Social explanations for credit problems of mortgage loan applicants

Material Information

Social explanations for credit problems of mortgage loan applicants differences by race and their effect on loan denials
Lake, Margaret Ann
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Physical Description:
viii, 111 leaves : ; 29 cm


Subjects / Keywords:
Discrimination in mortgage loans ( lcsh )
Mortgage loans ( lcsh )
Discrimination in mortgage loans ( fast )
Mortgage loans ( fast )
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )


Includes bibliographical references (leaves 108-111).
General Note:
Submitted in partial fulfillment of the requirements for the degree, Master of Arts, Sociology.
General Note:
Department of Sociology
Statement of Responsibility:
by Margaret Ann Lake.

Record Information

Source Institution:
|University of Colorado Denver
Holding Location:
|Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
37364442 ( OCLC )
LD1190.L66 1996m .L35 ( lcc )

Full Text
Margaret Ann Lake
B.A., Colorado State University, 1970
A thesis submitted to the
University of Colorado at Denver
in partial fulfillment
of the requirements for the degree of
Master of Arts

This thesis for the Master of Arts
degree by
Margaret Ann Lake
has been approved


Lake, Margaret Ann (M.A., Sociology)
Social Explanations for Credit Problems of Mortgage Loan Applicants:
Differences by Race and Their Effect on Loan Denials
Thesis Directed by Professor Karl H. Flaming
Minority groups historically have had lower rates of home
ownership than the majority White group in the United States. One
reason proposed for the difference in home-ownership rates has been
higher denial rates for home mortgages for minority groups than
Whites. Discrimination on the basis of race or ethnicity in mortgage
lending has been cited as the reason for persistent differences.
Mortgage lenders have countered that minority groups are denied loans
in higher proportions partly because of poor credit histories.
Previous research has focused on the differences in denial
rates using HMDA data, augmented with additional information. Little
or no research has examined the actual lending decision process.
This study examined one part of the mortgage lending process: the
letters of explanation written by loan applicants in response to
derogatory items on their credit reports. It investigated the
differences in explanations for credit history problems by racial or
ethnic groups and their effects on loan denials. The sample came
from a 1996 study of mortgage lending for the Colorado Civil Rights
Division by Karl Flaming and Richard Anderson. This study collected
information on 521 loan applications for the Denver MSA for 1994. Of
these, 101 loan applications contained borrowers' letters of
explanation, which were used in this study.
The content analysis showed no differences by racial or ethnic
group in reasons of explanation for credit derogatories. For all
credit problems from mild to severe, the most common explanation was
"I'm not at fault," followed by "I made a mistake." For the severest
credit problem, bankruptcy, the reasons were "Health Problems,"
"Employment Problems," and "Family Problems." All other factors
being equal for the applicants, mortgage lenders accepted most
explanations. If the delinquency was within the past year or there
were delinquencies following a bankruptcy, no type of explanation
sufficed for loan approval.
This abstract accurately
thesis. I recommend its
the content of the candidate's

I dedicate this thesis to my parents, Arthur and Herma Lankenau, who
gave me their love, support, and the confidence to accomplish my
goals. I also dedicate it to my children, Maria and Nathaniel, for
their love, patience, and understanding while I was engaged with this
research and pursuing my master's degree.

I would like to thank the Sociology Department, especially Karl
Flaming and Richard Anderson for giving me the opportunity to work on
the research study, and Candan Duran-Aydintug for her careful reading
of the thesis. I would especially like to thank Karl Flaming, my
adviser, for his advice, support, and encouragement. My thanks to
Peggy for her empathy and comaraderie. Finally, a special thanks to
Steve, my companion on the journey, whose sense of humor helped me
keep this all in perspective.

1. INTRODUCTION........................................... 1
2. REVIEW OF THE LITERATURE................................6
Theoretical Justification............................16
3. METHODS................................................20
Obtaining the Sample.................................22
Sample Characteristics...............................26
Data Analysis Procedures.............................42
4. RESULTS................................................46
Typology of Credit Derogatories......................46
Late Payments..................................50
Collections and Other Actions..................50
Public Records.................................51
Explanations of Credit Derogatories..................52
Not at Fault...................................57
Health/Medical Problems........................60
Employment Problems............................62
Family Problems................................65
Unexpected/Increased Expenses..................68
Moving/Mail Problems...........................69
Made a Mistake
.. 69

Explanations by Race or Ethnicity by
Credit Derogatories.................................71
Late Payments............................... 72
Collections and Other Actions.................75
Public Records................................78
Lender Responses to Explanations....................81
5. DISCUSSION.............................................92
Differences in Explanations by Credit Type..........96
Differences by Racial/Ethnic Groups for
Credit Problem Explanations.........................98
Lender Responses to Explanations...................100
Limitations of the Study...........................102
Policy Implications................................105

Owning your own home has long been part of the "American
Dream." Since the passage of the Housing Act of 1949, the goal of
federal policy has been the provision of a "decent home and a
suitable living environment" for all (quoted in McKinney and Pap
1978, p.1076). Buying a home is probably the most expensive purchase
consumers will make in their lifetimes, and most home buyers must
obtain a home mortgage loan to purchase their home. The mortgage
lenders and financial institutions control the process of financing a
home; thus they determine how and on what terms an individual can buy
a home. These mortgage lenders make decisions to grant loans based
on many factors related to the creditworthiness of the individual and
the value of the property offered as collateral. If these decisions
are made on the basis of non-economic criteria, such as race or
ethnic origin, certain groups will be excluded from the opportunity
to obtain credit to finance the purchase of a home.
Although the stated goal of federal policy has been decent
housing for all, the reality does not match the goal. Flaming and
Anderson (1993) cite 1990 census data which show that 64% of White
Colorado households live in owner-occupied housing as compared to 39%
of Black households, 50% of Hispanic households, 45% Native American
households and 50% of Asian American households. The Federal Housing
Administration (FHA) had policies until the 1950's which discouraged
giving loans to Black applicants for houses in White neighborhoods

(Dane 1993). Federal policy thus contributed to locking minorities
out of White neighborhoods and denying them access to a large housing
stock, but federal agencies were not the only actors limiting access
to housing.
The mortgage banking industry historically has used policies
and practices which discriminate against minorities and minority
neighborhoods (Dane 1993). Rapid suburbanization combined with the
abandonment of central city neighborhoods by middle-class Whites
after World War II resulted in deteriorating, dilapidated inner city
neighborhoods with high concentrations of minority residents (Dane
1993; Hula 1991; Massey and Denton 1988; Massey and Eggers 1990;
McKinney and Pap 1978; Moskowitz 1987; Shlay 1989, 1988).
Residential lenders played a vital role in the shaping of these
housing patterns by making loans easily available in the suburbs and
limiting the extension of credit to inner-city neighborhoods.
To remedy some of these disparities, Congress enacted a series
of Fair Lending Laws: the Fair Housing Act, Title VIII of the Civil
Rights Act of 1968; its amendments of 1974 and 1988; and the Equal
Credit Opportunity Act of 1976. These laws prohibited discrimination
in mortgage lending and residential real estate transactions on the
basis of race, ethnic origin, sex, religion, age, handicaps or
marital status. Despite the passage of these laws, during the
1970's, residents of deteriorating inner city neighborhoods became
frustrated by an inability to obtain credit to purchase or improve
their homes. They charged that the mortgage lenders were "redlining"
or refusing to give loans to properties in certain geographic

locations. These individuals and community organizations asserted
that the underlying basis of redlining was racial discrimination
because of the high proportion of racial and ethnic minorities
residing in the redlined areas. The bankers responded that they were
making loans on the basis of sound business practices and not
discriminating on the basis of race or ethnic origin.
Reacting to individual and group pressure and evidence of
redlining in certain specific areas, Congress passed the Home
Mortgage Disclosure Act of 1975 (HMDA) and the Community Reinvestment
Act of 1976 (CRA). HMDA gave researchers the opportunity to examine
the patterns of lending (the number and dollar volume of loans) to
specified geographic locations (census tracts). The variations in
lending patterns by geographic area shown by the analysis of HMDA
data were interpreted by some as evidence of illegal discrimination.
Others suggested the variations were due to differences in demand for
housing and home loans among individuals and across neighborhoods.
Lenders argued that the discrepancies in denial rates reflected the
legitimate application of credit standards by lenders as they
reviewed applications for home loans.
HMDA was amended in 1989 to require lenders to disclose the
disposition of all their loan applications, the race or ethnic
origin, sex, and annual income of their applicants. Researchers
began to study whether applicants were being rejected on the basis of
race, ethnic origin, or sex. Critics of the studies pointed out that
crucial variables in the lending decision process, the applicants'
credit histories, debt ratios, and the value of property, were not

included in the studies using HMDA data. HMDA did not require
lenders to report credit histories, debt ratios, and the properties'
appraised values, but these were needed to determine whether loan
denials were based on race or ethnic origin, instead of on flaws in
credit histories, high debt ratios, and low appraised values of
One aspect of the lending decision process concerns the
treatment all applicants receive when they fill out a loan
application. Since the lenders are interested in a person's credit
history, they ask prospective applicants to explain the reasons for
any observed credit problems. If lenders failed to give applicants
from all racial/ethnic groups the same opportunity to respond to
credit problems, they would be discriminating on the basis of race or
ethnicity. Then if they responded to the explanations based on the
race or ethnic origin of the applicants, that would also be
The purpose of this study was to examine the lending process
for discrimination on the basis of race or ethnic origin. The study
focused on one part of the loan application process, namely the
reasons given for credit derogatories and the lenders' responses to
them. The study investigated whether lenders' acceptance of
applicants' reasons for credit problems varied according to the
applicants' race or ethnicity. To establish the role of race or
ethnicity in the mortgage lending process, it was necessary to

understand the mortgage lending process, and this study contributed
to the understanding of that process.
For most people, their home is the most valuable thing they
own. Real estate increases in value over time, and it is one way of
building wealth for individuals. Studies have cited great
disparaties in the average net worth of individuals from different
racial/ethnic groups (Canner and Smith 1991). This may be due in
part to the differing rates of home ownership between racial/ethnic
groups. Since most people cannot afford to purchase a home with
cash, they have to obtain a mortgage loan. If mortgage lenders
prevent individuals in some groups from obtaining the credit to buy a
home because of race or ethnicity, they are preventing these groups
from achieving economic parity with Whites. Such discrimination
contributes to the stratification of society, causing discontent and

The debate over discrimination in mortgage lending has reached
beyond academic circles to government, the press, financial
institutions, residents of inner city neighborhoods, minorities, and
community organizations. There are basically two sides to the
debate: those who believe that mortgage lenders discriminate on the
basis of race or ethnicity in making loan decisions and those who
believe that lenders make loan decisions based on market and economic
criteria. The Federal Reserve Board, community organizations, and
academics have studied the question of discrimination in mortgage
lending with varying results. In general, the studies have shown
that the denial rates for home improvement and home mortgage loans
are higher for racial and ethnic minorities than Whites, even at
comparable levels of income and loan amount-to-income ratios. Most
of the studies have shown that census tracts with high percentages of
minorities have received a lower number and dollar volume of home
loans proportionate to the housing stock available in those areas.
The 1989 amendment of HMDA enabled researchers to study the
denial of mortgage loan applications by financial institutions on the
basis of race, gender, and annual income. Studies of 1990 HMDA data
for the nation as a whole indicated that Blacks, Hispanics, and
Native Americans applying for mortgage loans were significantly more
likely to be denied credit than Whites or Asian Americans (Canner and
Smith 1991). The denial rates for Blacks, Hispanics, and Native

Americans were two to three times higher than for Whites for
conventional loans. Asian Americans, however, had denial rates
comparable to those for Whites. The disparities in denial rates were
somewhat less for government-backed loans. Confirming the results of
the Federal Reserve, the Association of Community Organizations for
Reform Now (ACORN), using 1990 HMDA data, reviewed 6.4 million loan-
application records from over 9,000 lending institutions nationwide
and found that 33.9% of Black applicants were denied conventional
loans as compared to 14.4% of White applicants (Alexander 1992).
Since then HMDA studies of lending patterns nationwide for
1991, 1992, and 1993 have shown little change (Canner and Passmore
1995, 1992; Canner and Smith 1992) In 1993, HMDA data showed that
for conventional loans, Blacks were denied at a rate of 34.0%,
Hispanics 25.1%, compared to Whites who were denied at a rate of
15.3% (Canner and Passmore 1995). The denial rates for government-
backed loans were slightly lower, but a large gap persisted between
Blacks andWhites, 22.2% to 11.8%. The publication of these studies
generated much debate about whether the data showed that lenders were
discriminating and about whether HMDA data could be used to show
To counter some of the HMDA statistics, Lindsey (1995)
presented evidence that the numbers of Blacks and Hispanics getting
home loans were increasing in spite of the denial gaps between Whites
and minorities. Between 1993 and 1994, he claimed that conventional
home purchase loans to Blacks rose 54.7% and 42.0% for Hispanics.
The comparable increase for Whites was 15.7%. This was also a

multiyear trend. Starting in 1991, the number of home purchase loans
to Blacks increased 26.0%, and 36.0% in 1992. While these figures
were encouraging, Blacks and Hispanics had a long way to go to catch
up with Whites in terms of home ownership.
The Boston Federal Reserve conducted a study of the 1990 HMDA
data for the Boston metropolitan area and found that Black and
Hispanic mortgage loan applicants were rejected at a rate 2.7 times
greater than white applicants (Brown 1992; Brown and Scott 1993;
Munnell, Tootell, Browne, and McEneaney 1996; Syron 1993).
Attempting to overcome the limitations of research based just on HMDA
data, the Boston Fed asked the most active financial lenders in the
Boston area to provide additional information on thirty-eight
financial, credit history, and employment variables for all of the
1,143 Black and Hispanic applicants and a random sample of 3,300
White applicants. This information was combined with neighborhood
characteristics from the 1990 Census to test for race as a
significant factor in the mortgage lending process. The results of
the study revealed that the additional information reduced the
disparity in denial rates but that minority loan applicants with the
same financial, credit history, employment, and neighborhood
characteristics as Whites had denial rates of 17% as compared to 11%
for Whites (LaWare 1993; Munnell et al.; Syron 1993). Syron (1993,
p. 315) concluded that discrimination was a factor in the loan
granting process.
Flaming and Anderson (1993) analyzed 1990 HMDA data for the
state of Colorado. They included records for mortgage loan

