Neighborhood Patterns of Subprime Lending: Evidence from Disparate Cities
Paul S. Calem
Division of Research and Statistics
Board of Governors of the Federal Reserve System
Jonathan E. Hershaff
Division of Research and Statistics
Board of Governors of the Federal Reserve System
Susan M. Wachter
The Wharton School
University of Pennsylvania
June 8, 2004
The views expressed are those of the authors and do not represent official views of the Federal
Reserve Board of Governors or its staff. We thank Robert Avery, Glenn Canner for very helpful
comments.
1
Abstract
This paper estimates, for 7 cities, a model of prime versus subprime allocation of loans in 1997
and 2002 based on both individual loan and neighborhood attributes. The paper is directly
interested in the effect of neighborhood racial and ethnic composition on the likelihood of
receiving a subprime loan. The paper also allows for interaction of borrower race and ethnicity
with neighborhood attributes. A unique feature of the paper is that it provides additional
neighborhood controls for the aggregate level of credit risk and the neighborhood level of equity
risk.
The paper finds some evidence for tightening loan standards over the 5-year period in the
subprime market. In both years, even with risk controls, the minority share of neighborhood is
consistently significant and positively related to subprime share. Furthermore, neighborhood
education level is consistently significant and negatively related to subprime lending.
Keywords: Subprime mortgages; Mortgage lending and race; Mortgage lending and
neighborhood
2
I. Introduction
Subprime residential mortgage lending, characterized by relatively high credit risk and
high interest rates or fees has grown substantially in recent years, both in total volume and as a
share of overall mortgage lending. On the one hand, this growth represents an expansion in the
supply of mortgage credit among households who do not meet prime market underwriting
standards and, hence, might not otherwise have access to credit. On the other hand, the high cost
of subprime credit has prompted concerns that its rapid growth may in part be due to sales tactics
of lenders (“targeting”) or to a lack of financial sophistication among classes of borrowers that
lead some borrowers to pay more than is necessary for credit (Courchane et al. 2004). At the
same time, high default and foreclosure rates among subprime borrowers have prompted
concerns that it may not be in the best interest of some borrowers or of the neighborhoods where
they reside for such loans to be extended in the first place.
The growth of subprime lending and the ensuing debate over its role has attracted the
interest of policymakers and academic researchers. A number of empirical studies have analyzed
the degree to which subprime lending correlates with neighborhood economic and demographic
characteristics. Such studies are of interest because high subprime default rates are more likely
to have adverse consequences for communities to the extent that subprime loans are concentrated
in neighborhoods that are fundamentally more vulnerable to economic decline. Further, such
studies are of interest because a correlation with neighborhood demographic characteristics may
reflect “targeting” through more intensive marketing of subprime products to selected
populations.1
1
Arguably, such a process of “targeting” is more plausible than that where bank employees consciously persuade
individual minority customers to apply for subprime loans.
3
In this study, we conduct a detailed analysis of subprime mortgage lending patterns in
seven major cities (Atlanta, Baltimore, Chicago, Dallas, Los Angeles, New York, and
Philadelphia), representing a cross section of the nation as a whole. The analysis is conducted
separately for 2002, the most recent year for which required data are available, and 1997 to
assess the degree to which lending patterns may be evolving. We examine the relative likelihood
that a conventional, refinance loan is subprime in relation to a variety of neighborhood
demographic and economic variables, controlling for a number of individual borrower
demographic and economic characteristics. The study extends earlier work by Calem, Gillen,
and Wachter (CGW).2 The earlier study was limited to two cities and one year (Chicago and
Philadelphia in 1999). The present study also looks more closely at the demographic correlates
of subprime lending, through examination of the interaction between neighborhood and
individual borrower demographic classifications.
Most studies to date examine subprime mortgage lending in relation to a limited set of
neighborhood or individual demographic variables. Relatively few studies, however, include
both individual borrower characteristics and measures of neighborhood risk. A recent study
published by NCRC and a study by Apgar (2004) et al. partially replicate the CGW
methodology; the former does not include individual characteristics while the latter incorporates
a more limited set of neighborhood variables.3 Similarly, Scheessele (2002) incorporates a
variety of neighborhood variables but also does not control for individual characteristics.
Pennington-Cross, Yezer, and Nichols (2002) use a broad range of individual characteristics,
including borrower credit rating, but only one neighborhood variable (an indicator for
“traditionally underserved” neighborhood), and restrict attention to home purchase loans
2
See Calem, Gillen, and Wachter, Journal of Real Estate Finance and Economics, forthcoming.
4
originated in 1996 (whereas subprime mortgage loans are predominantly used for refinancing).
The present study incorporates a broad set of neighborhood variables and individual borrower
demographic characteristics, including a measure of property equity risk and a measure of the
credit quality composition of neighborhood residents.4,5
The paper is organized as follows: Section II reviews the existing literature in greater
detail. Section III describes the sources of data, variables, and statistical approach for the
analysis. Section IV presents the results and Section V concludes.
II. Literature Review
Subprime lending has grown rapidly over the past decade, which has increased policy and
research interest in the distribution of subprime lending across borrowers and neighborhoods.
Early studies show striking positive correlations between minority borrowers and frequency of
subprime lending and also between predominantly minority neighborhoods and frequency of
subprime lending. For example, Canner, Passmore, and Laderman (1999) report that in 1998,
more than 15 percent of loans to black households or in predominantly black neighborhoods
were subprime, compared with the only 6 percent of loans that were subprime overall. They also
present evidence that subprime lending has increased the amount of credit available to low- or
moderate-income and minority households and to residents of low- or moderate-income and
predominantly minority neighborhoods. They attribute more than one-third of the growth in
3
Unlike Calem & Wachter, the NCRC study is estimated on metropolitan data using which combines urban and
suburban markets with potentially different outcomes.
4
Unlike Pennington-Cross et al., however, we do not have access to individual borrower risk measures.
5
The measure used as a proxy for equity risk is the neighborhood rent to value ratio. Tootell (QJE, 1996) and Ross
and Tootell (JUE, 2004) both show that rent to value ratio is the most important neighborhood predictor of
underwriting outcomes. Deng, Ross, and Wachter (RSUE, 2003) show that a similar equity risk proxy is important
in explaining homeownership.
5
overall mortgage lending to predominantly minority Census tracts between 1993 and 1998, and
about one-fourth of the growth in lower income areas, to subprime lending.
Similar patterns are demonstrated by Immergluck and Wiles (1999), who provide an
analysis of lending patterns over time in Chicago in the late 1990s. They find that subprime
lending had been increasing in share in neighborhoods with large minority populations.
Bunce et al. (2000) calculate relative frequencies of subprime refinance lending in
predominantly minority neighborhoods and low- or moderate-income neighborhoods nationally
and for five individual metropolitan areas (New York, Chicago, Baltimore, Atlanta, and Los
Angeles) in 1999, again without control variables. This study finds that on average nationwide,
frequency of subprime lending was three times higher in low-income neighborhoods than in
upper-income neighborhoods, and five times higher in predominantly black neighborhoods than
in predominantly white neighborhoods. The study also finds that in black neighborhoods, half of
all refinance loans were subprime, compared to only one out of every 10 in white
neighborhoods.6 While these early studies demonstrate that minority and low-income status of
borrowers and neighborhoods are associated with a higher frequency of subprime lending, they
do not control for other possible explanatory factors, such as borrower credit risk or other
neighborhood characteristics.
