminate selection bias between treatment and control groups by matching
treatment and control units based on a set of covariates. This study employs both a difference-in-
differences model and propensity score matching to study the effects of foreclosures on the
neighborhood property values in the city of Atlanta from 2000-2010.
In addition, this study also distinguishes between real estate owned (REO) property and
REO sales. REO occurs when the borrower misses mortgage payments and the property becomes
owned by banks, government agencies or mortgage institutions; REO sale occurs when bank
sells the foreclosed property to individuals or investment corporations. There are very few
literatures distinguish REO from REO sales and they did not incorporate both variables in the
model to study the foreclosure effects. The hypothesis is that both REO and REO sales will
50
decrease surrounding houses? sales price. REO reduces surrounding houses? sales price by
introducing disordered communities. REO sale reduces surrounding houses? sales price because
house prices are usually set by comparables in the neighborhood. In order to quickly resolve
REOs, Banks usually sell houses to individual or companies at a great discount, so there is a
direct negative spillover effect.
2. Literature Review
There are a few recent studies addressing the effects of foreclosure on housing values using the
hedonic price model. Most studies are done by employing cross-sectional data, while a few of
them use panel data or repeat sales data.
Immergluck and Smith (2006) combine foreclosure data from 1997 and 1998 with
neighborhood demographic characteristics data and more than 9,600 single family property
transactions in Chicago in 1999. After controlling for forty characteristics of properties and their
respective neighborhoods, they find that foreclosure of conventional single-family loans have a
significant impact on nearby property values. Each conventional foreclosure within a 1/8 mile
(about 660 feet) of a single-family home results in a decline of 0.9% in value.
Leonard and Murdoch (2009) use four models to examine the foreclosure impacts on
single-family home values in and around Dallas County, Texas, in 2006. Maximum likelihood
estimation and GMM estimation are used to examine and compare the spatial lag model and
general spatial model. Regression results suggest that foreclosures within 250 feet, between 500
and 1000 feet and between 1000 and 1500 feet of a sale depreciate surrounding neighborhood
selling prices.
Lin, Rosenblatt and Yao (2009) use 20% of the mortgages made in the United States
from 1990 to 2006 to examine the spillover effects of foreclosures on neighborhood property
51
values in Chicago metropolitan area in 2003 and 2006. They also apply the Heckman (1979)
two-step model to correct for sample selection bias. The researchers use both loan characteristics
and borrower characteristics to examine foreclosure status. Though there is a statistically
significant bias, the effects on the hedonic model are quite small. The results show that spillovers
take effect within ten blocks and for up to five years from the foreclosure. The effect decreases
as time passes and as space between the foreclosure and the subject property increases. In
addition, foreclosures reduced surrounding 2006 house values by half compared to prices during
the boom period in 2003.
Schuetz, Been and Ellen (2008) use property sales and foreclosure filings data in New
York City from 2000 to 2005 to examine foreclosure effects on neighborhood property values.
Nine time and distance intervals were created to measure foreclosure effects according to the
foreclosure filing timeline. Regression results show that properties close to the foreclosures sell
at a discount, and the magnitude of price discount increases with the number of nearby
foreclosures, but not linearly.
Rogers and Winters (2009) apply a hedonic price model to study foreclosure impacts on
nearby property values in St. Louis County, Missouri, using single-family sales data from 2000-
2007 and foreclosure data from 1998-2007. They adjust the model to account for spatial
autocorrelation, since foreclosures are usually spatially clustered. The ten nearest neighboring
sales are used to construct the spatial weight matrix. This study supports the hypotheses that
foreclosure impacts decrease as distance and time between the house sale and foreclosure
increase. The results show a similar magnitude of foreclosure impacts compared to Immergluck
and Smith?s (2006) study, but a much smaller foreclosure impact compared to Lin, Rosenblatt
and Yao?s (2009) study.
52
Campbell, Giglio and Pathak (2009) use housing transaction data from Massachusetts
over the last twenty years to examine the effect of foreclosures on house prices at zip code level.
The results show that the average discount resulting from a forced sale is 28% of house value.
Unforced sales take place at efficient prices, while forced sale prices reflect time-varying
illiquidity in neighborhood housing markets. It also shows that the house price is reduced by 1%
when there is a foreclosure at a distance of 0.05 mile.
One limitation of these studies is that they do not distinguish whether foreclosures
themselves lead to depreciated surrounding housing price, or the external economic conditions
reduce the sales price around the whole neighborhood. There is only one paper mentioning this
issue. Harding, Rosenblatt and Yao (2009) use repeat sales model to examine the effect of
foreclosures on property values. However, for each sales property they arbitrarily pick just two
repeated sales during study period 1989-2007, the first sale had to occur in 1990 or later and the
resale had to be completed in 2007 or earlier. Because housing is sold randomly, some houses
are sold once, while others may be sold more than 3 times during the study period. In their study,
a number of observations are deleted in the analysis, and not every market transaction is
available to estimate foreclosure effects. In addition, the authors do not mention how the two
repeat sales are selected for each sales property. For example, if a house is sold three times in
2006, 2007 and 2008 respectively, why do they choose sales in 2006 and 2007 as repeat sales but
not those in 2006 and 2008 as repeat sales?
In contrast, this paper includes every valid sale that sold twice or more during the study
period. Every house is transacted at a different time, so this dataset comprises an unbalanced
panel.
53
3. Data
The transaction and foreclosure data are from the Board of Tax Assessors in Fulton County,
Georgia. It should be mentioned that a small part of Atlanta belongs to DeKalb County. Because
Fulton County and Dekalb County use different variables and codes to record properties sales, it
is difficult to combine the datasets. This study only includes the Atlanta sales in Fulton County.
The sales data from 2000 and 2010 include all the transaction information we need for the
analysis, including sales price, sales type, sales date, address, and the seller and buyer name,
which helps to identify real estate owned (REO) property and REO sales. Because the sales data
and the housing characteristics data are in different files, I combine the datasets according to
housing parcel id to get all the information needed for analysis. There are 74,424 transactions in
total from 2000 to 2010, including valid sales, REOs and REO sales. The REOs are either
transacted at a price of 0 when the borrower has less equity in the property than the amount owed
to the bank or is transacted at price great than 0 when the borrower has more equity in the
property than the amount owed to the bank. The REOs should be deleted from the transaction
data because they are not real sales and do not reflect the housing values. After deleting the
REOs and observations with missing variables, there are 26,352 one to four unit family house
sales records and all of them are sold twice or more.
The REO occurs if the individual sells the property to the bank, government agency or
mortgage institutions when the borrower cannot pay the loans. Then the bank usually sells the
property to other investment companies or individuals, which is REO sale. For some properties,
the REO sale transacted more than once during the years. Also, the REO sale could become the
REO again in subsequent years. To calculate the number of foreclosures nearby, each property?s
sales date and foreclosure date are used to determine how many REOs and REO sales in each
54
buffer. For example, a REO occurs on June 20th, 2008, it is then sold to an individual on July 30th,
2008. If there is a house within 300 feet sold on August 2nd, 2008 then I identify there is one
REO sale nearby. However, if the house sold on June 25th, then I identify there is one REO
nearby.
Each record is geocoded to get the longitude and latitude using ArcGIS according to the
property address, then intersected (overlaid) with census block group to identify which census
block group the property belongs to, and intersected with Atlanta school zones to identify which
school zone the property belongs to. In this study, census block group level data is used as a
proxy for neighborhood characteristics. Thus, the sales records can be merged with
neighborhood characteristics according to the census block group id number. The neighborhood
characteristics data are from the 1990 and 2000 Census Bureau. Neighborhood characteristics
include the average per capita income, percentage of black residents, average household size,
percentage of residents who own the property, and percentage of residents over 65 years old.
This study uses the difference-in-differences methodology to estimate the effects of
foreclosures on property values. Since foreclosures are expected to have fewer impacts on sales
at a greater distance, buffer rings for each sales record were created. DIS300 is the number of
foreclosures within 300 feet of sales occurred before sales. DIS600 is the number of foreclosures
within 600 feet of sales occurred before sales. DIS900 is the number of foreclosures within 900
feet of sales occurred before sales. DIS1200 is the number of foreclosures within 1200 feet of
sales occurred before sales.
The Georgia law permits lenders to declare a borrower in default and reclaim a house in
as little as sixty days, which speeds up the foreclosure process and puts foreclosures back on the
market quickly. The dataset shows that most of the REOs are sold by bank or mortgage
55
institutions within one year and usually sell at a discounted price, which is much lower than
surrounding house values. If the REOs are bought by an investment companies, the investment
companies usually can sell the house to the market at a much higher price than the REO sales
price made by the banks.
4. Model
The hedonic price model is a commonly used method for studying housing values. House price is
usually affected by its own physical characteristics and its location. Structural characteristics,
socio-demographic factors, and location to specific amenities or disamenities are included in the
hedonic model to attribute positive or negative effects on house prices. The hedonic model
estimated by OLS regression serves as the baseline for analysis. Difference-in-differences and
propensity score matching methods will then be used for identifying the effects of foreclosures
on property values.
4.1. Difference-In-Differences (DID)
The difference-in-differences (DID) model can not only remove biases from comparisons
between the treatment and control group that could be the result from permanent differences, but
also can remove biases from comparisons over time in the treatment group that could be the
result of trends (Wooldridge 2007). Although previous studies indicate that foreclosures
depreciate neighborhood house sales prices, there is a problem in that there could be a trend over
time that the prices of all the houses in a neighborhood, not only houses in foreclosure infestation
areas, are declining due to external economic conditions. The question is that whether the
foreclosures cause a decline in sales price through spillover effects or all the houses in a
neighborhood experience price decline due to external economic conditions.
