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This is to certify that the
thesis entitled
The Magnitude of Mortgage Redlining and Its
Relationship to Insurance Patterns in

Urban Environments

presented by

Janet Pualani Corniel

has been accepted towards fulfillment
of the requirements for

Master's degree in Urban & Regional Planning

 

 

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University

 

 

 

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TO AVOID FINES Mum on or baton dot. duo.

DATE DUE DATE DUE DATE DUE

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THE MAGNITUDE OF MORTGAGE REDLINING AND ITS
RELATIONSHIP TO INSURANCE PATTERNS IN
URBAN ENVIRONMENTS

By

Janet Pualani Comic]

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF URBAN & REGIONAL PLANNING

Department of Geography
Urban & Regional Planning Program

1995

ABSTRACT

THE MAGNITUDE OF MORTGAGE REDLINING AND ITS
RELATIONSHIP TO INSURANCE PATTERNS IN
URBAN ENVIRONMENTS

By

Janet Pualani Corniel

Does redlining remain a problem within urban environments in this country?
Since the Civil Rights Movement in the 1960’s, there has been a great deal of debate
regarding redlining and various methods have been used to study this social enigma.
Historically, the response has changed with the political climate.

This thesis describes a case study of the Tri-County Region, consisting of
Wayne, Oakland and Macomb Counties, in Southeast Michigan. The purpose of this
analysis is to analyze the extent of mortgage redlining and its relationship to
homeowner’s insurance patterns. The main hypothesis of this thesis is that there is a
significant degree of mortgage redlining and similarly it has a relationship to
homeowner’s insurance patterns. Furthermore, it was conjectured that: comparing
the difference between the means of white and black applicants denied, and analyzing
whether the variables of income, owner-occupied households and minority

populations do have an effect on applicants denied. These subsequent hypotheses

further the argument and offers a composite view of the current lending patterns in

the study area.

C0pyright by
JANET PUALANI CORNIEL
1995

To my family; Mom, Dad, Kim, Papa, Nana and Chris for
years of unconditional love and support.

ACKNOWLEDGMENTS

This entire process has been a learning experience and I would like to
acknowledge all of those instrumental in helping me through it.

First, I would like thank the members of my committee; my chair Dr.
Zenia Kotval and members, Dr. Bruce Pigozzi, Dr. Patrick McGovern and Dr.
Rex LaMore for their help and patience.

Second, I would like to thank Amy Christiansen and Gerald Gannod for the
countless late nights and understanding throughout my last year.

Next, my deepest appreciation goes to my family; Mom, Dad, Kim, Papa
and Nana. Your unconditional love and support is responsible for all of my
accomplishments.

l would also like to thank my best friend and love Chris. Your support and
patience has helped me through this entire process.

Lastly, I would like to thank the Browns for being my surrogate family in

Michigan and making me feel so welcomed.

TABLE OF CONTENTS

LIST OF TABLES ....................................................... ix
LIST OF FIGURES ...................................................... x
LIST OF MAPS .......................................................... xi
LIST OF APPENDICES ................................................ xii
CHAPTER 1
INTRODUCTION .............................................. 1
Statement of Purpose .................................. 2
Methods ................................................. 2
Summation .............................................. 6
CHAPTER 2
LITERATURE REVIEW ..................................... 7
Redlining and Its Historical Implications .......... 7
Theoretical Framework and Current Literature... 9
MortgageRedlining..........; ............... 10
Insurance Redlining .......................... 19
CHAPTER 3
LEGISLATION AND
NATIONAL TRENDS ...................... 24
Federal Legislation and Current Studies ........... 25

Mortgage Redlining .......................... 25

Insurance Redlining .......................... 27
Michigan Legislation ................................. 29
Mortgage Legislation ........................ 29
Insurance Legislation ........................ 3]
Mortgage Redlining as a
Current Problem in the U .S ................ 32
CHAPTER 4
DATA ANALYSIS FOR THE
TRI-COUNTY REGION ............................ 40
Geographic Perspective of the Region ............. 40
Data and Methods ..................................... 45
HMDA Analysis ...................................... 49
HMDA and Insurance Analysis ..................... 6O
Summation ............................................. 62
Analytical Limitations ................................ 63
CHAPTER 5
CONCLUSION ................................................ 65
APPENDIX ............................................................. 75
BIBLIOGRAPHY ...................................................... 77