applicants in the metropolitan statistical areas of Denver, Colorado
Springs, Pueblo, Boulder, Greeley, and Fort Collins. Their analysis
concluded that fewer minority households as a proportion of the
population applied for loans and that minorities were denied loans
more frequently than Whites in every income bracket. They compared
minority and White applicants with similar loan-to-income ratios and
found differences in approval rates for minorities and Whites. They
used 1990 Census information to describe neighborhood
characteristics, but they were unable to obtain additional
information on the financial characteristics and credit history of
the applicants.
Bankers and conservatives viewed these studies with skepticism.
They denied that the difference between mortgage loans to minority
and White borrowers was the result of racism but rather the result of
the application of sound credit standards (Brimelow and Spencer
1993). Brimelow and Spencer argued that the findings of the Boston
Fed study illustrated that banks made mortgage loan decisions based
on credit criteria because the Boston study showed that loan defaults
were about the same across census tracts. They argued that this
meant that bad credit risks had been eliminated in both minority and
White areas.
Constance Dunham, a financial economist affiliated with the
Urban Institute, countered with the argument that the opposite should
apply because Whites earned more, made larger down payments, and had
more net wealth compared with minorities (quoted in Brown and Scott
1993). Whites should have lower default rates because their average

credit characteristics are stronger than the averages for minorities
as a whole. Even for minorities who have passed the credit standards
of the lending institutions, they are at higher risk for default
because they put less money down and have higher debt ratios (Yinger
Becker (1993) claimed that the flaw in all mortgage lending
studies was that they had not determined the profitability rates of
loans to different groups based on the default rates, late payments,
and interest rates of those loans. Other researchers responded that
Becker's criticisms were invalid because examinations of loan default
rates could not explain the disparities in lending rates between
Whites and minorities (Tootell 1993; Yinger 1996). Studies of loan
denial rates constituted a valid approach to testing for
discrimination because of several reasons, according to Yinger
(1996). Loan default studies only looked at borrowers who got loans;
they told nothing about those who had been denied. The studies of
loan defaults also left out unobservable credit characteristics
(Yinger 1996). Studies of loan denial rates were a much more direct
approach to studying discrimination and therefore, more valid than
studies of default rates (Yinger 1996).
The claim that discrimination was responsible for the disparity
in loan denial rates has been disputed. Bankers, being business
people, are interested in making a profit and would not turn down
good loans because of racism (Rockwell 1993a, 1993b). Becker (1993)
argued that discrimination against minorities in lending meant that
the lenders would voluntarily give up profits in order to serve

prejudice. Others dismissed discrimination as a factor by saying
that credit flowed where demand was the highest (Shlay 1989).
Neighborhood disinvestment was not the cause of neighborhood decline
but its result. Some of the skeptics of the lending discrimination
studies saw them as an attack on the free market system and a first
step toward a policy of public allocation of credit (Hula 1991).
Even so, if bankers were good businessmen who would not turn down an
opportunity to make a profit, then why were loan denial rates higher
for minority applicants at all income brackets (Brown 1992; Canner
and Smith 1991; Flaming and Anderson 1993)?
Mortgage lenders have used the income of the loan applicant as
one indicator of the applicant's ability to repay a loan and
therefore, one of the determining factors in the loan decision
process. Income seemed to be a factor in denial rates for both
Whites and minorities. Canner and Smith (1991) argued that the
differences in denial rates between racial or ethnic groups reflected
in part differences in the proportion of each group in the low-income
categories. Nationally, the rate of loan denial declined as the
income of the residents of an area increased; however, the rates of
denial were still higher for racial and ethnic minority groups at all
income levels (Canner and Smith 1991) Furthermore, as the
percentages of minorities increased in census tracts in all income
categories, the proportion of minority home loan applicants denied
credit increased (Bradbury, Case, and Dunham 1989; Canner and Smith
1991; Galster 1992; Pol, Guy, and Bush 1982; Shlay 1989, 1988; Shlay,
Goldstein, and Bartelt 1992).

Racial and ethnic minorities were generally at a disadvantage
in comparisons of income and financial assets. For example, the 1986
Survey of Consumer Finances by the Federal Reserve System showed the
mean amount of financial assets held by Black families was $5,900
compared with $64,000 for white families. Differences in net worth
were illustrated by Black families with an average net worth of
$29,000 compared to White families with an average net worth of
$165,000 (quoted in Canner and Smith 1991, p.876). Canner and Smith
further quoted statistics which showed that in mid-1991, the
unemployment rate for Blacks was twice as high as for Whites and that
the median income of households headed by Blacks and Hispanics was 57
percent and 71 percent, respectively, of the median income of
families headed by Whites.
These income differentials between Whites and minorities had
additional consequences. Those in the low-income groups generally
had few assets for the down payment on a house. If they had consumer
debt, they tended to have high consumer debt burdens and to have
fallen behind in their debt repayments. They also had more periods
of involuntary employment and reduced work hours than those in the
middle- and high-income groups (Canner and Smith 1991). Lenders
considered these characteristics in their mortgage lending decisions
which further disadvantaged minorities. Nevertheless, financial
institutions have an obligation to serve qualified low- and moderate-
income clients of any ethnic or racial origin (Canner and Smith
1991) Anne Shlay (1989) suggested that the marketing of loans to

various income groupings and lenders1 policies on minimum loan
amounts needed to be studied.
Another important component of the mortgage lending decision
process cited by researchers was the value of the property serving as
collateral for the loan (Black and Schweitzer 1985; Bradbury et al.
1989; Canner and Smith 1991; Pol et al. 1982; Shlay 1989, 1988).
Some researchers, unable to obtain direct appraisals of properties in
their research, used the median dollar value of existing homes in the
census tracts as a measure of the value of the property (Pol et al.
1982; Shlay 1988) As expected in those studies, the value of the
housing stock was positively correlated with the dollar amount and
number of loans made on properties in the defined areas. When
housing values were held constant, race or ethnic origin became a
factor in the number and dollar amount of loans received.
Using the median value for homes in a defined area told nothing
about the individual properties offered as collateral for home loans,
and the median value should not be used as a proxy for appraised
value. A partial answer to that criticism were the studies done by
Bradbury, Case, and Dunham (1989) and Black and Schweitzer (1985).
Bradbury et al. looked at the geographic distribution of credit by
neighborhood in the Boston area by analyzing individual deed
transfers. The deed transfers listed the sales price of the home,
and when the values of the housing units (along with lower incomes,
less wealth, less housing development and other factors) were
controlled, the ratio of mortgage loans to potentially mortgageable
housing stock was substantially lower in Black neighborhoods. Black

and Schweitzer included the value of the residential property serving
as collateral in the net worth variable of their study, which
measured property worth less precisely than if it were measured by
itself, but they found that race and marital status played
significant roles in the terms of the loans granted.
Even if a loan were denied because the appraised value of the
property was too low, discrimination based on race or ethnicity could
be a factor in the denial. Stephen Dane (1993) in his study of the
history of mortgage lending discrimination in the United States
showed that appraisers have used appraisal methods which specifically
discriminated against racial and ethnic minorities. Appraisers would
systematically appraise properties lower in neighborhoods with higher
concentrations of minorities than in comparable neighborhoods with
lower concentrations of minorities. The Federal Financial
Institutions Examination Council identified unduly conservative
appraisal practices in minority areas, strict property standards
regarding size and age, and high minimum loan amounts as subtle forms
of discrimination (LaWare 1993). As already noted, when racial and
ethnic minorities, with lower incomes and fewer financial assets,
applied for home loans on properties of lesser value located in
minority areas, they experienced the effects of these policies.
The research described in this study has called into question
the claims of the mortgage lenders that they use objective criteria
to determine which loan applicants are granted loans. Munnell of the
Boston Federal Reserve has said that only 20% of the 4,500 applicants
in the expanded Boston Federal Reserve study of the 1990 HMDA data

were unquestionably qualified or unqualified for a home loan (quoted
in Brown and Scott 1993). The other 80% had a credit flaw which
could trigger a rejection of their loan application, and their loans
were denied or granted on the basis of the discretion of the loan
officer. Researchers know little about how mortgage lenders decide
to grant loans for less than perfect applicants.
Analysis of HMDA data describes the geographic flow of credit
by neighborhoods and what percentages of loan applicants were denied
by race or ethnic origin. It cannot explain the reasons why the loan
decision-making process resulted in disparate denial rates between
minorities and Whites. The use of pre-1990 HMDA data is even more
limited in that it can only address the pattern of uneven volume and
dollar amount of loans by census tract (Shlay 1989). Looking at the
disposition of loan applications by race in HMDA data from 1990 and
more recently cannot explain the housing market processes behind a
lending decision. Such processes include credit pre-screening, real
estate steering, marketing of loans to different communities, and the
consumer search and decision process for mortgage loans (Shlay 1989) .
The disposition of loans by sex, race, and annual income does not
explain the processes of the lending decision employed by financial
institutions. It does not demonstrate whether individual loan
officers follow the criteria and apply the standards fairly and
consistently to all individuals and groups. It does not show how
much the decision to lend rests on the individual discretion of the
loan originators and underwriters.

Because the research on mortgage lending and discrimination has
not investigated the actual process in granting a loan, and because
research indicates that the lending decision depends somewhat on the
discretion of lending personnel, this study looked at one part of the
mortgage lending process, the applicant's explanation for credit
derogatories. When applicants for a mortgage loan have problems on
their credit reports, the lenders ask them to write a letter of
explanation for those derogatories. This study examined the reasons
given for credit problems and determined the criteria used by lenders
in accepting or rejecting an applicant's explanation for credit
problems. It looked at whether those criteria were applied fairly
and consistently to all racial and ethnic groups. Because little or
no research has been conducted on the applicant's reasons for credit
problems and the lender's response to them, research of this kind can
help to explicate one part of the lending process.
Theoretical Justification
Researchers studying mortgage lending have been mostly
concerned with whether disparate loan rates between Whites and
minorities show discrimination. They have been less concerned about
the possible causes of discrimination, if it exists. One possible
cause of discrimination that has received attention by researchers is
that "the unobservable credit characteristics of minority applicants
are less favorable, on average, than those of majority applicants"
(Yinger 1996, p.52). This type of discrimination, called statistical
discrimination, fits the theories of Becker (1993) and others who say

that lenders are profit-maximizing institutions who make decisions on
this basis. However, if lenders base their decisions on unobservable
characteristics rather than the observable characteristics of each
applicant, it constitutes legal discrimination.
It does not seem probable that lenders make decisions solely on
the basis of business principles, when the decision of whether to
extend a loan to an applicant is not always clear cut. For
applicants with imperfections in their applications such as weak
credit histories or high debt-to-income ratios, race or ethnicity may
be a factor in granting the loan (LaWare 1993). In such cases, loan
officers might be more willing to exercise their discretion to grant
a loan to people who look or talk like themselves (Hunter and Walker
1995; Syron 1993) The personnel involved in the lending decision
may make decisions based on the perception of risk for a loan but not
on the actual risk (Black and Schweitzer 1985). They may perceive
minorities as being a greater credit risk because they perceive
minorities as being somehow "different" from them, given that most
people working in the lending business are White. They may perceive
minorities as the "other," and prefer to give loans to people who are
like them.
A couple of studies have tested this hypothesis. Calomiris,
Kahn, and Longhofer (1994) proposed that discrimination in mortgage
lending resulted from the lack of "cultural affinity." Hunter and
Walker (1995) tested this hypothesis by looking at whether lenders
perceived credit history information differently for Whites than for
minorities. They suggested that marginal White candidates had

"thicker loan files" than rejected minority applicants, meaning that
the White applicants had more documentation of their situations than
minority applicants, that White applicants had received more
counseling and coaching. Hunter and Walker found that the
"thickness" of the file made no statistical difference, but this may
have been due to the inadequate way they measured creditworthiness
and "thickness of file."
Another intriguing study testing the hypothesis that mortgage
lenders like to give loans to people who look like them was done by
Squires and Kim (1995) Their results showed that the likelihood of
a Black applicant being approved increased as the proportion of Black
employees increased, controlling for applicant and institutional
characteristics. They found this especially true for savings and
loans which did a large part of their business in mortgage lending.
They recommended that lending institutions hire more minorities to
facilitate loans to minorities.
Another cause of discrimination in mortgage lending may be
personal prejudice on the part of individuals working in the lending
business. Personnel involved in the decision to grant a loan may
deny loans to minorities based on their own prejudices (Galster 1992;
Yinger 1996) Because of the history of prejudice in this country,
it seems plausible to suppose that people working in the lending
business could be prejudiced and allow their prejudices to intrude
into their decision. However, it seems less likely for applicants
who make it into the formal application process because several
individuals at an institution usually review a file. Personal

prejudice might have a greater effect at the pre-application stage,
when the minority person comes in to the lender to apply for a loan.
This study was exploratory in nature and did not test
hypotheses per se. Not enough was known about the lending process
and interaction between applicants and lenders to formally test
hypotheses. Furthermore, the study used data from the field as the
best way to observe the actual process. The study analyzed the data
to see whether evidence existed for any of the theories of
discrimination discussed above, especially the "cultural affinity"
hypothesis. The development of theory has a long way to go in this
area, and most of the research on mortgage lending has been done by
economists who have not worried about social causes of

The data for this study came from the data obtained for the
1996 mortgage lending study for the Colorado Civil Rights Division
(CCRD) under contract from the US Department of Housing and Urban
Development by Karl Flaming, Ph.D. and Richard Anderson, Ph.D. of the
Sociology Department, University of Colorado at Denver. The CCRD
mortgage lending study looked at mortgage lending in the Denver
metropolitan area for the years 1990 through 1994 and gathered
information from Home Mortgage Disclosure Act (HMDA) data, mortgage
loan application files, and interviews with key personnel involved in
mortgage lending.
The CCRD mortgage lending study team consisted of the principal
investigators, Karl Flaming and Richard Anderson, plus other
consultants from the fields of economics, public policy, and
sociology. My role in the CCRD study was as a research assistant
with primary responsibility for supervision of the coding and data
entry procedures. Under the direction of the principal
investigators, I developed the coding manual, pre-tested it, and then
supervised the data extraction and data entry. In addition as part
of the research team, I carried out qualitative procedures including
in-depth interviews of key lending officials, observations of lending
institution offices, and analysis of documents found in the loan

