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Working Paper 942 1

UNDERSERVED MORTGAGE MARKETS:
EVIDENCE FROM HMDA DATA
by Robert B. Avery, Patricia E. Beeson,
and Mark S. Sniderman

Robert B. Avery is a professor in the Department
of Consumer Economics and Housing at Cornell
University, and Patricia E. Beeson is an associate
professor of economics at the University of Pittsburgh;
both are research associates of the Federal Reserve
Bank of Cleveland. Mark S. Sniderman is senior vice
president and director of research at the Federal Reserve
Bank of Cleveland. This paper was presented at the
Western Economic Association Annual Meeting in
Vancouver in July 1994; an earlier version was presented
at the 1994 winter meetings of the AREUEA Society.
Working papers of the Federal Reserve Bank of Cleveland
are preliminary materials circulated to stimulate discussion
and critical comment. The views stated herein are those of
the authors and not necessarily those of the Federal Reserve
Bank of Cleveland or of the Board of Governors of the
Federal Reserve System.
December 1994

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Abstract
The 1992 Housing and Community Development Act directed the two government-sponsored
housing enterprises -- Fannie Mae and Freddie Mac -- to increase their lending in "underserved areas" and
where there are "unmet housing needs." Unfortunately, Congress did not specify how unmet mortgage
needs were to be measured or how underserved areas were to be identified. To shed light on this issue, we
use data collected under the Home Mortgage Disclosure Act to provide a baseline evaluation of the
variation in mortgage credit flows from all lenders across different types of neighborhoods. These data
represent a virtual census of all mortgage loan applications in metropolitan areas for the years 1990 and
1991. Variations in both loan application and lender denial rates are examined separately, recognizing that
loan originations depend on both processes. An attempt is made to isolate the effect of neighborhood
characteristics by controlling for other factors, such as the borrower's income and race, market effects, and
lender behavior.
After other factors are controlled for, the study concludes that the racial composition of a
neighborhood appears to have little impact on either the likelihood that a loan application will be denied or
the rate at which applications are made. On the other hand, the race of the applicant appears to have a
strong impact on loan denial. Black applicants, in particular, have unexplainably high denial rates. The
income of a neighborhood does appear to impact both denial and application rates, with neighborhoods
below a median income of $20,000 being particularly disadvantaged. Finally, once other factors are
controlled for, the fact that a neighborhood is in a central city appears to have little impact on credit flows.
The study cautions that although these data represent the most comprehensive information available,
questions remain about both the coverage of the dataset and the impact of many omitted variables, such as
applicant credit history and property valuation.

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I. INTRODUCTION
When Congress enacted the Housing and Community Development Act (HCDA) in 1992, it added
another legislative initiative to a 25-year federal tradition of support for the goal of equal access to credit
markets for all segments of the community. The Act directed the two government-sponsored housing
enterprises (GSEs) -- Fannie Mae and Freddie Mac -- to increase their lending in "underserved areas" and
where there are "unmet housing needs." In the short run, interim targets specify that 30 percent of the
GSEs' purchased mortgages must be in central cities, rural areas, or other underserved locations, and 30
percent must be made to borrowers with incomes below their area's median. By 1995, the Department of
Housing and Urban Development (HUD) is to replace these targets with permanent ones.
The language and spirit of HCDA are very similar to those of the 1977 Community Reinvestment

Act (CRA), which requires depository institutions (mainly commercial banks and savings and loans) to
i

help meet the credit needs of their entire community, including low- and moderate-income neighborhoods,
in a manner consistent with safe and sound banking. Initial enforcement of CRA by the federal banking
regulatory agencies focused on procedures used to advertise and solicit loan applications (phcularly
mortgages) from low-income and minority (nonwhite) neighborhoods. Increasingly, however, community
groups have pressured regulators to shift enforcement toward quantitative standat&.' This has raised the
same issue about how unrnet mortgage needs are measured as HUD will face in devising permanent GSE
targets under HCDA. Unfortunately, there is little agreement about how to identify underserved areas.
The underlying premise of both HCDA and CRA is that some sort of market breakdown exists under which

well-qualified borrowers are willing to pay prevailing mortgage rates but are unable to secure a mortgage.
This might occur because of either supply constraints (lenders may discriminate against certain individuals
or neighborhoods, or they may incorrectly perceive the risk of such lending) or demand considerations
(borrowers might have incorrect perceptions about underwriting standards). Although the premise may be

See Neuberger and Schmidt (1994) md Avery (1989).
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clear, it is not clear how to identify the occurrence of a market breakdown empirically. Credit flows may
vary across individuals or neighborhoods for many reasons other than the presence or absence of a market
breakdown. Supply may vary because lending risk differs, and demand may vary for a host of reasons.
The objective of this paper is to provide a baseline evaluation of the variation in mortgage credit
flows across different types of neighborhoods. We focus on mortgage credit because of its heavily
geographic component and its specific citation in HCDA. In our analysis, we examine variation in loan
application and lender denial rates separately, recognizing that the variable of concern -- loan originations

-- depends on both processes. The spirit of our inquiry is descriptive; we do not pretend to answer
definitively the question of how to identify an underserved area Hopefully, a better understanding of the
reduced-form stylized facts can provide signs about where future research can best be directed.
We use data recently made available under the 1989 amendments to the Home Mortgage
Disclosure Act (HMDA). Starling in 1990, the amendments required covered lenders operating in
metropolitan areas (MSAs) to report on a census tract basis, among other things, detailed information on
individual mortgage loan applicants, including income and race and disposition of the applications.
Curiously, despite congressional interest in credit flows to specific types of neighborhoods, most analysts
have used post-1989 HMDA data to investigate charges of racial discrimination against individual loan
applicants. The role of property location remains largely unexplored with this dataset2

Canner (1981), Avery and Buynak (1981), Avery and Canner (1983), and Bradbury, Case, and Dunham (1989)
contrast the differences in mortgage credit originations between predominantly white and predominantly minority
neighborhoods in various MSAs. These studies use either pre-1990 HMDA data or lien title data to infer from the
neighborhoods' characteristics whether mortgage lenders treat neighborhoods differently depending on their racial
composition. In studies combining individual and neighborhood data, King (1980) and Schafer and Ladd (1981)
find little evidence of neighborhood effects, but they do uncover some evidence of higher denial rates for black and
Hispanic applicants. While quite informative, these studies are limited in their geographic coverage and in the
number and types of lenders surveyed. More recently, Munnell et al. (1992) conducted a special survey of home
purchase applications in Boston matched to the 1990 HMDA frame. They determined that once an individual's
race is factored in, neighborhood racial composition accounts for little. However, their sample contained a
relatively small number of minority neighborhoods. Similarly, Megbolugbe and Cho (1993) and Buist,
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We use HMDA data in two ways. Total loan applications for 1990 and 1991 are sorted into
census tracts and used to construct application rates by tract, scaled by the number of tract housing units as
measured in the 1990 Decennial Census. Application denial rates are also constructed by aggregating
actions on individual loan applications into tract averages. Our analysis focuses on how these two
variables differ across different types of neighborhoods -- specifically, neighborhoods sorted by median
family income and percent minority population. We examine the gross variation in these two measures as
well as the variation controlling for 1) individual characteristics of the borrower and loan and 2)
demographic characteristics of the tract.
Although HMDA data are by far the most comprehensive available on the geographic distribution
of mortgages, they raise several concerns. First, many applicant-level variables used in lenders' credit
decisions are not collected. These include the applicant's credit history, work history, debt burdens, and
wealth, for example. Second, no information is provided about the physical condition of the individual
property securing the mortgage being sought. To the extent that these individual and property
characteristics are correlated with neighborhood characteristics,this creates problems in identifying a pure
neighborhood effect.
Finally, concern has been expressed about the completeness of HMDA coverage. Evidence
suggests that some lenders, particularly mortgage bankers, may not be filing HMDA reports. If such
omissions are not random, then this presents a potentially serious drawback to the use of our application
rate variable. This is particularly troublesome because we have argued elsewhere (see Avery, Beeson, and

Megbolugbe, and Trent (1994) use post-1989 HMDA data to examine geographic variations in mortgage lending,
but they restrict themselves to MSA-level aggregates.
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Sniderman [1994]) that loan originations are the best measure of lender compliance with CRA. We show
that across lenders, and thus potentially across neighborhoods, application rate variation explains a much
larger percentage of the variation in origination rates than do denial rates. Because of its importance to the
debate over underserved neighborhoods and the lack of a better data source, we present evidence on the
distribution of application rates constructed from HMDA data. However, these results should be viewed
with caution until we have a better understanding of the potential bias stemming from undercoverage.
By way of preview, we find that once other factors are controlled for, the racial composition of a
census tract has little impact on either its application rate or the likelihood that a loan will be denied On
the other hand, tract income appears to be important. Ceteris paribus, low-income tracts, particularly those
with median incomes below $20,000, show significantly lower application rates and higher denial rates
than other areas. Although the racial composition of a tract doesn't appear to matter, we do find that the
race of an individual has a large impact on denial rates. Black applicants, in particular, have unexplainably
high denial rates. Finally, although the interim HCDA guidelines set specific targets for central city
lending, we find little evidence that central city tracts have either lower application rates or higher denial
rates once other tract characteristics are accounted for.
The remainder of the paper is organized as follows. The next section presents the framework for
the empirical analysis used to identify neighborhood effects. In section III, we discuss the dataset used in
the study, describe the steps used to prepare it, and give simple descriptive statistics. Section IV presents
the bulk of the analysis and a discussion of the results. Conclusions are reported in section V.

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11. EMPIRICAL FRAMEWORK
The purpose of this paper is to examine variation in mortgage lending patterns -- both application
rates and denial rates -- across neighborhoods (i.e., census tracts). Ideally, we would like to isolate true
neighborhood differences; that is, differences that stem from characteristics of the neighborhood itself
rather than from characteristics of either the individuals who apply for loans in the neighborhood or the
lenders that happen to serve them. Unfortunately, since we lack any information on persons who did not
apply for loans, analysis of application rates must be conducted at the neighborhood level without controls
for any individual or lender characteristics. Infomation in HMDA filings, however, does allow the
potential to control for some borrower and lender characteristics in the analysis of denial rates. This is
done through a two-stage procedure. In the first stage, we use the complete 1990/1991 HMDA filings to
identify neighbodmod differences in denial rates that cannot be explained by characteristics of the
application or lender.3 These neighbofhood residuals are then used as dependent variables in second-stage
regressions relating them to neighborhood characteristics drawn from the 1980 and 1990 Decennial
Censuses. This approach parallels the one we used in two earlier studies designed to isolate individual and
lender effects (Avery,Beeson, and Sniderman [1993a, 1993bl).
In the first stage, we assume that each mortgage applicant's risk can be represented as a function of

hisher race and economic characteristics (such as income), neighborhood (census tract), market (MSA),
and lender. We have no basis with which to select a particular econometric model specification. However,
the size of the dataset dictates that in practice we assume a linear-probabilitymodel specification4 Thus,