viii

LIST OF TABLES

Table 2.1

Riverdale Park/Winship Community Comparison ....... 12
Table 2.2

Control Group’s Treatment of Home-buyers ............. 15
Table 3.1

Bank Branch Ratios ........................................... 39
Table 4.1

Correlation Research Hypothesis ........................... 49
Table 4.2

T-test Research Hypothesis for PCT l ..................... 50
Table 4.3

T-test Research Hypothesis for PCT 2 ..................... 51
Table 4.4

T-test Research Hypothesis for PCT l & PCT 2 ......... 52
Table 4.5

Results of Multiple Regression Tests ....................... 54
Table 4.6

Correlation on state FAIR plan and voluntary market. . . . 60

Table 4.7
Correlation on state FAIR plan and PCT 2 ................. 61

LIST OF FIGURES

Figure 3.1
1990 Denials for Conventional
Government Backed Loans .......................... 33
Figure 3 .2
1991 Denials for Conventional
Government Backed Loans .......................... 34
Figure 3.3
1992 Denials for Conventional
Government Backed Loans .......................... 35
Figure 4.1
Multiple Regression Residuals for PCT 1 .................. 55
Figure 4.2
Multiple Regression Residuals for PCT 2 .................. 57
Figure 4.3
Multiple Regression Residuals for DIFFDEN ............. 59

LIST OF MAPS

Map 4.1

Tri-County Region within Southeast Michigan ............ 41
Map 4.2

Wayne County .................................................. 43
Map 4.3 .

Oakland County ................................................ 44
Map 4.4

Macomb County ............................................... 46

LIST OF APPENDICES

Appendix A
List of Minor Civil Divisions ............................... 75

xii

CHAPTER 1

Introduction

In order to determine the extent of redlining within urban environments, the
Tri-County Region of Southeastern Michigan, which includes Wayne, Oakland and
Macomb Counties, was examined. Historically, in the United States, mortgage
redlining was defined as (Taibi, 1994, p. 1486):

the practice of literally drawing a red line around certain neighborhoods on a

city map and refusing to make loans for property or businesses located within

the demarcated zones. Today, the term refers to any set of practices that
systematically denies credit to applicants from low- and moderate income, and
minority neighborhoods.
As seen by the mere definition of redlining, it has evolved over time and reactions to
such practices have changed as well.

Detroit, Michigan is a city that faces the problem of countering disinvestment.
Due to many years of decline, Detroit has been targeted for support. Using Detroit as
a model, this study analyzes two possible causes of disinvestrnent that will make it
difficult to spark economic growth; mortgage lending and homeowner’s insurance

patterns within urban environments.

Statement of Purpose

 

The purpose of this thesis is to analyze the extent of mortgage redlining and its
relationship to homeowner’s insurance patterns. It is conjectured that there is a
significant presence of redlining within the Tri-County Region, despite legislative
efforts to curtail it. In addition, it is hypothesized that there is a significant difference
between the means of white mortgage loan applicants’ denied and black mortgage loan
applicants’ denied. Additionally, it is proposed that income, owner—occupied
households and black population have an effect on mortgage loan applicants’ denied.
These subsequent tests offer a composite view of the current lending patterns of the

study area.

m
Several studies on redlining have been conducted. A few of those discussed in
the literature review of this analysis are Squires and O’Connor (1993), Squires and
Valez (1987), Dunham and Jackson (1993), and Galster (1991,1992). This analysis
uses some of the suggestions proposed by these authors and incorporates its own
examination through a case study of the Tri-County Region.
The Tri-County area encompasses Wayne, Oakland and Macomb Counties

within the state of Michigan and are the most urban of the counties, within the Detroit

Primary Metropolitan Statistical Area (PMSA). Furthermore, Wayne County includes
Detroit proper and outlying communities to the south and has the most diverse
population of the three counties studied with 40 percent of the population being black.
Oakland County is located to the northwest of Wayne and has experienced a large
amount of suburban growth over the past decade. Macomb County is located to the
north of Wayne County and has experienced some suburban growth over the past
decade and has the smallest percentage of black population of the three counties
studied, with 1.4 percent (SEMCOG, 1993).