This project used the data obtained by the CCRD study because
of its availability and the difficulty of accessing elites such as
mortgage lenders. The mortgage lenders, savings and loans, and banks
who make residential mortgage loans may be reluctant to participate
in a study of this kind for several reasons. First of all, they are
wary of negative publicity that could come from publicizing the
results of a study of discrimination in mortgage lending, such the
controversy surrounding the publication of the Boston Fed study
(Munnell, Tootell, Browne, and McEneaney 1996). If a study were to
claim that certain lenders were discriminating against racial and
ethnic minorities, those lenders could face investigation by federal
agencies and a potential loss of business resulting from negative
Second, federal regulations require institutions making
mortgage loans to maintain the privacy and confidentiality of the
information supplied by the loan applicants. If lenders violate
these standards, they can be liable for prosecution, so any research
involving mortgage loan applications must ensure the privacy of the
applicants. Many-lending institutions would rather not take the
chance unless they are compelled to participate by a regulating
Third, participating in a research study involves an intrusion
into the daily business practices of. the participating lenders.
Cooperating lenders must take employees away from their normal tasks
to pull the loan files and to make copies of the required documents.
This process can be time consuming because the loan application files

may not be accurately filed or easily obtainable. The files contain
many documents and sorting through them to make copies of the needed
documents can take many hours of employee time. Most lenders would
be reluctant to let members of a research team have unsupervised
access to their files, thus necessitating the use of lender
personnel. Participating institutions can, therefore, expect to be
compensated for their time and expenses in gathering the information
All of these are formidable obstacles for a single researcher
with limited funds to attempt to conduct this research alone.
Furthermore, gaining access to top personnel in the institution would
be difficult without some credentials or backing from an agency or
institution. Using data from the CCRD study, which had already
solved these problems, made this research possible.
Obtaining the Sample
Flaming and Anderson (1996) used data collected under the
requirements of the Home Mortgage Disclosure Act (HMDA) to complement
and verify the information in the mortgage loan files received from
the lenders. They obtained 1994 HMDA data, distributed by the
Federal Financial Institutions Examination Council (FFIEC), to
generate a list of all mortgage lenders in the Denver MSA for 1994.
The list was narrowed from over 400 lenders to 44 lenders whose
combined market shares comprised 80% of the mortgage loan
applications for the Denver MSA for 1994. From this list, it could
be determined how many loan applications a lender received and what

percentage of the market each lender controlled. The market shares
of the individual lenders accounting for 80% of the 1994 loan
applications in the Denver MSA ranged from 8.5% to .5% of loan
The CCRD study (Flaming and Anderson 1996) was conducted with
the cooperation of the Colorado Mortgage Lenders Association (CMLA),
principally through the help of its president Mr. Kim Starley. With
Mr. Starley's assistance, lenders were selected from the list of 44
lenders generated by HMDA data, and these lenders were sent a letter
introducing the project and its goals. The letters were followed up
with phone calls by Mr. Starley and the principal investigators to
determine which lenders would be interested in participating and to
try to convince those who might lean toward participating, but have
doubts. The lenders were assured that they would not be identified
individually in the study, thus protecting them from potential bad
publicity and possible reprisals.
Of those lenders who seemed agreeable, meetings were held
between the research team and key personnel at each institution to
discuss procedures and protocol for maintaining confidentiality of
loan applicants, what documents would be provided by the lenders, and
methods for providing these documents. The research team needed to
be flexible because each lender had slightly different requirements
for the procedures for providing the necessary documents. Five
lending institutions agreed to participate in the CCRD study. These
five lenders from the list of 44 together accounted for 13.7% of the
Denver MSA market for home purchase loans in 1994.

The procedure for drawing a probability sample of the mortgage
loan applications was the same for all participating lenders. The
lenders supplied a list of all of their mortgage loan applications
originated or denied for the Denver MSA for 1994 for home purchase,
owner-occupied properties, with race/ethnic origin of the
applicant(s) reported. A probability sample of 10% of all loan
applications to the lender meeting the above parameters was selected
from the list of loan applications. Seventy percent of this sample
was a general random sample, 15% was a minority oversample, and 15%
was an oversample of denied loans.
The lenders then pulled the loan application files from the
list of randomly selected loan applications. There were some
problems finding all the requested loan files due to storage
procedures of individual lenders and the age of the requested
applications. Mortgage lenders are required to keep denied loan
application files only for 25 months, which meant that at the time of
this study, 1996, some denied loan application files had been
destroyed. Other loan application files could not be located. These
files were replaced with additional loan files drawn randomly from
the lender's loan application lists.
The lenders made copies of four documents from the requested
files: (1) the mortgage loan application; (2) the underwriting
document; (3) the credit bureau report; and (4) the letter of
explanation for denial of the application if denied. Two of the
lenders supplied additional documents, the letters of explanation
from the prospective borrowers regarding derogatory items on the

credit bureau reports, and these letters and explanations were used
in this study. The identifiers on all documents, that is the names
of the applicants, social security numbers, and addresses, were
blacked out by the mortgage lenders and research personnel.
The researchers checked the loan application files received
from the lending institutions against the list of randomly selected
loan files to ensure the randomness and accuracy of the sample. Loan
application files that were received in error were discarded, and a
replacement was selected where possible. Also loan application files
received which did not fall within the sampling frame of the study,
such as those for properties outside the Denver MSA, applications
acted on in years other than 1994, loan applications which were
neither originated or denied, and those which were not for home
purchase and owner-occupied properties. Again where possible,
replacement loan applications were drawn to maintain the size of the
The research team then collected data on the type of mortgage
and terms of the loan, information on the property and purpose of the
loan, demographic information on the borrower and co-borrower, where
applicable, and data on the applicant's employment history, credit
history, monthly income, combined housing expense, assets and
liabilities, race and sex. Additional information was recorded
regarding the loan transaction costs, the loan-to-value ratio, the
debt ratio, and comments by the underwriters, if any. From
conversations with lending personnel such as loan processors,
underwriters, and originators, these were the pieces of information

they identified as crucial to the lending decision process. Other
published research, such as the Boston Fed study (Munnell et al.
1996), and an examination of sample loan files from one or two
lenders also confirmed the importance of these variables in the
lending decision process.
The data recorded in the code books were entered into an SPSS
data base and checked twice for accuracy of entry. It was this data
base and photocopies of letters from the applicants explaining
problems on their credit histories which formed the basis for this
study. The computerized data base provided much background and
supplemental information necessary for studying the reasons
applicants gave for credit delinquencies and how the lending
institutions responded to their requests for a mortgage loan.
Sample Characteristics
Data from two of the five lending institutions in the CCRD
study were used in this study because these two institutions
furnished the explanatory letters from the applicants. These two
lenders accounted for 196 loan applications, 46 from Bank 1 and 150
from Bank 2 (to maintain the anonymity of the institutions involved,
I call them "Bank 1" and "Bank 2") Bank 1 was a moderate-sized,
full-service commercial bank with 440 mortgage loan applications for
1994 meeting the criteria listed previously. Bank 1 had .9% of the
market share for mortgage loans in the Denver MSA for 1994. Bank 2
had 1,929 mortgage loan applications meeting the criteria in 1994 and
controlled 4.1% of the Denver MSA market share. Bank 2 was a large

mortgage lender, mainly providing home financing for purchasers of
homes built by its parent company.
The sample of 196 loan applications from these two lenders
included the general random sample, the minority oversample and the
denied oversample. In order to examine as many letters from
applicants as possible, I decided to use the total sample from these
lenders, not just the general random sample. Using the total sample
from the two institutions had the effect of increasing the number of
applications from minorities and applications denied by the lender,
such that the proportion of minority and denied applications were
greater than would be found in the general universe of mortgage loan
There were several ways to look at the sample of loan
applications used in this project. The first was to look at the
breakdown of the sample mortgage loan applications by minority
composition of the census tract, as shown in Table 3.1.
Table 3.1 Percent of Loan Applications by Percentage of Minority in
Census Tract
Less Number
than 10 to 20 to 50 to 80% or of
Bank 10% 19% 49 % 79% more Cases
Bank 1 18.9% 45.9% 21.6% 5.4% 8.1% 37
Bank 2 35.7% 45.2% 17.4% .9% .9% 115
Total Sample 31.6% 45.4% 18.4% 2.0% 2.6% 152
Number of Missing Observations: 44
Percents do not sum to 100 because of rounding.
Table 3.1 shows the sample of mortgage loan applications from
both institutions as having the greatest percentage of applications

for properties located in census tracts composed of 10 to 19% percent
minority inhabitants. The sample from Bank 2 had almost twice the
percentage of mortgage loan applications for properties in census
tracts with less than 10% minority composition, as compared with the
sample from Bank 1 (35.7% to 18.9%). The sample from Bank 1, on the
other hand, had 8.1% of its mortgage loan applications for properties
in census tracts with 80% or more minority inhabitants as compared
with .9% for the sample from Bank 2. These differences showed that
the sample from Bank 1 had a greater percentage of mortgage loan
applications from census tracts of high minority concentration than
the sample from Bank 2. Seventy-seven percent of the applications
for both lenders were for properties located in census tracts with
less than 20% minority inhabitants.
Another way to characterize the sample was to look at the
median income of census tracts containing the properties as a percent
of the 1990 Denver MSA median income. Table 3.2 illustrates this
relationship by showing the percent of area median income of the
census tract locations of the properties for both lenders.
Table 3.2 Percent of Loan Application Properties by Tract Income as
Percent of Denver MSA Median Income
Less than 80 to 120% or
80% 119% More of
Median Median Median Number of
Bank Income Income Income Cases
Bank 1 37.5% 30.0% 32.5% 40
Bank 2 .7% 10.9% 88.4% 147
Total Sample 8.6% 15.0% 76.5% 187
Number of Missing Observations: 9
Percents do not sum to 100 because of rounding.

Table 3.2 displays that the sample from Bank 1 had the greatest
percentage (37.5%) of its applications for properties in tracts with
median income less than 80% of the area median income, while the
sample from Bank 2 had an overwhelming percentage (88.4%) of
applications for properties in tracts with incomes of 120% or more of
area median income. This caused the sample to be weighted toward
properties located in tracts having 120% of more of the median
income. Table 3.1 and Table 3.2 illustrated the differences between
the samples from the two banks. The Bank 1 sample had a greater
percentage of its applications for properties in minority tracts and
tracts with lower median incomes than the sample from Bank 2.
Characteristics of the sample also included the breakdown by
the race/ethnicity of the applicants and whether their loan
applications were originated or denied. Table 3.3 displays the
composition of the borrowers on the sample applications by race or
ethnic origin.
Table 3.3 Borrowers on Loan Applications Classified by Race or
Ethnic Origin
Valid Cumulative
Race or Ethnicity Frequency Percent Percent Percent
Native American 2 1.0 1.0 1.0
Asian American 14 7.1 7.1 8.2
Black 18 9.2 9.2 17.3
Hispanic 23 11.7 11.7 29.1
White 139 70.9 70.9 100.0
Total 196 100.0 100.0
Number of Missing Observations: 0
Percents do not sum exactly to 100 due to rounding.

The above table shows that the sample contained the greatest
number of loan applications from borrowers who classified themselves
as white, 139, versus 67 loan applications from borrowers who
classified themselves as being different from White, or minority.
Based on their household proportion in the population (1.6%), Asian
Americans were the most over-represented group in the sample with
7.1% of applications from Asian Americans. I considered each loan
application as an application from a household because most
applications came from applicants who lived together as a household.
The other minority groups were also over-represented in the sample,
but not by as much. Native Americans made up .6% of Denver MSA
households, the sample had 1.0% applications from Native Americans.
Black household proportion was 5.6%, but applications from Blacks
composed 9.2% of the sample. Hispanic household proportion
represented 9.8% in the Denver MSA, but 11.7% of the applications
were from Hispanic households. Again these high percentages were due
to including the minority oversamples in the total sample used in
this study.
The next table illustrates the classification by race/ethnicity
of the borrowers by the two lending institutions making up the

Table 3.4 Percentage of Borrowers by Racial/Ethnic Groups by Sample
Lending Institutions
Native Asian Black His- of
Bank Amer. Amer. panic White Cases
Bank 1 2.2% 0.0% 15.2% 19.6% 63.0% 46
Bank 2 .7% 9.3% 7.3% 9.3% 73.3% 150
Total Sample 1.0% 7.1% 9.2% 11.7% 70.9% 196
Number of Missing Observations: 0
Percentages do not sum to 100 due to rounding.
The breakdown by racial/ethnic group of borrowers in Table 3.4
indicates that the Bank 1 sample had a much higher percentage of
applications from borrowers with minority status (37.0% to 26.7%).
The Bank 2 sample had a high percentage of applications from Asian
Americans (9.3%) in comparison with the household proportion of Asian
Americans of 1.6% in the Denver MSA. Otherwise, the racial/ethnic
make-up of borrowers from the Bank 2 sample matched more closely the
household proportions of those groups in the Denver MSA. The sample
from Bank 1 was weighted more heavily toward minorities.
The next area of interest concerned the percent of originations
and denials of loan applications in the sample. The sample included
only those applications which were originated or denied; applications
with other dispositions, such as application withdrawn, were excluded
from the sample. Table 3.5 presents the percentages of loan
applications originated and denied by lenders.

Table 3.5 Percent of Loan Applications Originated and Denied by
Lending Institution
Loans Loans Number of
Bank Originated Denied Cases
Bank 1 71.7% 28.3% 46
Bank 2 78.0% 22.0% 150
Total Sample 76.5% 23.5% 196
Number of Missing Observations: 0
The percentages of loan applications originated and denied in
the sample did not vary that much between the two lenders. For the
overall Denver MSA in 1994, the loan origination rate was 70.5%,
while the denial rate was 10.1% (Flaming and Anderson 1996). The
denial rate was higher for this sample due to the oversampling of
denied applications. Next I examined origination and denial rates
for the various racial/ethnic groups in the sample, shown in Table
3.6 below.
Table 3.6 Percent of Loan Applications Originated and Denied by
Racial/Ethnic Group
Action Taken on
Number of Cases
Native Asian
Amer. Amer.
50.0% 85.7%
50.0% 14.3%
1 2 14
Black panic
66.7% 73.9%
33.3% 26.1%
18 23
White cent
77.7% 76.5%
22.3% 23.5%
139 196
Number of Missing Observations: 0
As seen in Table 3.6, Asian American applicants had the highest
origination rates (85.7%) and Native Americans the lowest (50.5%),
although the number of applications from Native Americans was too
small for statistical analysis. With the exception of Native

Americans, Blacks and Hispanics had the highest denial rates of 33.3%
and 26.1%, respectively, as compared to that for Whites of 22.3%.
The denial rates for the various groups in the sample were not
reflective of the denial rates for the Denver MSA as a whole.
Although Flaming and Anderson (1996) did not report denial rates for
each minority group, they reported the denial rate for Whites as 9.8%
and as 16.0% for all minorities in the Denver MSA. Again the denial
rates were higher in the sample because the 196 loan applications
included the denied oversample.
Another characteristic of the sample that should be noted was
the percentage of loan types by racial groups. The loan applicants
in the sample applied for one of three types of loans, conventional,
FHA, and VA. The FHA and VA loans were guaranteed by the government,
while those applicants seeking conventional loans had to get private
mortgage insurance if the amount of the loan was 80% or more of the
value of the home. Table 3.7 displays the percentages of loan types
by racial/ethnic groups.
Table 3.7 Percent of Loan Applications by Loan Type by Racial/Ethnic
Native Asian His- Per-
Loan Type Amer. Amer. Black panic White cent
Conventional 0.0% 92.9% 44.4% 52.2% 65.5% 63.3%
FHA 0.0% 0.0% 22.2% 47.8% 24.5% 25.0%
VA 100.0% 7.1% 33.3% 0.0% 10.1% 11.7%
Number of Cases 2 14 18 23 139 196
Number of Missing Observations: 0
Total percents do not sum to 100 due to rounding.