- -

At the time this paper was written, 1992 HMDA data were also available. However, the geographic taxonomy
used for reporting loans changed from 1980 census tracts to 1990 tracts in 1992. Thus, the analysis was restricted
to 1990 and 1991 in order to utilize a consistent geographic framework.
AS discussed later, a large number of nonlinear transformations and interactions of the independent variables
are used. We do this to increase the robustness of the results and to reduce the potential impact of the arbitrary
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we estimate a model in which the probability of a random loan application being denied is linear in the
following terms:

where DENIAL is one if the ith application using the Lth lender in the Mth MSA and Tth census tract is
denied, and zero otherwise. MSA, TRACT, and LENDER are dummy variables indicating which MSA,
census tract, and lender the application relates to, and e is a residual. AC is a vector of application
characteristics, other than race, reported in the HMDA data It includes gender, marital status, occupancy,
income, loan amount, income-to-loan ratio, federal loan guarantee (Federal Housing Administration [FHA]

or Department of Veterans Affairs [VA]), and month of the year the application was acted upon.'n6 RACE
is a set of dummy variables indicating the race of the applicant and co-applicant; each is interacted with
FHA/VA status as well as income. The model is specified and estimated separately for each of three types

selection of the model form. With more than 2,000,000 observations, the use of either a logistic or probit model
form would have been impractical.
To help minimize the possibility that the differences we identify within and across neighborhoods reflect
nonlinearities in other effects that are correlated with location, we allow for a considerable degree of nonlinearity
in the effects of individual characteristics. Income and loan amount are entered as linear spline functions with
seven knots each (dummies are also used for small home improvement loans), and the income-to-loan-amountratio
is entered as a series of six dummy variables. A fiveknot linear spline for income is interacted with a dummy
variable indicating the presence of a co-applicant, and with dummy variables indicating that the application is for
an FHA or VA loan. Similarly, a five-knot linear spline of loan amount, and the six dummy variables indicating
ranges of values for the ratio of income to loan amount, are also interacted with a dummy variable indicating
applications for FHA or VA loans.
6
The month of the action date is included as a crude proxy for interest rates and other market conditions.
Lenders reported the date of both the application and loan action. The application month would be the ideal choice
as a proxy for interest rates, since most mortgage rates are locked in at that point. Unfortunately, the filing year is
defined by the action date, which is the date of denial for a denied application, but the closing date for accepted and
originated mortgages. Because the closing date is typically a month or two later than the approval date, this
creates a systematic bias in the HMDA data in the relationship between the loan action and application dates and
the loan's disposition. For example, more than half of the applications made in November or December 1991 and
filed for the 1991 calendar year were denials. Closing dates for accepted applications during those months were
likely to extend over the first of the year and thus were filed for the 1992 calendar year. Potentially, this problem
could be reduced by combining several years of data. However, this raises the issue of changing frling
requirements.

'

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of loan applications -- home purchases, refinancings, and home improvements -- and for each of the two
sample years, 1990 and 1991.
To reduce the computing requirements, the actual estimation was done in two steps. In the first
step, equation (1) was estimated with the individual application characteristics (AC) and separate intercepts
for each 1enderJcensustract combination included as single-component fixed effects. The MSA, lender,
and tract effects are thus intertwined in these intercepts. In the second step, an iterative procedure,
equivalent to regressing the fixed-effects intercepts against MSA, census tract, and lender dummies, was
used to identify the MSA, tract, and lender effects. Separate lender effects were estimated for each MSA,
thus defining lenders operating in multiple MSAs as multiple lenders. By construction, the MSA effects
were normalized to have overall sample means of zero, and within each MSA, lender and tract means were
normalized to zero. In cases where lender and tract effects were not identified (a lender was the only lender
in a tract and did dl of its business there), the effect was assigned to the tract.
The parameter estimates from equation (I), together with the characteristics of the applications
received (AC, RACE, and LENDER), are used to predict denial rates for each neighborhood.
Neighborhood denial residuals are measured as the difference between the neighborhood's predicted and
actual denial rates:

+ PRJRACET~
+ PLAENDERT~),
(2) DENIAL RESIDUfij = DENIfij - (PA~ACT~
where DENIAL (the actual denial rate), AC, RACE, and LENDER are tract averages for the jth loan type
(home purchase, refinance, home improvement) and Tth tract. Note that these residuals reflect relative
treatment, since, by construction, the average residual across all neighborhoods is zero. Also note that the
residuals include MSA effects (which are normalized to zero). Thus, the tract residuals reflect both within-

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and between-MSA effects. Including the between-MSA effect in the residual is consistent with the view
that it is the absolute characteristics of a tract, and its absolute denial rate, that matter. This would be the
case if the United States were truly one national m e e t , but may not be true if MSA market conditions are

important.
Although these residuals are constructed for each of the three types of loans and each-year, our
analysis combines 1990 and 1991 data for each loan type using a weighted average. A second set of
residuals that factor out the MSA effects, J3MjMSA~j,
were also constructed. These residuals are deviations
about MSA means, indicating that the relevant consideration for a tract is its relative position within an
MSA.

In the second stage of estimation, these neighborhood residuals are regressed on various
neighborhood chkacteristics. The general form of the estimation is as follows:
(3) DENIAL RESIDUAkj = xCENSUST + UTj,
where J indicates loan type, T specifies tract, and CENSUS is a vector of variables drawn from the 1980
and 1990 Decennial Censuses. Regressions are run for the whole sample and separately for center city and
suburban (non-central city) tracts. We use both absolute tract residuals, including between-MSA effects,
and relative residuals, specified as deviations about MSA means.
Consistent with the qualifications cited earlier, we also examine the relationship between loan
application rates and neighborhood characteristics. Applications are summed for each tract over the two
years for each loan type and are then deflated by the stock of 1-4 unit residential properties as defmed by
the 1990 Decennial Census. This variable is regressed against the same set of independent vaiiables as
used for the denial rate regressions in equation (3):

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+ VT~,
(4) APPLICATION R A m j = njCENSUS~
with j, T, and CENSUS as defined above.

111. DATA

Mortgage Loan Ap~licationand Disposition Data
Data on individual loan applications and dispositions for 1990 and 1991, used in the first-stage
estimation for the denial rate and to form the numerator of the application rate, are collected under the 1989
revisions to HMDA. The amended HMDA data form one of the most comprehensive sets of statistics on
mortgage lending available in the United States. Nearly all commercial banks, savings and loan
associations, credit unions, and other mortgage lending institutions (primarily mortgage banks) with assets
of more than $10 million and an office in an MSA are required to report on each mortgage loan purchased
and loan application filed during the calendar year. Lenders must report the loan amount, census tract of
the property, whether the property is owner occupied, purpose of the loan (home purchase, home
improvement, or refinancing), loan guarantee (conventional, FHA, or VA), loan disposition (loan approved
and originated, application approved but withdrawn, no lender action taken [incomplete data or application
withdrawn], or application denied), race and gender of the loan applicant (and co-applicant, if any), and
income relied on by the lending institution in making the loan decision.'*'

' See Canner and Smith (1991,1992) for a comprehensivediscussion of the HMDA data
'Instihltions with assets of less than $30 million are not required to report race, income, or gender for loan
applicants. In addition, the HMDA filings contain many errors and inconsistencieseven after extensive editing by
the receiving agencies. We dealt with missing and implausible data by using a "hot deck" imputation procedure
similar to that used by the U.S.Census Bureau. Applications with missing or implausible data were statistically
matched to applications for the same type of loan in the same census tract that came closest to them in reported
characteristics (race, loan action, income, and loan amount). Missing values were filled in using the variable value
of the matched observation. Overall, income was imputed for 4.9 percent, loan amount for 1.5 percent, gender for
4.0 percent, and race for 5.6 percent of the study sample applications.
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In total, 9,333 financial institutions filed HMDA reports for 1990 on 6,595,089 loans. In 1991,
9,365 institutions filed on 7,939,107 loans. Our analysis focuses on the 7,938,438 loan applications in the
two years for 1-4 unit residential properties that were acted upon (denied or accepted) by the

lender^.^ Of

these, 4,072,158 were for home purchase loans, 2,216,810 were to refinance an existing mortgage loan,
and 1,649,470were for home improvement loans (generally second or third mortgages).1° These
applications were received by 8,745 separate institutions operating in 40,008 census tracts in all 341 of the
MSAs defined as of 1990. For our analysis, we define lender at the MSA level; thus, an institution
reporting applications for two different MSAs is treated as two different lenders. There are 23,248 such
lenders in our sample.
Descriptive statistics for the applications reported for 1990 and 1991 under HMDA are presented

in table 1. Statistics are given separately for home purchase, refinancing, and home improvement loan
applications. Clearly, housing credit applicants are a select group of American families. Applicants'
median income ($49,000) is substantially higher than the median income of families in MSAs ($37,918) as

The following loan filings were omitted from the sample: 1) loans purchased from other institutions (because
they did not require an action by the reporting lender and often were missing geographic information) and
applications for properties outside the MSAs in which the lender had an office (5,670,768 loans dropped), 2)
applications for multifamily homes (55,703 loans dropped), and 3) applications that never reached the stage of
lender action because they were either withdrawn by the applicant or closed for incompleteness (869,287 loans
dropped). Overall in 1990 (1991), the sample consisted of 1,984,688 (2,087,470) home purchase loan applications,
7 16,595 (1,500,215) refmcing applications, and 787,952 (86 1 518) home improvement loan applications. The
f d sample includes some mobile home loans and condominium loans, since they were treated as 1-4family units
in the HMDA reporting guidelines.
lo The distinction between loan types may be blmed. Institutions were allowed to report home improvement
loans secured by a fnst lien as either home purchase or home improvement loans. Some home improvement loans
may also be reported as refinancings if a new first lien was issued. Some refinancing may not have been reported
at all. If a refmcing was undertaken primarily for a purpose other than home purchase or home improvement
(such as college expenses or to start a business), then it did not have to be reported. Similarly,unless the borrower
specifically noted home improvement as a reason for the loan, lenders did not have to report home equity or
second-lien mortgages.
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reported in the 1990 Decennial Census.'' The racial composition of the study sample also appears to differ
from that of all U.S. families. Blacks filed 7.4 percent of the HMDA housing loan applications for the

three loan types, yet headed 11.4 percent of the MSA households and represented 7.7 percent of all
homeowners in the 1990 Decennial Census. Asian loan applicants (5.2 percent), however, were
overrepresented compared with their numbers in the census (2.5 percent of MSA household heads and 2.2
percent of homeowners). The percentage of applicants who were white (8 1.9 percent) or Hispanic (7.5
percent) is approximately representative of their numbers (78.1 percent of household heads and 84.8
percent of homeowners for whites, and 7.5 percent of household heads and 5.0 percent of homeowners for
Hispanics).12 It is also apparent that denial rates differ substantially by race for all three types of loans.
Census Data
Data used as explanatory variables in the second stage of the analysis were drawn from the 1980
and 1990 Decennial Censuses. Unfomately, although most tracts remained the same, some boundary
definitions were changed between 1980 and 1990. In filing 1990 and 1991 HMDA reports, lenders were
required to use 1980 census tract definitions. However, the most relevant census information, that for
1990, is reported by the Census Bureau using 1990tract definitions. To resolve this problem, we decided
to use 1980 tract definitions as the mode of analysis and to use estimates of 1990 census information. Data
were obtained from Claritas Corporation, which aggregated block-level 1990 census data to 1980-defined
tract totals. Change variables were calculated using 1980 census information and Claritas's 1990
estimates.

l1 I
n the HMDA data, household income may be slightly understated, as it reflects only the portion of an
applicant's income needed for mortgage qualification.
l2 These figures exclude Puerto Rico, which is included in the table 1 statistics. If Puerto Rico is included,
Hispanics are 8.1 percent of the loan sample.