In order to analyze the magnitude of redlining and its relationship to
homeowner’s insm'ance patterns 1992 Home Mortgage Disclosure Act (HMDA) Data,
published in 1993 and 1992 homeowner’s insurance patterns for the Tri-County area
were examined. This thesis analyzes the direct relationship between racial disparity in
home mortgage lending and homeowners’ insurance patterns quantitatively and details
this relationship for the Tri-County area. It is assumed that the higher the percentage
of minority denial rates, the more difficult to obtain and maintain mortgage lending
and homeowner’s insurance services. If such services are available, often times they
are limited.

Several statistical tests are used to answer hypotheses in this analysis. T-tests
indicate a presence of racial disparity within mortgage lending. Multiple regression

further details possible cause and effect of such disparity. Correlation is used to find

two significant relationships; (1) between minority denial rates and involuntary markets
or state Fair Access to Insurance Requirements (FAIR) plan insurance and (2) between
cancellations and nonrenewals by voluntary markets or private insurance and a high
presence of involuntary markets (state FAIR plan insurance).

Prior to statistically analyzing the data, insurance data by zip codes and HMDA
data by census tracts were aggregated into Minor Civil Divisions or MCDs in order to
place these data into comparable geographic units. There are approximately 133 MCDs
within the Tri-County Region, but 10 were eliminated from further study because they
lacked sufficient data (SEMCOG, 1993).

The differences between the means of
white and black gaplicants denied

 

 

The difference of means in white mortgage loan applicants’ denied and black
mortgage loan applicants’ denied indicate a presence of racial disparity. Studies by
Canner and Smith (1991, 1992, 1994) and Squires and O’Connor (1993), further
discussed in the literature review, confirm the use of denial rates in establishing a
presence of racial disparity. This explains the use of denial rates in this analysis as an

indicator.

The effects of income, owner—occupied households
and minority population on applicants denied

 

 

In order to establish a causal model of the extent of redlining present in the
study area, the effects of independent variables income, percent owner—occupied
households and percent minority population on percent applicants denied were assessed
by linear regression. Similar variables were used in a study conducted by Dunham and
Jackson (1993) to test the market shares of both depositories and mortgage companies
in Lansing and Grand Rapids, Michigan. This thesis focuses on denial rates because

most racial disparity in mortgage lending exists in the denial rates.

The relationship between involuntarlmarket and
policy cancellations and nonrenewals by the private insurer

 

 

In an effort to establish the relationship between voluntary market (private
insm'ance) cancellations and nonrenewals by the insurer and the involuntary market
(state FAIR plan) insurance, correlation was used. The results of this correlation is
used to addresses the problem with obtaining and maintaining private insurance in areas

with a predominance of state FAIR plan insurance.

The relationship between homeowner’s
insurance and redlining

 

 

The percentage of involuntary market and the percent black mortgage loan
applicants’ denied were used in a correlation. This relationship indicates whether a
high presence involuntary market (state FAIR plan) insurance has a positive
relationship with a high percent of black mortgage loan applicants’ denied. The result
of this correlation explains a link between homeowner’s insurance and mortgage

redlining.

W

This thesis describes the interdependent relationship of homeowner’s insurance and
mortgage lending and current theoretical perspectives through a literary review and a
statistical analysis of the Tri-County Region. National HMDA data is discussed and
indicates problems still exist on that level. In addition, an analysis of governmental
responses discusses available remedies. Lastly, a description of the insurance and lending
patterns is detailed.

This discussion establishes the basis of the methodology used in the analysis, which
enables the use of correlation to test the strength of the relationship between homeowner’s
insurance and lending patterns. Multiple regression analysis on lending patterns ofi‘ers a
current composite of the Tri-County Region and the existence of racial disparity. The
results of these tests are used to explain the current magnitude of the problem and policy

implications.

CHAPTER 2

Literature Review

Although the United States has continuously had a presence of discrimination
within its housing market, little credence was given to this perplexing social dilemma
until the Civil Rights movement of the 1960’s made its impact on the nation. Once the
problem had been exposed, various objectives at curtailing it were considered. In an
effort to address the problem, theoretical questions were asked and social responsibility

became popular. This led to the onset of studies and legislation in the 1970’s.