According to Table 3.7, Native Americans, Blacks, and Hispanics
applied for conventional loans in smaller percentages than Whites and
Asian Americans. The Hispanics in the sample, however, tended to
apply more for conventional loans than either of the government-
sponsored loans (FHA and VA), although the difference between
applications for conventional and FHA was not great (52.2% to 47.8%).
A majority of Black applicants, 55.5%, applied for the government-
sponsored VA and FHA loans, rather than conventional loans. Loan
types were important because government-sponsored loans were more
expensive for the applicant, but were easier to get than conventional
The study also collected information regarding the personal
characteristics of the applicants, such as their age, years of
education, marital status, and number of dependents. Financial
information included monthly income, debt ratio (total obligations to
monthly income), housing ratio (proposed housing expense to monthly
income), loan amount, and liquid assets. The data captured the value
of the property serving as collateral for the loan in the loan-to-
value ratio. Table 3.8 summarizes the values of these variables by
racial/ethnic groups.

Table 3.8 Values for Applicants' Personal, Financial, and Property
Characteristics by Racial/Ethnic Group
Characteristic Native Amer. Asian Amer. Black His- panic White Sample Total
Mean Age (Bor) 53.5 34.3 38.3 35.3 37.6 37.3
Mean Age (Co-B) 33.6 32.3 34.3 36.8 36.1
Mean Years of Education (Bor) 14.0 15.3 15.0 13.5 15.3 15.1
Mean Years of Education (Co-B) 15.1 16.3 14.2 14.6 14.7
% Married (Bor) (Total Cases) 50.0% (2) 85.7% (14) 50.0% (18) 60.9% (23) 74.1% (139) 70.9% (196)
% Married(Co-B) (Total Cases) (0) 100.0% (10) 100.0% (7) 88.2% (17) 89.0% (109) 90.3% (143)
Mean Dependents 0.0 . 8 1.2 1.1 1.2 1.1
Median Monthly Income $4055 $3768 $3602 $3553 $4308 $4065
Debt Ratio 41.65 37.39 34.18 36.88 35.98 36.07
Housing Ratio 19.71 31.39 24.93 24.51 25.01 25.35
Mean Loan Amnt 84,450 129,742 102,747 97,648 120,459 116,653
LTV Ratio 102.88 79.42 93.55 90.29 86.01 86.83
Mean Liq. Assets 28,014 57,628 22,408 25,060 51,595 45,931
The data showed the mean age for borrowers among racial/ethnic
groups to be roughly the same, a mean of 37.3 years, with Native
American borrowers being much older, but there were only two Native
Americans in the sample. The co-borrowers were slightly younger than
the borrowers, a mean of 36.1 years, and all groups were close in
age. Hispanic borrowers had the least years of education, while
Asian American, Black, and White borrowers had almost 1.5 more years
of education than Hispanics. The mean years of education for the co-
borrowers was slightly less than the mean years for the borrowers.
Black co-borrowers, however, had the most years of education among
all the groups, 16.3%.
Asian Americans had the highest percentage of married
borrowers; whereas Blacks and Native Americans had the lowest. The

percentage of borrowers married ranged from 50.0% to 85.7%, showing a
fair amount of variation among groups. The co-borrowers had high
percentages reporting married, which would be expected as the most
common combination of borrowers applying for a mortgage loan was a
married couple. The total number of dependents varied little among
groups with one dependent the average.
The median monthly income included all income combined for the
borrower and co-borrower(s) per month. Hispanics had the lowest
median monthly income, followed by Blacks, with Whites having the
highest. The difference between the Hispanic and White median
monthly incomes was $755 a month. There was quite a difference in
the sample among the different groups with respect to mean liquid
assets, the amount of cash readily available to the applicants.
Blacks had the lowest mean of $22,408 and Asian Americans had the
highest mean of $57,628. Mean liquid assets for Whites ($51,595)
were closer to the mean for Asian Americans, while Hispanics and
Native Americans had mean liquid assets closer to the mean for Black
applicants. With the exception of Native Americans, who made up only
two cases in the sample, the debt ratios (total monthly obligations-
to-monthly income) varied little for all other groups. Housing
ratios (proposed monthly housing payment-to-monthly income) were
similarly close for Blacks, Hispanics, and Whites, but Asian
Americans had the highest housing ratio.
Asian Americans had the highest mean loan amount among the
groups in the sample. The average loan amounts varied from a low of
$84,450 for Native Americans to a high of $129,742 for Asian

Americans, a difference of $45,292. Whites had the second highest
mean loan amount of $120,459. Blacks and Hispanics had fairly close
mean loan amounts, $102,747 and $97,648, respectively. As far as the
loan-to-value (LTV) ratio which measured the appraised value of the
property to the amount of the loan, Asian Americans had the lowest
(79.42), meaning that they made the largest down payments on their
prospective properties. Whites had the second lowest LTV ratio
(86.01), followed by Hispanics (90.29), Blacks (93.55), and Native
Americans (102.88). Both cases of Native Americans in the sample
applied for VA loans, where the loan-to-value ratio can exceed 100%
when closing costs are financed.
So far, I have not characterized the borrowers and co-borrowers
in the sample by sex. Table 3.9 presents these percentages.
Table 3.9 Percent of Borrowers and Co-Borrowers by Seat
Type of Borrower Male Female Number of Cases
Borrower 81.6 18.4 196
Co-Borrower 8.3 91.7 144
Total Sample 50.6 49.4 340
Number of Missing Observations: 0
From Table 3.9, the borrowers on the sample applications were
overwhelmingly male (81.6%) and the co-borrowers overwhelmingly
female (91.7%). There were more female borrowers as a percentage
than male co-borrowers, however. When all applicants listed on the
applications, borrowers and co-borrowers were combined, the breakdown
by sex was roughly equal, 50.6% males and 49.4% females. The typical

set of applicants for a mortgage loan in the sample was a male
borrower and a female co-borrower. Out of 196 loan applications, 144
of them had a borrower and a co-borrower, and of these, 128 had a
male borrower and a female co-borrower.
The sample provided information on the credit history of the
applicants. If the credit history of the applicant(s) contained any
derogatory credit items, its type, frequency, and resolution, if any,
were noted. Of the 196 applications in the sample, 188 of them
included credit bureau reports showing the credit histories of the
applicants. The following Table 3.10 illustrates the presence of
derogatory items by racial/ethnic groups. If the credit history
contained any derogatories, the application was coded as "yes" on
this item.
Table 3.10 Percent of Applications Showing Credit Derogatory by
Racial/Ethnic Group
Presence of Credit Derogatory? Native Amer. Asian Amer. Black His- panic White Total
Yes (Number) 50.0 (1) 42.9 (6) 72.2 (13) 90.0 (18) 59.7 (80) 62.8 (118)
No (Number) 50.0 (1) 57.1 (8) 27.8 (5) 10.0 (2) 40.3 (54) 37.2 (70)
Number of Cases 2 14 18 20 134 188
Number of Missing Observations: 8
The majority of the applicants (62.8%) in the sample had at
least one derogatory item on their credit reports, as shown in Table
3.10. However, the Hispanic group had a very high percentage (90.0%)
of applicants with a derogatory item on their credit report. The

Asian American applicants had the lowest percentage reporting a
derogatory item (42.9%), while Blacks had the second highest
percentage (72.2%).
Of the 118 applications in the sample reporting at least one
credit derogatory, 88 were approved and 30 were denied. Table 3.11
exhibits the percentage breakdown of originations and denials for
applications with a credit derogatory by racial/ethnic group.
Table 3.11 Percent of Loan Applications Reporting at Least One
Derogatory Credit Item Originated or Denied by Racial/Ethnic Group
Native Asian His- of
Loan Action Amer. Amer. Black panic White Total
Originated 100.0 83.3 61.5 83.3 73.8 74.6
Denied 0.0 16.7 38.5 16.7 26.3 25.4
Number of Cases 1 6 13 18 80 118
Number of Missing Observations: 0
Percents do not sum to 100 due to rounding.
As shown above in Table 3.11, Blacks had the highest denial
race (38.5%) for applicants reporting at least one derogatory on
their credit report. Asian Americans and Hispanics had the lowest
(16.7% each) with Whites in the middle at 26.3%. There was only one
application from Native Americans in this category, which was
Of the 118 applications in the sample which reported at least
one derogatory on the credit bureau report, 101 of them included a
letter from the borrower explaining the reason or circumstances
causing the credit problem. Whether the other 17 applications did
not contain a letter of explanation or whether the bank personnel

failed to make a copy of the letter, for whatever reason, could not
be discerned. The breakdown of applicants by racial/ethnic group for
those applications having a letter is shown in Table 3.12.
Table 3.12 Percent of Applicants With at Least One Credit Derogatory
Who Have an Explanatory Letter by Racial/Ethnic Group
Explanatory Native Asian His- Total
Letter? Aster. Aster. Black panic White Percent
Yes 100.0 83.3 76.9 94.4 85.0 85.6
No 0.0 16.7 23.1 5.6 15.0 14.4
Number of Cases 1 6 13 18 80 118
Number of Missing Observations: 0
Excluding Native Americans because of only one case, Hispanic
applicants with at least one credit derogatory had the highest
percent (94.4%) with letters of explanation, and Black applicants had
the lowest percent (76.9%). White applicants and Asian American
applicants were in the middle, with 85% and 83.3%, respectively. Of
the total sample, 85.6% of the applicants with at least one credit
derogatory had a letter of explanation.
One final characteristic concerned the mortgage lender's reason
for denial of the denied loan applications. Table 3.13 shows the
percentages of first reasons for denial given by the mortgage lender
broken down by racial/ethnic group.

Table 3.13 Percent by First Reason for Denial for Denied
Applications by Racial/Ethnic Group
Reason for Denial Native Amer. Asian Amer. Black His- panic White Total Percent
Debt-to-Income Ratio 0.0 100.0 16.7 50.0 54.8 50.0
Employment History 0.0 0.0 0.0 0.0 3.2 2.2
Credit History 0.0 0.0 66.7 33.3 25.8 30.4
Collateral 100.0 0.0 0.0 0.0 3.2 4.3
Other 0.0 0.0 16.7 16.7 12.9 13.0
Number of Cases 1 2 6 6 31 46
Number of Missing Observations: 0
Percents may not sum to 100 due to rounding.
For the sample as a whole, the most common reason given for
denial was "Debt-to-Income Ratio," which meant that the applicants
had too much monthly debt causing the ratio to go over the guidelines
of 36% for conventional and 41% for FHA and VA loans. The second
most common reason given was "Credit History," which meant that the
applicants had too many derogatories or blemishes on their credit
history. The category of "Other" comprised miscellaneous reasons,
such as the denial of private mortgage insurance, loss of employment
before closing on the loan, and so on. The reason most frequently
given for denial of Black applicants was "Credit History." Black
applicants were more likely to be denied because of credit history
than any other group, with 66.7% of Blacks being denied for reasons
of credit history, 33.3% of Hispanics, and 25.8% of Whites.
Excluding Asian Americans with only two cases. Whites and Hispanics
were denied most often because of excessive debt ratios, 54.8% and
50.0% of the time, respectively.

Data Analysis Procedures
This study used content analysis based on the grounded theory
method developed by Glaser and Strauss (1967) and Glaser (1978) and
explicated by Charmaz (1983). As noted above in the sample
characteristics, 101 of the 196 loan applications included a letter
of explanation from the borrower(s) regarding blemishes on the credit
bureau reports. These letters, which were photocopies of the
original letters stored in the lenders' loan files, formed the data
base for the content analysis. All identifiers were removed from the
photocopies so the confidentiality of the applicants were maintained.
Because of the requirement of confidentiality, the letters
provided the best available explanation of applicants' credit
problems. A researcher could not examine the data in a loan
application file and then go to the applicant(s) for explanation and
confirmation. Some richness of detail of the borrowers' stories was
lost by using only the letters, but this method allowed access to all
the information that the lender had about the applicant(s). The loan
originator and loan processor would have direct contact with the
applicant(s), but the underwriter who usually makes the lending
decision might only see the documents in the file. The underwriter
would be somewhat affected by the impressions of the loan originator,
but if the applicant(s)' information on the application and the
credit bureau report did not warrant making the loan, the loan would
be denied. Therefore, using the letters without corroboration by the

applicant(s) would simulate to a certain degree the situation of the
According to underwriters and originators, applicants with a
late payment, judgment, or other derogatory on their credit bureau
reports would be given a chance to explain the circumstances of the
credit derogatory, to explain why this problem occurred, and what
they had done to correct it. Given that all other characteristics of
the borrower(s) and property met the lender's requirements for a
loan, the letters of explanation could become the determining factor
of whether the loan is originated or denied. The letters portrayed
the human and social side of the applicants who were reduced to facts
and figures regarding income, debt, employment, and payment history
on the applications.
The initial task of coding the letters consisted of
categorizing and sorting the explanations in the letters by the type
of derogatory, number of times it occurred, the severity, and the
dates of occurrence. I checked the accuracy of the information in
the letters by referring to the credit bureau reports. For example,
if an applicant discussed a derogatory, I checked whether it was
listed on the credit bureau report, whether the date, frequency, and
severity shown on the report matched what the applicant stated about
the credit problem. If the applicant described a credit problem that
did not appear on the credit report, I discarded these explanations
and letters. I also noted whether there were derogatories on the
credit bureau report not explained by the applicant.

The applicant's narrative for each derogatory was then analyzed
by transcribing each singular concept or idea given by the applicant
onto index cards. For example, some applicants gave more than one
reason for a credit problem, such as "I was in between jobs and fell
a couple of payments behind," and "I feel this is inaccurate." These
reasons were conceptually distinct and were recorded onto separate
cards, even though they were discussing the same credit derogatory.
This allowed the reasons to be easily sorted into categories by type.
Some of the explanations for a credit derogatory, such as a
bankruptcy, consisted of multiple reasons and involved a sequence of
causally-related events. In order to maintain the continuity of the
applicant's story, the explanations were still coded as distinct
concepts, but the cards were numbered and identified by derogatory so
that the story could be told as a whole, as well as analyzed by its
parts. Some of the applicants' reasons for credit problems, when
looked at in isolation, did not seem substantial, but when viewed as
a whole gained in weight and significance. These accounts served as
the basis for case histories of the applicants.
The initial coding developed descriptive categories for the
applicants' explanations of credit problems. This process involved
grouping reasons or explanations that seemed related and could be
defined as distinct categories. According to Charmaz (1983, p.lll),
the researcher then "delineates their properties, explicates their
causes, demonstrates the conditions under which they operate, and
spells out their consequences." The theoretical concepts, if
present, then emerge from further analysis of the descriptive

categories of the data. In this sense, the theory comes from the
The initial coding resulted in many different explanations,
some of which seemed related, but were too numerous to show trends in
the data. As described above, I synthesized these individual
explanations into conceptual categories based on their properties,
causes, conditions, and consequences. This reduced fifty or sixty
explanations to about fourteen categories. Again the data were
studied and examined to reduce the number of conceptual categories to
a small number of very distinct categories which could not be
construed as shades or variations of each other.
The index cards included the case number of the loan file, in
order to make verification and reference to supplemental information
about the applicants easy. The supplemental information was
available in the computerized data base, the code books collecting
the information from the loan documents, and the loan documents
themselves. Cross-tabulations were made between categories of
reasons given for derogatories and racial/ethnic groups. Statistical
tests of significance were not conducted because there were not
enough cases and too many categories. However, comparisons were made
on the basis of race or ethnicity to identify differences between the
groups as to kinds of reasons and explanations provided in the
applicants' letters. Having such a rich data base available from the
CCRD study facilitated the analysis and increased the scope and depth
of the study.