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Census and HMDA data could be aligned using a consistent taxonomy for most areas with the
methodology just described. However, for a few outer areas of some MSAs that were not tracted in 1980,
loan and census information had to be aggregated to the county level. In a few other instances, tracts had
to be dropped for a variety of reasons. We lacked census information on Puerto Rico and thus excluded it
from the analysis. We also dropped HMDA loans in tracts that had no residents, in those with insufficient
numbers to provide racial breakdowns, and in those with less than 50 dwellings. In total, the sample for the
second stage consisted of 38,697 of the original 40,008 HMDA census tracts, with 98.9 percent
(7,851,680) of the original HMDA loan applications. Puerto Rico accounted for the majority of the
omissions.
Specific census variables selected for the analysis include the following: 1) percent minority
population of each tract (defined here as all nonwhites -- Hispanic, black, Asian, native American, and
other race), 2) median family income, 3) median owner-occupied house value, 4) age distribution of
household heads, 5) distribution of residential dwellings by number of units in the structure, 6) percentage
of 1-4 unit residential properties that were vacant and rented, and 7) variables indicating the distribution of
the housing stock by vintage. 1990 values were used for each of these variables (except the housing age
variables, which used 1980 data) as well as for the change from 1980 to 1990.
The sample distribution of tracts, population, owner-occupied housing units, and total 1990/1991
HMDA loan applications for the three loan classes is reported in table 2. Information is given for the total
population and for minorities. Distributions are shown for census tracts sorted by minority population
share in 1990, change in minority population share from 1980 to 1990, share of black population, share of

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Hispanic population, median owner-occupied housing value in 1990, percentage change in median housing
value from 1980 to 1990,13median family income in 1990, and center citylsuburban and MSA size.
?he most interesting comparison in table 2 is between column 4 (the stock of 1-4 unit residential
properties as measured by the Decennial Census) and columns 5,7, and 9 (loan applications for
comparable units). Interestingly, those tracts with less than 5 percent minority population are
proportionately represented in loan applications, whereas 10 to 50 percent minority tracts have
disproportionately more loan applicants, and more than 50 percent minority tracts have disproportionately
fewer applicants. It appears that predominantly black tracts are particularly underrepresented. It also
appears that tracts with median home values above $100,000 or median incomes above !$40,000 have a
disproportionately large number of applicants, but that areas with substantial increases in housing value
from 1980 to 1990have less than their share of applicants.
Table 3 reports HMDA denial rates for white, black, and Hispanic applicants by tract using the
same taxonomy as in table 2. It appears that differences across racial groups dominate those across
neighborhood types. Interestingly, a neighborhood's racial composition seems to affect the treatment of
white applicants much more than it does blacks or Hispanics. Tract house value and income appear to
impact each racial group in roughly proportional ways. On the other hand, the change in housing value
seems to be unrelated to lender treatment. Finally, denial rates are somewhat higher in central cities than in
subudxv~areas, but at least for blacks and Hispanics, MSA size appears to have an even larger effect.

l3

Measured in nominal terms. The Consumer Price Index rose about 50 percent over this period.
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IV. ESTIMATION AND RESULTS
Parameter estimates for the first-stage regressions predicting the denial of an application are
presented in tables 4,5, and 6.14915In examining these numbers, a positive coefficient can be interpreted as
the expected increase in the probability that an applicant's loan will, be denied resulting from a one-unit
increase in the independent variable, holding all other variables constant (specifically, the applicant's MSA,
census tract, and lender). Thus, the coefficients on race, for example, represent the expected difference in
the probability that a white and black applicant with the same income, gender, FHA/VA status, loan
amount, month of action date, MSA, census tract, and lender will have their loan applications denied. Thus
interpreted, the estimated black/white (.I04 and .106) and Hispanic/white (.038 and .052) differences for
conventional home purchase loans are quite sigmficant. Differences are similar for refinance and home
improvement loans. This might appear to be tangential to our examination of neighbohood effects.
However, since minorities tend to live in segregated communities, if they are underserved as individuals,
then a policy of targeting minority neighborhoods may be warranted -- even if the neighborhood racial
composition per se does not appear to be related to denial rates.
The second stage of the analysis consists of examining the relationship between neighbo&d
characteristics and application and denial rates. Instead of gross denial rates, we use adjusted tract
residuals computed using the coefficients in tables 4-6 (see equation [2]). These can be thought of as tract

14

The model was actually estimated using deviations about the means, which is computationallyequivalent to a
single-component fixed-effectsmodel. For 1990 (1991), the home purchase sample had 1,984,688 (2,087,470)
observations located in 607,631 (662,571) unique combinations of 40,008 (39,963) tracts and 20,695 (26,508)
lenders spread across 340 (341) MSAs; thus, the average tract had about 15 lenders in each year, each of whom
served about 30 tracts per MSA. For the refinancing sample in 1990 (1991), the 716,595 (1500,215) observations
were located in 326,535 (563,380) unique combinations of 37,746 (38,912) tracts and 16,159 (23,284) lenders.
For the home improvement loan sample in 1990 (1991), the 787,951 (861,518) observations were located in
267,158 (285,605) unique combinations of 39,219 (39,216) tracts and 12,280 (13,276) lenders.
l5 The reported standard errors in tables 4-6 are those from a standard regression program. These may be biased
due to heteroskedasticity stemming from the linear probability model specification.
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denial rates adjusted for applicant and lender characteristics. Most of our analysis includes the MSA
effects in these residuals; however, we also duplicate our analysis using deviations about MSA means.
Means for the dependent and independent variables used in the second stage are given in table 7. Figures
are reported for all tracts as well as separately for center city and suburban areas. We do not give the
-

adjusted denial-rate means, since they are normalized (to zero) constructs.
Regression results are presented in tables 8-11. Independent variables are identical in each
regression However, the dependent variable and the sample are varied. Regressions were run separately
for home purchase, refinance, and home improvement loans. Table 8 presents results for the whole sample
using the adjusted denial-rate residuals. In these, and in all regressions using the adjusted denial rates,
tracts are weighted by the number of applications of each loan type in the tract Table 9 gives results of
regressions identical to those in table 8, except that all variables are expressed as deviations about MSA
means (equivalent to adding a dummy variable for each MSA). Tables 10 and 11 present results of
regressions identical to those in tables 8 and 9, except that the dependent variable is the tract application
rate, with observations weighted by the number of 1-4 unit residential properties in the tract
Clearly, the format of the results presented in tables 8-11makes it difficult to get a g o d sense of
the overall thrust of the data To put this information into a more easily understood form, we decided to
focus on only two neighborhood characteristics -- percent minority population in each tract and tract
median family income. We also tried to distill the information in the regressions into a few summary
variables. For each tract and loan type, the following were constructed: 1)gross denial rate, 2) denial rate
adjusted for lender and individual characteristics (the dependent variable used for the regressions in tables 8
and 9), and 3) gross application rate (the dependent variable in tables 10 and 11).

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In addition, predicted values from the regressions presented in tables 8-11 were used to construct

four variables. We subtracted these predicted values from the application and denial rates in each tract to
compute adjusted residuals. These can be thought of as the application (or denial) rate in the tract adjusted
for its demographic and economic characteristics (e.g., age of the housing stock and householders and
house usage) and, in the case of the denial rate, the individual's characteristics as well. Because of the
particular concern with minority population share and tract family income, we constructed two separate
adjusted residuals. To examine the impact of minority population share, we computed residuals using the
coefficients on all variables except those for minority population share and the change in minority share.
These residuals are based on the predicted tract application (or denial) rate if the tract were all-white and
had no change in racial composition from 1980 to 1990. The impact of tract income was examined using a
similarly constructed residual that incorporates all variable coefficientsexcept those for median family
income, the change in median income, median house value, and the change in house value. Again, these
residuals can be viewed as deviations from the predicted application (or denial) rate for a tract if it were
assumed to have an average tract income, home value, and average changes from 1980 to 1990.
Tracts were then sorted by minority share and median tract family income. Tract values for each
of these variables were averaged (using applications or 1-4unit residential properties as weights) for all
tracts with the same income or minority share and were summarized in graph form. In the subsections that
follow, we discuss several issues using these results.
Tract Racial Com~osition
Loan denial rates arrayed by minority percentage in the tract are presented in figure 1. Panels are
shown for each loan type using the same scale for comparison In each panel, three separate denial rates
are shown: 1) the gross denial rate controlling for nothing (equivalent to the numbers presented in table 3),

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2) the adjusted denial rate controlling for individual and lender characteristics (the dependent variable in the
regressions presented in table 8), and 3) the fully adjusted denial rate adjusting for individual and lender
characteristics, and for all tract characteristics except minority share (the residuals from the regressions
presented in table 8). In each case, the denial rates are normalized to have a value of zero in tracts having a
minority share of 2 percent or less.
The gap in denial rates between white and minority neighborhoods is huge. Moreover, although
much of the difference disappears when individual and other tract characteristics are controlled for, a
significant difference remains. The difference between all-white and all-minority tracts for home purchase
loan denial rates, for example, falls from .I67 when nothing is controlled for, to .084 when individual and
lender characteristics are controlled for, to .044when tract characteristics other than race are controlled
for. Similar reductions occur for refinance loans, where the gap narrows from .213 to .I18 to .OH.
Neighborhood effects seem more persistent for home improvement loans, with a comparatively wide gap of
.I56 remaining even after individual and nonracial neighborhood effects are taken into account
The data in figure 1 reflect both between- and within-MSA effects, implying that it is the absolute
characteristics of a tract that count. In figure 2, we present denial rate differences based only on withinMSA information (the gross denial rate data shown also have between-MSA differences removed).
Controlling for MSA appears to virtually eliminate the effect of neighborhood racial composition on denial
rates of home purchase and refinance loans, reducing the all-white and all-minority gap to .015 and .016,
respectively, when all other factors are controlled for. Thus, any relationship between the racial
composition of the tract and denial rates appears to stem from variation across MSAs, not within them.
Although reduced from figure 1, the fully adjusted denial rate gap between all-white and all-minority tracts
for home improvement loan applications is stiU a significant .048.