Redlining and its Historical Implications

 

Historically, in the United States, mortgage redlining was defined as (T aibi,
1994, p. 1486):

the practice of literally drawing a red line around certain neighborhoods on a
city map and refusing to make loans for property or businesses located within
demarcated zones. Today, the term refers to any set of practices that
systematically denies credit to applicants from low- and moderate income, and
minority neighborhoods.
Although this evolution transpired, inequity remains a problem in certain areas of the
lending process, such as the denial rates in mortgage lending.
Financial institutions were not alone in building barricades that made the

acquisition of property difficult to nearly impossible. Historically, the insurance

industry has had a role in redlining. Insurance redlining is defined as “a
discriminatory failure or refusal to provide property insurance on dwellings” (Dunn v.

Midwestern Indemnity, 1979, p. 1106). Insurance agencies have established a history

 

of discriminatory business practices. As further explained by Squires and Valez (1988,
p.64):

At least since the mid 1960’s, when several urban areas experienced race riots,

revolts and other forms of civil disobedience, many insurers have concluded that

urban communities are uninsurable. Underwriting manuals have frequently
included maps with red lines drawn on them to indicate areas where policies
should not be written, or should be written only after careful examination and
frequently at higher costs or with special exclusions.

Redlining began as intentional discrimination and has evolved into a
subconscious phenomenon. Prior to legislation, it was common knowledge that
neighborhoods were systematically groomed for different races and social classes. This
discrimination was encouraged and a part of common business practices for the real
estate market. Racial make-up was used as a criterion in evaluating real estate. Until
1950, the National Association of Real Estate Boards’ National Code of Ethics
specifically stated that (Squires & O’Connor, 1993, p. 85):

a realtor should never be instrumental in introducing into a neighborhood a

character of property or occupancy, members of any race or nationality, or any

individual whose presence will clearly be detrimental to property values in the
neighborhood.

With segregated neighborhoods as the historical basis for many urban areas and white
flight after the riots of the 1960’s, a pattern of urban disinvestrnent has made it difficult
for urban areas to grow economically.

For years, race has been a factor in determining an assumption of risk.
“Historically, insurers have used race and ethnicity to identify ‘good’ and ‘bad’ risks-
those categories of people who were more or less likely to experience compensable
losses” (Squires & Valez, 1987, p. 65). Although legislation has been enacted for
financial institutions, there has been no direct regulations created by the federal
government for insurance industries.

Theoretical Framework and Current Literature

 

There are many theories on redlining and studies have been conducted on both
mortgage and insurance redlining, such as analyses done by Galster (1991, 1992),
Dunham and Jackson (1993), Squires, Valez and Taeuber (1991). Although these
studies have recognized a link between the two, this study attempts to make a direct
quantitative link between the extent of racial disparity in mortgage lending and its
relationship to the lack of availability of private homeowner’s insurance.

Efforts to address the strength of this relationship between mortgage lending and
insurance patterns and the implications of redlining are incorporated into the literature
review and the statistical analysis of this thesis. Library research resulted in one study

that shares some of the methodology used in this analysis. This particular study

10

conducted by Squires & Valez (1987) used insurance and census data to focus on the
effects of insurance underwriting on disinvestment in urban areas and the profitability
of the industry, using Milwaukee as a case study.

MortgageRedlinirg

 

Until the passage of the Financial Institutions Reform, Recovery and
Enforcement Act (FIRREA) in 1989, which provided more detailed information on the
applicants in mortgage data, it was difficult to conduct analytical studies of HMDA
data. HMDA data provided aggregate information without the racial background of the
applicants; this made it difficult to conduct in-depth analyses of racial disparity.

Hula’s discourse on redlining, published in December 1991, illustrates the
problems encountered when testing aggregate HMDA data. Hula attempted to
reconsider the relationship between race and geographic location with the allocation of
home credit by private sector lending institutions by using a national data set for the
years of 1981 through 1987. Hula found no significant presence of redlining.

In particular, Hula’s (1991) analysis centered around two inconsistencies which
were identified by Shlay, Goldstein and Bartelt (1992, p. 130):

First, Hula used stepwise-regression procedures rather than a priori specifying
and then testing a theoretical model. Second, the reported results were
standardized regression coefficients (betas), not the unstandardized coefficients
(bs) typically reported in multivariate analyses of HMDA data.

Stepwise regression is an estimation process which includes variables in an

equation that produce the largest increase in R2. R2 is the proportion of variance and

11

the assumption is that variables which explain the most variance are the most important
(Shlay, Goldstein & Bartelt, 1992). Further emphasis added by Van Wagnen (1991), a
priori specifying is a method of parsimoniously selecting those variables upon which an
explanation is dependent.