56
Repeat sales data are powerful for estimating the foreclosure effect. Because number of
foreclosures is the interest of this study, and the treatment effect varies with different number of
foreclosures. With many time periods and arbitrary treatment patterns, we can use
(3.1)
where Pit is a vector of property sales price from year 2000 to 2010 deflated by year consumer
price index (CPI) expressed as the natural log form; ?t is a full set of time effects, representing
the overall market price level at time t; NFit is the number of foreclosures within certain buffers;
Xit are variables that change over time to affect sales price, which include vectors of property
characteristics6, sales type, and sales quarter dummy variables; ci is observed characteristics that
do not change over time; and di is unobserved characteristics that do not change over time.
Estimation by fixed effects can absorb observed and unobserved invariant characteristics ci and
di across time, provided the number of foreclosures, NFit, is strictly exogenous.
Bertrand, Duflo and Mullainathan (2004) point out that conventional DID standard errors
are understated due to a serial correlation problem. One factor causing serial correlation is that
the treatment variable itself changes very little within a state over time. However, they find that
the serial correlation problem can be eliminated by randomly choosing ten treatment dates
between study years, instead of just choosing one date after which all the states in the treatment
group are affected by the treatment. If the observation relates to a state that belongs to the
treatment group at one of these ten dates, the law is defined as 1, 0 otherwise. In other words, the
intervention variable is now repeatedly turned on and off, so its value in one year tells us nothing
about its value the next year. In this study, the treatment is the number of foreclosures within
certain buffers. Because the number of foreclosures changes over time, a property not
6 In Harding et al. (2008)?s paper, they assume that the property characteristics are fixed between sales in the
standard repeat sales methodology. However, the characteristics of some houses may change due to remodeling or
innovation. This study incorporates dummy variables to control these changes in the model.
57
surrounded by foreclosures at initial time may experience foreclosures in its neighborhood next
year, but when the foreclosures are resolved by a bank or third party, the property would not be
exposed to the nearby foreclosures. In other words, the treatment is repeatedly turned on and off.
Thus, the serial correlation problem can be avoided in this model.
4.2. Propensity Score Matching (PSM)
Propensity score matching (PSM) is often used in observational studies where subjects are not
randomly assigned to treatment and control groups. Randomization may assure that treatment
and control groups have identical characteristics, so that the differences between the groups after
applying a treatment can be attributed to the treatment effect. However, when the subjects are not
randomly assigned to groups, it causes causal inference complicated because we do not know
whether the differences of outcome come from the treatment itself or is a product of differences
among treatment group and control group. The propensity score matching method can achieve
randomized experiment effect by making the characteristics of subjects in treatment and control
groups close to identical. The idea is to estimate the probability (the propensity score) that a
subject would be assigned to the treatment given certain characteristics. If a treated subject has
the same propensity score as a control subject, then the difference between them is the result of
the treatment effect itself.
Previous studies indicate that foreclosures are more likely to occur in low-income and
minority districts. In other words, houses located in foreclosure infestation areas and non-
foreclosure infestation areas have systematically different characteristics, with respect to housing
characteristics and neighborhood characteristics. We could match the propensity score for
subjects in treatment and control groups to make sure they have similar probability of being
surrounded by foreclosures.
58
The first step is to estimate the propensity score for the sales property surrounded by
foreclosures. It is estimated by logistic regression in which the dependent variable is DIS300,
indicating whether there are foreclosures within 300 feet. The treatment DIS300 equals to 1 if
there are there are 1 or more than 1 foreclosures within 300 feet. In particular, 68% of the sales
properties in my sample from year 2000 to 2010 were affected with foreclosures.
( ) (3.2)
where M is a vector of mortgage characteristics, including percentage of low-cost/high-leverage
mortgage, high-cost/low-leverage mortgage, and high-cost/high-leverage mortgage in census
tract; H is a vector of the house characteristics, including lot size, living areas, and house age; N
is a vector of census block group socio-demographic characteristics, including the average per
capita income, percentage of black residents, average household size, percentage of residents
who own the property, and percentage of residents over 65 years old; Y is a vector of year
dummies.
After estimating the propensity score, there are various methods for matching the scores
between treatment and control groups. The most commonly used matching methods include the
nearest available neighbor and caliper matching. In the nearest available neighbor method, the
treatment unit is selected to find the closest control match if the absolute value of the difference
between their propensity scores are the smallest. The procedure is repeated for all the treated
units. If there is a replacement, then the matched control unit can be selected again to match
other treatment units. Otherwise, once it is matched, it will not be considered for matching
against other treatment units. The nearest available neighbor method guarantees that all the
treated units can find their control matches even if their propensity scores are not close enough.
59
Caliper matching is similar to the nearest available neighbor matching method but it adds
an additional restriction (Coca-Peraillon, 2006). The treated unit is selected to find its closest
control match based on the propensity score but only if the control unit?s propensity score is
within a certain radius. Thus, it is possible that not all the treated units will be matched to control
units, but the method can avoid bad matches.
5. Results
Table 3.1 reports the descriptive statistics for the variables in the model. The house
characteristics include lot size in square feet, living area in square feet, number of stories, age of
the house, number of bedrooms, number of full and half bathrooms, basement and attic condition,
heat type, overall dwelling condition, and the street condition for each parcel.
In addition to structural house characteristics, many neighborhood characteristics may
affect house value. Percentage of black residents, average household size, percentage of residents
who own a house, and percentage of people over 65 years old in each CBG are chosen to
represent neighborhood quality.
The effect of foreclosures is the focus of this study. The DID specification is used to
estimate the effect of foreclosures on property values. The distance interval is created for each
property. DIS300 is a vector of the number of foreclosures (including both REO and REO sales)
within 300 feet of sales occurring before sale, DIS600 is a vector of the number of foreclosures
within 600 feet of sales occurring before sale, DIS900 is a vector of the number of foreclosures
within 900 feet of sales occurring before sale, and DIS1200 is a vector of the number of
foreclosures within 1200 feet of sales occurring before sale. The average number of foreclosures
is 3.12 within 300 feet of a sales property, 9.91 within 600 feet of a sales property, 19.04 within
900 feet of a sales property, and 30.6 within 1200 feet of a property.
60
In this study, I also distinguish between REO and REO sales. The hypothesis is that both
REO and REO sales will depreciate neighborhood house sales price. REO reduces surrounding
house sales price because it causes disordered community, the vacant houses usually attract
criminals and rodent animals, which may cause both house physical appearance deterioration and
lead to mental stress for surrounding neighbors. REO sales could also depreciate its surrounding
neighborhood house sales price, because the price of the subject property is determined by its
comparable properties that are recently sold and close to the subject property (Lin et al., 2009;
Vandell, 1991). Banks usually sell REO to individuals or investment companies with a great
discount. The discount price thus has a spillover effect to surrounding sales price. The average
number of REOs is 1.1 within 300 feet, 3.49 within 600 feet, 6.71 within 900 feet, and 10.82
within 1200 feet. The average number of REO sales is 2.02 within 300 feet, 6.43 within 600 feet,
12.33 within 900 feet, and 19.78 within 1200 feet.
