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August 15, 1996

Federal Reserve Bank of Cleveland

Discri01ination in Mortgage Lending:
What Have We Learned?
by Stanley D. Longhofer


has now been nearly four years since
researchers at the Federal Reserve Bank
of Boston released their groundbreaking
study on residential mortgage lending
patterns in that city. 1 Their findings
showed that black and Hispanic applicants were over 50 percent more likely to
be denied a mortgage loan than whites,
even after taking into account many factors relevant to the credit-granting decision. In the end, they concluded that this
disparity was the result of taste-based
discrimination (bigotry) on the part of
lenders active in the area.
In the intervening years, much effort
has gone into dissecting the Boston researchers ' analysis, both to replicate
their results and to explain how such discrimination could persist in a market so
many view as being highly competitive.
With the final version of this paper recently published in one of the most
respected academic journals in the economics profession, we can now look
back on the debate over the presence of
discrimination in mortgage lending to
see what we have learned. 2


Origin of the Debate

In 197 5, the Home Mortgage Disclosure
Act (HMDA) was passed with the goal
of providing a better understanding of
the extent of redlining-the alleged
practice of denying loans solely because
of the location of the property being
mortgaged. At the time, the Act required
covered lenders to disclose only the geographic distribution of their residential
mortgage loans. Although these data did
show that substantially fewer mortgage
ISSN 0428-1 276

loans were originated in census tracts
with a high proportion of minorities,
they did not (and were not intended to)
provide evidence of discrimination
against individual applicants.
In 1989, Congress expanded HMDA to
require lenders to report key information
about each mortgage application received, including the applicant's income,
race, and gender, and the disposition of
the application. The initial release of
these data fueled new controversy, since
black and Hispanic applicants (but not
Asians) were shown to have a much
higher denial rate than whites. For example, in 1995 (the most recent year for
which data are available), 40.5 percent of
black and 29.5 percent of Hispanic mortgage applicants were denied, compared
with 20.6 percent of white applicants.
Despite these provocative disparities, the
HMDA data alone are inadequate to
draw any meaningful conclusions about
the presence of discrimination in the
nation 's mortgage markets. After all, key
underwriting factors, including an applicant's credit history, debt burden, loanto-value ratio, liquid assets, and employment history, are not included in the data.
As it turns out, most of these factors are
correlated with race, making it impossible to determine whether minorities are
more likely to be turned down because
they are less creditworthy on average or
because lenders discriminate. 3


The Boston Fed Study

In 1992, researchers at the Federal
Reserve Bank of Boston (Munnell et al.)


T he Federal Reserve Bank of Boston's
groundbreaking study on residential
mortgage lending patterns in that city,
published in 1992, sparked national
interest and led many researchers to
look more closely at the role of race in
mortgage underwriting decisions.
With the Boston study's recent publication in a respected academic journal, now is a good time to look back
on the debate over the presence of discrimination in mortgage lending to
see what we have learned.

began an ambitious effort to augment the
HMDA data by collecting additional
information believed to be relevant to
the credit-granting decision. Using 1990
HMDA data for lending institutions in
the Boston Metropolitan Statistical Area,
they worked with examiners, bankers,
and other experts to develop a list of
additional variables that lenders use to
determine an applicant's creditworthiness, with the goal of better isolating the
effect of race on an applicant's chance of
being approved for a loan.
As expected, their analysis showed that
much of the difference in denial rates
across races is due to the fact that black
and Hispanic loan applicants have, on
average, less wealth, higher loan-to-value
ratios (smaller down payments), and
more credit blemishes than their white
counterparts. Nonetheless, even after
controUing for these factors, the Boston
researchers concluded that minority

applicants were over 50 percent more
likely to be denied a loan than whites:
" ... minority applicants with the same
economic and property characteristics as
white applicants would experience a
denial rate of 17 percent rather than the
actual white denial rate of 11 percent." 4
Munnell et al. seemed to provide hard
statistical evidence that widespread, systematic discrimination against blacks
and Hispanics occurs in the Boston-area
home mortgage market. Yet, after four
years of debate, many economists
remain unconvinced. Why haven ' t the
results been universally embraced?