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Figures 3 and 4 present similar information for application rates. Since we have no control for
individual characteristics, we plot the gross application rate and the rate adjusted for tract characteristics
other than race. Although it is necessary to bear in mind our concern about the adequacy of HMDA
coverage, several conclusions emerge. The gross difference in home purchase loan application rates
between all-white and all-minority tracts presented in figure 3 (.042) is relatively large, especially when
compared with the average tract application rate of .07 1 in the sample. However, this gap narrows to .007
when characteristics other than race are controlled for. Indeed, nearly all differences in application rates
across tracts of different racial composition disappear when adjusted rates are used This is true whether
between- and within-MSA data are used or just within-MSA numbers (figure 4).
Tract Median Family Income
Denial rates arrayed by tract median family income (measured in $1,800~)are presented in figure

5. The variables plotted are similar to those used for figure 1 except that the fully adjusted rate represents
the denial rate residual controlling for all tract characteristics except income, house value, and the change
in both variables from 1980 to 1990. Each denial rate is normalized to have a value of zero for all
neighborhoods with a median income of $110,800 or more.
Unlike neighborhood racial composition, it appears that neighborhood income has a significant
impact on home purchase and refinance denial rates even after other factors are controlled for. This is
particularly true for loans in neighborhoods with median incomes below $20,000 (the median income for
the average tract is $37,800). Ceteris paribus, home purchase loans in tracts with a median income of
$20,000 are .073 more likely to be denied than loans in tracts with a $110,000 median, and .022 more
likely than loans in tracts with a $40,800 median. Differences for refinance loans are even more

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pronounced, at .I65 and .066, respectively. On the other hand, after controlling for other factors,
neighbofhood income appears to have virtually no effect on home improvement loan denial rates.
Although the magnitudes change somewhat, these findings also hold when only within-MSA
differences are plotted (figure 6). 'Ihe only conclusion with a substantive change is the appearance that
neighbofhood income may affect home improvement denial rates when MSA is controlled for, even though
it has little effect when MSA is not considered.
The income of a tract also appears to have a strong impact on home purchase and refinance (but
not home improvement) application rates (figure 7). This is true for both gross and adjusted rate
comparisons, when MSA is not controlled for, and when only within-MSA differences are used (figure 8).
The effect is monotonic, with the application rate steadily increasing in income up to the $65,000 to
$70,000 level.
Center Citv/Suburban
Interim targets set up under HCDA require the GSEs to meet minimum gods for lending in center
cities. This suggests a belief by Congress that central city neighbofhoods are more likely to be underserved
than are other neighborhoods. HMDA data provide little evidence to support this view. Controlling for
other factors, denial rates for home purchase loans are slightly higher (.002) in central city tracts than in
other neighbofhoods (table 8). However, ceteris paribus, denial rates are actually lower for refinance and
home improvement loans (table 8). We note, though, that when deviations about MSA means are used, the
findings for refinance and home improvement loans reverse (table 9). There also appears to be little
evidence that, ceteris paribus, application rates differ significantlybetween center city and suburban tracts
(table 10). Indeed, the regression results suggest that home purchase and home improvement loan
application rates are actually higher in central city tracts.

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To explore this further, we use the same data as in figures 1,3,5, and 7, but graph central city and
suburban tracts separately (figures 9-12). It is apparent from the plots that overall, the difference among
tracts within central city or suburban areas is much larger than the gap between the two. Moreover, it is
not always the case that central city denial rates are larger. For example, among the poorest
neighborhoods, suburban home purchase denial rates are actually higher than those for central cities. The
only exception to the general conclusion that central city does not matter is the relationship between home
purchase and refinance application rates and neighborhood racial composition (figure 10). However, most
of this difference disappears when the fully adjusted residuals are compared16
Neighborhood versus Individual

The data presented in figures 1-12reflect overall neighborhood effects. Clearly, there may be
interaction effects; that is, neighborhood effects may be different for different individuals. Moreover,
neighborhood characteristics may be important -- not in and of themselves, but because certain types of
people tend to live there. The interaction between an individual's race and the racial composition of hisher
neighborhood is examined in figures 13 and 14. In figure 13, the gross and adjusted (for individual
characteristics other than race) differences between blackJwhite and Hispaniclwhite applicant denial rates
are arrayed by neighborhood racial composition. Unlike data presented in other figures, these are absolute
differences and are not normalized. Although a quite noisy series, the gap is generally widest in the
predominantly white neighborhoods and lowest in the predominantly minority neighborhoods.
This effectis mirrored in figure 14, which gives the adjusted denial rate residuals (similar to the
dependent variables in the table 8 regressions) calculated separately for each racial group. These are each
normalized to have a value of zero in tracts with a minority share of 2 percent or less. Interestingly, the

16

Although not shown here, similar results emerge when within-MSA data are used.
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racial composition of a neighborhood affects the denial rate of white applicants much more than that of
black or Hispanic applicants. For example, ceteris paribus, a black applicant for a home purchase loan is
.037 more likely to have hisher application denied in an all-minority tract than in an all-white tract; a white

applicant, however, would be .I15 more likely.
Similar data are presented for tracts arrayed by income in figures 15 and 16. Here, tract income
appears to affect all racial groups in approximately the same way. Except for home improvement loans --

and here only for middle-income tracts -- there is virtually no difference in tract effects by the individual's
race.

V. CONCLUSIONS

We have examined how a neighborhood's racial composition and median family income affect
application and denial rates for home mortgage loans. Several findings emerge. We show that controlhg
for nothing else, the racial composition of a tract appears to be strongly related to the likelihood that a loan
application will be denied. However, when other factors, particularly the individual's race and MSA, are
controlled for, the difference largely disappears for home purchase and refinance loans (but not for home
improvement loans). Similar findings emerge for application rates.
It is important to note that this does not mean that '"ace doesn't matter." Indeed, in our analysis
of HMDA data, the most significant and persistent factor in explaining denial rates is the applicant's race
(see Avery, Beeson, and Sniderman [1993a]). The current paper attempts to sort out the difference
between the effects of an individual's race and the racial composition of the neighborhood. This, however,
is an imperfect process, and strong interaction effects may exist. Indeed, the data suggest that the racial
composition of a neighborhood strongly affects the denial likelihood of white applicants. Moreover, even

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if, ceteris paribus, the racial make-up of a neighborhood doesn't matter, neighborhood targeting by race
may be a way of helping individual minorities and thus offsetting what appears to be their adverse
treatment in the denial process.
We do find evidence that, ceteris paribus, a neighborhood's income does matter. Although many
effects are monotonic with no clear-cut breakpoints, tracts with median income below $20,000, in
particular, show significantly higher denial rates, even when applicant characteristics (including income)
and other tract characteristics are accounted for. Median tract income also appears to have a strong
relationship with application rates, particularly for home purchase and refinance loans. These effects
remain even when other tract characteristics are controlled for.
Evidence from HMDA data does not appear to support the congressional decision to single out
central city tracts in setting targets for the GSEs under HCDA. Although denial rates are marginally
higher for home purchase loans in central cities, there is little evidence that central city and suburban tracts
differ in either denial or application rates once individual tract characteristics are accounted for. This does
not mean that the selection of central city tracts for loan targets is necessarily wrong if, for example, most
of these tracts are also low income and/or predominantly minority. However, it would appear to be more
effective to set targets according to tract-level characteristics than to use central city as a proxy.
We caution that these results come from reduced-form regressions. Differences in application or
denial rates related to the racial composition or income of a neighborhood may stem from either unobserved
variables related to risk or demand that we have failed to control for, coverage gaps in our data, inherent
differences in mortgage demand, or differences in supply. Only if we eliminated the first three "causes"
could we conclude unequivocally that low-income neighboxhoods (or minority individuals) are underserved.

On the other hand, the results make a prima facie case that neighboxhood income and individual race do

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matter. Ceteris paribus, persons in low-income tracts are less likely to apply for loans and, if they do, are
more likely to be denied Similarly, loan applications by minorities (particularly blacks) are significantly
more likely to be denied than those by whites, even after other factors are controlled for. These are not
results that stem from one market or one loan product; rather, they are pervasive and appear to be
widespread. Thus, although our results are inconclusive, they are strongly suggestive of the need for
further research.

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REFERENCES
Avery, Robert B. 1989. Making Judgments about Mortgage Lending Pattern. Economic Commentary,
Federal Reserve Bank of Cleveland (December 15).
Avery, Robert B., Patricia E. Beeson, and Mark S. Sniderman 1993a Accounting for Racial Differences
in Housing Credit Markets. Proceedings of a Conference on Discrimination and Mortgage
Lending, U.S. Department of Housing and Urban Development (forthcoming). Also, Working
Paper 9309, Federal Reserve Bank of Cleveland (August).

. 1993b. Lender Consistency in Housing Credit Markets.
,and
Proceedings of the 1993 Conference on Bank Structure,Federal Reserve Bank of Chicago,
pp. 339-358. Also, Working Paper 93 10, Federal Reserve Bank of Cleveland (August).

.

. 1994. Cross-lender Variation in Home Mortgage Lending.
, and
Economic Review, Federal Reserve Bank of Cleveland, vol. 30, no. 4 (Quarter 4), pp. 15-29.
Avery, Robert B., and Thomas M. Buynak. 1981. Mortgage Redlining: Some New Evidence. Economic
Review, Federal Reserve Bank of Cleveland (Summer), pp. 18-32.
Avery, Robert B., and Glenn B. Canner. 1983. Mortgage Redlining: A Multicity Cross-Section Analysis.
Unpublished working paper, Board of Governors of the Federal Reserve System, Washington,
D.C.
Bradbury, Katharine, Karl E. Case, and Constance R. Dunham. 1989. Geographic Patterns of Mortgage
Lending in Boston, 1982-1987. New England Economic Review, Federal Reserve Bank of Boston
(September/October), pp. 3-30.
Buist, Henry, Isaac F. Megbolugbe, and Tina R. Trent. 1994. Racial Homeownership Pattern, the
Mortgage Market, and Public Policy. Journal of Housing Research, vol. 5, no. 1, pp. 1-27.
Canner, Glenn B. 1981. Redlining and Mortgage Lending Patterns. In Research in Urban Economics,
edited by J. Vernon Henderson, Greenwich, CT:JAI Press, pp. 67-101.
Canner, Glenn B., and Delores S. Smith. 1991. Home Mortgage Disclosure Act: Expanded Data on
Residential Lending. Federal Reserve Bulletin, vol. 77 (November), pp. 859-881.

. 1992. Expanded HMDA Data on Residential Lending: One Year Later.
,and
Federal Reserve Bulletin, vol. 78 (November), pp. 801-824.
King, A. Thomas. 1980. Discrimination in Mortgage Lending: A Study of Three Cities. Working Paper
No. 91, Office of Policy and Economic Research, Federal Home Loan Bank Board (February).
Megbolugbe, Isaac F., and Man Cho. 1993. An Empirical Analysis of Metropolitan Housing and
Mortgage Markets. Journal of Housing Research, vol. 4, no. 2, pp. 191-243.

clevelandfed.org/research/workpaper/index.cfm

Munnell, Alicia H., Lynne E. Browne, James McEneamey, an8 Geoffrey M. B. Tootell. 1992. Mortgage
Lending in Boston: Interpreting HMDA Data Working Paper 9207, Federal Reserve Bank of
Boston (October).
Neuberger, Jonathan A., and Ronald H. Schmidt. 1994. A Market-Based Approach to CRA. FRBSF
Weekly Letter, Federal Reserve Bank of San Francisco (May 27,1994).
Schafer, Robert, and Helen F. Ladd. 1981. Discrimination in Mortgage Lending. Cambridge, MA: MIT
Press.

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FIGURE 1
DENIAL RATES, MINORITY PERCENTAGE IN TRACT
NEW PURCHASE LOANS
...- - .... ~ j u s l e dfor lndlvlduals
-.. -. . -

0.25

Fully Adjusted

REFINANCE LOANS

T

Adjusted for lndlvlduab

Adjusled for lndlvlduats

,p

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FIGURE 2
DENIAL RATES, DEVIATIONS ABOUT MSA MEANS, MINORITY PERCENTAGE IN TRACT
NEW PURCHASE LOANS
Gross Rale

1

I

-------- Adjusted for I ~ I v M u a I s
.. . ... -

Fully Adjusted

REFINANCE LOANS
Gmss Rate
-

--

-.
.