More recent studies have improved on the methods used to identify neighborhood
concentration of subprime lending in early research. Scheessele (2002) identifies the type of
neighborhoods in the nation as a whole where borrowers are likely to rely on subprime loans for
refinancing. The paper finds that, even after controlling for several neighborhood characteristics
(but not for the credit quality distribution of neighborhood residents), the percentage of African
6 In New York, 60 percent of refinance loans in predominantly black neighborhoods were subprime, 52% in
Chicago, 49 % in Baltimore, and 33% in Atlanta and Los Angeles.
6
Americans in a given neighborhood is positively related to the share of refinance loans that are
subprime. Also using nationwide data from 1996, Pennington-Cross, Yezer, and Nichols (2002)
analyze the factors associated with whether a borrower obtained a subprime, prime, or FHA
mortgage when obtaining a home-purchase loan (although they do not analyze refinance loans).
A major innovation of the study is that it incorporates measures of individual borrower credit
risk, using data from a major national credit bureau that was linked (based on Census tract
location, loan amount, and lender identification) with HMDA data. The analysis relates
differences across metropolitan statistical areas (MSAs) to MSA level variables but only includes
one tract level variable: a dummy variable identifying “underserved” (low income and
predominantly minority) neighborhoods. After including these controls for borrower risk, the
study finds that subprime mortgages are not necessarily more prevalent in these “underserved”
neighborhoods or among lower income borrowers, but they are more prevalent among minority
borrowers.
In a recent study, CGW examines how subprime lending activity varies with a broad
range of neighborhood economic and demographic characteristics within Philadelphia and
Chicago. Regression equations are estimated at the Census tract level, where the dependent
variable is share of HMDA-reported conventional loans that are subprime, and logit equations
are estimated at the borrower level, where the dependent variable is whether or not an originated
loan is subprime and where individual demographic characteristics are included for both homepurchase and refinance loans. Statistically significant relationships are observed between
subprime lending and the credit quality composition of neighborhood residents and other
neighborhood economic variables. The study yields mixed results with respect to the
relationship between subprime lending activity and the minority composition of neighborhoods.
7
In particular, in the borrower-level analysis, a statistically significant association with percent
African-American homeowner population is observed only for Chicago.
The National Community Reinvestment Coalition (NCRC 2003) partially replicates the
CGW study analyzing subprime lending in ten large metropolitan areas. The analysis is
conducted only at the Census tract level (individual characteristics are not included). Regression
equations are estimated, where the dependent variable is the proportion of HMDA-reported
conventional loans that are subprime, and the independent variables are Census tract
characteristics. As in CGW, measures of the credit-quality composition of neighborhood
residents are included along with a number of neighborhood economic and demographic
variables. The NCRC study finds that, in nine of the ten cities7, the proportion of subprime
refinance lending increased as the proportion of minorities in a neighborhood increased, all else
equal. For home-purchase subprime lending, this relationship was seen in six of the ten cities.
Apgar et al. (2004) extend this analysis to more MSAs, replicating the logistic analysis of CGW,
but with a more limited set of neighborhood variables. In particular, they do not include
measures of the credit-quality composition of neighborhood residents. They find that the
proportion of minorities in a neighborhood is significant and negatively related to the market
share of prime lenders.
III. Data Sources, Variables, and Statistical Approach
Data used in this analysis comes from four primary sources. First, we use data collected
by the Federal Financial Institutions Examination Council (FFIEC) in accordance with the Home
Mortgage Disclosure Act (HMDA) to obtain mortgage-loan information and borrower-level
7
Specifically, the NCRC study looked at Atlanta, Baltimore, Cleveland, Detroit, Houston, Los Angeles, Milwaukee,
New York, St. Louis and Washington, D.C.
8
characteristics for 1997 and 2002.8 Second, like all previous studies, we use the Department of
Housing and Urban Development’s (HUD) annual lists of HMDA-reporting lenders that
specialize in subprime loans.9 Third, we use the 2000 Census to construct Census variables and
measures of neighborhood-related credit risk for Census tracts. Finally, we obtain information
on the distribution of credit ratings of individuals within tracts from CRA Wiz®, a product of
PCI Services in Boston that provides comprehensive, geography-based information. This
information is available to us only for 1999. The original source of these ratings distributions for
CRA Wiz® is the consumer credit reporting agency Experian.
Under HMDA, lending institutions with more that $30 million in assets and that have
branches in MSAs are required to provide information about their mortgage loan applications.
This information includes type of loan, the purpose of the loan, the dollar amount of the loan, the
Census tract where the home is located, and whether the application was approved or denied.
Demographic information on the applicant is also provided, including income, gender, and race
or ethnicity. Each loan application is also coded to identify the lending institution to which the
applicant applied.
Since the HMDA data do not separately identify subprime loans, loans originated by
these institutions are used as a proxy for subprime loans. That is, any loan originated by a lender
on the HUD list of subprime specialists is classified by us as subprime; all other loans are
considered prime. Lenders on the HUD list either identified themselves as subprime lenders or
had more than fifty percent of their originated conventional mortgages classified as subprime.
Significant potential measurement error due to the omission of smaller lenders that do not report
under HMDA and to the inability to classify loans of lenders that originate both types of loans is
8
9
We also use 1999 HMDA data for supplemental analysis, referred to below.
www.huduser.org/datasets/manu.html
9
unavoidable (Lax, Manti, Raca, Zorn, 2000). Using our approach, some prime loans will be
classified as subprime, and any subprime loans from large lenders that have less than half of their
business in the subprime market will be incorrectly classified as prime. Such misclassifications
could potentially introduce measurement error. Thus, since HMDA data exclude small lenders,
our analysis may be subject to measurement error to the extent that lenders not reporting under
HMDA originate a substantial share of subprime loans. However, with the information that is
available at this time, our approach comes as close as is possible to measuring relative frequency
of origination of subprime compared to prime loans.
Census tract definitions in the 2000 Census are often inconsistent with the 1990 Census
tract definitions employed in 2002 HMDA data and the CRA Wiz® data. To reconcile these
differences, we employed a special database providing demographic characteristics based on the
2000 Census for Census tracts as defined in 1990.10
Neighborhood characteristics. From the 2000 Census, we obtain a number of tract-level
economic and demographic variables for use in the analysis. These include the log of tract
median family income (LNMFI), the percent of individuals 25 years of age or older with a
bachelor’s degree (PCT_COLLEGE), and the proportion of occupied housing units that are
renter-occupied (PCT_RENTAL). Since economic conditions tend to be better in neighborhoods
where residents have higher incomes or educational attainment, and since borrower financial
sophistication tends to be inversely related to educational attainment, we expect subprime
borrowing to be inversely related to these variables. Three measures of percent minority
population: the percent of households headed by a person classified as African American, the
percent headed by a person classified as Asian, and the percent headed by a person classified as
Hispanic, (respectively PCT_BLACK; PCT_ASN_HISP), also are used.