61
Table 3.1 Descriptive Statistics for One to Four Unit Family Sales House Characteristics and
Neighborhood Characteristic, Atlanta, 2000-2010 (N=26,352)
Variable Description Mean SD Minimum Maximum
Price Sales price (*$1000) 180.99 289.51 1 6,499
Street1 The street condition in the parcel is "paved" 0.99 0.08 0 1
Street2 The street condition is ?semi-improved? 0.002 0.05 0 1
Street3 The street condition is ?dirt? 0.004 0.06 0 1
Lotarea Lot area sqft (*1000) 10.91 10.05 0.06 387.68
Lvarea Living area sqft (*1000) 1.61 0.99 0.12 24.55
Stories Number of stories 1.19 0.39 1 3
Rmbed Number of bedrooms 2.98 0.96 0 14
Fixbath Number of full bathrooms 1.70 0.90 0 11
Fixhalf Number of half bathrooms 0.25 0.48 0 7
Bsmt1 No basement 0.06 0.24 0 1
Bsmt2 Crawl basement 0.60 0.49 0 1
Bsmt3 Part basement 0.16 0.37 0 1
Bsmt4 Full basement 0.17 0.38 0 1
Heat1 No heat 0.02 0.14 0 1
Heat2 Central heat 0.05 0.22 0 1
Heat3 Central air condition 0.17 0.38 0 1
Heat4 Heat pump 0.76 0.43 0 1
Attic1 No attic 0.88 0.33 0 1
Attic2 Unfinished attic 0.05 0.22 0 1
Attic3 Part finished attic 0.03 0.18 0 1
Attic4 Fully finished attic 0.03 0.18 0 1
Attic5 Fully finished/wall height attic 0.01 0.09 0 1
Age Age of sales house 51.00 29.13 0 140
Cdu1 Dwelling condition is excellent 0.10 0.30 0 1
Cdu2 Dwelling condition is very good 0.21 0.41 0 1
Cdu3 Dwelling condition is good 0.20 0.40 0 1
Cdu4 Dwelling condition is average 0.39 0.49 0 1
Cdu5 Dwelling condition is fair 0.06 0.23 0 1
Cdu6 Dwelling condition is unsound 0.03 0.16 0 1
Cdu7 Dwelling condition is poor 0.01 0.11 0 1
Cdu8 Dwelling condition is very poor 0.003 0.05 0 1
Black Percentage of black residents in CBG 0.79 0.32 0 1
Hsize Average household size in CBG 2.65 0.44 1.28 4.19
Own Percentage of residents own the house in
CBG
0.49 0.21 0 0.98
Income Per capital income in 1999 in CBG (*$1000) 19.37 17.74 2.76 120.93
Old Percentage of people over 65 years old in
CBG
0.11 0.06 0 0.51
DIS300 Number of total foreclosures (including REO
and REO sales) within 300 feet of sales
house
3.12 3.90 0 37
62
DIS300REO Number of foreclosures within 300 feet of
sales house
1.10 1.64 0 21
DIS300REOS Number of foreclosure sales within 300 feet
of sales house
2.02 2.71 0 26
DIS600 Number of total foreclosures (including REO
and REO sales) within 600 feet of sales
house
9.91 11.75 0 91
DIS600REO Number of foreclosures within 600 feet of
sales house
3.49 4.54 0 41
DIS600REOS Number of foreclosure sales within 600 feet
of sales house
6.43 7.88 0 57
DIS900 Number of total foreclosures (including REO
and REO sales) within 900 feet of sales
house
19.04 22.26 0 153
DIS900REO Number of foreclosures within 900 feet of
sales house
6.71 8.42 0 77
Dis900REOS Number of foreclosure sales within 900 feet
of sales house
12.33 14.72 0 94
DIS1200 Number of total foreclosures (including REO
and REO sales) within 1200 feet of sales
house
30.60 35.45 0 268
DIS1200REO Number of foreclosures within 1200 feet of
sales house
10.82 13.33 0 132
DIS1200REOS Number of foreclosure sales within 1200 feet
of sales house
19.78 23.30 0 152
63
Table 3.2 reports the descriptive statistics for dummy variables. Quarter dummies are
created to control for sales seasonal effects. It is hypothesized that houses are usually sold at a
lower price in the winter than in the summer. There are many types of sales within the sample,
and the sales type affects the sales price directly. Thus, we add sales type dummies into the
regression. D1 is valid sale, D2 is the sale to or from exempt or utility, D3 is remodeled or
changed after sale, D4 is related to individual or corporation sale, D5 is liquidation or foreclosure
sale, D6 is land contract or unusual financing sale, and D7 is the sale that includes additional
interest. School zone variable is another important factor determining house sales price. Good
quality schools usually attract good quality teachers who receive higher salaries. Parents fund the
schools through buying houses located in good school zones. Houses located in good school
zones are usually more expensive because parents thus can fund the school by paying more
property tax. Fifty elementary school zone dummies were added in the model.
64
Table 3.2 Descriptive Statistics for Dummy Variables, Atlanta, 2000-2010 (N=26.352)
Variable Description Mean SD Minimum Maximum
Sale1 Valid sale 0.35 0.48 0 1
Sale2 To/from exempt or utility 0.03 0.17 0 1
Sale3 Remodeled/Changed after sale 0.06 0.24 0 1
Sale4 Related individuals or corporation 0.08 0.27 0 1
Sale5 Liquidation/Foreclosure 0.43 0.49 0 1
Sale6 Land contract/Unusual financing 0.05 0.21 0 1
Sale7 Includes Additional interest 0.002 0.05 0 1
Quarter1 Sold in the first quarter 0.25 0.43 0 1
Quarter2 Sold in the second quarter 0.28 0.45 0 1
Quarter3 Sold in the third quarter 0.26 0.44 0 1
Quarter4 Sold in the fourth quarter 0.21 0.41 0 1
SD1 Adamsville Elementary School 0.004 0.06 0 1
SD2 Benteen Elementary School 0.01 0.10 0 1
SD3 Mary Mcleod Bethune Elementary School 0.02 0.15 0 1
SD5 Capitol View Elementary School 0.02 0.15 0 1
SD6 Cascade Elementary School 0.002 0.05 0 1
SD7 Cleveland Avenue Elementary School 0.01 0.08 0 1
SD8 William M. Boyd Elementary School 0.01 0.12 0 1
SD9 Warren T. Jackson Elementary School 0.02 0.14 0 1
SD10 Morris Brandon Elementary School 0.02 0.14 0 1
SD11 Garden Hills Elementary School 0.01 0.11 0 1
SD12 E. Rivers Elementary School 0.02 0.14 0 1
SD13 Bolton Academy 0.01 0.11 0 1
SD14 Beecher Hills Elementary School 0.02 0.12 0 1
SD15 Daniel H. Stanton Elementary School 0.03 0.17 0 1
65
SD16 John Wesley Dobbs Elementary School 0.03 0.18 0 1
SD17 Hill-Hope Elementary School 0.01 0.11 0 1
SD18 Connally Elementary School 0.06 0.24 0 1
SD19 Centennial Place Elementary School 0.01 0.09 0 1
SD20 Continental Colony Elementary School 0.004 0.07 0 1
SD21 Ed S. Cook Elementary School 0.04 0.20 0 1
SD22 Deerwood Academy 0.01 0.11 0 1
SD23 Paul L. Dunbar Elementary School 0.01 0.11 0 1
SD25 Margaret Fain Elementary School 0.01 0.10 0 1
SD26 Fickett Elementary School 0.01 0.08 0 1
SD27 William Finch Elementary School 0.05 0.22 0 1
SD28 Charles L. Gideons Elementary School 0.07 0.25 0 1
SD29 Grove Park Elementary School 0.04 0.19 0 1
SD30 Heritage Academy Elementary School 0.01 0.09 0 1
SD31 Alonzo F. Herndon Elementary School 0.03 0.17 0 1
SD32 Joseph Humphries Elementary School 0.01 0.07 0 1
SD33 Emma Hutchinson Elementary School 0.01 0.10 0 1
SD34 M. Agnes Jones Elementary School 0.06 0.25 0 1
SD35 L. O. Kimberly Elementary School 0.004 0.07 0 1
SD36 Mary Lin Elementary School 0.003 0.06 0
1
SD37 Leonora P. Miles Elementary School 0.006 0.08 0 1
SD38 Morningside Elementary School 0.02 0.14 0 1
SD39 Parkside Elementary School 0.03 0.16 0 1
SD40 Perkerson Elementary School 0.03 0.17 0 1
66
SD41 Peyton Forest Elementary School 0.006 0.08 0 1
SD42 William J Scott Elementary School 0.02 0.13 0 1
SD43 Thomas Heathe Slater Elementary School 0.03 0.16 0 1
SD44 Sarah Rawson Smith Elementary School 0.02 0.12 0 1
SD45 Springdale Park Elementary School 0.01 0.12 0 1
SD46 F. L. Stanton Elementary School 0.03 0.18 0 1
SD47 Thomasville Heights Elementary School 0.01 0.09 0 1
SD49 George A. Towns Elementary School 0.02 0.12 0 1
SD50 Bazoline Usher Elem School 0.03 0.16 0 1
SD52 West Manor Elementary School 0.01 0.09 0 1
SD53 Walter F. White Elementary School 0.03 0.18 0 1
SD55 Carter G. Woodson Elementary School 0.01 0.12 0 1
5.1. OLS Regression
Table 3.3 reports the foreclosure effects within different buffers estimated by OLS regressions.
The standard deviations are heteroskedatic-corrected, so they are robust. All the structure
variables and neighborhood variables have their expected signs. For example, 1000 more square
feet living areas increases the sales price by 13%. The Cdu is the general dwelling condition for
each property. Eight dummy variables are created to distinguish among different dwelling
conditions, with Cdu1 being excellent condition and Cdu8 being very poor condition. In the
regression, we delete one dummy Cdu8, so the coefficients of Cdu1 to Cdu7 should be
interpreted as the differences from the coefficient of Cdu8. Within 300 feet, properties with
excellent condition (Cdu1) sells at a 39% higher price on average than houses with very poor
67
condition (Cdu8), while properties with fair condition (Cdu5) sells at only 18% higher price on
average than houses with very poor condition (Cdu8).
In the OLS regression, one more foreclosure within 300 feet reduces the property sales
price by 1.4%, one more foreclosure within 600 feet reduces the property sales price by 0.6%,
one more foreclosure within 900 feet reduces the property sales price by 0.3%, and one more
foreclosure within 1200 feet reduces the sales price by 0.2%.