Through examination of the 101 letters in the sample, I first
established a typology for the various credit derogatories on the
applicants' credit histories. Further analysis revealed that reasons
and explanations for credit problems could be grouped in broad
categories and that types of reasons varied according to the type of
delinquency. When the categories of credit explanations were
compared by racial/ethnic groups, some differences emerged, but on
the whole, there were more similarities among groups than
differences. The mortgage lenders' responses to the letters varied
little from group to group. This chapter is organized as follows:
first I discuss the typology of credit delinquencies, then the
categories of explanations for credit problems, next the findings by
race or ethnic origin, and finally, responses by the lenders to the
Typology of Credit Deroqatories
One of the first things that the lender did when a person
applied for a home mortgage was to order a credit bureau report.
This document contained many pieces of information about the
applicant regarding employment, marital status, dependents, rental
history or mortgage loan payment history, credit card and other
outstanding debts, and derogatories on those accounts. This study
focused on the many different types of credit derogatories found in

the credit report. Each report listed how many times the subject
made late payments on loans and credit card accounts. The late
payments were characterized as 30-59 days late, 60-90 days late, and
over 90 days late. If the applicant had any collections, charge-
offs, accounts closed by the grantor, foreclosures, or repossessions,
these were also noted. Finally, a section for public records
including judgments, bankruptcies, wage garnishments, and tax liens
completed the credit report. I will now explain some of the terms
listed above.
Collection: When a person was slow paying on an account or
debt or refused to pay, the creditor turned the debt over to a
collection agency which vigorously pursued payment of the debt.
Collections were listed as paid or unpaid. Unpaid collections
could cause the applicant to be denied the loan, and the
lenders required the applicant to pay off any unpaid
collections before getting the loan.
Charge-off: This occurred when the applicant did not pay his
bill, and the creditor wrote off the amount of the debt as a
loss. Charge-offs were also listed as paid or unpaid, and the
lenders regarded charge-offs much the same as collections.
Account Closed by the Grantor: The applicant might have been a
slow-paying customer, and the creditor decided to revoke his or
her credit privileges.
Foreclosure: This was shown when the applicant had previously
owned property and for some reason, usually non-payment, the
lender holding the mortgage legally reclaimed the property.

Foreclosures could be either voluntary or involuntary, but
lenders frowned on prospective applicants having lost a
property to foreclosure in the past.
Repossession: In the sample, this happened when the applicant
had a car loan and stopped making the payments or was late with
payments. The creditor then legally repossessed the car,
sometimes with the consent of the car owner.
Judgment: If a judgment appeared in the public record section
of the credit report, this meant that a creditor had gone to
court to get payment for a debt, and the debtor had been
ordered to pay the debt by the court. The judgment was entered
as part of the public record and remained there as unsatisfied
or as satisfied after it was paid off.
Garnishment: Sometimes when a judgment 'was entered, the court
could order the person's wages to be garnished, or a portion
withheld and given to the creditor until the debt was repaid.
Garnishment became part of the public record.
Tax Lien: The tax liens that appeared in the credit bureau
reports were usually due to the person owing money to the IRS,
and even though he or she may have been making payments to the
IRS or creditor, a lien for the amount due was put on the
property until the owed amount was paid. A tax lien also
became a public record.
Bankruptcy: When a person had too many debts that he or she
could not repay, then declaring bankruptcy to gain protection
from creditors was an option. The most common type of

bankruptcy in the sample was a Chapter 7 bankruptcy, through
which the person liquidated all assets, and the proceeds went
to the creditors. In a Chapter 13 bankruptcy, the person
worked out a payment plan to pay off his creditors. If an
applicant had a clean credit record for two years after the
discharge of a bankruptcy, then the lender considered him
eligible for a loan, provided he met the other requirements.
Bankruptcy was also shown on the public record.
In thinking about the late payments to creditors and other
derogatories, I decided to group them to facilitate analysis. From
interviews with lending personnel and examination of credit bureau
reports and other documents, three categories of credit derogatories
were developed: (1) late payments, (2) collections/other actions,
and (3) public records. I used seriousness of the credit problem in
terms of loss to the creditor and amount of action taken by the
creditor to recover his money as the criteria for creating the
categories in this study. I put all late payments (30, 60, and 90
days past due) in one category called "Late Payments." I created
another category for credit problems including collections, charge-
offs, accounts closed by grantor, foreclosures, and repossessions and
called it "Collections/Other Actions." All derogatories in the
public record section became the third category, "Public Records."
These included bankruptcies, judgments, garnishments and tax liens.

Late Payments
Late payments were the most common credit derogatory found in
the sample. The total number of late payments, combining the 30, 60,
and 90 day lates, ranged from no late payments to 96 per application.
For those 118 applications showing at least one credit derogatory,
the mean number of all late payments was 9.98.
Applicants with late payments varied from those who simply
overlooked a payment to those who were deeply in debt and unable to
meet their payments. Late payments were the least serious credit
delinquency because the creditor generally received payment, although
late. The creditor took no extra actions with this type of
delinquency other than sending statements to the debtor and reporting
the person as late to the credit agencies. Late payments became more
serious if the debtor decided that he/she could not meet his/her
obligations and declared bankruptcy. Then a creditor might not
receive his money.
Collections and Other Actions
This category comprised more serious credit delinquencies than
the late payment category. Collections, charge-offs, accounts
closed, foreclosures, and repossessions required the creditor to take
extra action against the debtor and probably to lose money in the
process. For example in a collection, the creditor hired another
company to collect the debt, and if the debtor paid the collection
company, the original creditor got about half of the money owed.
Charge-offs meant that the creditor wrote off the debt as a loss. In

foreclosures and repossessions, the creditor had to follow legal
procedure, physically take possession of the property or car, and
then resell the item, costing the creditor time and expense.
As might be expected, loan applicants had fewer occurrences of
these kinds of credit problems than late payments. The number of
collections combined ranged from zero to 29 for those applicants
showing at least one derogatory on their credit report. The mean for
these applicants was 2.05 collections/other actions per application.
Public Records
Bankruptcies, judgments, garnishments, liens fell naturally
into a third category representing the most serious delinquencies.
These credit derogatories appeared in their own section on the credit
bureau report and were a matter of public record. They usually
necessitated the creditor to get an attorney to represent her/him in
court and to take legal action. A creditor would not take such
action unless the delinquency was viewed as extremely serious and
worth the cost of going to court. In a bankruptcy, the creditors
most likely did not receive full payment on the debts.
For the applications in the sample with at least one credit
derogatory of any kind, the mean number of public records was .47.
The number of public records ranged from zero to 4 for those
applications. Although these were the most serious derogatories and
occurred the least frequently, lenders said they were seeing more
bankruptcies among loan applicants than in previous years. Table 4.1

gives the means for total late payments, total collections/other
actions, and total public records by racial and ethnic group.
Table 4.1 Mean Number of Total Late Payments, Total
Collections/Other Actions, and Total Public Records by Racial/Ethnic
Type of Native Asian His- All
Derogatory Amer. Amer. Black panic White Groups
Late Payments 1.00 2.67 12.08 10.94 10.09 9.98
Collections 0.00 1.00 4.38 2.11 1.76 2.05
Public Records 0.00 0.00 1.31 .39 .39 .47
Number of Cases 1 6 13 18 80 118
Black applicants had the highest average number of credit
derogatories in all three categories, while Native Americans had the
lowest average. There was only one Native American case among the
118, so too much emphasis should not be placed on their statistics.
Asian Americans had the second lowest means for all categories, and
the means for Whites were slightly lower than those for Hispanics.
Explanations of Credit Derogatories
When an applicant(s) for a mortgage loan had any derogatories
appearing on the credit bureau report, the lender gave the
applicant(s) a chance to explain in writing the circumstances of the
derogatory item. Therefore, applicants wrote the letters in response
to the lender's request, and they were trying to put the best face on
their credit problems. Presumably, the applicants wanted to convince
the lender of their creditworthiness. The lender, on the other hand,
was trying to discern whether the applicant(s) had mitigating

circumstances for the delinquencies or whether the delinquencies
resulted from a poor attitude on the part of the applicant(s) toward
debt obligations. Lenders reasoned that if applicants disregarded
their other credit obligations, why would they treat mortgage
payments any differently?
The letters of explanation took two forms. Some applicants
submitted both kinds. One form was a preprinted page with the
individual derogatories listed and space given after them for the
applicants' explanations. This type of letter brought to the
attention of the applicant every problem on the credit bureau report,
and the applicant was less likely to leave out any item in the
explanation. If he or she left a derogatory item unexplained, the
lender would be likely to notice and require the applicant to
The second form of the letters of explanation was a free form
letter where the applicant discussed in general his or her credit
problems and might not refer to any credit problems in particular.
Some of the free form explanation letters did list delinquent items
one by one, but with this type, the applicants were more apt to leave
out or ignore certain items. Some of the letters were very long
recounting all life events during the period of time covered by the
credit bureau report.
The free form letters also discussed things other than the
derogatory credit items. Some of the letters included reasons why
the applicants wanted to buy the home and declarations of how
important the home was to them. Some applicants emphasized that they

had learned a lesson from their previous problems and would never
permit themselves to be in that situation again. Others pointed out
that their life prospects were now good due to steady employment or
an increase in their income. These additional statements served to
make these applicants come alive as people, but I could not tell
whether this made any difference to the lenders or whether the
lenders were more interested in just the facts and figures. For the
purposes of this study, I will analyze only the responses to credit
problems and exclude the other kinds of statements.
The categorization of the credit explanations applied to all
three types of credit derogatories. This allowed a comparison
between types of derogatories as to reasons for the derogatories.
For example, would the explanations for late payments be the same as
explanations for public records? In order to derive common
categories for all types of derogatories, I examined the data base of
letters as a whole and noted every reason given for all credit
Often the letters gave more than one reason for a single
derogatory item, and I noted all unique reasons for each credit
problem. This resulted in over 80 different statements by the
applicants as a whole. The letters had many more statements than 80,
but if the language or meaning of a statement was very similar to the
language or meaning of another statement, I counted it as an
occurrence of the same statement. For example, the respondent on
application #25 said, "I don't remember this late payment on my car
loan," and the respondent on #38 said, "I don't really recall this

late payment." I considered these as the same statement and counted
them twice.
While most of the explanations were clear and easily
understood, there were a few cases of explanations which did not
really explain anything. Some of these were due to vagueness and
lack of specificity. For example, respondent #501 gave as a reason
for 25 late payments, "was in transition from Arizona to Michigan."
The respondent did not specify whether the late payments stemmed from
the move to Michigan and a delay in receiving his mail, or whether he
was unable to find employment.
Other letters had statements that were just a restatement of
the derogatory item without an explanation. Respondent #481 wrote,
"the federal tax lien was filed on my previously owned home as I was
on a monthly payment plan with the IRS," but failed to explain why he
was on a monthly payment plan with the IRS. Another example of
stating the obvious was this statement from respondent #8, "this is
another situation where we have been making regular payments, but
they are mailed after the next billing period," in explanation for
six late credit card payments.
Both of these examples and others presented coding problems,
which I solved by making a decision, based on interpretation of the
meaning, as to which category the statement belonged. I decided that
the statement from #401 implied that the tax lien was not due to any
action on their part but to action taken by the IRS, and therefore,
they felt they were not really at fault. In the case of the
statement from respondent #8, I judged it as an admission from the

applicants that they were at fault, but were trying to comply with
their creditor's terms.
For every application that had a letter of explanation for a
credit derogatory, I verified the presence of the derogatory item on
the credit bureau report. If the applicant discussed an item that
did not appear on the report, I excluded that explanation from the
pool of explanations. I also checked the credit bureau reports to
see whether the applicant(s) had explained all derogatories,
especially in the free form letters. I found over 40 derogatory
items on the credit bureau reports that were not accounted for in the
101 letters. There was no way to ascertain why these items were left
Although I examined every reason given for derogatory items to
develop the categories, I entered up to three different categories of
reasons in the SPSS data base for each type of credit derogatory for
each letter. I counted the occurrence of each type of reason just
once. In other words, if the applicant had late payments to several
different creditors, but claimed health problems caused all the late
payments, "health problems" was noted only once as a reason for late
payments. I was looking at what kinds of reasons applicants gave for
credit problems, not how frequently the applicant cited a particular
From these many varied reasons for credit problems, broad
themes emerged that made it possible to group the reasons into seven
categories. The categories were "Not at Fault," "Health/Medical
Problems," "Employment Problems," "Family Problems,"