The Critics Respond

One of the first problems other researchers faced when trying to verify Munnell
et al .'s findings was the questionable
quality of much of the data. When the
study was publicly released, many researchers questioned the usefulness of
the information because of what they
believed to be data entry errors, missing
data, and unreliable recording techniques. 5 Of course, such errors are not
uncommon with economic data (particularly those derived from surveys), and
even if these criticisms are correct, they
do not in and of themselves invalidate
Munnell et al.'s results. Nonetheless,
their prevalence in the data makes many
economists uncomfortable with the information's reliability and usefulness
for research.
Second, Munnell et al. used a sophisticated statistical technique known as logit
analysis to determine the impact of an
applicant's race on his chance of being
denied a mortgage. Many researchers
have questioned the applicability of this
technique to mortgage lending. Indeed,
several studies have shown that logit
analysis is unreliable in testing for discrimination, since it can provide misleading results. For example, it has been
demonstrated that logit analysis can
"detect" discriminatory behavior even at
institutions where none exists, yet fail to
uncover even egregious cases of bias. 6

In addition, there are practical reasons to
be skeptical of their conclusions. If discrimination is so rampant in the marketplace, why have regulators been so
unsuccessful in detecting it? Should we

believe these resuJts when similar regressions suggest that black-owned banks
discriminate against black applicants?7
And why do the authors insist that they
have uncovered taste-based discrimination when evidence on default rates
seems to contradict this conclusion? 8


Other Evidence

Of course, statistical analyses like that of
Munnell et al. are not the only way to
detect discrimination. The traditional
method used by the Federal Reserve and
other bank regulators is known as paired
file review. Here, examiners probe an
institution's loan files to see if they can
find minority applicants (or members of
other protected classes) who have been
denied loans while essentially similar
white applicants have been accepted.
Although such reviews can provide valuable insight into a lender's underwriting
decisions, in practice, individual applications often differ enough that an institution can provide a seemingly valid reason
for minority denials. In contrast, statistical analyses look for systematic trends,
which are more difficuJt for institutions to
explain away. Not surprisingly, paired file
reviews rarely uncover any but the most
egregious cases of illegal discrimination.
More recently, there has been some
interest in the use of paired testers. Here,
regulators "create" two applicants who
are virtually identical except for their
race. Each requests, in person, information about a mortgage loan at a target
institution. This method has two advantages. First, unlike paired file reviews,
when done properly and repeated a number of times it virtually ensures that any
differential treatment is due to race
rather than to subtle differences in the
applicants' creditworthiness. Second, it
can also uncover discriminatory treatment that may occur before the application is ever filed.
Unfortunately, paired-tester analysis is
expensive, making it difficult to justify
for widespread fair lending enforcement.
Furthermore, many have questioned the
appropriateness of federal regulatory
agencies "sponsoring deception." 9 More
important, it is difficult to ensure the
objectivity of such tests, since testers can
easily (and perhaps unknowingly) elicit
the very behavior they are attempting to

detect. Nonetheless, the few pairedtester studies that have been done suggest that differential treatment may be a
problem even before a formal application is made. 10


The Verdict?

So, does widespread discrimination exist
in the home mortgage market? Ultimately, the answer must be "we don ' t
know." Taken together, the problems
with the Boston Fed data set (including
its limited geographic focus), questions
about the robustness of logit analysis,
and limitations of other methods for
detecting discrimination all combine to
lead most economists to conclude that
we still don't have a definitive answer
about the presence of widespread and
systematic discrimination in the home
mortgage market.
Of course, nearly all economists would
agree that isolated incidences of discrimination occm for a variety of reasons.
Clearly, such cases are important to detect
and eliminate. Nevertheless, the more
important policy question is whether
widespread systematic discrimination
persists either at individual institutions or
in the mortgage market as a whole. On
this issue, opinions are more divided.
There are a few researchers on both
sides of the issue who are certain of the
answer; perhaps they have prior convictions, and no amount of evidence either
way will sway them. But many more
researchers remain unconvinced. While
Munnell et al.'s study poses challenging
questions and raises the debate to a new
level, it alone cannot definitively determine whether widespread discrimination
exists. Prior intuition that such discrimination cannot persist in a competitive
market, coupled with the limitations of
our techniques for detecting such acts
and the numerous problems with their
data set, cause many economists to
remain skeptical.
Nonetheless, the Boston Fed data are the
best (only) we have. 11 And even with
their problems, if om conjecture that discrimination should not persist in the
home mortgage market is correct, it
seems unlikely that the authors would
have found such a strong racial effect in

their data. Furthermore, the anecdotal
evidence of discriminatory acts, including the few paired-tester studies that
have been done, does have strong
appeal, even if it cannot prove the existence of widespread discrimination.


What Have We Learned?