68
Table 3.3 The Effect of Foreclosures within Different Buffers, Regression Coefficients for
Heteroskedasticity ? Corrected OLSa
Variable Within 300 Feet Within 600 Feet Within 900 Feet Within 1200 Feet
Intercept 8.10***
(38.73)
8.06***
(38.39)
8.05***
(38.28)
8.04***
(38.29)
DIS300 -0.014***
(-7.65)
DIS600 -0.006***
(-8.82)
DIS900 -0.003***
(-8.22)
DIS1200 -0.002***
(-8.84)
Street2 -0.10
(-0.74)
-0.10
(-0.75)
-0.10
(-0.72)
-0.10
(-0.72)
Street3 -0.11
(-1.22)
-0.10
(-1.21)
-0.10
(-1.15)
-0.10
(-1.16)
Lotarea 0.006***
(8.59)
0.006***
(8.42)
0.006***
(8.51)
0.006***
(8.56)
Lvarea 0.13***
(9.98)
0.13***
(10.05)
0.13***
(10.09)
0.13***
(10.10)
Stories 0.07***
(3.13)
0.07***
(3.27)
0.07***
(3.27)
0.08***
(3.30)
Age 0.003***
(10.48)
0.003***
(10.43)
0.003***
(10.45)
0.003***
(10.46)
Rmbed 0.03***
(4.73)
0.03***
(4.66)
0.03***
(4.67)
0.03***
(4.67)
Fixbath 0.05***
(4.78)
0.05***
(4.79)
0.04***
(4.72)
0.04***
(4.73)
Fixhalf 0.01
(0.77)
0.01
(0.66)
0.01
(0.64)
0.01
(0.67)
Bsmt2 0.02
(0.95)
0.02
(1.11)
0.02
(1.13)
0.03
(1.23)
Bsmt3 0.08***
(3.45)
0.09***
(3.57)
0.09***
(3.63)
0.09***
(3.77)
Bsmt4 0.09***
(3.54)
0.09***
(3.62)
0.09***
(3.71)
0.09***
(3.81)
Heat2 0.07*
(1.74)
0.07*
(1.81)
0.07*
(1.84)
0.07*
(1.85)
Heat3 0.13***
(3.52)
0.13***
(3.64)
0.13***
(3.65)
0.13***
(3.63)
Heat4 0.20***
(5.70)
0.20***
(5.80)
0.20***
(5.78)
0.20***
(5.78)
Attic2 0.07***
(3.20)
0.07***
(3.30)
0.07***
(3.35)
0.07***
(3.38)
Attic3 0.04 0.01 0.01 0.01
69
(0.13) (0.18) (0.20) (0.19)
Attic4 0.01
(0.38)
0.01
(0.43)
0.01
(0.51)
0.01
(0.50)
Attic5 0.10*
(1.92)
0.10**
(1.99)
0.10**
(2.04)
0.11**
(2.07)
Cdu1
0.39***
(3.75)
0.38***
(3.73)
0.39***
(3.79)
0.39***
(3.80)
Cdu2 0.31***
(3.12)
0.31***
(3.10)
0.32***
(3.17)
0.32***
(3.18)
Cdu3 0.27***
(2.70)
0.27***
(2.69)
0.28***
(2.75)
0.28***
(2.76)
Cdu4 0.21**
(2.08)
0.21**
(2.06)
0.21**
(2.12)
0.22**
(2.15)
Cdu5 0.18*
(1.66)
0.17*
(1.65)
0.17*
(1.71)
0.18*
(1.73)
Cdu6 0.07
(0.65)
0.07
(0.65)
0.07
(0.71)
0.08
(0.75)
Cdu7 0.11
(1.04)
0.12
(1.11)
0.12
(1.12)
0.13
(1.16)
remodel 0.13***
(3.72)
0.14***
(3.80)
0.14***
(3.82)
0.14***
(3.81)
Black -0.11**
(-2.10)
-0.09
(-1.57)
-0.09
(-1.60)
-0.08
(-1.48)
Hsize -0.01
(-0.64)
-0.01
(-0.41)
-0.01
(-0.42)
-0.01
(-0.35)
Own 0.09**
(2.51)
0.10***
(2.78)
0.10***
(2.86)
0.11***
(3.04)
Income 0.005***
(6.49)
0.005***
(6.42)
0.005***
(6.32)
0.005***
(6.18)
Old -0.01
(-0.09)
-0.02
(-0.20)
-0.01
(0.08)
-0.002
(-0.01)
Quarter1 0.04**
(2.51)
0.03**
(2.30)
0.03**
(2.30)
0.03**
(2.21)
Quarter2 0.02*
(1.67)
0.02
(1.51)
0.02
(1.52)
0.02
(1.47)
Quarter3 0.04***
(3.03)
0.04***
(2.97)
0.04***
(3.00)
0.04***
(2.98)
Year Dummy Yes Yes Yes Yes
Adj. R2 0.57 0.57 0.57 0.57
a School district dummy, sales type dummy and year dummy variable parameter estimates not
reported here for sake of space.
Note: ***Statistically significant at 1%; **Statistically significant at 5%; *Statistically
significant at 10%.
The asymptotic t statistics are in parentheses.
70
I also distinguish between REO and REO sales to examine their effects on neighborhood
property sales price respectively. Table 3.4 reports that one more REO within 300 feet reduces
surrounding sales price by 4.3% after controlling housing characteristics and neighborhood
characteristics, and one more REO sale reduces surrounding sale price by 0.8%. If both REO and
REO sales are included in the regression model, Model 3 reports that one more REO within 600
feet reduces surrounding sales price by 2.7%, while one more REO sales increases surrounding
sales price by 0.6%. It makes sense because REO sales would resolve REO. Once a REO is sold
by the bank, it is not considered as a REO, and instead it becomes a REO sale if it occurs before
the subject property sale. Although REO sales themselves may affect surrounding house sales
price in a negative way, REO sales help eliminate vacant houses and rebuild community stability,
and one more REO sale indicates that there is one less REO, REO sales thus help raise
surrounding houses? sales price per se after controlling REOs. The effect of REO and REO sales
are consistent with different buffers although the magnitudes are different.
71
Table 3.4 The Effect of REO and REO sales on Neighborhood Property Sales Value within Different Buffers7
Variable Within 300 Feet Within 600 Feet Within 900 Feet Within 1200 Feet
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
DIS300REO -0.043***
(-11.24)
-0.045***
(-11.05)
DIS300REOS -0.008***
(-3.37)
0.003
(1.35)
DIS600REO -0.022***
(-13.49)
-0.027***
(-14.02)
DIS600REOS -0.004***
(-4.54)
0.006***
(5.10)
DIS900REO -0.012***
(-12.79)
-0.019***
(-14.84)
DIS900REOS -0.003***
(-4.45)
0.006***
(7.89)
DIS1200REO
-0.009***
(-14.18)
-0.015***
(-17.62)
DIS1200REOS -0.002***
(-4.75)
0.005***
(10.53)
Note: ***Statistically significant at 1%; **Statistically significant at 5%; *Statistically significant at 10%.
The asymptotic t statistics are in parentheses.
7 Regression Coefficients for Heteroskedasticity ? Corrected OLS
72
5.2. Difference-In-Differences
One disadvantage of OLS regression is that the omitted variable problem may exist. Although
we have controlled for housing characteristics, neighborhood characteristics, and school districts,
there are still some unobserved variables may affect housing sales price, including some location
variables which are unchanged over time but cannot be measured with in the dataset. Difference-
in-differences method could help eliminate those invariant observed and unobserved variables,
and thus avoid the omitted variable problem. Table 3.5 reports the difference-in-differences
regression results. Model 1 serves as the base model, which includes the foreclosure variable
DIS300, DIS600, DIS900, and DIS1200 (including both REO and REO sales) respectively and
variant house characteristics, including house age, sales type, sales quarter and whether it is
remodeled or not. On average, one more foreclosure within 300 feet reduces its surrounding sales
price by 2.5%, one more foreclosure within 600 feet reduces its surrounding sales price by 1.1%,
one more foreclosure within 900 feet reduces its surrounding sales price by 0.6%, and one more
foreclosure within 1200 feet reduces its surrounding sales price by 0.4%. The coefficients
estimated by the difference-in-differences method are larger than those estimated by OLS
regression. In Model 2, after separating REO and REO sales, the estimated coefficients are also
larger than those estimated by OLS regression. For example, one more REO within 600 feet
reduces surrounding sales price by 3.7%, while one more REO sales within 600 feet increases
sales price by 0.6%. In general, the difference-in-differences models produce a better fit than
OLS models indicated by higher adjusted R2 value.
73
Table 3.5 The Effect of Foreclosures within Different Buffers, Difference-In-Differences Model
Variable Within 300 Feet Within 600 Feet Within 900 Feet Within 1200 Feet
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
DIS300 -0.025***
(-7.91)
DIS300REO -0.068***
(-11.64)
DIS300REOS -0.001
(-0.13)
DIS600 -0.011***
(-9.52)
DIS600REO -0.037***
(-14.49)
DIS600REOS
0.006***
(3.23)
DIS900 -0.006***
(-9.64)
DIS900REO -0.024***
(-15.30)
DIS900REOS 0.006***
(5.38)
DIS1200 -0.004***
(-10.31)
DIS1200REO -0.018***
(-16.77)
DIS1200REOS 0.005***
(7.11)
Age -0.07***
(-14.15)
-0.07***
(-14.21)
-0.07***
(-14.08)
-0.07***
(-14.17)
-0.07***
(-14.07)
-0.07***
(-14.26)
-0.07***
(-14.11)
-0.07***
(-14.26)
Sale1 1.31***
(8.13)
1.30***
(8.10)
1.32***
(8.21)
1.31***
(8.13)
1.32***
(8.21)
1.30***
(8.11)
1.33***
(8.23)
1.30***
(8.11)
Sale2 0.53***
(3.22)
0.52***
(3.18)
0.54***
(3.28)
0.53***
(3.20)
0.54***
(3.27)
0.52***
(3.17)
0.54***
(3.30)
0.52***
(3.16)
74
Sale3 0.70***
(4.28)
0.69***
(4.25)
0.70***
(4.33)
0.70***
(4.29)
0.70***
(4.33)
0.69***
(4.27)
0.71***
(4.35)
0.69***
(4.28)
Sale4 0.82***
(5.06)
0.81***
(5.02)
0.84***
(5.14)
0.82***
(5.06)
0.84***
(5.15)
0.82***
(5.05)
0.84***
(5.17)
0.82***
(5.06)
Sale5 0.65***
(4.00)
0.63***
(3.93)
0.66***
(4.06)
0.63***
(3.94)
0.66***
(4.06)
0.63***
(3.90)
0.66***
(4.08)
0.62***
(3.89)
Sale6 0.20
(1.22)
0.19
(1.18)
0.21
(1.29)
0.20
(1.25)
0.21
(1.29)
0.20
(1.22)
0.22
(1.32)
0.20
(1.23)
Sale7 1.28***
(6.33)
1.26***
(6.26)
1.29***
(6.38)
1.26***
(6.26)
1.29***
(6.39)
1.26***
(6.25)
1.30***
(6.42)
1.26***
(6.25)
Quarter1 0.02
(1.01)
0.02
(1.08)
0.01
(0.81)
0.02
(1.01)
0.01
(0.76)
0.02
(1.02)
0.01
(0.65)
0.02
(0.99)
Quarter2 0.004
(0.27)
0.01
(0.43)
0.002
(0.10)
0.01
(0.33)
0.001
(0.07)
0.01
(0.33)
-0.00
(-0.01)
0.01
(0.31)
Quarter3 0.04***
(2.95)
0.05***
(2.97)
0.05***
(2.85)
0.05***
(2.93)
0.05***
(2.83)
0.05***
(2.92)
0.05***
(2.78)
0.05***
(2.83)
Remodel 0.06
(0.92)
0.06
(0.85)
0.05
(0.78)
0.04
(0.62)
0.04
(0.66)
0.03
(0.44)
0.03
(0.53)
0.03
(0.41)
Adj. R2 0.76 0.76 0.76 0.76 0.76 0.76 0.76 0.76
a School district dummy and year dummy variable parameter estimates not reported here for sake of space.