10
A proxy for the price of risk in real estate investment, the tract’s capitalization rate
(CAP_RATE), defined as a ratio of the tract’s annualized median rent divided by the median
house value, also is constructed using 2000 Census data. A larger value for this measure is
consistent with lower expected price appreciation or more uncertain future house prices and,
hence, indicates increased credit risk. Hence, we expect this variable to be positively associated
with relative likelihood of a loan being subprime.
Using CRA Wiz®, we calculate two measures of the credit quality composition of
neighborhood residents. These are the percent of adult individuals in a tract that have been
classified as very high credit risk (PCT_VHIGH_RISK), defined as the bottom quintile of the
credit rating distribution, and the percent with no credit rating (PCT_NA_RISK). Both are
expected to be positively associated with relative likelihood of a loan being subprime.
Individual borrower characteristics. From HMDA, we obtain borrower-specific
demographic variables for use in the analysis. We create dummy variables identifying AfricanAmerican, Asian-American, and Hispanic borrowers (BLACK, ASIAN, HISPANIC,
respectively) and those for whom race is unreported (MISSING). In addition, we separately
identify single borrowers (those without co-borrowers) and then create dummy variables
classifying these borrowers by gender (SINGLE MALE; SINGLE FEMALE). From HMDA, we
also obtain the natural log of the borrower’s income (LNINCOME).
Statistical models. Logit models are estimated where the dependent variable is whether
or not a HMDA-reported loan is subprime (SUBPRIME); that is, is originated by a lender on the
HUD list. Loans having dollar amounts larger than the conforming size limit for the year of
analysis are excluded, since such loans are unlikely to be subprime quality.
10
See Avery, Calem, and Canner (2003) for a description of this database.
11
For each city and year, two models are estimated. The first includes, as independent
variables, each of the variables defined above. In the second specification, we account for
interactions between neighborhood and individual demographic characteristics. Interaction terms
are included to potentially shed light on the source of any association between the percent
African-American and minority population of a neighborhood and subprime borrowing. That is,
we replace the Census tract demographic variable PCT_BLACK with a set of five measures
representing the interaction of this variable with each of the individual borrower race
classifications. Thus, PCT_BLACK is interacted with, respectively, BLACK, HISPANIC,
ASIAN, MISSING, and a dummy variable (WHITE) that identifies whites and others. In
addition, we replace PCT_VHIGH_RISK and PCT_NA_RISK with the respective interactions of
these variables with the individual borrower race classifications.
IV. Logistic Estimation Results
Mean values of the explanatory variables in each city for 1997 and 2002 are reported in
Table 1 (where statistics for borrower and neighborhood median income are reported prior to
applying the log transformation). The table indicates a few notable distinctions between the two
years. The average income of borrowers was substantially higher in 2002 compared to 1997, and
borrowers in 2002 were less likely to be members of minority groups.11 Similarly, borrowers in
2002 resided in higher-income areas and areas with larger proportions of college graduates and
lower percentage minority populations. Moreover, a smaller percentage of loans were subprime
in 2002 compared to 1997. These differences likely reflect the relatively large proportion of
11
The average income of borrowers is substantially higher in 2002 compared to 1997 even after adjusting the
nominal figures reported in Table 1 for inflation (using the GDP deflator of 1.12).
12
refinance mortgages originated in 2002, and also in part may reflect trends over the intervening
period.12
Table 1 also highlights differences across individual cities, most notably in minority
composition and in the frequency with which borrower race is unreported. For instance,
Baltimore and Atlanta have relatively high percentages of African-American borrowers and
relatively few Hispanic or Asian borrowers, while Los Angeles has a relatively high percentage
of Hispanic borrowers. The cities differ along other dimensions as well. In 2002, for example,
the mean borrower in Atlanta was in a Census tract where about half the adult population had a
college degree, while the mean borrower in Philadelphia was in a Census tract where about one
quarter of the adult population had a college degree.
Tables 2a and 2b report estimation results for the first model (no interaction terms).
Overall, we observe consistency across cities and years with our a priori expectations. In each
city and year, neighborhood educational attainment (PCT_COLLEGE); neighborhood median
income (LNMFI); and borrower income (LINCOME), when statistically significant, are
inversely associated with subprime borrowing. Also, in each city and year, the estimated
coefficients on neighborhood credit quality (PCT_VHIGH_RISK, PCT_NA_RISK) and property
risk (CAPRATE) are positively associated with subprime borrowing when statistically
significant.
We observe some differences across years with respect to degree of consistency across
cities. In 2002, in every city the relative likelihood of a loan being subprime is inversely related
to the proportion of neighborhood residents with a bachelor’s degree, while in 1997, this
relationship holds for three cities. CAPRATE is positively associated with a loan being
12
For example, there were modest declines in the proportion of black borrowers (from 29 to 25 percent) and the
percentage of loans that were subprime (from 33 to 26 percent) between 1997 and 1999.
13
subprime in three cities in 1997 and four cities in 2002. The neighborhood credit quality
measures are more strongly related to relative likelihood of a loan being subprime in 1997
compared to 2002. In particular, PCT_VHIGH_RISK is statistically significant and positively
related to the relative likelihood of a loan being subprime for five cities in 1997 but for only two
cities in 2002. This may reflect a tightening of subprime credit standards in more recent years,
observed, for example, by Chomsisengphet and Pennington-Cross (2004).13
We find that Census tract percent African-American population (PCT_BLACK), AfricanAmerican borrower (BLACK), and the indicator for missing race information (MISSING) are
consistently, positively associated with relative likelihood of a loan being subprime. Results for
other minority categories are more mixed. Census tract percent Asian or Hispanic, when
statistically significant, is positively associated with relative likelihood of a loan being subprime.
In some cases, however, Hispanic or Asian borrower is inversely associated with relative
likelihood of a loan being subprime.14 Single borrower (male or female) tends to be positively
associated with relative likelihood of a loan being subprime.
Tables 3a and 3b report estimation results for the second model, which contains the
interaction terms. Only results pertaining to the interaction terms are reported, other results are
similar to those shown in Tables 2a and 2b. A striking finding is that the positive association
observed in Tables 2a and 2b between subprime lending and the percent African-American
population of a neighborhood is particularly strong among white borrowers. The estimated
coefficient on the interaction between PCT_BLACK and WHITE is positive and statistically
13
Supplemental analysis conducted with 1999 HMDA data yielded results quite similar to those for 2002, except in
the case of the neighborhood credit quality measures, which were more consistently associated with relative
likelihood of subprime lending, as in 1997. The 1999 results are available from the authors upon request.
14
We also included percent of population over 65 as a measure of targeting to the elderly. This variable was not
significant. Results were robust to the inclusion of age variables and also were robust to the exclusion of various
other variables such as single status and missing race.
14
significant in each city in both years, and is consistently substantially larger than the estimated
coefficient on the interaction between PCT_BLACK and minority race categories (which often
are not statistically significant). Similarly, the estimated coefficient on the interaction between
PCT_BLACK and HISPANIC is usually statistically significant and, when it is, is positive and
comparable in magnitude to the estimated coefficient on WHITE.