Despite the controversy over the core
question of discrimination, we have
made progress in understanding the role
of race in the mortgage market. First of
all, despite their problems, denial-rate
studies can provide valuable insights
into the possibility of illegal discrimination, especially at individual institutions.
But the results of such studies are at best
imprecise; at worst they can be inaccurate and misleading. They must be interpreted with care in order to draw meaningful conclusions.
For example, although the Federal
Reserve performs statistical analyses of
the denial-rate patterns of large lenders as
a regular part of its fair lending exams, a
positive relationship between denial rates
and race is not used as conclusive evidence of discrimination. Rather, the
results of these analyses are used to target
further judgmental review by examiners.
Only if such follow-up is unable to adequately explain disparate denial rates is
an institution referred to the Justice
Department for further investigation.
Second, we have learned that defa ult
rates, while an important piece of the
puzzle, cannot provide insight about
whether discrimination exists at an individual institution or in the mortgage
market as a whole. A few years ago,
many questioned the validity of focusing
on denial rates to detect discrimination
(as Munnell et al. do), suggesting that
we should instead be focusing on default
rates. 12 It is now well established, however, that different causes of discrimination have different implications for the
relative default rates of marginal minority and marginal white applicants. For
example, bigotry would lower the default rate of marginally qualified minority borrowers, while statistical discrimination and discrimination arising from
cultural affinity problems would make
them default more often. 13 Hence,
knowing how race is associated with
default rates may point to the source of

any discrimination that exists, but it cannot help us determine whether it occurs
in the first place. Consequently, focusing
on denial rates is probably still the best
way to detect systematic discrimination.
Third, studies using the new HMDA
data, including Munnell et al., have fairly
well established that redlining per se is
not as severe a problem as once thought.
In other words, banks and other lending
institutions do not appear to arbi trariJ y
deny loans in neighborhoods solely
because of their racial composition.
Rather, differences in credit flows across
neighborhoods appear to be directly
related to the demand for credit and the
risk of lending in those areas. 14 This is
not to say, however, that other market
failures might not result in suboptimal
credit flows to low-income and minority
neighborhoods. 15 Rather, the cause of
any suboptimal credit flows is probably
not discrimination.
Perhaps the most important lesson to be
learned from the debate over systematic
discrimination in the mortgage market is
that the question itself may be largely
misunderstood. That there are major disparities in the allocation of mortgage
credit across races is not disputed; these
disparities are an important social problem regardJess of whether they result
from discrimination, differences in average creditworthiness across races, or
some other market failure. The true
magnitude of the debate lies in how it
can help us better deal with these disparities. Understanding why they exist, and
in particular whether racial discrimination is at their source, is a crucial first
step in developing policies that can
effectively address this fundamental
social problem.



1. Alicia H. Munnell, Lynn E. Browne, James
McEneaney, and Geoffrey M.B. Tootell,
"Mortgage Lending in Boston: Interpreting
HMDA Data," Federal Reserve Bank of
Boston, Working Paper WP-92-7, October
1992 (hereafter referred to as Munnell et al.).
2. Alicia H. Munnell, Geoffrey M .B. Tootell,
Lynn E. Browne, and James McEneaney,
"Mortgage Lending in Boston: Interpreting
HMDA Data," American Economic Review,
vol. 86, no. 1 (March 1996), pp. 25 - 53.