"Unexpected/Increased Expenses," "Moving/Mail Problems," and "Made a
Mistake." The following text defines and describes these categories.
Not at Fault
This was a very broad category that included any sentiments
expressing the idea that the applicant(s) was in some way not
responsible for the credit derogatory shown on the credit report.
This analysis made no judgment as to whether the respondent was
correct in claiming that he or she was not at fault. For example,
the letter from respondents on #394 expressed the idea that the
creditor had "a strange billing cycle," which made them not
responsible for the late payment. The applicants in this case did not
define "a strange billing cycle," or describe how that would make
them late. Although the lender might view these applicants as at
fault, or reject their explanation, the purpose of the coding was to
define the reasons from the viewpoint of the letter writer.
Therefore, this reason was coded in the category of "Not at Fault."
Over fifty statements which were coded as "Not at Fault"
expressed the thought that the applicant did not know that she or he
was late or that he/she owed money. Respondent #3, for example,
said, "I was unaware of this bill until the mortgage credit report
was run," and respondent #45, "this is the first I have become aware
of this and is now paid in full" in regard to collection actions
appearing on their credit reports. Pleading ignorance of the
delinquency took another form in over thirty statements, such as this
from respondent #442, "I have no idea why there are late dates listed

after 1988." These respondents didn't remember or recognize the
delinquency, so their statements were also classified as "Not at
Fault." I interpreted these statements as saying that they could not
be responsible if they didn't know or acknowledge the credit problem.
Other responses which fell in this category were ones where the
respondent thought that the credit bureau report was incorrect, that
they had never made late payments, or that the account on the report
was not their account. This was cited over thirty times, so often
that it raised a question as to the accuracy of the credit bureau
reports. In one case, a respondent claimed that the derogatory
belonged to his father, and another declared that the derogatory
belonged to someone with the same name as the respondent. Other
respondents had no idea why the erroneous credit derogatory had
appeared on their report.
Disputes with creditors formed another large group of responses
(over 80) in this category. Some of the disputes involved purchases
of items that turned out to be unsatisfactory, services the
applicants were charged for but never received, and charges that were
not disclosed at the time of purchase but were billed later. One
respondent thought he had purchased a refrigerator with no interest
or payments required for ninety days, but the creditor reported him
as late on payments before the ninety days had passed. Another
respondent #504 who had 46 late payments for a student loan asserted,
"this was in dispute while I was in school- they wouldn't take
partial payments." Other respondents said they were in the process
of trying to work out a payment schedule or solve the dispute with

the creditor when they were put in collection. This category would
have had even more responses if I had included disputes with health
insurance companies, but I put those in the "Health/Medical Problems"
This category also included responses (about 20) where the
applicants thought the derogatory was due to a misunderstanding or
miscommunication with the creditor. These respondents often claimed
confusion about the bill from the creditor and expressed surprise
when they were put in collection or reported as late. Clearly, they
understood the situation differently from the creditor; they didn't
say that the creditor was wrong, but they thought they were doing the
right thing. One example was from respondent #3 who made a late
house payment and explained it this way, "the mortgage company told
me that I could miss a payment and make it up the next month." She
felt that she had been given permission to be late and should not
have been reported. Respondent #11 said regarding three late
payments, "I would not have done this if they had told me it would
hurt my credit report." Another good example of misunderstanding was
respondent #29 who "was told it would be all right to pay this
balance on her [his daughter's] last visit," but was put in
collection before the last visit.
The last group of responses fitting in this category concerned
statements that someone else was responsible for paying the debt.
Respondent #481 had transferred a cellular phone contract to a co-
worker with the permission of US West, who continued to bill him and
report him as late. Other respondents claimed things such as their

mother was responsible for the bill and had not paid it, that
roommates had run up high phone bills or other expenses and then
stuck the applicant with the bills, that other family members had
used the applicant's credit card and not repaid the applicant, that a
friend failed to mail the bill while the applicant was out of town,
and so on. In all of these cases, the response of the applicants was
that they were not at fault and should not be judged as
Health/Medical Problems
This was an easy category to define, requiring less
interpretation than the previous category. Health or medical
problems often led to other problems, such as problems with
employment, but if an injury or illness was the underlying cause for
job loss or credit problems, the responses were put in this category.
There were three kinds of statements occurring in the letters which
were classified as "Health/Medical Problems." The first kind dealt
with a loss of income due to an inability to work. The second
concerned the inability of the applicant(s) to pay the medical bills,
and the third related to disputes with health insurance companies.
The applicants made over a dozen statements which related that
an injury, surgery, or birth of a child temporarily reduced their
ability to work. They then said that they could not keep up with
their bills because of the loss of income. Some statements of this
kind were the one from respondent #2, "injured in '87, was off work
seven months- tried to pay off bills but couldn't keep up," or from

self-employed respondent #23 regarding two back-to-back surgeries,
"the second surgery was completely unexpected . this put me in a
position of a longer recovery period and I was forced to cut back on
work at that time." For some respondents in this category, they were
barely keeping up with their bills when an injury destroyed them
financially. The pair of respondents from application #495 had just
moved to Denver on a "shoe-string budget," when the male borrower was
involved in a near-fatal car accident which left him unable to work
for several months. The couple had to declare bankruptcy.
For other respondents, the injury or illness caused them to
incur additional expenses their budget could not handle. This was
the case mainly for respondents who were not covered by health
insurance, but even respondents with health insurance had problems
with the added bills. More than 25 statements were made regarding
the hardship imposed by medical bills. A typical example was from
the respondents on #40 who described having custody of their
granddaughter who needed a spinal tap to test for meningitis. They
explained their situation this way, "we had no insurance because we
couldn't afford to carry the old insurance, and there was one month
before mine was in effect." These respondents couldn't pay all the
bills and were advised by their attorneys to declare bankruptcy.
Many applicants with health problems said they had problems
with their health insurance companies, such as the insurance company
not covering certain things or not covering them fully. Respondents
on #471 had 15 collections due to a dispute with their health
insurance company covering only 80% of the cost of numerous items

instead of 100%. The respondents on #40, put in collection for a
medical bill, thought they were covered by their insurance company,
but as shown below, they were not.
[borrower's] company was changing insurance companies and the
last day of the old insurance company, I had some tests done.
I fought with the old insurance company because I thought they
should pay. I finally paid it after it went to collection.
Some applicants, such as those on #461, were surprised by additional
bills for medical services, "[co-borrower] paid the initial medical
bill and was not aware of any smaller bills," but nonetheless she was
put in collection. These and many other examples numbered over 30
responses regarding disputes with health insurance companies.
Employment Problems
Employment problems involved losing a job, inability to find a
job, business failure for the self-employed, or low paying jobs that
did not provide enough income to meet expenses. Since employment
provided the income for most people, it was no surprise that many
responses to credit problems were in this category. Employment
problems, comprising over a hundred responses, resulted in a loss or
lack of income, and when combined with medical problems, family
problems, or unexpected expenses, they caused the applicants to pay
their bills late or to not be able to pay them at all. Some
applicants admitted that they had already overextended themselves
when they had lost their jobs, making a bad situation impossible.
Some of the responses regarding employment problems were as
simple as "I was in between jobs and fell a couple of payments .

behind," respondent #425. Others told more elaborate narratives,
such as respondent #405:
I had been working full time for McLean Trucking for twelve
years. In fall 1985, they asked for a 15% pay cut from all of
their employees in exchange for a stock option. In January
1986, my employer filed for bankruptcy and I was out of a job
by no fault of my own. They owed me a significant amount of
money which was settled two years later at ten cents on the
For a time, respondent #405 could only find part-time jobs, and after
getting a job with a construction company, this company went out of
business also. Job loss was a significant problem for some, but
others had less trouble dealing with it.
Several applicants related that low-paying jobs were the
problem, and one pair of respondents on #501 described being in debt
to the company the borrower worked for, as illustrated by this
The late payments were due to the fact that I went from a
steady income in retail sales to an unfamiliar industry of
door-to-door sales. It took several months to gain the
necessary experience to make enough sales to cover the weekly
draw. For the weeks that sales were low, the company would pay
me a draw which I was required to pay back out of future sales.
When I quit working for this company, I owed money back which I
felt I should not have to pay because the leads I had generated
resulted in future sales for the company.
This respondent had a judgment entered against him which he
eventually paid. In another example, respondent #380 discussed
working a construction job where he made $16.51 an hour and having no
problem to pay off his credit cards. However, he was laid off and
could only find part-time work typing for a neighbor and cleaning
carpets, jobs which paid at a much lower rate. Unable to pay his
bills now because of reduced income, he declared bankruptcy.

Sometimes even respondents with relatively good paying jobs
complained that they did not make enough to pay their bills, such as
the co-borrower from application #481. She was a newly appointed
prosecutor with a local district attorney's office, but declared
bankruptcy because of a heavy debt burden.
As you can see from my disclosures, my student debt is
substantial. As a new lawyer working in the public sector
making only $25,000 annually, meeting my financial obligations
together with the student debt was impossible.
Inability to find employment after leaving a job voluntarily or
involuntarily compounded the problems of many respondents. The pair
of respondents on #36 gave the account of leaving a good-paying job
in Denver and moving to a small fanning community to help out a sick
mother. Unable to find work in the small town, they began to use
their "credit card to buy everyday necessities," with the consequence
of being unable to pay off the credit cards and incurring high
interest charges. Another example concerned the borrower from
application #485 who left a job in Washington DC working for a
Colorado Congressman and could not find employment in public
relations for eight months. This caused him to make late payments.
This example was not typical because most of the respondents unable
to find work were not in the professional or managerial class.
Job transfers also caused hardship for some respondents.
Included in this group were respondents who were doing military
service and were sent to the Persian Gulf or other destinations.
These people often had little time to get their affairs in order
before leaving, which meant that bills from creditors never reached
them or came so late that they were already past due. One set of

respondents on #444 were given one week-end by their employer to get
their affairs in order before being transferred from Pueblo to
Denver. These respondents owned a house which they still had to make
the payments on, but now they had rental expenses for housing in
A few respondents were self-employed and experienced business
failure or additional expenses to keep their business going. The
borrower on application #26 had worked for over thirty years in the
banking industry when he decided to start his own graphic arts and
typesetting business. He used his savings from his employment in the
banking business and credit cards to pay business and living
expenses. In the latter half of the 1980's, Macintosh Computer came
out with a system of desk top publishing which made his business
unnecessary to many of his former clients. He closed his business,
but he was already in a lot of debt.
Family Problems
Family problems constituted an area with over 40 responses, and
the category included any credit problem related to a family matter.
Family problems embraced a wide variety of situations, such as
divorce or break-up of an intimate relationship, death or illness in
the family, and a family member needing extra help and resources. I
have excluded the medical bills of a family member which caused
difficulties for the respondents to pay, and I have put those
instances under the category of "Health/Medical Problems," as already
discussed above.

Divorce and break-up of a relationship were cited the most
often in this category. Divorce imposed financial hardship on the
respondent due to the costs associated with getting a divorce and
losing the income of the partner. Respondent #3 accounted for late
payments by saying she had been living with her boyfriend, had two
incomes, and when they broke up, she "had to make decisions as to
what bills were to be paid." The co-borrower on application #38
faced foreclosure on a house she received in a divorce settlement,
which she described thus, "I managed to make payments for a year, but
the payment went up and became overwhelming for me." The borrower
from #25 expressed the financial difficulties associated with divorce
this way, "the divorce caused some financial hardship also, as I was
trying to start a new life, and continue to pay financial obligations
as best as I could including child support and alimony."
Ex-spouses and ex-partners who were unable or refused to pay
their share of the debt obligations, leaving the respondent to take
care of the debts, were another source of financial problems. The
borrower from #25 again provided a good example of this by naming
several debts, credit card accounts, and a state tax lien which "my
ex-wife verbally agreed to one-half of the federal and state income
taxes. . my ex-wife did not pay this owed tax and I ended up having
it withdrawn directly from my Navy FCU." Respondent #12 said that
his daughter, who was living with his ex-girlfriend, went to see the
doctor, and "my ex-girlfriend told me the amount of the bill, and I
gave her a check to pay the expense that same day. Apparently, she

kept the money and. didn*1 pay the doctor hill.** He was put in
collection ton his daughter*s medical hill.
Respondents sometimes had to declare bankruptcy against their
own will when their ex-"spouses declared bankruptcy during the course
of a divorce. They were also assigned certain debt obligations by
the divorce proceedings which they may or may not have been able to
pay. From these narratives,, one could draw the conclusion that
divorce was an effective way to spoil a person* s credit record. Xn
fact, the borrower from ir 25 said, "X have my ex-v/ife on tape saying
that she was intentionally ruining my credit because of the divorce./7
The effect of illness of a family member or a death in the
family was succinctly described by the respondent on ^4 68, "we had
two sudden deaths in the family andr needless to say, the entire
family was in turmoil.** Distracted respondents would forget to pay
bills during this time, but death or illness also resulted in extra
expenses. Respondents on rrlQ explained, "with [borrower] having to
gr. out of state for a week due to his mother* s illness which resulted
in a lot of extra money needed.** Xn a few cases, respondents moved
to a different town or state to care for a dying family member,
thereby jeopardizing their family finances when they could not find
employment in the new location or had other problems.
The final kind of family problem, involved family members who
needed financial help. Respondents would loan family members money
for things such as lawyers or cars. An example was the pair of
respondents on frS who loaned the down payment for a car to their sen.
The son was inducted into the Navy, leaving the parents with the

additional expense of the car payments and an unpaid loan. This
might not have been a problem if these respondents didn't have other
problems such as loss of employment. Their letter summed it up this
way, "Now if we had known all of this was going to happen, we never
would have gotten into it in the first place."
Unexpected/Increased Expenses
This category with only about a dozen responses was developed
to classify expenses which caused a hardship to the applicants, but
did not fit under the categories of "Health/Medical Problems,"
"Employment Problems," or "Family Problems." The most common event
in this category was the breakdown of a car, which drained the
respondents' resources. The second most often cited event was
expenses associated with moving or job transfer. A couple of
respondents had little time to prepare for the move by selling or
leasing their previous homes. This entailed the respondents to make
double payments for housing expenses. A couple of respondents had
roommates who didn't pick up their share of the living expenses,
leaving the respondents to cover them. Also mentioned in this
category were wedding expenses and increased child care expenses,
such as going from practically nothing to over $600 a month. These
expenses could have been placed under "Family Problems," but I
decided to put them here because they were not caused by problems
internal to the family.

Movinq/Mail Problems
Problems associated with receiving mail after a move and
problems with the mail in general generated over 30 responses in this
category. These moving problems do not concern the expense of
moving, only difficulty in receiving mail, particularly in a timely
fashion. Respondents stated that they forgot to fill out change of
address forms or put the wrong dates on them. Others, mainly college
students, described moving so often that the mail had a hard time
catching up with them. Specifically mentioned were problems with
Public Service Company because Public Service kept sending the final
bill to the old address, which the respondents claim they never
received and were consequently put in collection. Other responses in
this category were that the creditor had the wrong address, that
payments were lost in the mail, and that the respondent never
received the bill from the creditor. Generally, problems with
receiving mail resulted in the respondents showing late payments on
their credit reports unless the payments were so late often enough
that the creditor put them in collection.
"Made a Mistake"
The fifty-plus responses in this category comprised any
statements where the applicants acknowledged that they were
responsible for the credit derogatory, that it was due to an error on
their part. These responses were sometimes as simple as the
derogatory "was due to my negligence," from the respondent on #498 or
that the respondent had misplaced or overlooked the bill, which was a

very common response in this category. Some respondents claimed that
they did not notice when a bill fell in the crack in the seat of the
car or that the bill was mailed without a stamp. Several said that
they went on vacation and forgot to mail the bill before leaving.
One pair of respondents on #518 blamed a late mortgage payment on
losing their checkbook.
The next type of response in this category consisted of
respondents admitting that they did not meet the payment terms of the
creditor without really explaining why. A few respondents said that
they paid their bills once a month and that a particular creditor had
a billing cycle that fell in between their other bills, so they made
late payments. Similar to this were the respondents who made double
payments every other month. A couple of respondents thought that if
they sent a postdated check or the bill was postmarked by the due
date, they had met the requirements of the creditor. Finally, one
pair of respondents simply said that they mailed the payment after
the next billing period.
Out of the whole sample, only six respondents said that their
credit problems were due to overspending. Among those that admitted
abusing their credit privileges, the pair of respondents on #14
attributed it to when they were young and inexperienced, "[borrower]
opened the account when he was 18 and didn't have his priorities in
order." Respondent #447 said, "I experienced a misjudgment in my
ability to repay my overextended credit cards." These respondents
implied that they had learned their lessons about wise use of their
credit cards.