-

- - -

Adjusted for IndMduals
Fully Adjusted

HOME IMPROVEMENT LOANS

I I I I I I I I I I I I I ,
I I I I I I I I I , I I , I

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FIGURE 3
APPLICATION RATES, MINORITY PERCENTAGE IN TRACT

T

NEW PURCHASE LOANS

I

I-

T

I

G ~ S Rme
S
-

Adlusted for Trad Demographics

REFINANCE LOANS

-

P

2 -0.02
8

-.- --- -.-- AdJusledfor Trad Dernogra~hia

HOME IMPROVEMENT LOANS

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FIGURE 4
APPLICATION RATES, DEVIATIONS ABOUT MSA MEANS, MINORITY PERCENTAGE IN TRACT

0.02

NEW PURCHASE LOANS

[
--

Gmss Rate

.-- --- --- Adjusted for Trad Demographics

1

L

REFINANCE LOANS

HOME IMPROVEMENT LOANS

/-

G m s Rate

1

--------- Adjusted for Trad Demographlcs

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FIGURE 5
DENIAL RATES, TRACT MEDIAN INCOME
NEW PURCHASE LOANS

Gmss Rate
.-------.Adiusted for lndlvlduals
-.

0.25

-

--

Fully Adjusted

REFINANCE LOANS

0.2 -~djustedfor lndlviduals

I I I I I I I I I I I I ( I I I t I I ; ~ ~ ; ; I ~ ~ I ~ ~

HHtKtftll'll'lt"ll't"'lll

0.25

T '-,

HOME IMPROVEMENT LOANS

Adjusted for lndlvlduals

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T m t M.dh lncarn

FIGURE 6
DENIAL RATES, DEVIATIONS ABOUT MSA MEANS, TRACT MEDIAN INCOME
NEW PURCHASE LOANS

Gmss Rate
..
--

--- -.Adlusled for lndlvlduals

-. --

Fully Adjusted

REFINANCE LOANS

Adjusted for lndlvlduals

4.05

1

HOME IMPROVEMENT LOANS

Gmss Rate

1

Fully Adjusted

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FIGURE 7
APPLICATION RATES, TRACT MEDIAN INCOME

0.02

--

NEW PURCHASE LOANS

0.04--

REFINANCE LOANS

Gmss Rate

4.04

T

.-- --

---- Adlusted for Tm d Demogmphb

HOME IMPROVEMENT LOANS

G r w Rate

Adjusted for Tred Demogmphlcs

1

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FIGURE 8
APPLICATION RATES, DEVIATIONS ABOUT MSA MEANS, TRACT MEDIAN INCOME

0.02

I

NEW PURCHASE LOANS

Gmss Rate
.-- - -.- -.Adjusted for Tma Demographics

REFINANCE LOANS

T

HOME IMPROVEMENT LOANS

Gmss Rate

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FIGURE 9

DENIAL RATES, CENTER CITYISZIBURBAN, MINORITY PERCENTAGE IN TRACT
0.25

-

0.2

--

NEW PURCHASE LOANS

Suburban Gmss Rate
Center City Fully Adjusted

0.25

REFINANCE LOANS

T

Suburban Gmss Rate

y * b ~ 2 ~ C M % B Z 8 % S 3 S 8 $ 8 % 6 f 6 P E e E a % 9 1 6 t 6 ;

HOME IMPROVEMENT LOANS

-Center CHy Gmse Rate
.---- - -- - Suburban Gmss Rate

-

. Cenler Ci Fully Adjusted

.........-. .., Suburban Fully Adjusted

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FIGURE 10
APPLICATION RATES, CENTER CITYISUBURBAN, MINORITY PERCENTAGE IN TRACT
NEW PURCHASE LOANS

0.02

i

O

I1
8

Suburban ~

m s ~sa t e s

Center Cky Adjusted for Trad
-0.04

-0.m

i1

suburb^ Mjusted for Tmd

REFINANCE LOANS

0.02

if:.

Suburban Gmss Rate
Center Cky Adjusted for Trad

I

Demographics
.........

Suburban AdJwled for Trad
Demographla

HOME IMPROVEMENT LOANS

I ---------

Suburban Gmsa Rates

. Center Cky Adjusled for

Trad

I

Demographla
.................

Suburban Adjusled for Trad

Demogmphlcs

1

clevelandfed.org/research/workpaper/index.cfm

FIGURE 11
DENIAL RATES, CENTER CITYISUBLRBAN, TRACT MEDIAN INCOME
NEW PURCHASE LOANS

Subumn Gloss Rate
Center Clty Fully Adjusted

015

1

REFINANCE LOANS
Center Clty Gmos Rate

I

1

Subumn Gmss Rate
Center Clty Fully Adjusted

HOME IMPROVEMENT LOANS

Subuhan Gmss Rate
t I Iy~djusted
~
Center ~ ~F U

.................

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FIGURE 12
APPLICATION RATES, CENTER CITYISUBURBAN, TRACT MEDIAN INCOME
NEW PURCHASE LOANS

Center Cky Adjusted for Trad

REFINANCE LOANS

1-

Center Cky Gmss Rate
.-- ----.Suburban Gmss Rate
- -. .-.,.

I

1

1

1 \ fl

Center Cky Adjusted for Trad
Demographlcs

............. Suburban Adjusted
Demographics

I'

b

7

Y

'

I

HOME IMPROVEMENT LOANS

0.02

11

O

I
I

Suburban Gmss Rate

'.02

1

-

1 _.....-Suburban Adjusld for Trpd
Demogmphb

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FIGURE 13
DENIAL RATE DIFFERENCES BY RACE, MINORITY PERCENTAGE IN TRACT

0'25

T

NEW PURCHASE LOANS
Gmsr Black-Whne
.--- - - -.
.

Adjusted Black-WhYs

---

Gross Hispanic-We

........ Adjusted Hispanic-WhHe

REFINANCE LOANS
Adjusted Bladc-Whne

.-..-

..

~ m s Hispanbmne
r

.................

0'25

T

HOME IMPROVEMENT LOANS

Gmss Bid-WMte

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FIGURE 14
DENIAL RATES BY RACE, MINORITY PERCENTAGE IN TRACT

NEW PURCHASE LOANS

-I

Black Applicanb
.-------Hlsp~fllc
Appll~nts

0.25

f
1

0.2
0.15

8

Y

1I

i

a

0.1

O.O5

0

-0.05
0.25

-

HOME IMPROVEMENT LOANS

HkpanlcApplkmta

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FIGURE 15
DENIAL RATE DIFFERENCES BY RACE, TRACT MEDIAN INCOME
NEW PURCHASE LOANS
Adjusted Black-White

Gross Hispanlo-White

-0.05

4.05

I

1

I

!

REFINANCE LOANS

..

~

Z

~

R

A

R

Z

S

S

~

Z

HOME IMPROVEMENT LOANS

S

~

~

...

~

I;':,,:, '

X

;

' ' ' '

S

'

'I'~ "
ii

'A'I
S

/:

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R

E

R

R

ROWJU 1
-

~hcb
conbolha FW IIU#WWS

RehUva Tmct Elhca Conbollin#For IndMdmtr

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~TnctElhcaConbdElnOForlndMdurb

Table 1: Characteristics of Mortgage Applications, National Sample, 1990 and 1991 HMDA
Home Purchase
Percent Percent Denial
Sample Loan$ Rate

Refinance
Percent Percent Denial
Sample Loan$ Rate

Home Imvrovement
Percent Percent Denial
Sample Loan$ Rate

Race ofApplicant
Native American
Asian (or Pacific Islander)
Black
Hispanic
White
Other
Race of Co-applicant
No Co-applicant
Same Race as Applicant
Different Race than Applicant

28.7
69.3
2.0

Income ofApplicant

Less than $25,000
$25,000 to $50,000
$50,000 to $75,000
$75,000 to $100,000
More than $100,000

Loan Request

Less than $50,000'
$50,000 to $75,000'
$75,000 to $125,000'
More than $125,000'

Gender
Male Applicant, Female Co-applicant
Female Applicant, Male Co-applicant
Male Applicant and Co-applicant
Female Applicant and Co-applicant
Single Male Applicant
Single Female Applicant

64.0
4.3
1.9
1.3
16.9
11.8

Owner-occupied

93.6

Loan Type
Conventional

FHA
VA
FmHA
Lender Action
Loan Denied
Loan Accepted and Withdrawn
Loan Originated
Loan Kept by Originator (% of originations)
Loan Sold to FNMA (% of originations)
Loan Sold to GNMA (% of originations)
Loan Sold to FHLMC (% of originations)
Loan Sold Elsewhere (% of originations)

15.3
2.7
82.0
42.9
15.2
11.0
9.4
21.5

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Table 1: (Continued)
Home Purchase
Percent Percent Denid
Sample Loan$ Rate

Refinance
Percent Percent Denial
Sample Loan$ Rate

Home Im~rovement
Percent Percent Denial
Sample Loan$ Rate

Reasons for Denial (of Loans ~ e n i e q ~
No Reason Given
Debt-to-Income Ratio
Employment History
Credit History
Collateral
Insufficient Cash
Unverifiable Information
Application Incomplete
Mortgage Insurance Denied
Other
Memo Items:
Median Income ($1,000~)
Median Loan Request ($1,000~)
Number of Loans

Loan categories for home improvement loans are 1) under $10,000,2) $10,000-$25,000, 3) $25,000-$50,000, and 4) over $50,000.
Up to three reasons for denial could be given, and answers were voluntary. Each category gives the percentage of a l l denials citing
that reason as one of the three.

Source for a l l tables: Authors.

clevelandfed.org/research/workpaper/index.cfm

Table 2: Distribution of 1990 Census Population and 199011991HMDA Loan Applications by Tract characteristics'
1990 Census
Total Total Minor 1-4
Units
Tract Pop Pop

HMDA Loan Ap~lications
Home Purch
Refinance
Home Improve
Total Minor Total Minor Total Minor

Black Population Share, 1990
Less than 5 Percent
5 to 10 Percent
10 to 50 Percent
50 Percent or More

59.0
10.6
17.2
12.2

62.6
11.9
17.0
8.5

35.8
11.2
25.1
27.9

65.1
11.3
15.4
8.2

69.2
12.5
14.4
4.0

48.0
14.0
23.0
15.0

75.6
10.3
10.6
3.4

56.9
12.9
17.2
13.1

67.0
10.4
13.5
9.1

37.0
9.4
19.2
34.3

Hispanic Population Share, 1990
Less than 5 Percent
5 to 10 Percent
10 to 50 Percent
50 Percent or More

65.0
12.0
18.0
5.0

62.4
13.0
19.0
5.6

39.8
11.8
30.9
17.5

66.9
12.7
16.5
3.9

65.5
14.5
17.2
2.9

39.0
15.8
33.8
11.4

58.2
15.9
22.0
3.9

25.4
17.0
41.3
16.3

68.6
12.2
16.0
3.2

51.4
11.4
25.7
11.5

Median Owner-occupied House Value, 1990
Less than $50,000
$50,000 to $100,000
$100,000 or More

21.6
39.2
39.2

15.6
42.7
41.7

25.5
33.2
41.3

17.2
43.1
39.7

9.6
44.2
46.2

12.5
34.4
53.0

5.7
31.0
63.3

4.9
17.4
77.8

17.5
43.4
39.1

32.2
30.2
37.6

Change in House Value, 1980-1990
Rose Less than 25 Percent
Rose 25 to 50 Percent
Rose 50 to 100 Percent
Rose 100 to 150 Percent
Rose More than 150 Percent