What do these findings imply? We find the strong association between educational
attainment of neighborhood residents and subprime borrowing to be of particular interest. This
result suggests that to some extent, lack of financial sophistication may lead borrowers to choose
subprime products. In turn, this suggests that financial education might be effective in helping
borrowers obtain lower cost credit. Alternatively, PCT_COLLEGE may simply be a relatively
robust measure of credit risk at the neighborhood level. Once again, however, this variable is
consistently associated with subprime lending even after the inclusion of risk and interact
variables.
We also test for interaction terms by including borrower minority status interacted with
neighborhood status. We find a significant interaction of white with percent black. A likely
explanation is that whites with poor credit history buy homes in predominantly minority
neighborhoods15. However, this result also is consistent with institutions active in subprime
lending targeting their lending efforts to minority neighborhoods, either directly or through
broker networks.16
The strong association between CAP_RATE and subprime borrowing in a subset of cities
suggests that subprime lenders to some degree may be extending credit in areas where prime
15
See Schill and Wachter (1994) and Lundberg and Startz (2002) for similar results.
Since black borrowers, wherever their neighborhood, are relatively more likely to choose or obtain subprime
loans, targeting of African-American neighborhoods by subprime lenders may increase subprime borrowing among
non-black borrowers more than among black borrowers.
16
15
lenders are hesitant to lend due to risks related to property value. Such risks may be more
significant in some cities than in others.
Finally, the results pertaining to missing race information are interesting. One
conceivable interpretation of the correlation between subprime loans and missing race
information particularly in minority neighborhoods is that brokers are more active in minority
neighborhoods and are less apt to report race information. Moreover, the results suggest that
HMDA data may be less useful for screening subprime lenders for potential discriminatory
patterns than for screening prime lenders17.
Policy implications. The strong association between the racial or ethnic composition of a
neighborhood and relative likelihood of subprime borrowing suggests that enforcement of fair
lending laws in the subprime market should remain a priority. Our results cannot be construed as
evidence of discriminatory practices; as noted, they are consistent with a variety of alternative
scenarios. They do however, indicate, that concerns about fair lending cannot be dismissed, and
continued vigilance is required. For instance, lenders or brokers failing to inform potential
borrowers located in minority neighborhoods of the full range of loan products available to them
would raise fair lending concerns.
Recently, the Federal Reserve Board tightened rules for collecting and reporting race and
ethnicity information in applications taken by telephone, with the intended effect of reducing the
incidence of missing race and ethnicity information in mortgage applications. The greater
availability of race and ethnicity information would enhance fair lending enforcement
17
The results follow earlier findings concerning missing race information in HMDA. Holloway and Wyly
(Economic Geography, 2002) discuss this issue in detail. Dietrich (OCC working paper, 2001) finds higher denial
rates for the sample of loans where race is not reported, and even more convincingly Ross and Yinger (2002, MIT
Press) find evidence of higher levels of racial discrimination (e.g. after controlling for underwriting variables) for
the portion of the Boston Fed sample that could not be matched to HMDA data (where missing race information was
the most common cause for why a match could not be obtained). See also Huck (2001).
16
opportunities and would allow all parties a greater ability to monitor developments in the
subprime market.
The observed relationship between educational attainment in a neighborhood and
likelihood of subprime borrowing suggests that some subprime borrowers may be receiving less
favorable loan terms than are potentially available to them, due to a lack of financial
sophistication. This interpretation suggests a continuing policy focus on financial education as a
tool for ensuring equal access to the prime market to all borrowers of prime credit quality.
V. Conclusion
This paper tests for neighborhood patterns of subprime lending in the distribution of
subprime mortgage loans over time and across multiple cities. We include measures of
neighborhood composition by race and ethnicity along with measures of neighborhood risk. We
also include a measure of neighborhood education levels; an important policy question raised in
the discussion of subprime lending is the extent to which financial sophistication or the lack
there of influences the likelihood of subprime borrowing.
A major focus of this paper is the impact of race and ethnicity of neighborhood,
controlling for neighborhood risk and individual borrower characteristics. Results of logistic
estimations show that individual race, ethnicity, and income are significant and positively related
to the likelihood of subprime borrowing, along with single borrower (no co-borrower) status.
Because we lack individual credit risk measures, we expect to find individual borrower race and
ethnicity status variables to be significant. We have no a priori expectations regarding the
relationship of minority status of neighborhood to subprime borrowing, assuming we have
adequately controlled for sources of risk at the neighborhood level. Yet in each of the seven
cities and for both periods, we find that minority status is significantly related to subprime
17
borrowing. In addition, the result pertaining to missing race information is interesting, and
suggests that HMDA data may be less useful for screening subprime lenders for potential
discriminatory patterns than for screening prime lenders, although newly instituted HMDA
reporting rules may help dispel such concerns.
We also find, across the seven cities and in both years, education levels are consistently
related to subprime lending, all else equal. Relative likelihood of subprime borrowing tends to
be inversely associated with neighborhood median income and positively associated with the
neighborhood rent to value ratio, a measure of equity risk. Key variables measuring borrower
credit risk by neighborhood tend to be positively related to subprime borrowing, although more
strongly in 1997 than in 2002. These results are consistent with the neighborhood risk literature
which shows that default and foreclosure rates are related to both property risk and individual
credit quality (Calem and Wachter, 1999). The apparent increasing impact of neighborhood
equity risk variables and decreasing impact of neighborhood credit risk on subprime borrowing
over time are consistent with a trend towards more conservative credit quality standards in
underwriting.
The observed patterns are subject to the caveat that we cannot directly identify subprime
loans but must rely as a proxy on the lender’s identity as a subprime specialist as determined by
HUD. Thus, to some extent the observed patterns may simply reflect the historical lending
patterns of these institutions.
18
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22
Authors
Susan M. Wachter is Richard B. Worley Professor of Financial Management and Professor of
Real Estate and Finance in the Real Estate Department at The Wharton School of the University
of Pennsylvania.