Note: ***Statistically significant at 1%; **Statistically significant at 5%; *Statistically significant at 10%.
The asymptotic t statistics are in parentheses.
75
5.3. Propensity Score Matching
Table 3.6 and Table 3.7 report the propensity score matching results. Table 3.6 shows the
baseline characteristics of the treatment and control groups. The treatment group (N=18,030) is
very different from the control group (N=8,312) in all selected variables. Houses are more likely
to be surrounded by foreclosures within 300 feet if they have smaller living areas, older, located
in the census block group with higher percentage of subprime mortgages, higher percentage of
black residents, larger household size, lower home ownership, lower per capita income, and
lower percentage of people older than 65 years old.
Table 3.7 shows the matching results using the caliper matching method with
replacement with a caliper of 1*E-4. There are 2,183, i.e. 12% treated units are matched. The
samples appear to be well balanced except lot size. Then, t test is used to compare the sales price
mean between treated and control group. The average sales price in treatment group is $128,712,
and the average sales price in control group is $141,052. Thus, the treatment effect of foreclosure
within 300 feet is to reduce average sales price by $4,728, which is about 8.7% less. The result is
larger than the coefficient estimated by OLS and difference-in-differences regression. Propensity
score matching reports whether foreclosure reduces surrounding house sales price. The treatment
is a binary variable, if the number of foreclosures within 300 feet for a house is not 0, the house
is considered to be treated with foreclosures. Thus, foreclosures within 300 feet reduce property
sales price by 8.7% on average.
76
Table 3.6 Baseline Characteristics (Treatment: DIS300>0)
Characteristic Treatment (N=18,030) Control (N=8,312) Analysis
Mean SD Mean SD T-test P-value
Pct_lchl 0.06 0.03 0.10 0.05 t = 66.05 <0.0001
Pct_hcll 0.31 0.10 0.20 0.14 t = -75.88 <0.0001
Pct_hchl 0.19 0.06 0.12 0.08 t = -79.48 <0.0001
Lot size 8.97 6.37 15.11 14.36 t = 48.10 <0.0001
Living area 1.43 0.57
2.02 1.48 t = 46.69 <0.0001
Age 52.45 29.73 47.86 27.51 t = -11.93 <0.0001
Black 0.89 0.20 0.59 0.42 t = -77.61 <0.0001
Household size 2.74 0.39 2.46 0.48 t = -50.52 <0.0001
Own 0.46 0.19 0.56 0.23 t = 40.48 <0.0001
Income 14039.9 8507.2 30933.4 25397.2 t = 80.11 <0.0001
Old 0.11 0.06 0.12 0.07 t =4.08 <0.0001
a School district dummy variable parameter estimates not reported here for sake of space.
Note: ***Statistically significant at 1%; **Statistically significant at 5%; *Statistically
significant at 10%.
77
Table 3.7 Propensity Score Matching, Caliper (1*E-4) method (Treatment: DIS300>0)
Characteristic Treatment (N=2,183) Control (N=2,183) Analysis
Mean SD Mean SD T-test P-value
Pct_lchl 0.07 0.04 0.07 0.04 t = -0.24 0.81
Pct_hcll 0.29 0.11 0.29 0.11 t = -0.76 0.45
Pct_hchl 0.17 0.06 0.17 0.06 t = 1.21 0.23
Lot size 9.75 6.77 10.76 6.40 t = 5.05 <0.0001
Living area 1.45 0.64
1.48 0.63 t = 1.57 0.12
Age 52.44 28.81 51.21 26.90 t = -1.46 0.14
Black 0.85 0.25 0.85 0.26 t = 0.50 0.62
Household size 2.67 0.42 2.67 0.43 t = -0.51 0.61
Own 0.48 0.19 0.49 0.21 t = 0.75 0.45
Income 15810 10236.2 16375.1 10938.3 t = 1.76 0.08
Old 0.12 0.07 0.12 0.07 t =-0.36 0.72
Price 128,712 127,935 141,052 12,340.4 t = 2.72 0.01
a School district dummy variable parameter estimates not reported here for sake of space.
Note: ***Statistically significant at 1%; **Statistically significant at 5%; *Statistically
significant at 10%.
6. Conclusion
By using repeat sales, this study applies quasi-experiment models, difference-in-differences and
propensity score matching methods to analyze the impacts of foreclosures on neighborhood one
to four unit residential property values in city of Atlanta. The regression results by the OLS,
difference-in-differences and propensity score matching are analyzed and compared. Using
repeat sales, the difference-in-differences and propensity score matching methods avoid omitted
variable bias which is likely a problem in hedonic models.
78
In this study, I also distinguish between REO and REO sales. The difference-in-
differences model reports that one more foreclosure (including REO and REO sales) reduces
surrounding house sales prices by 2.5% within 300 feet, 1.1% with 600 feet, 0.6% within 900
feet, and 0.4% within 1200 feet. However, after separating REO and REO sales, the effect of
REO increases dramatically, one more REO reduces surrounding sales price by 3.7%, while one
more REO sales increases sales price by 0.6% within 600 feet.
Propensity score matching also indicate that after balancing treatment and control groups,
foreclosures within 300 feet reduces average sales price by 8.7% less. The result is larger than
the coefficient estimated by both OLS and difference-in-differences regression. Propensity score
matching only reports whether foreclosure reduces surrounding house sales price. The treatment
is a binary variable, if the number of foreclosures within 300 feet for a house is not 0, the house
is considered to be treated with foreclosures. However, difference-in-differences reports how
much surrounding house sales price will be reduces by increasing one more foreclosure within
certain buffer. Compared with difference-in-differences method, propensity score matching
method reports an aggregate effect of foreclosures. Thus, it makes sense that the coefficient of
foreclosures estimated by propensity score matching is larger than those estimated by difference-
in-difference method.
Compared to Harding, Rosenblatt and Yao?s (2009) work, this study improves their
model from several aspects. First, besides number of nearby foreclosures, this study controls
more property characteristics that are expected to change between sales, including whether the
house remodeled or not, sales quarter and sales type. Second, instead of arbitrarily picking out
two repeat sales, this study includes every transaction record sold more than once during the
study periods, which gives me more precise estimates due to the efficiency gain bout by more
79
data. Third, using transaction buyers and sellers? names, this study distinguishes between REO
and REO sales. Because each REO sale resolves a REO, it helps increase surrounding house
sales price as a result. The study separates effects of the price trend over time and the contagion
effects of foreclosures. The results confirm negative contagion effects of foreclosures for
surrounding sales properties
80
CHAPTER 4
Irrigation and Income Inequality in the Southeast United States
1. Introduction
Irrigation is often promoted as a technology that can increase crop production, improve
agriculture income and alleviate poverty. However, irrigation is a relatively expensive
technology for small-scale farmers and poor farmers, which impedes their opportunities to adopt
irrigation technology. Income inequality may increase due to the adoption barriers.
According to the agricultural treadmill theory, anyone of a number of small farms
produce the same products cannot affect the commodity?s price; hence farmers who initially
adopt new technology and thus increase productivity are able to gain significant benefits. The
income inequality increases between technology adopters and non-adopters. However, after
some time, others follow and commodity prices tend to fall with the increased supply. Thus,
increased efficiency in agricultural production can drive down prices. The downward pressure on
crop price directly has two results: (1) those who have not yet adopted the new technology must
now do so lest they lose income because of price squeeze and (2) those who are too old, sick,
poor or indebted to innovate eventually have to exit from farming. Their resources are then
absorbed by those who make the windfall profits or ?scale enlargement? (Cochran, 1958; Bai,
2008). In effect, the consequence of the first situation decreases farmers? income inequality when
more and more farms adopt irrigation technology, and the second situation may result in
redistribution of natural resources and rural income and further exacerbates inequality.
81
Lorenz curves and Gini coefficient are used to measure the income inequality. A Lorenz
curve plots the cumulative percentages of total income received against the cumulative
percentages of recipients, starting with the poorest farms. With perfect equality, the number of
percent of the farms would receive exactly number of percent of the income. The corresponding
Lorenz curve would therefore be a straight 45 degree line. A Gini index helps to compare income
inequality among farms. It is the area between a Lorenz curve and the line of absolute equality,
as a percentage of the triangle under the line of absolute equality. A Gini index of 0% represents
perfect equality and a Gini index of 100% implies perfect inequality.