12.6
21.5
28.4
14.1
23.3

12.4
22.2
30.2
14.4
20.8

10.1
16.2
31.7
16.4
25.5

12.7
23.1
30.7
13.9
19.5

12.3
23.6
32.0
15.8
16.2

9.5
16.4
30.6
20.5
23.1

6.8
16.7
28.0
22.7
25.8

2.9
7.3
20.9
30.9
38.0

12.5
23.9
31.2
16.8
15.6

11.0
18.5
34.5
17.8
18.2

Median Family Income, 1990
Less than $20,000
$20,000 to $30,000
$30,000 to $40,000
$40,000 or More

11.5
21.6
28.3
38.7

7.6
19.6
29.7
43.1

21.2
29.4
24.7
24.7

6.6
18.7
29.8
44.8

2.6
13.3
29.1
55.0

7.1
19.4
28.1
45.3

1.9
10.1
24.8
63.1

5.3
16.7
24.8
53.1

5.3
16.7
30.8
47.2

16.5
25.3
25.4
32.8

24.2
7.5
20.2

21.5
6.6
17.4

22.2
9.5
32.8

22.5
6.7
15.0

20.6
6.2
13.1

18.3
8.0
24.4

14.8
4.8
15.7

10.6
5.9
28.7

19.6
6.9
15.4

20.5
10.5
30.8

19.4
7.7
21.1

21.3
8.8
24.3

10.2
4.6
20.8

22.6
9.1
24.0

22.4
10.0
27.7

10.9
6.6
31.7

20.7
8.7
35.3

8.1
4.9
41.8

22.3
9.0
26.8

9.3
4.4
24.5

Level & Change in Minority Population Share
Less than 5 Percent Minority, 1990
5 to 10 Percent Minority, 1990
Rose < 5 Percent from 1980
Rose > 5 Percent from 1980
10 to 50 Percent Minority, 1990
Rose < 5 Percent from 1980
Rose 5 to 15 Percent from 1980
Rose > 15 Percent from 1980
50 Percent or More Minority, 1990
Rose < 5 Percent from 1980
Rose 5 to 15 Percent from 1980
Rose > 15 Percent from 1980

Center City, MMSA Size, 1990
Center City
MSA Less than 1Million
MSA 1to 2 Million
MSA More than 2 Million
Non-Center City
MSA Less than 1Million
MSA 1to 2 Million
MSA More than 2 Million

Percentages sum to 100 for each group for each column.

clevelandfed.org/research/workpaper/index.cfm

Table 3: Percentage of Applications Denied by Census Tract Characteristics, 199011991 HMDA'
Home Purchase
Refinance
Home Improvement
White Black Hispanic White Black Hispanic White Black Hispanic

Level & Change in Minority Population Share
Less than 5 Percent Minority, 1990
5 to 10 Percent Minority, 1990
Rose < 5 Percent from 1980
Rose > 5 Percent from 1980
10 to 50 Percent Minority, 1990
Rose < 5 Percent from 1980
Rose 5 to 15 Percent from 1980
Rose > 15 Percent from 1980
50 Percent or More Minority, 1990
Rose < 5 Percent from 1980
Rose 5 to 15 Percent from 1980
Rose > 15 Percent from 1980
Black Population Share, 1990
Less than 5 Percent
5 to 10 Percent
10 to 50 Percent
50 Percent or More
Hispanic Population Share, 1990
Less than 5 Percent
5 to 10 Percent
10 to 50 Percent
50 Percent or More
Median Owner-occupied House Value, 1990
Less than $50,000
$50,000 to $100,000
$100,000 or More
Change in House Value, 1980-1990
Rose Less than 25 Percent
Rose 25 to 50 Percent
Rose 50 to 100 Percent
Rose 100 to 150 Percent
Rose More than 150 Percent
Median Family Income, 1990
Less than $20,000
$20,000 to $30,000
$30,000 to $40,000
$40,000 or More
Center City, M M Size, 1990
Center City
MSA Less than 1 Million
MSA 1 to 2 Million
MSA More than 2 Million
Non-Center City
MSA Less than 1 Million
MSA 1 to 2 Million
MSA More than 2 Million
clevelandfed.org/research/workpaper/index.cfm

Application denial percentage for each category.

Table 4: Linear Probability Model of Loan Denial (1) or Acceptance (0),Home Purchase
1990
Coefficient Standard Error
Owner-occupied (Dummy)
Race (Dummies, "White"Is Base Group)
Native American Applicant
Asian Applicant
Black Applicant
Hispanic Applicant
Other Race Applicant
Mixed Race, Minority Co-applicant (Dummy)
Mixed Race, Non-minority Co-applicant (Dummy)

1991
Coefficient Standard Error

.00649***

.02636***
.00171
.10385*'*
.03841m*
.03043"'
.00764'*
-.02324"'

Income, Interacted with Race
Native American Applicant
Asian Applicant
Black Applicant
Hispanic Applicant
White Applicant
Other Race Applicant
Income Splines (31,000's)
Income Spline at $20.000
Income Spline at $40,000
Income Spline at $60,000
Income Spline at $80,000
Income Spline at $100,000
Income Spline at $150,000
Income Spline at $200,000
Loan Amount (31,000's)
Loan Amount
Loan Amount Spline at $20,000
Loan Amount Spline at $40,000
Loan Amount Spline at $60,000
Loan Amount Spline at $80,000
Loan Amount Spline at $100,000
Loan Amount Spline at $150,000
Loan Amount Spline at $200,000
Loan-to-Income Ratio (Dummies, Less than 1.5 Is Base Group)
Ratio of 1.5 to 2.0
Ratio of 2.0 to 2.25
Ratio of 2.25to 2.5
Ratio of 2.5 to 2.75
Ratio of 2.75to 3.0
Ratio over 3.0

-.01012*'*
-.01158"*
-.01176"*
-.00713**'
.00362
.05105***

Applicant Gender (Dummies, Female Applicant, No Co-applicant Is Base Group)
Male Applicant, Female Co-applicant
-.01875*
Female Applicant, Male Co-applicant
-.00726
Male Applicant and Co-applicant
-.00354
Female Applicant and Co-applicant
-.00984
Male Applicant, No Co-applicant
.02815***

clevelandfed.org/research/workpaper/index.cfm

Table 4: (Continued)

1990
Coefficient Standard Error

1991
Coefficient Standard Error

Income, Interacted with No Co-applicant
Income
Income Spline at $20,000
Income Spline at $40,000
Income Spline at $60,000
Income Spline at $80,000
Income Spline at $100,000
Race and Marital Status, Interacted with VA Loan
Native American Applicant
Asian Applicant
Black Applicant
Hispanic Applicant
White Applicant
Other Race Applicant
No Co-applicant
Race and Marital Status, Interacted with FHA Loan
Native American Applicant
Asian Applicant
Black Applicant
Hispanic Applicant
White Applicant
Other Race Applicant
No Co-applicant
Income, Interacted with VA or FHA Loan
Income
Income Spline at $20,000
Income Spline at $40,000
Income Spline at $60,000
Income Spline at $80,000
Income Spline at $100,000
Loan Amount, Interacted with VA or FHA Loan
Loan Amount
Loan Amount Spline at $20,000
Loan Amount Spline at $40,000
Loan Amount Spline at $60,000
Loan Amount Spline at $80,000
Loan Amount Spline at $100,000
Loan-to-Income Ratio, Interacted with VA or FHA Loan
Ratio of 1.5 to 2.0
Ratio of 2.0 to 2.25
Ratio of 2.25to 2.5
Ratio of 2.5 to 2.75
Ratio of 2.75to 3.0
Ratio over 3.0

clevelandfed.org/research/workpaper/index.cfm

Table 4: (Continued)

1990
Coefficient Standard Error

1991
Coefficient Standard Error

Month of Decision (Dummies, December Is Base Group)
January
February
March
April
May
June
July
August
September
October
November
Memo Items:
Number of Observations
Mean Denial Rate in Regression Sample
Number of Tracthstitution Dummies
R Squared (Including Tradhstitution Dummies)
R Squared (Variation around Tmcilhtitution Means)

at the percent level.
...* Significant
Significant at the percent level.
Significant at the percent level.
5
1
.1

clevelandfed.org/research/workpaper/index.cfm

Table 5: Linear Probability Model of Loan Denial (1) or Acceptance (0), Refinance
1990
Coefficient Standard Error

1991
Coefficient Standard Error

Owner-occupied (Dummy)
VA Loan (Dummy)
Race (Dummies, "White"Is Base Group)
Native American Applicant
Asian Applicant
Black Applicant
Hispanic Applicant
Other Race Applicant
Mixed Race, Minority Co-applicant (Dummy)
Mixed Race,Non-minority Co-applicant (Dummy)

.02245
.04053*"
.06370°**
.04342*'*
.03812'**
.00340
-.02737***

Income, Interacted with Race
Native American Applicant
Asian Applicant
Black Applicant
Hispanic Applicant
White Applicant
Other Race Applicant
Income Splines (31,000's)
Income Spline at $20,000
Income Spline at $40,000
Income Spline at $60,000
Income Spline at $80,000
Income Spline at $100,000
Income Spline at $150,000
Income Spline at $200,000
Loan Amount (31,000's)
Loan Amount
Loan Amount Spline at $20,000
Loan Amount Spline at $40,000
Loan Amount Spline at $60,000
Loan Amount Spline at $80,000
Loan Amaunt Spline at $100,000
Loan Amount Spline at $150,000
Loan Amount Spline at $200,000
Loan-to-Income Ratio (Dummies, Less than 1.5 Is Base Group)
Ratio of 1.5 to 2.0
Ratio of 2.0 to 2.25
Ratio of 2.25 to 2.5
Ratio of 2.5 to 2.75
Ratio of 2.75 to 3.0
Ratio over 3.O

-.00241
.00433
.00667*
.01452"*
.02524***
.08519."