23
Table 1
Summary Statistics: Sample Mean Values
VARIABLE
ATLANTA
2002
ATLANTA
1997
BALTIMORE
2002
BALTIMORE
1997
CHICAGO
2002
CHICAGO
1997
DALLAS
2002
DALLAS
1997
LOS
ANGELES
2002
LOS
ANGELES
1997
NEW YORK NEW YORK PHILADELCITY 2002 CITY 1997
PHIA 2002
PHILADELPHIA 1997
WHOLE
SAMPLE
2002
WHOLE
SAMPLE
1997
TRACT-LEVEL VARIABLES
CAPRATE
0.00
0.00
0.01
0.01
0.00
0.00
0.01
0.01
0.00
0.00
0.01
0.01
0.01
0.01
0.00
0.01
MEDIAN
FAMILY
INCOME
71950
69281
57865
45609
64196
56063
72320
76056
60595
63828
64053
60759
49772
41648
63105
57817
PCT ASIAN
0.01
0.01
0.02
0.01
0.04
0.03
0.03
0.03
0.07
0.07
0.07
0.06
0.03
0.03
0.05
0.04
PCT
COLLEGE
0.47
0.43
0.35
0.23
0.33
0.26
0.39
0.41
0.30
0.32
0.30
0.28
0.24
0.16
0.32
0.28
PCT BLACK
0.40
0.49
0.33
0.57
0.16
0.33
0.10
0.11
0.09
0.12
0.21
0.28
0.26
0.45
0.16
0.29
PCT
HISPANIC
0.03
0.03
0.02
0.01
0.11
0.11
0.10
0.09
0.18
0.17
0.11
0.11
0.03
0.04
0.12
0.10
PCT RENTAL
0.47
0.45
0.39
0.41
0.40
0.42
0.35
0.34
0.42
0.43
0.50
0.53
0.35
0.34
0.42
0.43
PCT VHIGH
RISK
0.17
0.17
0.17
0.18
0.16
0.17
0.17
0.17
0.18
0.18
0.17
0.17
0.17
0.18
0.17
0.18
PCT N_A
RISK
0.17
0.17
0.17
0.18
0.17
0.18
0.16
0.17
0.17
0.17
0.16
0.16
0.17
0.17
0.17
0.17
BORROWER-LEVEL VARIABLES
INCOME
($1000)
92.48
68.29
77.44
48.43
80.39
59.91
97.94
93.83
87.54
75.30
90.62
76.97
66.15
42.69
85.79
65.42
ASIAN
0.01
0.01
0.01
0.01
0.05
0.03
0.04
0.02
0.08
0.06
0.06
0.05
0.02
0.01
0.05
0.03
BLACK
0.21
0.34
0.15
0.40
0.11
0.24
0.05
0.06
0.05
0.11
0.14
0.15
0.11
0.27
0.09
0.20
HISPANIC
0.01
0.01
0.01
0.01
0.15
0.12
0.10
0.05
0.18
0.13
0.07
0.05
0.02
0.02
0.13
0.09
MISSING
0.22
0.17
0.28
0.22
0.13
0.14
0.18
0.10
0.24
0.16
0.31
0.29
0.35
0.29
0.22
0.19
SINGLE
0.73
0.67
0.66
0.63
0.52
0.49
0.51
0.43
0.56
0.49
0.63
0.56
0.64
0.59
0.56
0.52
SINGLE
FEMALE
0.28
0.28
0.25
0.28
0.20
0.21
0.16
0.15
0.20
0.22
0.21
0.18
0.22
0.27
0.20
0.22
SINGLE
MALE
0.45
0.39
0.41
0.35
0.32
0.28
0.35
0.28
0.35
0.26
0.41
0.38
0.42
0.32
0.36
0.30
SUBPRIME
0.15
0.39
0.15
0.52
0.12
0.32
0.14
0.13
0.13
0.27
0.18
0.36
0.19
0.45
0.14
0.33
NUMBER OF
LOANS
9920
3403
8476
4816
95849
31612
32419
3293
110158
20923
56144
14113
16768
9423
329734
87583
Table 2a
Loan-Level Logistic Regression Results for Refinance Loans: 1997
Variable
ESTIMATE
ATLANTA
1997
BALTIMORE
1997
CHICAGO
1997
DALLAS
1997
LOS
NEW YORK PHILADELANGELES
CITY 1997 PHIA 1997
1997
TRACT-LEVEL VARIABLES
CAP RATE
Coefficient
89.91
76.37
28.36
-8.46
14.23
-0.66
8.38
CAP RATE
ProbChiSq
0.01
0.00
0.01
0.78
0.44
0.62
0.36
LNMFI
Coefficient
-0.49
-0.77
-0.14
0.02
-0.46
-0.45
-0.69
LNMFI
ProbChiSq
0.04
0.00
0.11
0.96
0.00
0.00
0.00
PCT ASIAN
Coefficient
-3.67
1.51
0.68
-4.74
-0.48
0.83
2.44
PCT ASIAN
ProbChiSq
0.14
0.56
0.12
0.10
0.12
0.02
0.00
PCT BLACK
Coefficient
1.25
0.00
0.89
2.64
1.10
1.06
1.14
PCT BLACK
ProbChiSq
0.00
1.00
0.00
0.00
0.00
0.00
0.00
Coefficient
4.90
0.90
1.28
0.60
1.71
1.49
0.82
ProbChiSq
0.02
0.71
0.00
0.55
0.00
0.00
0.09
Coefficient
0.34
-0.72
-1.08
-1.15
-0.13
-1.28
0.85
ProbChiSq
0.65
0.14
0.00
0.15
0.66
0.00
0.02
Coefficient
-0.19
0.18
0.02
-0.22
-0.17
-0.64
-0.52
ProbChiSq
0.57
0.55
0.87
0.67
0.28
0.00
0.05
Coefficient
-12.31
5.86
2.42
-1.29
2.50
5.79
4.68
ProbChiSq
0.00
0.04
0.01
0.80
0.15
0.00
0.02
Coefficient
13.93
-6.15
-0.39
-0.12
1.98
4.06
0.77
ProbChiSq
0.00
0.01
0.68
0.98
0.20
0.00
0.71
PCT
HISPANIC
PCT
HISPANIC
PCT
COLLEGE
PCT
COLLEGE
PCT
RENTALS
PCT
RENTALS
PCT VHIGH
RISK
PCT VHIGH
RISK
PCT N_A
RISK
PCT N_A
RISK
BORROWER-LEVEL VARIABLES
ASIAN
Coefficient
-1.73
-0.22
-0.15
-0.51
-0.41
-1.21
-1.02
ASIAN
ProbChiSq
0.10
0.64
0.16
0.34
0.00
0.00
0.00
BLACK
Coefficient
0.24
0.86
0.79
0.31
0.65
0.27
0.77
BLACK
ProbChiSq
0.05
0.00
0.00
0.18
0.00
0.00
0.00
HISPANIC
Coefficient
-2.06
0.95
-0.17
-0.27
-0.06
-0.12
0.00
HISPANIC
ProbChiSq
0.05
0.02
0.00
0.33
0.29
0.24
0.99
LN INCOME
Coefficient
-0.75
-0.62
-0.41
-0.57
-0.20
-0.92
-0.73
LN INCOME
ProbChiSq
0.00
0.00
0.00
0.00
0.00
0.00
0.00
MISSING
Coefficient
1.34
1.45
1.10
0.99
1.44
1.54
1.42
MISSING
ProbChiSq