In this study, farm is ordered as the unit of analysis to calculate the Gini index. The
Lorenz curve is constructed by plotting the cumulative percentage of farms and cumulative
percentage of market value of agricultural products sold. In a perfectly equal system, all farms
would contribute the same share to the market value of agricultural products sold.
Figure 4.1 Lorenz Curve
82
There are several research studies examine the relationship between irrigation and
income distribution as well as poverty in the developing countries. However, the existing
literature does not adequately address the endogeneity of irrigation adoption and thus not
convincingly establish a causal relationship. Because irrigation is a relatively expensive
technology for small-scale farmers and poor farmers, low farm income may impede their
opportunities to adopt irrigation technology. Thus, irrigation may be endogenous to agricultural
income and profit. This paper corrects the endogeneity problem and uses county level data from
the Census of Agriculture to examine effects of irrigation technology adoption on agriculture
income and income inequality in the 9 Southeast states, including Alabama, Arkansas, Florida,
Georgia, Louisiana, Mississippi, North Carolina, South Carolina, and Tennessee. Compared to
the household level data in the previous studies, county level data may be more comprehensive
to study the impact of irrigation in the country on average.
2. Literature Review
Huang et al. (2005) conducted survey in rural China, using household level data, they find that
irrigation increases farmers? cropping income and total income. Holding other household
characteristics constant, increasing irrigated land per capita by one hectare will lead to an
increase of 3082 yuan in annual cropping income per capita, and an increase of 2628 yuan in
annual total income per capital.
Using household data from 26 irrigation systems, Hussain (2007) states direct and
indirect irrigation benefits. The direct benefits include increasing crop yields, reducing climate
risk and increasing employment opportunities for irrigation system construction and maintenance
work. The indirect benefits include increasing farming labor demand and activating the rural
83
community through various water related activities (such as fish farming). The indirect benefits
could be larger than direct benefits through the multiplier effect.
Chamber (1988) cites several empirical studies across countries that irrigation increases
wage rates by increasing labor demand. Irrigation raises employment for landless labors via
increased working days per hectare and increased working days during a cropping season and
additional employment in a second or third irrigation season (Smith, 2004).
However, irrigation adoption decisions are usually affected by many factors. Adoption of
irrigation may be difficult for poor farmers because it requires capital, familiarization and is cash
intensive to operate. At first it raises inequality, as only a few of farmers share the initially high
income generated (Smith, 2004).
Caswell and Zilberman (1985) apply a multinomial logit framework to predict irrigation
choice as a function of water cost, farm location, water source, and crops grown in the San
Joaquin Valley of California. Skaggs (2000) find that the probability a grower will be a higher
tech irrigator decreases as age increases. Farmers owning larger farms are more likely to install
or expand drip irrigation technology. Shreastha and Gopalakrishnan (1993) use a probit model to
examine the choice of drip technology as a function of differential water use and yields, plant
cycle, soil type, temperatures and field gradients. They find that advanced irrigation technologies
tend to be adopted first in areas with relatively low land quality and expensive water (particularly
deep groundwater) areas.
Huang et al. (2006) report that a higher proportion of good quality land leads to higher
cropping income, but has no effect on off-farm income and other income8. Good quality land
8 Other income includes livestock income, income from gifts (non-remittances), rental income, income from
subsidies and pensions, income from interest, income from asset sales, net value of commercial agricultural (e.g.
vegetable and fruit), value of crop subsidiaries (e.g. fodders), net value of processed crop products, and
miscellaneous income.
84
with higher water-holding capacity reduced farmers? propensity to adopt irrigation technology.
Lichtenberg (1980) also find that ?irrigated agriculture was restricted to areas with
relatively flat topography and good soils.? Irrigation on sloped land is not economics due to labor
consumption and irrigation on sandy soils may cause runoff and thus waste water.
3. Data
Agricultural production and related data come from the 1997 and 2002 Census of Agriculture.
For confidentiality reasons, counties are the finest geographic unit of observation in these data.
In the subsequent regression analysis, the total market value of crops sold per acre, the total
market value of agricultural products sold per acre and total profit of agricultural products sold
per acre are the dependent variables. The market value of agricultural products sold represents
the gross market value before taxes and production expenses of all agricultural products sold or
removed from the place regardless of who received the payment. It is equivalent to total sales
which include sales from crops, some livestock and animal specialties. The figure also includes
the value of commodities placed in commodity credit corporation (CCC) loans. The market value
of crops sold only includes sales from crops. The average total profit of agricultural products
sold is constructed as the difference between the average market value of agricultural products
sold and average production expenses in a county. The sales value may be a better measure of
the economic size of farm because it represents all income resources from production operation,
other than income from farm-related sources.
The soil quality data come from National Resource Inventory (NRI). The NRI is a
massive survey of soil sample and land characteristics from roughly 800,000 sites which is
conducted in census years. Follow Desch?nes and Greenstone?s (2007) work, a number of soil
quality variables are selected as controls in the following regressions, including fraction of sand,
85
soil erosion (K-Factor), susceptibility to flood, slope length, permeability, moisture capacity,
wetland, and salinity.
Number of wells in each county is received from U.S. Geological Survey (USGS)
Groundwater Watch. The USGS provides wells? location, site number, site name and
measurement begin date and end date. Using these measurement begin date and end date, I could
calculate number of wells in 1997 and 2002 in each county. Then, number of wells could be
merged with irrigation data by county fips code and year.
The Gini coefficient is calculated from Census of Agricultural total agricultural products
sold values categories. The 12 sales value categories range from less than $1000 to $500,000 or
more. In each category, the number of farms and their total sales values are provided, so I could
get cumulative percentage of farms from lowest to highest agricultural sales income and
cumulative percentage of market value of agricultural produce sold. Following Foster and Sen
(1997), the Gini coefficient9 is calculated as
(4.1)
where Ai is the number of farms in each sales value category, ?A is the sum of all the farms in
each category in the county, Ei is the total sales value in each sales category, ?E is the sum of
total sales value in each category, Gi is Gini coefficient in each sales category, ?G is sum of each
category?s Gini coefficient.
9
Group Farm per Group Income per Group Accumulated Income Gini
1 A1 E1 K1=E1 G1=(2*K1-E1)*A1
2 A2 E2 K2=E2+K1 G2=(2*K2-E2)*A2
3 A3 E3 K3=E3+K2 G3=(2*K3-E3)*A3
4 A4 E4 K4=E4+K3 G4=(2*K4-E4)*A4
Total ?A ?E ?G
Inequality
Measure
Gini=1-?G/?A/?E
86
4. Model
In this study, the determinants of income can be analyzed by making sales value and total profit
of agricultural products sold a function of irrigation and a set of other county level agricultural
characteristics. The basic model is
(4.2)
where is total market value of crops sold per acre, total market value of agricultural
products sold per acre, or total profit of agricultural products sold per acre in county i in year t
expressed as the natural logarithmic form. There is a reason to express dependent variables as the
natural logarithmic form. The dependent variables are county-level sales or profit value per acre,
the estimates of sales values vary from county to county since they have different type of
operations with different sizes. The descriptive statistics show that sales values have large
variations because the standard deviations are larger than the means of sales values. Thus, the
transformed form of corrects for the heteroskedasticity resulting from differences in
operations. The vector contains farmer age, commodity credit loan per acre, and
precipitation. This study includes both growing season accumulative precipitation from April to
October and precipitation standard deviation derived from growing season precipitation. When
the rain level is not consistent, farmers will more likely to adopt irrigation to help increase
agriculture productivity. It is thus hypothesized that larger precipitation standard deviation will
increase the level of irrigation adaption. Variable Irrigation is the interest of this study, it is
measured as percentage of irrigated land in each county. The term ai captures all unobserved,
time-invariant factors that affect yit; and the error term ui is the idiosyncratic error or time-
varying error, it represents unobserved factors that change over time and affect yit.
87
There are three problems with this basic model. First, because this dataset concludes
agricultural data for census year 1997 and 2002, the poolability test is used to examine if data are
poolable so that individual time periods have the same constant slopes of regressors. The large F
statistics rejects the null hypothesis of poolability10, so the panel data are not poolable with
respect to time. Thus, a year dummy is added in the regression, which is called least squares
dummy variable (LSDV) regression. Second, even if it is assumed that the idiosyncratic error uit
is uncorrelated with xit and the variable of interest irrigationit, the estimations by OLS is biased
and inconsistent if ai is correlated with xit or irrigationit. The bias is often called heterogeneity
bias, it is really just bias caused from omitting a time-constant variable. In order to correct for it,
a vector of soil quality variables are added in the regression, which is time-invariant in the
dataset. Third, there are no state fixed effects to account for all unobserved differences across
states, such as state agricultural programs. The improved model is
(4.3)
where d2 is a dummy variable that equals zero when t=1 and one when t=2; state is a vector of
state dummies; ai represents time-constant variable soil quality, which includes measures of sand
content, susceptibility to floods, soil erosion (K-Factor), slope length, permeability, wetland,
moisture capacity, and salinity.