Applicant Gender (Dummies, Female Applicant, No Co-applicant Is Base Group)
Male Applicant, Female Co-applicant
-.09152***
Female Applicant, Male Co-applicant
-.08392***
Male Applicant and Co-applicant
-.06548*'*
Female Applicant and Co-applicant
-.08076***
Male Applicant, No Co-applicant
.02499***

clevelandfed.org/research/workpaper/index.cfm

Table 5: (Continued)
1990

1991

Coefficient Standard Error

Coefficient Standard Error

Income, Interacted with No Co-applicant
Income
Income Spline at $20,000
Income Spline at $40,000
Income Spline at $60,000
Income Spline at $80,000
Income Spline at $100,000
Interactions with VA or FHA Loan
Native American Applicant
Asian Applicant
Black Applicant
Hispanic Applicant
White Applicant
Other Race Applicant
No Co-Applicant
Income
Loan Amount
Month of Decision (Dummies, December Is Base Group)
January
February
March
April
May
June

July
August
September
October
November
Memo Items:
Number of Observations
Mean Denial Rate in Regression Sample
Number of TractAnstitution Dummies
R Squared (Including Tract5stitution Dummies)
R Squared (Variation around Tracthmtitution Means)

at the 5 percent level.
.....Significant
Signilicant at the 1percent level.
Significant at the percent level.
.1

clevelandfed.org/research/workpaper/index.cfm

Table 6: Linear Probability Model of Loan Denial (1) or Acceptance (0), Home Improvement
1990
Coefficient Standard Error

1991
Coefficient Standard Error

Owner-occupied (Dummy)
VA I-4x3n (Dummy)
Race (Dummies, "White"Is Base Group)
Native American Applicant
Asian Applicant
Black Applicant
Hispanic Applicant
Other Race Applicant
Mixed Race, Minority Co-applicant (Dummy)
Mixed Race, Non-minority Co-applicant (Dummy)
Income, Interacted with Race
Native American Applicant
Asian Applicant
Black Applicant
Hispanic Applicant
White Applicant
Other Race Applicant
Income Splines (31,000's)
Income Spline at $20,000
Income Spline at $40,000
Income Spline at $60,000
Income Spline at $80,000
Income Spline at $100,000
Income Spline at $150,000
Income Spline at $200,000
Loan Amount (Dummies or %l,OOO1s)
$1,000 or $2,000 Loan (Dummy)
$3,000 or $4,000 Loan @Immy)
$5,000 or $6,000 Loan (Dummy)
$7,000 or $8,000 Loan (Dummy)
$9,000 or $10,000 Loan (Dummy)
Loan Amount Spline at $10,000
Loan Amount Spline at $25,000
Loan Amount Spline at $50,000
Loan Amount Spline at $100,000
Loan Amount Spline at $150,000
Loan Amount Spline at $200,000
Loan-to-Income Ratio (Dummies, Less than 1.51s Base Group)
Ratio of 1.5 to 2.0
Ratio of 2.0 to 2.25
Ratio of 2.25 to 2.5
Ratio of 2.5 to 2.75
Ratio of 2.75 to 3.0
Ratio over 3.0

clevelandfed.org/research/workpaper/index.cfm

Table 6: (Continued)
1990

1991

Coefficient Standard Error

Coefficient Standard Error

Applicant Gender (Dummies, Female Applicant, No Co-applicant Is Base Gro,ug)
-.11149
Male Applicant, Female Co-applicant
Female Applicant, Male Co-applicant
-.07509"'
Male Applicant and Co-applicant
-.04764'*'
Female Applicant and Co-applicant
-.0803 1.'.
Male Applicant, No Co-applicant
.03643-'
Income, Interacted with No Co-applicant
Income
Income Spline at $20,000
Income Spline at $40,000
Income Spline at $60,000
Income Spline at $80,000
Income Spline at $100,000
Interactions with VA or FHA Loan
Native American Applicant
Asian Applicant
Black Applicant
Hispanic Applicant
White Applicant
Other Race Applicant
No Co-applicant
Income
Loan Amount
Month of Decision (Dummies, December Is Base Gmup)
January
February
March
April
May
June
July
August
September
October
November
Memo Items:
Number of Observations
Mean Denial Rate in Regression Sample
Number of Tracthstitution Dummies
R Squared (Including Tmdhstitution Dummies)
R Squared (Variation around Tracthstitution Means)

787,952
.238
267,159
.474
.029

.....

Signiticant at the 5 percent level.
Significant at the 1 percent level.
Signiticant at the .1 percent level.

clevelandfed.org/research/workpaper/index.cfm

Table 7: Variable Means, All Tracts, Center City, and Suburban ~racts'
All Tracts

Center City Tracts

Suburban Tracts

Loan Application Rate (199011991 HMDA Applications Divided by Total 1-4 Unit Structures)
Home Purchase Loans
.07 143
. W O
Refinance Loans
.03930
.03 145
Home Impmvement Loans
.02871
.02721
Minority Population Share, I990

.20884

Change in Minority Share, 1980-1990 (Dummies)
Change in Share Less than 0
Change in Share between 0 and .05
Change in Share between .05 and .10
Change in Share between .10 and .15
Change in Share More than .l5

.I2162
.54155
.I6055
.08302
.09326

Median Family Income, 1990 ($100,000'~)

44354

Change in Median Family Income, 1980-1990 (Dummies)
Change in Income Less than 25%
Change in Income between 25% and 50%
Change in Income between 50% and 100%
Change in Income More than 100%

.01803
.08958
.62223
.27004

.28837

.40118

Age of Household Head, 1990
Share of Household Heads under 25
Share of Household Heads 25-34
Share of Household Heads 35-44
Share of Household Heads 45-54
Share of Household Heads 55-64
Share of Household Heads 65-74
Share of Household Heads 75 or Older
Median Owner-occupied House Value, 1990 ($100,000'~) 1.33740
Change in Median House Value, 1980-1990 (Dummies)
Change in Value Less than 25%
Change in Value between 25% and 50%
Change in Value between 50% and 100%
Change in Value between 100%and 150%
Change in Value More than 150%

1.22233

.lo819
.21743
.30740
.I7918
.I8780

Structure Variables, 1990
Share of Structures Single Unit Detached
Share of Structures Single Unit Attached
Share of Structures 2 Units
Share of Structures 3-4 Units
Share of Structures 5 or More Units
Share of Structures Mobile Homes

clevelandfed.org/research/workpaper/index.cfm

Table 7: (Continued)
All Tracts

Center City Tracts

Suburban Tracts

Usage of 1-4 Unit Structures, 1990
Share of Housing Units Owner Occupied
Share of Housing Units Rented
Share of Housing Units Vacant
Change in House Usage, 1980-1990
Growth Rate of Total Housing Units
Growth Rate of 1-4 Unit Structures
Change in Share of 1-4 Units Rented
Change in Share of 1-4 Units Vacant
Age of Housing Stock, 1980
Share of Housing Stock Built 1979-1980
Share of Housing Stock Built 1975-1978
Share of Housing Stock Built 1970-1974
Share of Housing Stock Built 1960-1969
Share of Housing Stock Built 1950-1959
Share of Housing Stock Built 1940-1949
Share of Housing Stock Built Prior to 1940
Number of Tracts

Tracts weighted by the total number of loan applications of all types in 1990 and 1991.

clevelandfed.org/research/workpaper/index.cfm

Table 8: All Tracts, 199011991 HMDA, Denial Rates
Home Purchase
Parameter Standard
Estimate Error

Refinance
Parameter Standard
Estimate Error

Home Irn~rovement
Parameter Standard
Estimate Error

Intercept
Center City (Dummy)

Minority Population Share, 1990
Minority Share
Minority Share Spline at .05
Minority Share Spline at .10
Minority Share Spline at .25
Minority Share Spline at .50

.07100*
.18832'"
-24255'"
-.01885
.05376*"

.03418
.05120
.02841
.01359
.01038

Change in Minority Share, 1980-1990 (Dummies,Less than 0 Is Base Group)
Change in Share between .Oand .05
,00363'" .00089
Change in Share between .05and .lo
.01049- .00113
Change in Share between .10and .15
.01367'" .00140
.02115*" .00149
Change in Share More than .15
Median Family Income. 1990
Median Family Income ($100,000'~)
Median Family Income Spline at $25,000
Median Family Income Spline at $40,000
Median Family Income Spline at $55,000

-.20070b" .02470
.01757 .02648
.00328 .01305
.08475*" .01W

.43291°"
-.18898"
-.17373*"
49782'"
.06608"

.04509
.06832
.03824
.01771
.01303

.01698*"
.02413'.03277'"
.04528*"

.00120
.00151
.00184
.00196

-.4705"'1
.17271b"
.05235"
.15230'"

.03644
.03919
.01737
.01183

Change in Median Family Income, 1980-1990(Dummies,Less than 25% Is Base Group)
Change in Income between 25% and 50%
.00647" .00237
.01850'" .00410
Change in Income between 50% and 100%
.00601' .00239
.02021'" .00407
Change in Income More than 100%
.01065'" .00256
.03746*" .00422
Age of Household Head, 1990
Share of Household Heads 25-34
Share of Household Heads 35-44
Share of Household Heads 45-54
Share of Household Heads 55-64
Share of Household Heads 65-74
Share of Household Heads 75 or Older

.06214"
.03678"
.14615*"
.19472'"
-.00325
.09518*"

.01402
.01287
.01788
.01906
.01724
.01360

.28383*"
.08186'"
.31 139'"
.33159'"
.16530'"
.22982*"

.01994
.01778
.02350
,02519
.02382
.01944

Median Owner-occupied House Value,1990
Median House Value ($100,000'~)
Median House Value Spline at $50,000
Median House Value Spline at $100,000
Median House Value Spline at $150,000

-.02922"
.04170b"
.01039'
-.01573*"

.01026
.01115
.00428
.00264

.06909*"
-.03141
-.02573'"
-.01903*"

.01701
.01842
.00586
.00331

Change in Median House Value,1980-1990 (Dummies,Less than 25% Is Base Group)
Change in Value between 25% and 50%
.00303" .00098
.00620'"
.00836'" .00105
Change in Value between 50% and 100%
.01112'"
Change in Value between 100% and 150%
.01515'" .00140
.01622*"
Change in Value More than 150%
.02265*" .00162
.02080'"

.00163
.00169
.00203
.00222

clevelandfed.org/research/workpaper/index.cfm

Table 8: (Continued)
Home Purchase
Parameter Standard
Estimate Error

Refinance
Parameter Standard
Estimate Error

Home Irn~rovement
Parameter Standard
Estimate Error

House Usage Variables, 1990
Share of Structures Single Unit Attached
Share of Structures 2 Units
Share of Structures 3 4 Units
Share of Structures 5 or More Units
Share of Structures Mobile Homes
Share of 1 4 Unit Structures Rented
Share of 1 4 Unit Structures Vacant
Change in House Usage, 1980-1990
Growth Rate of Total Housing Units
Growth Rate of 14Unit Structures
Change in Share 14 Units Rented
Change in Share 1 4 Units Vacant
Age of Housing Stock, 1980
Share of Housing Stock Built 1979-1980
Share of Housing Stock Built 1975-1978
Share of Housing Stock Built 1970-1974
Share of Housing Stock Built 1960-1969
Share of Housing Stock Built 1950-1959
Share of Housing Stock Built 1940-1949
Memo Items:
R Squared (Weightedby Loan Applications)
Dependent Variable Mean
Number of Tracts

Significant at the 5 percent level.
Significant at the 1percent level.
*"
Significant at the .I percent level.
u

clevelandfed.org/research/workpaper/index.cfm

Table 9: All Tracts, 1990/1991 HMDA, Denial Rates, Deviations about MSA Means
Home Purchase
Parameter Standard
Estimate Error
Center City (Dummy)

.00006

Refmance
Parameter Standard
Estimate Error

.00062

Home Imurovement
Parameter Standard
Estimate Error

.00393*"

Minoriry Population Share, 1990
Minority Share
Minority Share Spline at -05
Minority Share Spline at .10
Minority Share Spline at .25
Minority Share Spline at .50
Change in Minority Share, 1980-1990 (Dummies,Less than 0 Is Base Group)
Change in Share between .O and .05
-.00064
.00081
Change in Share between .05 and .10
.00226* .00102
Change in Share between -10 and .15
.00160
.00126
Change in Share More than -15
.00347* .00138
Median Family Income, 1990
Median Family Income ($100,000'~)
Median Family Income Spline at $25,000
Median Family Income Spline at $40,000
Median Family Income Spline at $55,000