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Coefficient
-0.08
-0.25
0.13
0.39
0.12
-0.19
-0.15
ProbChiSq
0.51
0.00
0.00
0.03
0.01
0.00
0.02
Coefficient
0.01
-0.30
0.16
0.64
0.02
0.03
0.29
ProbChiSq
0.94
0.00
0.00
0.00
0.60
0.59
0.00
C STATISTIC
0.83
0.77
0.78
0.79
0.75
0.84
0.80
MODEL CHI
SQUARE
1157
1171
6718
503
3384
4896
2727
SINGLE
FEMALE
SINGLE
FEMALE
SINGLE
MALE
SINGLE
MALE
Table 2b
Loan-Level Logistic Regression Results for Refinance Loans: 2002
Variable
ESTIMATE
ATLANTA
2002
BALTIMORE
2002
CHICAGO
2002
DALLAS
2002
LOS
NEW YORK PHILADELANGELES
CITY 2002 PHIA 2002
2002
TRACT-LEVEL VARIABLES
CAP RATE
Coefficient
-22.27
-9.69
69.18
38.10
89.54
-0.39
22.45
CAP RATE
ProbChiSq
0.36
0.61
0.00
0.00
0.00
0.68
0.02
LNMFI
Coefficient
-0.05
-0.39
-0.02
0.02
-0.17
-0.27
0.30
LNMFI
ProbChiSq
0.78
0.09
0.75
0.89
0.02
0.00
0.05
PCT ASIAN
Coefficient
-1.15
6.88
0.19
-0.86
0.48
-0.47
0.49
PCT ASIAN
ProbChiSq
0.65
0.00
0.54
0.32
0.00
0.03
0.48
PCT BLACK
Coefficient
1.22
0.61
1.14
1.54
1.08
1.00
1.27
PCT BLACK
ProbChiSq
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Coefficient
3.88
-3.55
1.60
1.79
0.73
1.43
0.99
ProbChiSq
0.02
0.11
0.00
0.00
0.00
0.00
0.05
Coefficient
-1.95
-1.98
-2.08
-1.83
-2.09
-3.16
-1.16
ProbChiSq
0.00
0.00
0.00
0.00
0.00
0.00
0.00
PCT
HISPANIC
PCT
HISPANIC
PCT
COLLEGE
PCT
COLLEGE
PCT
RENTALS
PCT
RENTALS
PCT VHIGH
RISK
PCT VHIGH
RISK
PCT N_A
RISK
PCT N_A
RISK
Coefficient
-0.54
-1.08
0.03
-0.46
-0.09
-0.68
-0.03
ProbChiSq
0.03
0.00
0.78
0.00
0.34
0.00
0.90
Coefficient
-3.11
3.38
1.34
-0.56
0.79
2.81
-1.81
ProbChiSq
0.25
0.29
0.07
0.72
0.42
0.00
0.31
Coefficient
0.72
-3.79
-0.10
2.94
1.07
0.92
1.29
ProbChiSq
0.73
0.14
0.89
0.04
0.21
0.20
0.46
BORROWER-LEVEL VARIABLES
ASIAN
Coefficient
-0.35
0.25
-0.01
-0.60
0.07
-0.56
-0.41
ASIAN
ProbChiSq
0.46
0.58
0.91
0.00
0.06
0.00
0.03
BLACK
Coefficient
0.84
0.46
1.08
1.02
0.79
0.45
0.21
BLACK
ProbChiSq
0.00
0.00
0.00
0.00
0.00
0.00
0.01
HISPANIC
Coefficient
0.39
0.70
0.63
-0.37
0.33
0.38
-0.27
HISPANIC
ProbChiSq
0.25
0.04
0.00
0.00
0.00
0.00
0.09
LN INCOME
Coefficient
-0.50
-0.57
-0.31
-0.54
0.03
-0.23
-0.44
LN INCOME
ProbChiSq
0.00
0.00
0.00
0.00
0.14
0.00
0.00
MISSING
Coefficient
1.09
1.20
1.04
0.96
0.15
0.80
0.39
MISSING
ProbChiSq
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Coefficient
-0.01
0.04
0.38
0.23
0.37
0.19
0.28
ProbChiSq
0.93
0.67
0.00
0.00
0.00
0.00
0.00
Coefficient
0.18
0.24
0.40
0.16
0.18
-0.33
0.48
ProbChiSq
SINGLE
FEMALE
SINGLE
FEMALE
SINGLE
MALE
SINGLE
MALE
0.05
0.01
0.00
0.00
0.00
0.00
0.00
C STATISTIC
0.82
0.78
0.81
0.78
0.70
0.76
0.72
MODEL CHI
SQUARE
1697
1115
13261
4652
6437
7071
1588
Table 2c
Loan-Level Logistic Regression Results for Refinance Loans: 1999
Variable
ESTIMATE
ATLANTA
1999
BALTIMORE
1999
CHICAGO
1999
DALLAS
1999
LOS
NEW YORK PHILADELANGELES
CITY 1999 PHIA 1999
1999
TRACT-LEVEL VARIABLES
CAP RATE
Coefficient
-29.49
26.95
41.78
22.90
17.40
-6.50
34.23
CAP RATE
ProbChiSq
0.29
0.08
0.00
0.09
0.15
0.06
0.00
LNMFI
Coefficient
0.18
-0.51
-0.21
-0.01
-0.49
-0.64
-0.73
0.00
0.00
LNMFI
ProbChiSq
0.37
0.01
0.01
0.96
0.00
PCT ASIAN
Coefficient
1.10
4.41
0.02
-2.83
-0.07
0.66
-0.10
PCT ASIAN
ProbChiSq
0.60
0.05
0.96
0.02
0.77
0.01
0.89
PCT BLACK
Coefficient
0.46
0.30
1.00
1.10
0.74
0.89
0.97
PCT BLACK
ProbChiSq
0.09
0.03
0.00
0.00
0.00
0.00
0.00
Coefficient
0.18
-0.80
1.17
0.64
0.82
0.99
0.24
ProbChiSq
0.92
0.69
0.00
0.14
0.00
0.00
0.56
Coefficient
-2.51
-1.00
-0.83
-1.27
-0.60
-1.67
0.00
ProbChiSq
0.00
0.01
0.00
0.00
0.01
0.00
0.99
Coefficient
-0.21
-0.28
0.15
-0.02
-0.48
-0.82
-0.56
ProbChiSq
0.45
0.29
0.12
0.93
0.00
0.00
0.02
Coefficient
2.17
-1.83
2.93
-1.12
0.05
3.92
2.88
ProbChiSq
0.47
0.44
0.00
0.63
0.97
0.00
0.11
Coefficient
6.03
2.03
2.13
2.84
2.77
2.20
1.38
ProbChiSq
0.01
0.35
0.01
0.20
0.02
0.02
0.46
PCT
HISPANIC
PCT
HISPANIC
PCT
COLLEGE
PCT
COLLEGE
PCT
RENTALS
PCT
RENTALS
PCT VHIGH
RISK
PCT VHIGH
RISK
PCT N_A
RISK
PCT N_A
RISK
BORROWER-LEVEL VARIABLES
ASIAN
Coefficient
0.34
1.03
0.06
-0.56
-0.10
-0.75
-0.21
ASIAN