Using stepwise regression has been criticized because it has been known to
produce biased estimates and has little or no value in analyzing statistical models.
(Shlay, Goldstein & Bartelt, 1992) Fmthermore, Galster and Hoopes state that one
lesson to be learned from Hula’s discourse is that “disaggregation should be a
guidepost for analysts of HMDA data” (1993, p. 149). Hula’s 1991 study encountered
these problems because he used aggregate data as a base and ran step-wise regression to
test such data instead of using planned comparisons on a theoretical model.

Perle, Lynch and Homer (1994) describe a perspective on redlining in Detroit
using the same methodology as Hula (1991) used in his discourse. The goal of the
study was to specifically address an earlier study by Blossom, Everett and Gallagher
(1988) that found racial bias with some lending institutions in Detroit. Using mortgage
approval rates for the city, the data was tested for statistical significance and it was
found that there was no statistical significance to conclude a presence of redlining in
Detroit.

Perle et a1. (1994) used stepwise regression as did Hula (1991). As mentioned

earlier, the article published by Hula received a great deal of criticism because of the

12

quantitative method used in his analysis. The publication of the study by Perle et al.
followed academic criticisms of what Hula’s study should have done, yet no re-
evaluation of the method used or an address of the limitations of using stepwise
regression and aggregate HMDA data were discussed. Although the analysis by
Blossom et al. (1988) was based on 48 census tracts out of 363, it used disaggregate
HMDA data as its base which made for more conclusive results. The analysis
attempted to study two control groups that had similar characteristics other than racial

makeup. Table 2.1, illustrates the two communities used.

Riverdale Park Winship Community

 

 

 

 

 

 

Race Predominantly White Predominantly Black
Median Household Income $20,585 $23,980
Median Assessed Home Value $27, 900 $29,200
Average Age of Homes 43 years 43 years
Number of Houflgf Units 1,066 1,268

Home Loans (including banks & 48% 18%

IthfiftS)

 

 

 

 

 

Table 2.1: Note: Illustrates the two neighborhoods used in a 1988 study by the
Detroit Free Press. According to the study, home loan ratios were 2.72

white to 1 black from all sources and 4.77 white to 1 black from banks &

thrifts. Source: Teresa Blossom, David Everett and John Gallagher, “The Race for
Money.” Detroit Free Press. (Jul. 24-27, 1988, p. 3A)

 

Blossom et al. used two communities for the analysis, Riverdale Park, which is

predominately white and Winship Community, which is predominately black.

13

There are 1,066 single-family houses in Riverdale Park and 1,268 in Winship.
Financial Institutions made loans per 1,000 houses nearly five times as often in
Riverdale than in Winship. They received applications at a rate only 3.4 times
greater in Riverdale Park than in Winship (Blossom et al., 1988, p. 3A).

The study did prove to be conclusive on a minuscule level of comparison
because it attempted to disaggregate HMDA data. The methods used in Blossom’s et
al. (1988) article provided a clear picture of the problem, using a large set of aggregate
HMDA data as its base. The problems encountered in the analysis of aggregate
HMDA data found in the analysis done by Hula (1991) and Perle et al. (1994) was
used as a guidepost in this analysis. This study avoided using national aggregated
HMDA data as a basis for the statistical analysis to overt problems the aforementioned
studies encountered. Instead, this analysis used raw HMDA data records as a basis and
then aggregated these data to concise geographic units that corresponded to the study
area, which consisted of 123 Minor Civil Divisions, MCDs.

There are various reasons why the practice of redlining continues. Due to the
presence of subjective criteria in the mortgage process, such behavior is endemic to the
situation and there is a greater potential for discrimination both consciously and
unconsciously. The screening process for applicants is especially sensitive to subjective
criteria, such as the applicants ability and willingness to repay (Fischl v General Motors

Acceptance Corp., 1983).

 

As disclosed in a recent study, conducted by Weink (1992), discrimination is

evident in all phases of the mortgage lending process. This particular study focused on

14

an audit of sale agents and their performance given different data on perspective
financial purchases. The auditing technique allows for an in-depth analysis of the
records and/ or business practices of an organization which provides detailed qualitative
and quantitative results that may not be obtainable through statistical analyses alone.
As illustrated in Table 2.2, the sale agents involved reacted differently to home buyers
according to their ethnicity and economic status. Stereotypical assumptions, such as
ethnicity equals inability to repay, were made regarding given data (Weink, 1992).
Although this study involved only one control group, it illustrated that the auditing
technique is a useful tool in evaluating the opportunities that allow discriminatory

practices to contaminate the lending process.