Irrigation is suspected endogenous in the model since there are obstacles for poor farmers
to adopt irrigation technology due to its high cost. In other words, the farmers? wealth may affect
their decisions of adopting irrigation technology. Thus, the crop sales value or the total
agricultural products sales value could affect farmers? irrigation level. Two-stage least squares
(2SLS) regression is appropriate to address the endogeneity problem. An instrumental variable
10 ( ? ) ( )
? ( ) [( ) ( )]
88
for irrigation is needed to conduct the first stage regression. Caswell and Zilberman (1986) find
that farmers in locations with relatively low land quality and expensive water (i.e. deep wells or
groundwaters) are more likely to adopt drip and sprinkler irrigation systems. The same findings
are also found in Shreastha and Gopalakrishnan?s (1993) work. Another recent study conducted
by Molnar and Sydnor (2010) shows that a major reason that farmers are reluctant to adopt
irrigation technology in some Southeast U.S. counties is due to lack of groundwater. In this
study, I use the number of wells in each county as a proxy to measure the groundwater
availability, which works as an instrumental variable for irrigation adoption. The first stage
model is
(4.4)
where Xit includes all the independent variables in the second stage; wells is number of wells in
each county.
Income inequality is analyzed as a function of a set of irrigation and other farm
characteristics. The model is expressed as
(4.5)
The dependent variable is Gini coefficient calculated from Census of Agricultural total
agricultural products sold values categories, independent variables include average farm size,
average commodity credit loans and irrigation. I use average acreage of irrigated land, number of
irrigated farm, and percentage of irrigated land to represent irrigation respectively. According to
the treadmill theory, it is hypothesized that an increase in the acreage or number of irrigated farm
will lead to an increase in the agricultural income inequality at first, but continually increasing
irrigated land or irrigated farms will drive down marginal profit of agricultural products and thus
decrease income inequality. So it is hypothesized that the relationship between irrigation and
89
income inequality is nonlinear. Thus, the square forms of irrigation variables are added to
examine this hypothesis.
5. Results
Table 4.1 reports the descriptive statistics for study variables. For the nine states, the average
total market value of crops sold is $354 per acre, ranging from $5 per acre to $23,172 per acre.
The average total market value of agricultural products sold is $466 per acre, ranging from $20
per acre to $24,836 per acre. The average profit of agricultural products sold is $85 per acre,
ranging from -$674 per acre to $4,542 per acre. The operational product sales values appear
unequal to some extent. The average irrigated land is 6%, ranging from 0.1% to 78%. The
number of wells in each county ranges from 0 to 4247.
90
Table 4.1 Descriptive Statistics for Study Variables, 9 Southeast States, 1997 and 2002 (N=568)
Variable Mean Std. Dev. Minimum Maximum
Income variables
Total market value of crops sold ($/acre) 353.94 940.56 5.01 23172.41
Total market value of agricultural
products sold ($/acre)
466.24 835.78 19.62 24836.12
Total profit of agricultural products sold
($/acre)
85.27 199.86 -673.97 4542.88
Gini coefficient 0.81 0.12 0 0.98
Irrigation
Percentage of irrigated land 6.07 12.54 0.01 78.08
Land quality
Fraction sand 0.25 0.37 0 1
Fraction flood-prone 0.18 0.24 0 1
K Factor 0.26 0.11 0.03 0.49
Slope length 148.33 80.46 21.26 785.71
Permeability 4.96 4.35 0.21 20
Wetlands 0.18 0.16 0.004 0.81
Moisture capacity 0.13 0.05 0.04 0.30
salinity 0.003 0.02 0 0.25
Precipitation
April average precipitation 4.05 1.01 1.79 6.32
May average precipitation 4.58 0.86 2.86 6.98
June average precipitation 4.71 0.92 3.18 8.77
July average precipitation 5.03 1.07 2.78 8.41
August average precipitation 4.54 1.34 2.51 9.4
September average precipitation 4.11 0.92 2.72 7.77
October average precipitation 3.54 0.61 2.26 5.69
Standard deviation 0.93 0.47 0.20 3.22
Cumulative precipitation 30.57 3.65 24.48 42.15
Other variables
Number of wells 170.39 336.60 0 4247
Average age of principle operator 55.82 2.02 44.80 62.20
Average commodity credit loans ($/acre) 5.29 9.74 0 79.63
Table 4.2 reports the first stage OLS regression for irrigation adoption. Besides all the
exogenous variables in the second stage, the instrumental variable number of wells is added in
the equation. The relationship between the number of wells and irrigation is strongly positive.
1% increase in number of wells will increase irrigated land by 2.2% The Hausman test is used to
test endogeneity in all the models, large chi-square statistics 8.25 (P=0.004) and 8.17 (P=0.0043)
confirm that there are endogeneity problems in the regression for total market value of crops
sold. However, there is no robust evidence that the irrigation is endogenous to total market value
of agricultural products sold and total profit of agricultural products sold. Thus, IV-2SLS
91
regression is preferred to the OLS regression for dependent variable total market value of crops
sold.
Table 4.2 First Stage OLS Regression Results for Irrigation
Dependent Variable
Percentage of irrigated land
Instrumental variable Model 1 Model 2
ln(Number of wells) 2.20***
(0.41)
2.27***
(0.41)
Land quality
Fraction sand 0.04
(0.03)
0.01
(0.03)
Fraction flood-prone -0.05*
(0.03)
-0.05
(0.28)
K Factor 0.23
(0.14)
0.19
(0.14)
Slope length -0.002
(0.01)
0.002
(0.01)
Permeability -0.66*
(0.39)
0.09
(0.37)
Wetlands -0.07*
(0.04)
-0.003
(0.04)
Moisture capacity -0.04
(0.38)
0.12
(0.38)
Salinity 0.76**
(0.35)
0.83**
(0.36)
Precipitation
Standard deviation of precipitation
7.85***
(3.07)
Cumulative precipitation -0.50**
(0.22)
Other variables
ln(Average commodity credit loans) ($/acre) 1.74***
(0.32)
1.90***
(0.32)
Age -34.53***
(7.40)
-34.12***
(7.43)
Age2 0.30***
(0.07)
0.29***
(0.07)
Year2002 2.53***
(0.88)
2.16**
(0.88)
State fixed effect Yes Yes
Adj R2 0.67 0.67
Note: ***Statistically significant at 1%; **Statistically significant at 5%; *Statistically significant at 10%.
The standard errors are in parentheses.
92
Table 4.3 reports the OLS regression and Table 4.4 reports the IV-2SLS regression for
the effect of irrigation on total market value of crops sold, total market value of agricultural
products sold and total profit of agricultural products sold respectively. Most of the coefficients
are statistically significant and have the expected sign. Most importantly, irrigation is positively
related to total market of crops sold and total market value of agricultural products sold. The
coefficients estimated in IV-2SLS are about two and half times the magnitude as those estimated
in the OLS regression. Holding other variables constant, an increase of 1% of irrigated land leads
to total market value of crop sold increasing by 5% per acre, and total market value of
agricultural product sold increasing by 0.5% per acre. On average, in 2002, the total market
value of crops sold decreases by 36% per acre compared to 1997.
A surprising result is that there are no statistically significant relationships between the
average commodity credit loan and the market value of crops sold estimated by all the models in
2SLS regression. In addition, there are no statistically significant causality between commodity
credit loan and market value of agricultural products sold as well as total profit of agricultural
products sold in the OLS regression.
93
Table 4.3 OLS Regression Results for Determinants of Income
Dependent variable
ln(Total market value of
crops sold)
($/acre)
ln(Total market value of
agricultural products sold)
($/acre)
ln(Total profit of agricultural
products sold)
($/acre)11
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Irrigation
Percentage of irrigated land 0.02***
(0.002)
0.02***
(0.002)
0.002
(0.002)
0.005*
(0.003)
0.003
(0.005)
0.005
(0.005)
Land quality
Fraction sand -0.001
(0.002)
-0.002
(0.002)
0.002
(0.002)
0.002
(0.002)
0.006
(0.004)
0.007*
(0.004)
Fraction flood-prone 0.001
(0.001)
0.001
(0.001)
-0.002
(0.002)
-0.001
(0.002)
-0.002
(0.003)
-0.001
(0.003)
K Factor 0.01
(0.01)
0.01
(0.01)
-0.02**
(0.01)
-0.02**
(0.01)
-0.03
(0.02)
-0.02***
(0.02)
Slope length 0.0003
(0.0004)
0.003
(0.0004)
0.001**
(0.0004)
0.001**
(0.0005)
0.0003
(0.001)
0.0001
(0.001)
Permeability 0.06***
(0.02)
0.07***
(0.02)
-0.01
(0.02)
0.02
(0.02)
0.01
(0.05)
-0.002
(0.05)
Wetlands 0.01***
(0.002)
0.01***
(0.002)
-0.01***
(0.002)
-0.005**
(0.002)
-0.003
(0.005)
-0.005
(0.005)
Moisture capacity -0.03
(0.02)
-0.03
(0.02)
0.04
(0.02)
0.02
(0.02)
0.06
(0.05)
0.03
(0.05)
salinity -0.001
(0.02)
-0.007
(0.02)
0.01
(0.02)
-0.01
(0.02)
0.05
(0.05)
0.02
(0.05)
Precipitation
Standard deviation of precipitation 0.30**
(0.14)
0.80***
(0.16)
0.45
(0.33)
Cumulative precipitation 0.01
(0.01)
0.05***
(0.01)
0.09***
(0.03)
Other variables
ln(Average commodity credit loans)
($/acre)
0.05***
(0.02)
0.06***
(0.02)
-0.01
(0.02)
0.005
(0.02)
-0.07
(0.04)
-0.06
(0.04)
Age 0.38
(0.40)
0.41
(0.40)
0.99**
(0.42)
1.06**
(0.47)
-0.52
(1.11)
-0.61
(1.10)
Age2 -0.003
(0.004)
-0.004
(0.004)
-0.01**
(0.004)
-0.01**
(0.004)
0.004
(0.01)
0.004
(0.01)
Year2002 -0.30***
(0.05)
-0.31***
(0.05)
0.001
(0.05)
-0.03
(0.05)
-0.16
(0.11)
-0.18
(0.11)
State fixed effect Yes Yes Yes Yes Yes Yes
Adj R2 0.52 0.51 0.45 0.43 0.22 0.23
Note: ***Statistically significant at 1%; **Statistically significant at 5%; *Statistically significant at 10%.