ProbChiSq
0.51
0.00
0.53
0.03
0.09
0.00
0.32
BLACK
Coefficient
0.83
0.81
0.93
1.06
0.79
0.65
0.73
BLACK
ProbChiSq
0.00
0.00
0.00
0.00
0.00
0.00
0.00
HISPANIC
Coefficient
0.52
0.38
-0.06
-0.22
0.04
0.23
0.09
HISPANIC
ProbChiSq
0.19
0.22
0.19
0.03
0.36
0.00
0.56
LN INCOME
Coefficient
-0.55
-0.78
-0.52
-0.82
0.03
-0.78
-0.77
LN INCOME
ProbChiSq
0.00
0.00
0.00
0.00
0.29
0.00
0.00
MISSING
Coefficient
1.88
1.45
1.27
1.17
0.92
1.67
1.32
MISSING
ProbChiSq
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Coefficient
-0.10
-0.14
0.19
-0.23
0.23
0.05
-0.15
ProbChiSq
0.33
0.07
0.00
0.00
0.00
0.29
0.02
Coefficient
0.01
-0.30
0.03
-0.30
0.05
-0.18
-0.45
ProbChiSq
0.90
0.00
0.38
0.00
0.14
0.00
0.00
C STATISTIC
0.83
0.76
0.80
0.79
0.68
0.82
0.81
MODEL CHI
SQUARE
1662
1523
10980
2176
2728
7720
3749
SINGLE
FEMALE
SINGLE
FEMALE
SINGLE
MALE
SINGLE
MALE
Table 3a
Loan-Level Logistic Regression Results for Refinance Loans: 1997
LOS
NEW YORK PHILADELANGELES
CITY 1997 PHIA 1997
1997
STAT
ATLANTA
1997
BALTIMORE
1997
CHICAGO
1997
DALLAS
1997
ASIAN* PCT BLACK
Coefficient
-27.24
4.10
1.90
-7.91
-0.21
1.27
-0.16
ASIAN* PCT BLACK
ProbChiSq
0.87
0.05
0.00
0.48
0.82
0.17
0.89
Coefficient
610.27
-37.17
-12.68
-55.53
0.13
-1.70
33.80
ProbChiSq
0.89
0.28
0.09
0.17
0.99
0.86
0.19
ASIAN* PCT N_A RISK
Coefficient
1036.06
2.92
-1.73
-4.52
1.13
9.21
-23.42
ASIAN* PCT N_A RISK
ProbChiSq
0.87
0.91
0.81
0.93
0.87
0.38
0.21
BLACK* PCT BLACK
Coefficient
0.09
-1.79
0.15
1.66
0.31
0.07
0.04
BLACK* PCT BLACK
ProbChiSq
0.82
0.00
0.22
0.01
0.20
0.73
0.87
Coefficient
-14.68
3.39
0.00
-12.18
1.65
1.69
1.00
ProbChiSq
0.00
0.40
1.00
0.29
0.67
0.51
0.76
BLACK* PCT N_A RISK
Coefficient
17.41
1.84
2.74
7.62
4.73
3.44
-2.47
BLACK* PCT N_A RISK
ProbChiSq
0.00
0.62
0.06
0.62
0.18
0.20
0.47
HISPANIC* PCT BLACK
Coefficient
51.99
6.54
1.41
2.99
1.18
1.33
0.99
HISPANIC* PCT BLACK
ProbChiSq
0.76
0.06
0.00
0.02
0.00
0.00
0.12
Coefficient
-556.38
-115.54
1.96
-6.31
-5.84
-0.62
-7.69
ProbChiSq
0.85
0.13
0.50
0.74
0.16
0.91
0.47
Coefficient
-425.43
59.03
-1.65
-28.26
-3.06
4.27
-13.72
ProbChiSq
0.89
0.20
0.55
0.12
0.39
0.40
0.18
MISSING* PCT BLACK
Coefficient
1.43
0.64
0.54
2.24
1.31
1.29
1.25
MISSING* PCT BLACK
ProbChiSq
0.00
0.03
0.00
0.01
0.00
0.00
0.00
Coefficient
-6.73
5.94
-1.28
-6.30
4.66
9.09
4.43
ProbChiSq
0.35
0.32
0.55
0.54
0.19
0.00
0.22
Coefficient
10.96
-17.09
-1.99
-1.91
3.36
4.24
-1.87
ProbChiSq
0.13
0.00
0.33
0.84
0.29
0.09
0.59
OTHER* PCT BLACK
Coefficient
2.45
0.62
1.76
3.51
1.96
0.92
1.43
OTHER* PCT BLACK
ProbChiSq
0.00
0.02
0.00
0.00
0.00
0.00
0.00
Coefficient
-10.97
10.57
7.58
5.92
5.15
7.70
10.06
ProbChiSq
0.05
0.05
0.00
0.37
0.04
0.00
0.01
OTHER* PCT N_A RISK
Coefficient
9.91
-7.95
1.29
3.68
2.28
4.60
7.83
OTHER* PCT N_A RISK
ProbChiSq
0.04
0.05
0.43
0.55
0.31
0.05
0.01
C STATISTIC
0.83
0.78
0.78
0.80
0.75
0.84
0.80
MODEL CHI SQUARE
1216
1284
7056
528
3444
4956
2818
Variable
INTERACTION TERMS
ASIAN* PCT VHIGH
RISK
ASIAN* PCT VHIGH
RISK
BLACK* PCT VHIGH
RISK
BLACK* PCT VHIGH
RISK
HISPANIC* PCT VHIGH
RISK
HISPANIC* PCT VHIGH
RISK
HISPANIC* PCT N_A
RISK
HISPANIC* PCT N_A
RISK
MISSING* PCT VHIGH
RISK
MISSING* PCT VHIGH
RISK
MISSING* PCT N_A
RISK
MISSING* PCT N_A
RISK
OTHER* PCT VHIGH
RISK
OTHER* PCT VHIGH
RISK
Table 3b
Loan-Level Logistic Regression Results for Refinance Loans: 2002
LOS
NEW YORK PHILADELANGELES
CITY 2002 PHIA 2002
2002
STAT
ATLANTA
2002
BALTIMORE
2002
CHICAGO
2002
DALLAS
2002
ASIAN* PCT BLACK
Coefficient
6.89
-1.51
1.96
-0.89
0.68
1.37
1.12
ASIAN* PCT BLACK
ProbChiSq
0.01
0.52
0.00
0.53
0.10
0.00
0.16
Coefficient
-87.63
-23.85
-1.82
1.87
-0.80
5.38
0.40
ProbChiSq
0.04
0.53
0.73
0.88
0.80
0.21
0.98
ASIAN* PCT N_A RISK
Coefficient
-10.50
54.47
-6.52
-16.94
1.68
-1.97
28.04
ASIAN* PCT N_A RISK
ProbChiSq
0.77
0.17
0.19
0.10
0.57
0.63
0.05
BLACK* PCT BLACK
Coefficient
0.00
-0.44
0.46
1.08
0.51
0.35
0.41
BLACK* PCT BLACK
ProbChiSq
1.00
0.20
0.00
0.00
0.00
0.00
0.08
Coefficient
3.81
-6.37
-1.58
-4.32
-4.50
2.45
-3.05
ProbChiSq
0.34
0.31