15

 

 

 

 

 

 

 

 

 

Control Group’s Treatment of
Home Buyers
Regarding Financing by Race
Agent’s Information Whites Blacks Same
Regarding Financing Only % Only % Treatment %
Agent offered to help
obtain financing 24.4 13.3 62.3
Agent said conventional
fixed-rate financing available 32.7 11.1 56.2
Agent said adjustable-rate
finmcimailable 23. l 6. 8 70. 1
Agent said FHA/VA
financing available 11.2 18.3 70.5

 

 

 

 

Table 2.2: Note: Illustrates the auditing technique of sales agents’ reaction
to home buyers and possible financing by ethnicity.
Source: Ronald E. Weink, “The Home-buying Process and Equality
of Opportunity in Mortgage Credit Markets.” Journal of Housing
Policy Debate. (1992: p. 219)

 

 

George Galster published an article in 1990 that relied heavily on auditing. The
article focused on racial steering within the real estate industry. Racial steering is
defined as sets of behaviors by a real estate agent that will direct a client toward a
particular neighborhood and eliminate others based on the racial composition of the
locality. The article details the audits of several large real estate firms in Cincinnati
and Memphis. The study found that black auditors did not receive additional
commentary within the process of home showings whereas white auditors were
encouraged to consider areas with less than 10 percent black populations (Galster,

1990).

16

The article specifically identifies several reasons for the continuation of racial
steering. These reasons include; agents may object to racial integration, agents may
feel obliged to steer to meet the needs of their cheats or retain them as clients and
lastly, agents may steer because they feel white home sellers will not sell to a black
client. These reasons illustrate why agents steer and they vary from direct segregation
to assumptions of client desires (Galster, 1990).

The auditing technique does provide a microcosm of information that details the
presence of discriminatory practices. In 1992, the Boston Federal Reserve Bank
conducted a study that enlisted researchers to review more than 3,000 loan applications
at 131 Boston lending institutions. The researchers were able to ascertain the actual
applications which allowed them to include important variables (e.g. credit record,
debt/equity ratios, employment, characteristics of property) that were previously
unexamined. The researchers found that after taking into consideration these variables,
black applicants were rejected 60 percent more often than whites with similar financial
characteristics (Squires & O’Connor, 1993).

Although the auditing technique provides a great deal of information, such as
debt/loan ratio and credit worthiness, it can be costly and time consuming. Thus, in
many instances, especially for governmental policy and procedure, it may not be
feasible as a constant monitoring tool. Instead, it could be used as an on-site test for

companies with questionable business practices.

17

A study by Dunham and Jackson in 1993 examined two comparable Michigan
cities, Lansing and Grand Rapids. The study mirrored a Chicago study conducted in
1991 which was sponsored by the same organization, The Woodstock Institute. The
Chicago study was a larger undertaking which analyzed over 90 mortgage companies in
the Chicago MetrOpolitan Area, which consists of six counties. The purpose of these
studies were to uncover the roles of the lending institutions within their communities by
comparing mortgage lenders and depository institutions and the market each fulfilled
(Dunham & Jackson, 1992).

Data used in the Lansing and Grand Rapids report included nation-wide and the
two cities market shares of both depositories and mortgage companies. For an in-depth
analysis, mortgage lending patterns were separated by loan type, i.e. conventional and
FHA/VA loans, for both Lansing and Grand Rapids. After they were separated, they
were compared and statistically analyzed (Dunham & Jackson, 1992). The findings of
the second report were very similar to those of the first, which confirmed their
hypothesis.

Both reports found that mortgage companies have been supporting a diverse
market. Service areas of mortgage companies are found in both the central city and the
fringe areas unlike their depository counterparts. Lending patterns did not vary

between predominately black and white areas for mortgage companies, whereas the

 

 

131
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depository institutions’ patterns varied greatly. Dunham and Jackson’s (1992, p. 61)
analysis found that:

for every 1,000 single-family properties, mortgage lenders in the Grand Rapids

and the Lansing metropolitan areas would have made about forty-four mortgage

loans in the black neighborhood, but about sixty mortgage loans in the
comparable white neighborhood.