Explanations by Race or Ethnicity by Credit Derogatories
After establishing the categories of reasons for credit
derogatories and coding the reasons expressed in the letters, I
counted how many different categories of reasons each applicant or
set of applicants gave for each of the three types of credit
derogatories. I analyzed each racial/ethnic group to see which
reasons were the most frequently cited within the group. Some
applicants named more than one category for each type of credit
derogatory, so the total frequencies of cited categories could be
greater than the number of applicants. First for each type of credit
derogatory, I examined how many categories the respondents mentioned,
and then I compared the kinds of reasons given by racial/ethnic
For each application with at least one credit derogatory and a
letter of explanation, up to three reasons for late payments,
collections/other actions, and public records were entered in the
data base. For the following analyses, I combined all reasons for a
given type of credit derogatory. For example, if the letters from
Whites named the category of "Not at Fault" three times as their
first reason for late payments, two times as their second reason, and
one time as their third reason, I counted "Not at Fault" as being
named six different times. Again for any single letter, a category
would be counted only once.

Late Payments
Table 4.2 shows the percent of times a category of reasons for
late payments was given by each racial/ethnic group.
Table 4.2 Percent of Reasons Given for Late Payments by Category by
Racial/Ethnic Group
Category of Reason Native Aster. Asian Aster. Black His- panic White Total Percent
0.0 66.7 36.3 26.1 28.3 29.2
Not at Fault (0) (2) (4) (6) (28) (40)
0.0 0.0 9.1 13.1 9.1 9.5
Health/Medical (0) (0) (1) (3) (9) (13)
0.0 0.0 9.1 21.7 14.1 14.6
Employment (0) (0) (1) (5) (14) (20)
0.0 0.0 18.2 8.7 9.1 9.5
Family (0) (0) (2) (2) (9) (13)
Unexpected/ 100.0 0.0 0.0 8.7 8.1 8.1
Increased (1) (0) (0) (2) (8) (11)
0.0 0.0 9.1 4.3 10.1 8.7
Moving/Mail (0) (0) (1) (1) (10) (12)
0.0 33.3 18.2 17.4 21.2 20.4
Made a Mistake (0) (2) (2) (4) (21) (28)
Total Number of Citations 1 3 11 23 99 137
For all groups, the most frequently cited category for late
payments was "Not at Fault," with 29.2% of the enumerations. The
second category given most often was "Made a Mistake," with 20.4%,
followed by "Employment Problems," 14.6%. "Family Problems," and
"Health/Medical Problems" were tied at fourth with 9.5% each, and
"Moving/Mail Problems," with 8.7% was close to "Unexpected/Increased
Expenses," at 8.1%. The sample had 74 applications which had at
least one late payment and a letter of explanation. The seven
categories of reasons as a whole were cited a total of 137 times, or
an average of 1.8 categories per application.

The Native American and Asian American groups had so few cases
that it was difficult to draw conclusions about how often these
groups would give a certain reason for late payments. Native
Americans gave one reason, "Unexpected/Increased Expenses," and of
the three reasons given by Asian Americans, two of them fell in the
"Not at Fault" category, and one of them in the "Made a Mistake."
Even with their small number of reasons, Asian Americans followed the
overall pattern with their first and second most often cited
The reasons given by Blacks deviated from the overall pattern
slightly. "Not at Fault" was still the most often mentioned category
at 36.3%, but "Made a Mistake" and "Family Problems" were tied for
second with 18.2% each. "Health/Medical Problems," "Employment
Problems," and "Moving/Mail Problems" were tied for third most cited
category with 9.1% each. "Unexpected/Increased Expenses" was not
mentioned at all.
For Hispanics, "Not at Fault" was again the most often cited
category with 26.1%, but "Employment Problems" received the second
highest percentage of mentions with 21.7%. "Made a Mistake" with
17.4% was third, and "Health/Medical Problems" received the fourth
greatest number of citations (13.1%). "Family Problems" and
"Unexpected/Increased Expenses" were a few percentage points down,
tied at 8.7% each. "Moving/Mail Problems" was mentioned the least
often at 4.3%.
Whites generally followed the overall pattern, which made sense
given that their numbers were so much larger than the other groups.

They gave the reason "Not at Fault" most often (28.3%), followed by
"Made a Mistake" (21.2%). "Employment Problems" was third with
14.1%, "Moving/Mail Problems," was fourth with 10.1%.
"Health/Medical Problems" and "Family Problems" were each given 9.1%
of the time, and "Unexpected/Increased Expenses" was cited the least
often (8.1%).
When I compared categories across groups, no great differences
were noted (excluding Native Americans and Asian Americans because of
small numbers). Blacks cited "Not at Fault" a greater percentage of
the time than Hispanics and Whites, 36.3% to 26.1% and 28.3%,
respectively. Hispanics were likely to mention two categories,
"Health/Medical Problems" and "Employment Problems," more often than
Blacks and Whites. For "Health," the percentages were 13.1% for
Hispanics and 9.1% for both Blacks and Whites. In the category of
"Employment," Hispanics gave it 21.7% of the time, Blacks 9.1%, and
Whites 14.1%.
Blacks cited the category of "Family Problems," 18.2%, more
than twice as often as either Hispanics, 8.7%, or Whites, 9.1%. For
the category of "Unexpected/Increased Expenses," Hispanics and Whites
had about the same percentages, 8.7% and 8.1%, respectively, with
Blacks not citing this category at all. For "Moving/Mail Problems,"
Black and White percentages were close at 9.1 and 10.1%,
respectively, but Hispanics mentioned this category only 4.3% of the
time. The last category, "Made a Mistake," was cited about equally
by all groups, Blacks 18.2%, Hispanics 17.4%, and Whites 21.2%.

Whites seemed to be slightly more likely to admit that they made a
mistake than Blacks or Hispanics.
Collections and Other Actions
For this type of derogatory, Table 4.3 shows the percentage of
times each category was chosen by racial/ethnic group.
Table 4.3 Percent of Reasons Given for Collections/Other Actions by
Category of Reason by Racial/Ethnic Group
Category of Reason Native Amer. Asian Amer. Black His- panic White Total Percent
Not at Fault 0.0 (0) 42.8 (3) 35.7 (5) 40.0 (4) 47.1 (24) 43.9 (36)
Health/Medical 0.0 (0) 0.0 (0) 21.4 (3) 20.0 (2) 19.6 (10) 18.3 (15)
Employment 0.0 (0) 14.3 (1) 7.1 (1) 20.0 (2) 3.9 (2) 7.3 (6)
Family 0.0 (0) 14.3 (1) 0.0 (0) 20.0 (2) 15.7 (8) 13.4 (11)
Unexpected/ 0.0 14.3 0.0 0.0 2.0 2.4
Increased (0) (1) (0) (0) (1) (2)
Moving/Mail 0.0 (0) 14.3 (1) 14.3 (2) 0.0 (0) 7.8 (4) 8.6 (7)
Made a Mistake 0.0 (0) 0.0 (0) 21.5 (3) 0.0 (0) 3.9 (2) 6.1 (5)
Total Number of Citations 0 7 14 10 51 82
Of the 101 applications with a letter of explanation and at
least one credit derogatory, 57 of them had a collection or other
action, as defined previously. This meant that each letter for these
applications mentioned an average of 1.4 categories of reasons, or
slightly more than one category of reason per application. As with
late payments, the reason given most often was "Not at Fault," which
was cited 43.9%. "Health/Medical Problems" was the second most cited

category of reason at 18.3%. Respondents chose "Family Problems"
13.4% of the time, making it the third reason in frequency of times
given. "Moving/Mail Problems," 8.6%, "Employment Problems," 7.3%,
and "Made a Mistake," 6.1% made up the fourth, fifth, and sixth
percentages of frequencies, and "Unexpected/Increased Expenses" was
mentioned the least often, 2.4%.
Native Americans did not have any collections/other actions on
their credit reports, and therefore gave no reasons for
delinquencies. Asian Americans declared the category "Not at Fault"
the most often (42.8%) of all the categories. They cited
"Employment," "Family," "Unexpected/Increased Expenses," and
"Moving/Mail" as reasons in equal percentages of 14.3% each.
Finally, Asian Americans did not mention "Health/Medical" or "Made a
Mistake" as explanations for this type of credit problem.
Like the other groups giving reasons for collections, Blacks
gave "Not at Fault" (35.7%) as the most common explanation. Their
second and third chosen reasons, "Made a Mistake" (21.5%) and
"Health/Medical Problems" (21.4%), were almost the same in
percentages. "Moving/Mail" (14.3%) and "Employment" (7.1%) were the
categories given least often, but "Family" and "Unexpected/Increased
Expenses" were not named at all.
Hispanics selected "Not at Fault" the most often (40.0%) of all
the categories. They cited second in equal percentages of 20.0%
"Health/Medical," "Employment," and "Family." They did not mention
"Unexpected/Increased," "Moving/Mail," or "Made a Mistake" for
collections or other actions.

Whites also cited "Not at Fault" as a reason, the most often
(47.1%). The next two most common reasons given were
"Health/Medical" (19.6%) and "Family" (15.7%). The rest of the
reasons given were in the following order: "Moving/Mail" (7.8%),
"Employment" (3.9%), "Made a Mistake" (3.9%), and
"Unexpected/Increased" (2.0%).
Were there differences by racial/ethnic group in kinds of
reasons given for collections and other actions? On the whole, the
different groups chose different categories of reasons in about the
same percentages. Blacks gave "Not at Fault" the least often
(35.7%), and Whites gave it the most often of all the groups (47.1%),
but the differences don't seem to be that great. Except for Asian
Americans who did not cite "Health/Medical," Blacks, Hispanics, and
Whites all mentioned "Health/Medical" about equally. Asian
Americans, Hispanics, and Whites selected "Family" as a reason in
percentages that ranged from 14.3% to 20.0%; again these percentages
don't show any large variation. Blacks did not mention "Family" as a
"Employment," "Made a Mistake," "Unexpected/Increased," and
"Moving/Mail" were categories showing some variation. "Employment"
had the most variation with Hispanics citing it the most often
(20.0%) and Whites the least (3.9%). Asian Americans and Blacks were
in the middle with 14.3% and 7.1% respectively.
"Made a Mistake" stood out because Blacks cited it 21.5% of the
time with Asian Americans (0.0%), Hispanics (0.0%), and Whites (3.9%)
mentioning it hardly or not at all. The same was true for

"Unexpected/Increased" where Asian Americans chose it 14.3%, but
Blacks (0.0%), Hispanics (0.0%), and Whites (2.0%) did not include it
as a reason with any frequency.
"Moving/Mail" was selected by Asian Americans and Blacks as a
reason with 14.3% frequency each, Whites, 7.8% frequency, and
Hispanics not at all. Whites had the greatest variety of reasons
given for collection problems, and this was probably due to the fact
that there were so many more Whites in the sample than the other
groups. If the sample had included greater numbers of applications
from the other groups, their reasons might have also shown a greater
variety of types, and the percentages might have been closer to the
percentages for Whites in all categories.
Public Records
Table 4.4 illustrates the types of reasons given for public
records by racial ethnic group. Public records were the most serious
type of credit derogatory, comprising judgments, tax liens,
garnishments, and bankruptcies.

Table 4.4 Percent of Reasons Given for Public Records by Category of
Reason by Racial/Ethnic Group
Category of Reason Native Airier. Asian Amer. Black His- panic White Total Percent
0.0 0.0 0.0 28.6 34.5 28.6
Not at Fault (0) (0) (0) (2) (10) (12)
0.0 0.0 33.3 0.0 20.7 19.0
Health/Medical (0) (0) (2) (0) (6) (8)
0.0 0.0 16.7 28.6 20.7 21.4
Employment (0) (0) (1) (2) (6) (9)
0.0 0.0 33.3 28.6 17.2 21.4
Family (0) (0) (2) (2) (5) (9)
Unexpected/ 0.0 0.0 0.0 0.0 6.9 4.8
Increased (0) (0) (0) (0) (2) (2)
0.0 0.0 0.0 16.7 0.0 2.4
Moving/Mail (0) (0) (0) (1) (0) (1)
0.0 0.0 16.7 0.0 0.0 2.4
Made a Mistake (0) (0) (1) (0) (0) (1)
Total Number of Citations 0 0 6 7 29 42
The total number of unique reasons cited was 42, while there
were 29 applications with at least one public record and a letter of
explanation. That meant that these applications had an average of
1.4 reasons per application. As with the other two types of credit
derogatories, the applicants gave the reason "Not at Fault" most
often for public records (28.6%). "Employment" (21.4%), "Family"
(21.4%), and "Health/Medical" (19.0%) were also major categories of
reasons to explain public records. The categories of
"Unexpected/Increased" (4.8%), "Moving/Mail" (2.4%), and "Made a
Mistake" (2.4%) were mentioned hardly at all as reasons. Since this
is the most serious credit problem category, it made sense that the
respondents would give more substantial, serious reasons.
Native Americans and Asian Americans had no problems with
public records, and therefore, they will be excluded from analysis.

Blacks cited "Health/Medical" and "Family" with 33.3% each as the
first main reasons for public records derogatories. "Employment"
(16.7%) and "Made a Mistake" (16.7%) were the second main reasons
given as explanation. Blacks did not mention "Not at Fault,"
"Unexpected/Increased Expenses," or "Moving/Mail."
Hispanics gave three categories in equal numbers as the first
reason for derogatories of this type. These were "Not at Fault"
(28.6%), "Employment" (28.6%), and "Family" (28.6%). The only other
category chosen as a reason was "Moving/Mail" with 16.7%.
Whites' reasons were in order: first, "Not at Fault" (34.5%);
"Health/Medical" (20.7%) and "Employment" (20.7%) tied for second;
"Family" followed closely at third with 17.2%; and
"Unexpected/Increased Expenses" received minor mention with 6.9%.
Whites did not claim any of the other categories as a reason for
public records.
Given the fact that the numbers are small, especially for
Blacks and Hispanics, I did not see any great differences in
frequency of type of reason cited, but several things should be
noted. For the category "Not at Fault," Blacks did not cite it all,
while it was the category most often given for Hispanics and Whites
whose percentages were close at 28.6 and 34.5, respectively.
Blacks selected "Health/Medical" 33.3% compared to Whites'
20.7%. Hispanics did not give "Health/Medical" as a reason at all
for this type of derogatory.
For "Employment," Blacks and Whites had percentages not too far
apart with 16.7% and 20.7%, respectively. Hispanics had the highest

percentage for this category, 28.6%, indicating that employment
problems contributed more to bankruptcy and other public records for
them than any of the other groups.
The percentages for the category "Family" ranged from 33.3% for
Blacks, 28.6% for Hispanics to the lowest for Whites at 17.2%. Again
because of the small numbers, it is difficult to tell whether this
would be a significant difference. It did indicate that Blacks and
Hispanics might have more family problems contributing to public
record derogatories than Whites.
The other three categories had only minor frequencies of
citation. Hispanics were the only group giving the category of
"Moving/Mail" (16.7%); Blacks were the only group mentioning "Made a
Mistake" (16.7%); and Whites the only one citing
"Unexpected/Increased Expenses" (6.9%) as a reason. The numbers were
too small to observe a pattern in these categories.
Lender Responses to Explanations
In terms of lender responses to reasons for derogatories, I
focused on the loan applications which were denied because of credit
history. I reasoned that the lender accepted the reasons given for
credit problems for the loans which were originated, but I wanted to
see whether the reasons cited as explanation were important in the
decision to deny. I selected from the pool of applications those
applications with explanatory letters for credit derogatories which
were denied because of poor credit history. Out of the 101
applications with a credit derogatory and a letter of explanation,