The standard errors are in parentheses.
11 Total profit of agricultural product sold is calculated by subtracting total farm production expenses from total
market value of agricultural product sold.
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Table 4.4 Two Stage Least Squares (2SLS) Regression Results for Determinants of Income
Dependent variable
ln(Total market value of crops sold)
($/acre)
Model 1 Model 2
Irrigation
Percentage of irrigated land 0.05***
(0.01)
0.05***
(0.01)
Land quality
Fraction sand -0.002
(0.002)
-0.002
(0.002)
Fraction flood-prone 0.003
(0.002)
0.003*
(0.002)
K Factor 0.004
(0.01)
0.005
(0.01)
Slope length 0.0003
(0.0004)
0.0002
(0.0004)
Permeability 0.07***
(0.02)
0.07***
(0.02)
Wetlands 0.01***
(0.003)
0.01***
(0.002)
Moisture capacity -0.03
(0.02)
-0.03
(0.02)
salinity -0.03
(0.02)
-0.03
(0.02)
Precipitation
Standard deviation of precipitation 0.08
(0.18)
Cumulative precipitation 0.02*
(0.01)
Other variables
ln(Average commodity credit loans)
($/acre)
-0.01
(0.03)
-0.005
(0.03)
Age 1.22**
(0.57)
1.21**
(0.56)
Age2 -0.01**
(0.005)
-0.01**
(0.005)
Year2002 -0.36***
(0.06)
-0.36***
(0.06)
State fixed effect Yes Yes
Adj R2 0.42 0.43
Note: ***Statistically significant at 1%; **Statistically significant at 5%; *Statistically significant at 10%.
The standard errors are in parentheses.
Table 4.5 reports the effects of irrigation on income equality. Three variables are chosen
to measure irrigation, they are average irrigated land (acres/farm), irrigated farm number, and
percentage of irrigated land. The square forms of these variables are added to examine the
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agricultural treadmill theory. All three models confirm that income inequality increases with
increased irrigation adoption, but when more and more farmers adopt this technology, the
marginal benefit becomes smaller and smaller, the income inequality thus decreases. An 10%
increase in irrigated number of farms increases Gini coefficient by 0.021, but when the number
of irrigated farms exceeds 48312 on average, the Gini coefficient then start to decrease, which
means income inequality begins to drop. Similar results can also be calculated by using
percentage of irrigated land, when more than 20% of land is irrigated, income inequality will
drop.
Table 4.5 Regression Results for Farm Sale Value Inequality
Dependent variable: Gini coefficient
(1) (2) (3)
Constant 0.85***
(0.01)
0.23
(0.16)
0.82***
(0.01)
Irrigation
ln(Average irrigated land)
(acres/farm)
0.016***
(02)
ln(Average irrigated land)2 -0.0036***
(0.0004)
ln(Irrigated farm number) 0.21***
(0.05)
ln(Irrigated farm number)2 -0.017***
(0.004)
Percentage of irrigated land 0.002***
(007)
(Percentage of irrigated land)2 -0.00005***
(0.00001)
Other variables
Average farm size (acres/farm) -0.0002***
(0.00002)
-0.00002
(0.00002)
-0.0002***
(0.00002)
ln(Average commodity credit loans)
($/farm)
0.001
(0.002)
-0.000002**
(8.88*10-7)
-0.006***
(0.002)
Year2002 0.04***
(0.005)
0.035***
(0.006)
0.04***
(0.006)
Adj R2 0.48 0.42 0.44
Note: ***Statistically significant at 1%; **Statistically significant at 5%; *Statistically significant at 10%.
The standard errors are in parentheses.
12 The maximum number of irrigated number of farms is calculated as 2*0.017*x=0, x=6.18. So when more than
e6.18 = 483 farms adopt the irrigation technology, income inequality begins to drop.
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6. Discussion and Conclusion
This paper addresses a major methodological problem that lies at the core of empirical literature
on agricultural income, the potential endogeneity of irrigation used as explanatory variable.
Using number of wells as instrumental variable for irrigation adoption, I find that irrigation has a
dramatic causal impact on the market value of crops sold. In addition, this study supports the
treadmill theory that irrigation increases income inequality at first when a few farmers adopt this
technology, but when more and more farms are involved in the system, income inequality
decreases.
The implication of this research is potentially important from a public policy perspective.
Farm consolidation, characterized by growing farm sizes, decreasing farm numbers, and
shrinking agricultural GDP, is a dynamic process over the past five decades in the U.S.
agricultural sector. Rich farmers have power to increase the agricultural supply and affect
agricultural price by adopting new technologies. Small scale and poor farmers have to leave the
scene because marginal profit was decreasing, their resources are absorbed by those who make
the windfall profits or ?scale enlargement. Thus, maybe instead of providing substantial
subsidies for specific crops, the government should provide loans and technique supports to the
poor farmers to adopt irrigation to help them increase on-farm income and alleviate income
inequality in the long run.
There is one limitation in this study. Agricultural operation profit maybe a more ideal
way to measure income inequality. However, due to data limitations, I can only use the total
market value of agricultural products sold to calculate the Gini coefficient. Although it may not
reflect the true farmers? income inequality, this is the most innovative and the best method to
proxy the income inequality using the Census of Agriculture dataset.
97
CHAPTER 5
Three chapters in the dissertation cover research topics including house foreclosure effects in
housing economics and irrigation adoption effects in development economics. There are some
connections among three chapters.
Chapter 1 and Chapter 2 use the same dataset to study the effects of house foreclosures
on surrounding property sales values. However, the research methods are different. Chapter 1
uses cross-sectional data and employs spatial models. The GS2SLS regression is more appealing
when the residuals are heteroskedatic and when the finite samples do not meet the normality
requirement. The foreclosure effects extend up to 1500 feet of a property. The results present a
slight larger spillover effects when compared to other studies. The marginal foreclosure impact is
-1.57% within 300 feet, - 0.54% between 300 feet and 600 feet, -0.3% between 600 and 1200
feet, and -0.37% between 1200 feet and 1500 feet.
By using repeat sales from 2000 to 2010, Chapter 2 employs quasi-experiment models,
difference-in-differences and propensity score matching methods to analyze the impacts of
foreclosures on neighborhood one to four unit residential property values in city of Atlanta.
Using repeat sales, the difference-in-differences and propensity score matching methods avoid
omitted variable bias which is likely a problem in hedonic models using cross-sectional data.
Compared to Harding, Rosenblatt and Yao?s (2009) work, this study improves their
model from several aspects. First, besides number of nearby foreclosures, this study controls
more property characteristics that are expected to change between sales, including whether the
house remodeled or not, sales quarter and sales type. Second, instead of arbitrarily picking out
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two repeat sales, this study includes every transaction record sold more than once during the
study periods, which gives more precise estimates due to the efficiency gain bout by more data.
Third, using transaction buyers and sellers? names, this study distinguishes between REO and
REO sales. Because each REO sale resolves a REO, it helps increase surrounding house sales
price as a result. The study separates effects of the price trend over time and the contagion effects
of foreclosures. The results confirm negative contagion effects of foreclosures for surrounding
sales properties. The difference-in-differences model reports that one more foreclosure
(including REO and REO sales) reduces surrounding house sales prices by 2.5% within 300 feet,
1.1% with 600 feet, 0.6% within 900 feet, and 0.4% within 1200 feet. However, after separating
REO and REO sales, the effect of REO increases dramatically, one more REO reduces
surrounding sales price by 3.7%, while one more REO sales increases sales price by 0.6% within
600 feet.
Both Chapter 1 and Chapter 3 address potential endogeneity problems in the regression.
The endogeneity problems in Chapter 1 and Chapter 3 are caused by reverse causality. Because
neighborhood house values depreciated by foreclosures may lead to more foreclosures,
foreclosures may thus be endogenous to the sales price. The contributions of Chapter 1 include
creating an innovative way to examine endogeneity through accounting for foreclosure timing
and it also addresses the endogeneity of the spatially lagged dependent variable by using
GS2SLS procedures.
Chapter 3 deals with endogeneity with 2SLS regression. Because irrigation is a relatively
expensive technology for small-scale farmers and poor farmers, it impedes their opportunities to
adopt irrigation technology. Thus, irrigation is potentially endogenous to agricultural sales
income. The coefficients estimated in IV-2SLS are about two and half times the magnitude as
99
those estimated in the OLS regression. Holding other variables constant, an increase of 1% of
irrigated land leads to total market value of crop sold increasing by 5% per acre, and total market
value of agricultural product sold increasing by 0.5% per acre.
The research results in three Chapters provide important public policy implications.
Because property taxes fund local public goods, losses in the property taxes revenues would have
a multiplier impact in degrading provision of local public goods. The estimated property tax loss
for 10,121 one-to-four unit family houses at Atlanta is about $2.2 million in 2008. If the full
spectrum of houses types and foreclosures were considered, reducing foreclosures would result
in an even higher social benefit. Policy makers should consider programs to make foreclosures
resolve in a timely manner to avoid tax loss. The resolved foreclosures actually could help
increase surrounding house sales prices which is proved by the study results in Chapter 2.
The implication of Chapter 3 indicates that the government should provide loans and
technique supports to the poor farmers to adopt irrigation to help them increase on-farm income
and alleviate income inequality in the long run.
100
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