-.14083*" .02207
.05912* .02330
.02711b .01170
.02424" .00913

.00187
.00246
.00302
.00501"
-.13641b"
.10859"
-.02005
.01484

Change in Median Family Income, 1980-1990 (Dummies,Less than 25% Is Base Group)
Change in Income between 25% and 50%
.00659" .00210
.00233
Change in Income between 50% and 100%
.00453* .00216
-.00324
Change in Income More than 100%
.00327
.00233
-.00172
Age of Household Head, 1990
Share of Household Heads 25-34
Share of Household Heads 35-44
Share of Household Heads 45-54
Share of Household Heads 55-64
Share of Household Heads 65-74
Share of Household Heads 75 or Older

.01780
.01515
.08894*"
.08581b"
-.05728*"
-.01050

.01318
.01179
.01652
.01741
.01588
.01246

.04764"
.02805
.10133*"
.08441b"
-.05359*
.02377

Median Owner-occupiedHouse Value, 1990
Median House Value ($100,000'~)
Median House Value Spline at $50,000
Median House Value Spline at $100,000
Median House Value Spline at $150,000

-.08682*"
.05970b"
.01896*"
.00025

.00947
.01003
.00399
.00255

-.12467'"
.04366"
.03090b"
.03035*"

Change in Median House Value,1980-1990 (Dummies,Less than 25% Is Base Group)
Change in Value between 25% and 50%
.00447*" .00106
.00092
Change in Value between 50% and 100%
.00825*" .00 128
-.00040
Change in Value between 100%and 150%
.0073o0" .00162
-.00549*
.00203
.00190
-.01459*"
Change in Value More than 150%

clevelandfed.org/research/workpaper/index.cfm

Table 9: (Continued)
Home Purchase
Parameter Standard
Estimate Error
House Usage Variables,1990
Share of Structures Single Unit Attached
Share of Structures 2 Units
Share of Structures 3-4 Units
Share of Structures 5 or More Units
Share of Structures Mobile Homes
Share of 1-4 Unit Structures Rented
Share of 1-4 Unit Structures Vacant

-.05225*"
-.02634*"
-.029 13'"
-.00204
.02777*"
.05 867'"
.07225*"

.00307
.00595
.00696
.00260
.00358
.00453
.00582

Change in House Usage,1980-1990
Growth Rate of Total Housing Units
Growth Rate of 1-4 Unit Structures
Change in Share 1-4 Units Rented
Change in Share 1-4 Units Vacant

.00185
-.00263'
-.01693"
-.01846"

.00123
.00126
.00516
.00711

Age of Housing Stock, 1980
Share of Housing Stock Built 1979-1980
Share of Housing Stock Built 1975-1978
Share of Housing Stock Built 1970-1974
Share of Housing Stock Built 1960-1969
Share of Housing Stock Built 1950-1959
Share of Housing Stock Built 1940-1949

-.02764*"
-.01397'"
-.01409'"
-.01772'"
-.01377*"
-.02070b"

.00506
.00337
.00311
.00266
.00277
.00443

Memo Items:
R Squared Total (Weighted by Loan Applications)
R Squared about MSA Means
Dependent Variable Mean
Number of Tracts

**
'U

Refiiance
Parameter Standard
Estimate Error

Home Improvement
Parameter Standard
Estimate Error

.464
.206
.00OOO
38,609

Significantat the 5 percent level.
Significant at the 1percent level.
Significant
at the .1percent level.

clevelandfed.org/research/workpaper/index.cfm

Table 10: All Tracts, 199011991 HMDA, Application Rates
Home Purchase
Parameter Standard
Estimate Error

Refinance
Parameter Standard
Estimate Error

-.00352
-.01778
.00822
.01734
-.01645*

-.28203*"
.27596*"
.02191
-.01337*
.00955*

Home Im~rovement
Parameter Standard
Estimate Error

Intercept
Center City (Dummy)

Minority Population Share, 1990
Minority Share
Minority Share Spline at .05
Minority Share Spline at .10
Minority Share Spline at .25
Minority Share Spline at .50

.02313
.03553
.02043
.00939
.00654

Change in Minority Share, 1980-1990 (Dummies.Less than 0 Is Base Group)
Change in Share between .O and .05
.00089
.00060
Change in Share between .05 and .10
-.00029
.00077
Change in Share between .10 and .15
.00110
.00094
Change in Share More than .15
.00295" .00098
Median Family Income, 1990
Median Family Income ($100,000'~)
Median Family Income Spline at $25,000
Median Family Income Spline at $40,000
Median Family Income Spline at $55,000

.04136*"
-.00826
.01790*
-.05958*"

.01249
.01342
.00889
.00742

.00237*"
.00214*"
.00212*"
.00285*"
-.04371*"
-.00010
-.05250°"
.00157

Change in Median Family Incorn, 1980-1990 (Dummies,Less than 25% Is Base Group)
Change in Income between 25% and 50%
.00277* .00120
.00073
Change in Income between 50% and 100%
.00655*" .00124
.00477*"
Change in Income More than 100%
.00527*" .00140
.00161
Age of Household Head. 1990
Share of Household Heads 25-34
Share of Household Heads 35-44
Share of Household Heads 45-54
Share of Household Heads 55-64
Share of Household Heads 65-74
Share of Household Heads 75 or Older

.11266*"
.07361"
.05945*"
-.00100
.005!n
.08399*"

.00923
.00863
.01177
.01228
.01124
.00900

-.03383*"
-.01274*
.02128"
-.12428'"
-.05485*"
-.01269*

Median Owner-occupied House Value, 1990
Median House Value ($100,000'~)
Median House Value Spline at $50,000
Median House Value Spline at $100,000
Median House Value Spline at $150,000

.01617"
.01488"
-.01822"
-.00345

.00508
.00571
.00298
.00195

.01748*"
.03508'"
.01375"
-.03202*"

Change in Median House Value, 1980-1990 (Dummies,Less than 25%Is Base Group)
Change in Value between 25% and 50%
-.00115
.00067
.00242*"
Change in Value between 50% and 100%
-.00195" .00072
.00354'"
Change in Value between 100 and 150%
-.00887*" .00096
.00593*"
Change in Value More than 150%
-.02742*" .00110
-.01706"*

clevelandfed.org/research/workpaper/index.cfm

Table 10: (Continued)
Home Purchase
Parameter Standard
Estimate Error

Refinance
Parameter Standard
Estimate Error

Home Im~rovernent
Parameter Standard
Estimate Error

House Usage Variables, 1990
Share of Structures Single Unit Attached
Share of Structures 2 Units
Share of Structures 3-4units
Share of Structures 5 or More Units
Share of StructuresMobile Homes
Share of 1-4Unit Structures Rented
Share of 1-4Unit Structures Vacant
Change in House Usage, 1980-1990
Growth Rate of Total Housing Units
Growth Rate of 1-4 Unit Structures
Change in Share 1-4Units Rented
Change in Share 1-4Units Vacant
Age of Housing Stock, 1980
Share of Housing Stock Built 1979-1980
Share of Housing Stock Built 1975-1978
Share of Housing Stock Built 1970-1974
Share of Housing Stock Built 1960-1969
Share of Housing Stock Built 1950-1959
Share of Housing Stock Built 1940-1949
Memo Items:
R Squared (Weighted by 1-4Units)
Dependent Variable Mean
Number of Tracts

Significant at the 5 percent level.
Signif~cantat the 1 percent level.
*"
Significant at the .Ipercent level.
**

clevelandfed.org/research/workpaper/index.cfm

Table 11: All Tracts, 1990/1991HMDA, Application Rates, Deviations about MSA Means
Home Purchase
Parameter Standard
Estimate Error
Center City (Dummy)

Refinance
Parameter Standard
Estimate Error

.00251e" .00046

-.00075'"

.00023

Change in Minority Share, 1980-1990 (Dummies,Less than 0 Is Base Group)
Change in Share between .Oand .05
.00104 .00059
Change in Share between .05and .10
.OOO37 .00075
Change in Share between .10and .15
.00178 .00092
Change in Share More than .15
.00339*" .00097

-.00063*
-.00038
-.00069
-.00071

.00029
.00036
.00045
.00047

Median Family Income, 1990
Median Family Income ($100,000'~)
Median Family Income Spline at $25,000
Median Family Income Spline at $40,000
Median Family Income Spline at $55,000

-.02502*"
.03775*"
.01680e"
-.02719'"

.00606
.00634
.00418
.00351

Home Im~rovement
Parameter Standard
Estimate Error

Minority Population Share, 1990
Minority Share
Minority Share Spline at .05
Minority Share Spline at .10
Minority Share Spline at .25
Minority Share Spline at .SO

.05511e"
.00512
.03670e"
-.06490°"

.01227
.01282
.00861
.00724

Change in Median Family Income, 1980-1990 (Dummies,Less than 25% Is Base Group)
Change in Income between 25% and 50%
-00068 .00116
-.00032
Change in Income between 50% and 100%
.00065 .00123
-.00018
.00191 .00140
Change in Income More than 100%
.00046
Age of Household Head, I990
Share of Household Heads 25-34
Share of Household Heads 35-44
Share of Household Heads 45-54
Share of Household Heads 55-64
Share of Household Heads 65-74
Share of Household Heads 75 or Older

.MI057
.OM1
.00069

.14284*"
.08264*"
.06724*"
.03307"
.01739
.08753*"

.00924
.00847
.O1 161
.01203
.01107
.00884

-.01318" .00450
.01436*" .00413
.05643*" .00565
-.02508*" .00586
-.02420e" .00539
-.00363 .00430

.00407
.01616"
-.02455*"
-00342

-00525
.00565
.00302
.00203

.01217*"
-.OW
.00835*"
-.01094*"

.00259
.00278
.00146
.00098

Change in Median House Value. 1980-1990 (Dummies,Less than 25% Is Base Group)
Change in Value between 25% and 50%
-.00012 .00077
.00416*"
Change in Value between 50% and 100%
.00339*" .OOO95
.00943*"
Change in Value between 100% and 150%
.00398" .00122
.01411*"
Change in Value More than 150%
-.00062 .00145
.00970e"

.00038
.00046
.00059
.00071

Median Owner-occupiedHouse Value. 1990
Median House Value ($100,000'~)
Median House Value Spline at $50,000
Median House Value Spline at $100,000
Median House Value Spline at $150,000

clevelandfed.org/research/workpaper/index.cfm

Table 11: (Continued)
Home Purchase
Parameter Standard
Estimate Error

Refinance
Parameter Standard
Estimate Error

Home Improvement
Parameter Standard
Estimate Error

House Usage Variables, 1990
Share of Structures Single Unit Attached
Share of Structures 2 Units
Share of Structures 3 4 Units
Share of Structures 5 or More Units
Share of Struchlres Mobile Homes
Share of 1
4Unit Structures Rented
Share of 1
4Unit Structures Vacant
Change in House Usage, 1980-1990
Growth Rate of Total Housing Units
4Unit Structures
Growth Rate of 1
Change in Share 1
4 Units Rented
Change in Share 1
4Units Vacant
Age of Housing Stock, 1980
Share of Housing Stock Built 1979-1980
Share of Housing Stock Built 1975-1978
Share of Housing Stock Built 1970-1V4
Share of Housing Stock Built 1960-1969
Share of Housing Stock Built 1950-1959
Share of Housing Stock Built 1940-1949

Memo Items:
R Squared Total (Weighted by 1
4Units)
R Squared about MSA Means
Dependent Variable Mean
Number of Tracts

11
1
"

Significant at the 5 percent level.
Significant at the 1 percent level.
Significant at the .1percent level.

clevelandfed.org/research/workpaper/index.cfm