there were 21 applications which were denied. Of these, eleven were
denied because of credit history.
I examined four of these in depth as case studies because they
each illustrated a principle of lender response to credit
derogatories and explanations. The last seven applications, I
summarized the lender responses because they fit the pattern of the
first four. Five of these applications were from Bank 1 and six were
from Bank 2. Three of the applications had a Black applicant, one
had a pair of Hispanic applicants, and the remaining seven had White
For the first example, application #3, the borrowers, two White
females, applied on May 2, 1994. The borrower was a 44 year old,
married woman with 13 years of education. She had worked in the
retail grocery business for 10 years, with a monthly salary of
$2,000. The borrower had no dependents, and although she was
married, the husband was not included on the application. The co-
borrower was a 70 year old retired female, living on Social Security
of $800 per month, who was separated from her husband. Her years of
education were not listed, and the applicants lived separately and
owned their own homes at the time of application.
No reason was given for the borrower's husband not being
included on the application, or why the borrower's grandmother
applied as the co-borrower. Sometimes loan applicants would not put
a spouse on an application if the spouse's credit record was worse
than the applicant's, and this may have been the case here. I saw
the complete loan file for these applicants, and there was a

completed pre-application with the husband included. The grandmother
who had a completely clean credit record and little debt would have
made the application look better to the lenders.
Borrower #3 had five late payments on her credit report, one of
which was a late payment to the mortgage company on her present house
in September 1993, less than a year prior to the date of application.
Her explanation was that she needed money to repair her car to keep
her job as a floater, someone who was assigned to different branch
stores as needed. The mortgage company told her that she could miss
a payment and make it up the next month, which she did. The
president of the lending company named the late mortgage payment as
one of the reasons for denying the loan.
Another reason for denial was the fact that the borrower had
late payments on a credit card as recent as February 1994, and she
failed to explain two paid collections and one judgment obtained by
the State of Colorado in 1992. She explained the February 1994 late
payment as due to overlooking the bill. The lender did not reject
her explanation; it was just the fact that the late payment was so
She explained four paid collections as due to health problems,
a break-up with a boyfriend causing a shortfall in income, confusion
over a student loan, and a dispute with an ex-husband over paying for
medical treatment for her son who lived with him. The lender had no
problem with these explanations, but it was the unexplained
collections, the late mortgage payment, and the recency of the last
late payment which caused her to not get the loan.

The next case was application #4 from a Black 30-year old male
borrower and a White 21-year old female co-borrower. The borrower
had 14 years of education, one dependent, and was not married to the
co-borrower. He had worked as a car salesman for three years, but
with his present company for six months. The co-borrower had 13
years of education and had worked as a billing clerk for an auto
dealership for three years. The borrower earned $2,787 a month, the
co-borrower $1,693, and they rented an apartment at the time of the
The borrowers had 4 late payments on credit cards from 8/93 to
11/93, which they said were due to moving and not filing a change of
address in a timely fashion. They also attributed one late payment
to the envelope with the payment falling unnoticed in the crack of
the car seat. The borrower discussed four collections on his credit
report as a result of becoming involved with drugs when he was 23.
His father came and took him to San Francisco for a six-month stay in
a drug rehabilitation center. He left town without paying off the
utilities and landlord, who put him in collection. He explained
another collection as a medical bill for a knee injury not covered by
After the date of application, March 16, 1994, the borrower
paid off three of the collection actions and a judgment from 1989,
upon the lender's request. He had an additional two paid collections
which he did not mention in his letter, but his biggest credit
problem was an unpaid collection for $2,367. His narrative
concerning this credit problem was as follows:

Upon leaving the military, $3,000 in unemployment checks were
sent to my girlfriend's address which I was told I must pay
back. I never received these or cashed them. I am disputing
this, but beginning to pay them back, and if I win, my monies
will be refunded.
Although these borrowers' application had other problems,
including a low appraisal and an inadequate down payment, the
lender's main reason for rejection was delinquent credit obligations.
It appeared that the lender did not accept the borrowers'
explanations as shown by the comment, "very questionable credit
pattern," on internal written communication between bank president
and loan personnel. The lender disliked and noted the fact that the
late payments continued late into 1993, less than six months before
the date of application. This document also noted that the credit
explanation letter "does not address all items and is incorrect on
some." The most serious credit problem, according to the lender, was
the open collection for $2,969, even though the borrower said he was
disputing the collection.
A White married couple comprised the borrowers on application
#8, the next case. The male borrower was 42, had 12 years of
education, and had worked as a pipe fitter/welder for 18 years. The
female co-borrower was 37, also with 12 years of education and had
worked as a senior programmer/analyst for a computer company for 9
years. The couple had one 20 year old son who caused some of their
problems, as described later. The wife made $4,219 a month and the
husband, $3,279. They applied for the loan on October 19, 1993, but
the final loan decision was not made until 1994, which is why it was
included in the sample. The couple had recently sold their home and

anticipated moving into a home which was under construction, and it
was this home that they hoped to finance.
This couple had 4 6 late payments on credit cards, the most
recent of which were from 1/93 to 9/93, one month prior to their loan
application. Their explanation for several of the late accounts
involved the loss of employment by the borrower whose company went
out of business, and forgetting to make the payment on one account.
The two delinquent accounts with late payments during 1993, the year
of application, were due to helping their son. Apparently, their son
applied to join the Navy after high school graduation in 1992, but
was told that he would not be inducted. The borrowers helped him buy
a car by loaning him the down payment and money for insurance,
amounting to $1,600. Shortly after this, the Navy called and picked
up their son four days later in January 1993. When he was in boot
camp, they said they had no way of contacting him, and they made his
car and insurance payments. This caused them to be short on their
other bills, and hence, the late payments.-
This lender took the original application, but another
institution was underwriting the loan. It was the underwriter who
rejected these borrowers. Unfortunately, these borrowers had
explained two of their accounts with 1993 late payments this way,
"this is another situation where we have been making regular
payments, but they are mailed after the next billing period." This
explanation did not satisfy the underwriter who wrote, "Credit
explanation not acceptable because borrowers state that they make the
monthly payments after the next billing date." The underwriter also

gave as reasons for decline, "credit history," and "borrowers have
had 12 x 30-day lates and 2 x 60-day lates in 1993, in 1992, 14 x 30-
day lates and 7 x 60-day lates."
The institution which originally took the application had the
borrowers write another letter explaining the situation with their
son, and the originating institution sent the following to the
underwriting institution:
We are requesting that you consider the rejection of this case.
Please read the attached letter for proposed borrower. Our
personal contact with these borrowers has been positive and we
feel that they had some circumstances beyond their control. To
look at their overall credit and the credit reported late, we
feel that overall they are a good credit risk. . . they have
never had a late payment on their mortgage.
There was no response to this from the underwriter in the file and
the loan was still rejected. Even though the lender which took the
application would have approved this loan, the underwriter did not,
thus illustrating that interpretation of credit reports and
explanatory letters could vary between institutions. For these
borrowers, late payments too close to the date of application and an
inadequate explanation were the deciding factors.
The final application selected for case study was #471 also
from a married couple, both of whom were White. The borrower, a 51
year old male with 14 years education, had worked as a sales
executive for twenty years, earning a monthly income of $4,750. The
48 year old female co-borrower also had 14 years of education and had
worked for five years as a buyer for a communications company,
earning $2,333 monthly. They owned their own home and had an 18 year
old dependent.

The date of application for this couple was April 19, 1994, and
their credit report showed numerous derogatories. They had 17 unpaid
collections, many of which were from the same collection agency
during 1993. They had 7 paid collections, 7 charge-offs, and 5 late
payments on their mortgage in 1989. In 1987, they filed a Chapter 7
bankruptcy which was discharged in January, 1988, but they also had
had a civil judgment since the bankruptcy, which was satisfied
January, 1993.
The borrowers dismissed 6 of the collections by writing,
"UNKNOWN! NOT OURS!" Two of the collections were described as, "old
account, not previously known now paid." The borrowers noted
another collection account as a duplicate listing of an account
already listed, although the credit report showed different dates and
amounts for the two accounts which happened to be from the same
collection agency. The borrowers claimed that a charge-off was
included in the bankruptcy, but the date of the charge-off was one
year after the discharge of the bankruptcy.
Ten of the paid and six of the unpaid collections were from the
same collection company concerning payments due to borrowers' health
insurance company for medical services. The application file
included a letter the co-borrower wrote to the insurance company
disputing the owed amounts. The co-borrower claimed that certain
medical items should have been covered 100%, but were only covered at
80%, due to miscoding. The reply from the health insurance company
said that all claims had been properly processed, that it was too
late to dispute any claims prior to 1993, and those from 1993 would

be looked at on a case by case basis. The co-borrower was told that
she needed to dispute each one individually in writing. The
insurance company further stated that she had been sent an adjustment
check in January, which she could have applied to the account, but
did not and still owed $576.34. Furthermore, the letter stated that
the insurance company no longer updated any accounts to the
collection agency after 1993.
Although most of the explanations of the derogatories on the
form letter were very terse, there was one explanation regarding a
satisfied civil judgment from July, 1990. This note is shown below:
[unreadable] double billed insurance co. who refused to pay
double & they sued me court then ordered double bill deleted
with $150 left paid in 3 pmts. We paid two by check and last
by money order. Dishonest people at [collection agency] said
they never received M.O. They then went back to court without
notifying us and lied to judge. Got a default judgment. Cost
less to pay than fight. I still have M.O. receipt.
Clearly, these applicants thought they were being unfairly treated by
dishonest creditors, and implied that they were somewhat helpless
against their creditors. Not enough information was given to assess
the truth of this situation, leaving question marks in the reader's
mind. Although the borrowers claimed to have a receipt for the money
order, they paid the judgment. This letter was more indignant and
less apologetic in tone than most of the others examined. It also
blamed others for their credit problems, disputed many items on the
credit report, and left unexplained many items. The credit report
showed many unpaid or outstanding delinquent debts for these

The lender's comments explaining reasons for rejection were
also very short. The first under the section entitled "Branch
Comments" said, "Delinquent credit after bankruptcy. Not very strong
letter of explanation." The underwriter's comments were "Delinquent
credit after bankruptcy not acceptable. If borrower disputes credit
report, he should contact them to correct." The lender did not
explain why the letter was not convincing, but possibly they were
reacting to the briefness of the explanations and flippant tone.
Because the credit report had so many derogatory items, the lender
might have had a hard time believing that so many of the derogatories
were mistakes on the credit report.
Of the seven remaining applications rejected for poor credit
history, they fit the pattern described above, namely that they had
derogatories too close to the date of the application or unpaid
delinquent debt obligations. Application #9 had 15 open collections,
five of them during 1993, the year of application, and the letter of
explanation discussed only the bankruptcy and not the other
delinquencies. One pair of applicants from application #10 had run
into problems because of job lay-offs, illness in the family and
increased child care expenses, but they had paid off all their
creditors through Consumer Credit Counseling Service (CCCS). They
applied for a loan a month after making their final payment to CCCS,
which the lender thought was too soon. The same was true with
applications #476, #485, and #493.
Another unacceptable credit pattern was credit derogatories
occurring after a bankruptcy. This was the case with application

#484 and probably the case with #495, although the lender gave merely
a brief reason for rejection of #495 of "Delinquent Credit
Obligations." In the case of application #484, the lender wrote:
Borrowers have unacceptable credit history after bankruptcy.
Borrowers have both been with current employers since before
bankruptcy. Explanation for credit problems is weak.
The lender never explained why the explanation for the credit
problems was "weak."
The co-borrower gave as reasons for derogatories that she
overlooked the bill because of holidays and her mother taken ill at
the same time, and a dispute with the health insurance company over
coverage. These reasons did not seem that different from reasons
given in other letters, but perhaps it was her explanation of a
collection from Public Service in June, 1992:
We were getting ready to move, I had called for the final
bill, we both thought each of us had paid it. When I received
a bill months later from this company I did nothing, because I
thought it was paid. The amount was not rolled over to the new
billing. After 1 year I received another bill, this time I
called and was told it was from the other home and the bill was
not paid. The bill is paid. Sorry for this mistake.
The co-borrower described doing nothing when receiving a bill, which
lenders would dislike, but she said it was a mistake. In this case
since the derogatories came after a bankruptcy, and she disregarded a
bill, the lender denied the loan. This example showed that a
combination of things caused the loan to be denied, that similar
situations in another case might not have the same effect.

The mortgage lender requested many documents and pieces of
information from the prospective applicant in order to gauge his or
her ability and willingness to repay the loan. The mortgage lender
could make a direct estimate of the applicant's ability to pay by
looking at his/her income, debts, and proposed mortgage amount. The
lender applied a formula using these numbers and made a determination
of ability to pay. In determining the willingness of the applicant
to pay, there was no formula where the lender could plug in the
numbers. The lender relied on the applicant's past behavior in
paying her or his bills, which was not necessarily an indicator of
what the applicant would do in the future. An applicant with credit
problems had to demonstrate to the lender that his or her past
behavior would not occur in the future and that it was due to
circumstances beyond his or her control. One of the ways the
applicant did this formally was by writing a letter explaining past
credit derogatories.
The lender had something that the applicant wanted, so the
applicant tended to take very seriously the request to explain his or
her credit problems. Many of the applicants wrote heart-felt
narratives regarding their lives and credit problems, while a few
gave only very brief dismissive explanations. The letters became the
deciding factor in the lending decision when the applicant(s) had met
the lender's requirements for income and ratios, but had derogatories

on the credit report. However, if the applicant(s) had open
delinquent debts, delinquencies within a year of the application, or
delinquencies after a bankruptcy, the letter did not make a
difference; the applicant(s) did not get the loan, regardless of what
the reasons were for the delinquencies.
The applicants wanted to present their case in the best
possible light, and in some cases, they might not tell the truth or
leave out certain facts. The lender could always ask for further
verification if, for example, the applicant claimed he/she had a
dispute with a creditor or that a mistake had been made on his/her
credit report. Generally, the lender had to base the decision on the
word of the applicant, even though some of the explanations might not
sound credible. Given the propensity of the applicant to tell
his/her story to make him/her look his/her best, it seemed logical
that the lender would not place a lot of emphasis on the letters from
the applicants, unless the letters revealed a poor attitude toward
credit obligations. The lenders tended to accept most explanations,
as evidenced that out of the 101 applications with a credit
derogatory and a letter of explanation, only 21 of them were denied,
and out of those, 11 were denied because of poor credit history.
A poor explanation of credit problems could be the basis to
deny a loan in cases where the applicants had a poor credit history,
and the explanation of derogatories then became part of the credit
history. The lender would be affected by both the tone and content
of the letters, and for borderline applicants, the lender had to make
subjective judgments. This was one area where prejudice against