0.23
0.27
0.10
0.07
0.46
Coefficient
4.12
-3.80
3.21
3.08
3.24
-0.77
3.52
BLACK* PCT N_A RISK ProbChiSq
0.21
0.45
0.01
0.48
0.20
0.54
0.38
HISPANIC* PCT
BLACK
HISPANIC* PCT
BLACK
HISPANIC* PCT VHIGH
RISK
HISPANIC* PCT VHIGH
RISK
HISPANIC* PCT N_A
RISK
HISPANIC* PCT N_A
RISK
Coefficient
-0.20
0.17
1.18
1.51
0.79
1.16
1.76
ProbChiSq
0.90
0.91
0.00
0.00
0.00
0.00
0.01
Coefficient
33.08
-1.55
0.25
-12.39
-1.33
0.10
7.45
ProbChiSq
0.29
0.96
0.88
0.00
0.46
0.97
0.49
Coefficient
5.14
18.68
0.78
-10.02
-5.28
-0.45
-28.45
ProbChiSq
0.73
0.53
0.63
0.01
0.00
0.85
0.00
MISSING* PCT BLACK
Coefficient
2.04
0.97
1.36
1.69
1.25
1.12
1.42
MISSING* PCT BLACK
ProbChiSq
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Coefficient
-3.22
10.92
2.54
11.80
4.72
1.69
-3.50
ProbChiSq
0.50
0.02
0.14
0.00
0.02
0.18
0.19
Coefficient
-6.74
-6.76
0.75
14.32
3.91
2.69
0.94
ProbChiSq
0.09
0.08
0.65
0.00
0.02
0.03
0.71
OTHER* PCT BLACK
Coefficient
1.34
0.45
1.55
1.76
1.95
1.03
1.36
OTHER* PCT BLACK
ProbChiSq
0.00
0.11
0.00
0.00
0.00
0.00
0.00
Coefficient
-9.05
1.86
3.42
-1.51
2.66
5.47
-0.55
ProbChiSq
0.04
0.74
0.03
0.51
0.10
0.00
0.85
Coefficient
2.17
-0.74
-3.18
2.49
3.16
2.44
2.24
ProbChiSq
0.56
0.87
0.02
0.22
0.02
0.09
0.40
C STATISTIC
0.82
0.78
0.81
0.78
0.70
0.76
0.72
MODEL CHI SQUARE
1742
1175
13434
4782
6620
7142
1627
Variable
INTERACTION TERMS
ASIAN* PCT VHIGH
RISK
ASIAN* PCT VHIGH
RISK
BLACK* PCT VHIGH
RISK
BLACK* PCT VHIGH
RISK
BLACK* PCT N_A RISK
MISSING* PCT VHIGH
RISK
MISSING* PCT VHIGH
RISK
MISSING* PCT N_A
RISK
MISSING* PCT N_A
RISK
OTHER* PCT VHIGH
RISK
OTHER* PCT VHIGH
RISK
OTHER* PCT N_A
RISK
OTHER* PCT N_A
RISK
Table 3c
Loan-Level Logistic Regression Results for Refinance Loans: 1999
LOS
NEW YORK PHILADELANGELES
CITY 1999 PHIA 1999
1999
STAT
ATLANTA
1999
BALTIMORE
1999
CHICAGO
1999
DALLAS
1999
ASIAN* PCT BLACK
Coefficient
0.60
0.27
1.17
4.39
1.47
2.48
2.28
ASIAN* PCT BLACK
ProbChiSq
0.80
0.82
0.04
0.06
0.00
0.00
0.01
Coefficient
-15.81
35.10
-0.89
-59.57
-1.77
8.88
-13.26
ProbChiSq
0.63
0.26
0.90
0.03
0.72
0.16
0.51
ASIAN* PCT N_A RISK
Coefficient
49.08
-27.25
-12.60
21.22
-6.52
-7.09
13.83
ASIAN* PCT N_A RISK
ProbChiSq
0.30
0.36
0.07
0.36
0.15
0.27
0.36
BLACK* PCT BLACK
Coefficient
-0.82
-0.59
0.39
0.81
0.11
0.59
-0.26
BLACK* PCT BLACK
ProbChiSq
0.02
0.02
0.00
0.01
0.55
0.00
0.23
Coefficient
5.28
-3.74
-1.06
-9.79
2.30
6.33
-1.98
ProbChiSq
0.21
0.28
0.41
0.05
0.48
0.00
0.57
BLACK* PCT N_A RISK
Coefficient
6.93
5.53
4.91
12.41
3.45
0.38
4.06
BLACK* PCT N_A RISK
ProbChiSq
0.06
0.09
0.00
0.05
0.26
0.81
0.25
HISPANIC* PCT BLACK
Coefficient
-0.29
2.76
1.03
0.25
0.21
0.82
2.18
HISPANIC* PCT BLACK
ProbChiSq
0.84
0.10
0.00
0.65
0.43
0.01
0.00
Coefficient
-21.31
-42.76
4.58
1.69
-2.96
-1.08
4.11
ProbChiSq
0.42
0.31
0.05
0.79
0.34
0.76
0.71
Coefficient
44.94
31.59
-1.44
-3.90
-2.53
3.95
-12.85
ProbChiSq
0.13
0.31
0.54
0.49
0.35
0.25
0.16
MISSING* PCT BLACK
Coefficient
0.82
1.14
1.18
1.29
0.69
1.08
1.21
MISSING* PCT BLACK
ProbChiSq
0.02
0.00
0.00
0.00
0.00
0.00
0.00
Coefficient
2.48
-6.05
2.99
1.48
1.80
1.64
1.23
ProbChiSq
0.62
0.19
0.11
0.72
0.49
0.33
0.66
Coefficient
0.52
-4.93
0.34
-2.13
6.05
2.74
1.02
ProbChiSq
0.90
0.21
0.84
0.61
0.01
0.10
0.71
OTHER* PCT BLACK
Estimate
1.92
-0.14
1.20
1.44
1.83
0.73
0.87
OTHER* PCT BLACK
ProbChiSq
0.00
0.53
0.00
0.00
0.00
0.00
0.00
Estimate
-0.83
5.24
8.75
3.11
-0.45
4.99
10.68
ProbChiSq
0.88
0.25
0.00
0.36
0.83
0.01
0.00
OTHER* PCT N_A RISK
Estimate
3.58
4.53
4.88
6.52
3.93
4.44
1.70
OTHER* PCT N_A RISK
ProbChiSq
0.46
0.20
0.00
0.03
0.03
0.01
0.57
C STATISTIC
0.84
0.77
0.81
0.79
0.68
0.82
0.81
MODEL CHI SQUARE
1719
1579
11151
2203
2810
7755
3831
Variable
INTERACTION TERMS
ASIAN* PCT VHIGH
RISK
ASIAN* PCT VHIGH
RISK
BLACK* PCT VHIGH
RISK
BLACK* PCT VHIGH
RISK
HISPANIC* PCT VHIGH
RISK
HISPANIC* PCT VHIGH
RISK
HISPANIC* PCT N_A
RISK
HISPANIC* PCT N_A
RISK
MISSING* PCT VHIGH
RISK
MISSING* PCT VHIGH
RISK
MISSING* PCT N_A
RISK
MISSING* PCT N_A
RISK
OTHER* PCT VHIGH
RISK
OTHER* PCT VHIGH
RISK