3. For evidence that these factors are correlated with race, see Munnell et al. ( 1996),
footnote 2, table 1.
4. Munnell et al. (1992), p. 2. It should be
noted that the authors' focus on denial rates
makes tbis disparity look particularly egregious. They could have noted that white applicants were 7 percent more likely than blacks
or Hispanics to be approved (assuming that all
nondenied applications were approved).
5. See, for example, Ted Day and Stan J.
Leibowitz, "Mortgages, Minorities, and Discrimination," University of Texas at Dallas,
unpublisbed manuscript, 1993; and David K.
Horne, "Evaluating the Role of Race in Mortgage Lending," FDIC Banking Review, vol. 7,
no. I (Spring/Summer 1994), pp. 1-15. See
also tbe Boston Fed researchers' response in
Lynn E. Browne and Geoffrey M.B . Tootell,
"Mortgage Lending in Boston-A Response
to the Critics," Federal Reserve Bank of
Boston, New England Economic Review,
September/October 1995, pp. 53-78.
6. See Paul W. Bauer and Brian A. Cromwell,
"A Monte Carlo Examination of Bias Tests in
Mortgage Lending," Federal Reserve Bank of
Cleveland, Economic Review, vol. 30, no. 3
(Quarter 3 1994), pp. 27-40; and Anthony
M.J. Yezer, Robert F. Phillips, and Roben P.
Trost, "Bias in Estimates of Discrimination
and Default in Mongage Lending: The Effects
of Simultaneity and Self-Selection," Joumal
of Real Estate Finance and Economics, vol. 9,
no. 3 (November 1994), pp. 196-215.
7. See Harold A. Black, M. Cary Collins, and
Ken B. Cyree, "Do Black-Owned Banks Discriminate against Black Borrowers?" Joumal
of Financial Services Research (fortbcoming).
8. See James A. Berkovec, Glenn B. Canner,
Stuart A. Gabriel , and Timothy H. Hannan,
"Race, Redlining, and Residential Mongage
Loan Performance," Journal of Real Estate
Finance and Economics, vol. 9, no. 3 (November 1994), pp. 263-94. The authors show
that marginally qualified minority borrowers
default more frequently tban their white counterparts, a result that is more consistent with
statistical discrimination or discrimination
arising from cultural affinity than from bigotry. Nonetheless, Munnell et al. (1996) conclude that "tbe dearth of any evidence tbat
minorities default more frequently, given their
economic fundamentals, makes a conclusion
of [statistical] disctimination problematic."
(footnote 2, p. 45).
9. Board of Governors Chairman Alan
Greenspan expressed this concern in 1991
when the Federal Reserve decided against
using this practice as a part of its fair lending
exams. See "Fed Rejects Plan to Uncover
Bias by Using Phony Mortgage Seekers,"
American Banke1; September 26, 1991 , p. 1.

10. See Cathy Cloud and George Galster,
"What Do We Know about Racial Discrimination in Mortgage Markets?" Review of Black
Political Economy, vol. 22, no. I (Summer
1993), pp. 101-20.
11. The Federal Reserve current! y uses statistical analysis in the course of its fair lending
exams. The data collected for these exams
may eventually provide a new resource for
researchers focusing on mortgage lending
12. See, for example, Gary S. Becker, "Nobel
Lecture: The Economic Way of Looking at
Behavior," Journal of Political Economy, vol.
LOI, no. 3 (June 1993), pp. 385 - 409.
13. Bigoted lenders would reject marginally
profitable minority applicants in order to satisfy their "taste for discrimination," implying
that the least qualified minority borrower will
be less likely to default than the least qualified
white borrower. In contrast, statistical discrimination arises from profit-maximizing behavior.
For a more complete discussion of this issue,
see Stanley D. Longhofer, "Rooting Out Discrimination in Home Mortgage Lending," Federal Reserve Bank of Cleveland, Economic
Commentary, November 1995.

Federal Reserve Bank of Cleveland
Research Department

P.O. Box 6387
Cleveland, OH 44101
Address Correction Requested:
Please send corrected mailing label to
the above address.

Material may be reprinted provided that
the source is credited. Please send copies
of reprinted materials to the editor.

14. See, for example, Munnell et al. (1992);
Robert B. Avery, Patricia E. Beeson, and
Mark S. Sniderman, " Underserved Mortgage
Markets: Evidence from HMDA Data," Federal Reserve Bank of Cleveland, Working
Paper 9421, December 1994; George J.
Benston, "Mortgage Redlining Research :
A Review and Critical Analysis," Journal of
Bank Research, vol. 12, no. l (Spring 1981),
pp. 8-23; Glenn B. Canner, Stuart A.
Gabriel, and J. Michael Woolley, "Race,
Default Risk, and Mortgage Lending: A
Study of the FHA and Conventional Loan
Markets," Southern Economic Journal, vol.
58, no. l (July 1991 ), pp. 249-62; and
Michael H. Schill and Susan M. Wachter,
"Borrower and Neighborhood Racial Characteristics and Financial Institution Mortgage
Application Screening," Journal of Real
Estate Finance and Economics, vol. 9, no. 3
(November 1994), pp. 223-39.


Stanley D. Longhofer is an economist at rhe
Federal Reserve Bank of Cleveland. The

author thanks Paul Calemfor helpful comments and suggestions.
The views stated herein are those of the
author and not necessarily those of the Federal Reserve Bank of Cleveland or of the
Board of Governors of the Federal Reserve
Economic Commentary is now available
electronically through the Cleveland Fed 's
home page on the World Wide Web:

15. See Lenoard I. Nakamura, "Information
Externalities: Why Lending May Sometimes
Need a Jump Start," Business Review, Federal Reserve Bank of Philadelphia, January/
February 1993, pp. 3-14.

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