A common denominator throughout the report was that the number of FHA/VA
loans were extremely high in ethnically diverse and lower income areas (Dunham &
Jackson, 1992). The importance of this finding is that current political trends that
harbor less government involvement may jeopardize a lending source available to

ethnically diverse and lower income populations.

Redliningg Is it Profitable? If forms of redlining continue to exist, is the

 

practice of redlining profitable? An analysis done by Squires and O’Connor in the
Spring of 1993 did a comparison to find if lenders who practice redlining make more
money than those lenders who do not. The study “examined the relationship between
lender profitability and the percentage of their loans and loan dollars that are invested
in the Milwaukee’s central city and to racial minorities throughout the metropolitan
area” (Squires & O’Connor, 1993, p. 83).

Squires and O’Connor chose Milwaukee because an analysis conducted by the

Atlanta Jom‘nal Constitution in 1989 found that of black and white loan denial rates,

 

Milwaukee had the highest presence of racial disparity in the country. In order to

measure the profitability of redlining, Squires & O’Connor (1993, p. 90) “examined

19

lender profitability, lending activity in the central city, and for 1990, lending to black
borrowers.”

A target area was designated that encompassed the outlying areas of the central
business district which included 12 percent of Milwaukee’s population and 54 percent
of the area’s minority population. The findings of the report were to the contrary of
what was expected. The study concluded “that lenders who conduct a relatively
greater amount of their business in predominantly white outlying neighborhoods are not
more profitable than lenders doing a relatively greater amount of their business in
predominantly minority and central city areas” (Squires & O’Connor, 1993, p. 99).
This is an important conclusion. The results of the study illustrate that profitability is
not compromised in predominately minority urban areas and lenders who perceive these
risks are misled.

Insurance Redlining

 

In the late 1970’s, insurance redlining began to receive greater attention by the
federal government, industry and academics. The difficulty of obtaining insurance and
the extensive costs of insurance in urban areas was recognized as a growing problem
and one that needed attention. As early as 1979, the link between secming
homeowner’s insurance in order to obtain a mortgage loan was made. The National
Commission on Neighborhoods concluded that the “availability of financial services is

essential for the viability of any community. Efforts to revitalize declining urban

20

neighborhoods must incorporate provisions for securing adequate financial services”
(Squires & Valez, 1987, p. 64).

The practice of insurance redlining has contributed to urban disinvestment. In
1987 , a study on insurance redlining, published by Squires and Valez, further
elaborates on the effects of such business practices when profitability is the primary
focus. The statistical analysis found that “the racial effect remains substantial even
after controlling for variables such as income level, poverty status, age of housing and
turnover rates” (Squires & Valez, 1987, p. 73).

The methodology used in the Squires and Valez analysis of insurance redlining
was comprehensive. The three data sets that were used in the Squires and Valez (1987)
analysis were; the number of policies enforce by zip code for Milwaukee County,
direct premiums earned and losses for the state of Wisconsin and 1980 census data of
the demographics of the area. Some of the methods found in the Squires and Valez
article are used to conduct the study of the Tri-County Region for this report.

Further study by Squires, Valez and Taeuber in 1991 offer further information
on the problems of insurance redlining. This particular analysis used two data sets
from the Milwaukee area which consisted of insurance agencies and neighborhood
characteristics. The purpose of the study was first, to find if there was a suburban bias
in the location of insurance agents and second, to find out if the location of these agents

is associated with the ethnic composition of the neighborhoods.

21

After the statistical analysis was completed, the study concluded that (Squires,
Valez & Taeuber, 1991):

race clearly is associated with agency location, and the effect of race remains

significant even after accounting for the effects of income, housing condition,

owner-occupancy and related neighborhood characteristics that presumably

influence the location of insurance agencies. (p. 581)

The report further explained that “the racial effect is in part a result of the
concentration of the metropolitan area’s minority population coupled with the industry’s
emerging preference for suburban locations” (p. 580).

In December of 1994, the National Association of Insurance Commissioners
(NAIC) published a report on urban insurance problems. The report was a result of an
investigation conducted in response to several anti-redlining bills introduced in
Congress. These bills were introduced after a publication by The Association of
' Community Organizations for Reform Now, ACORN, alleged discriminatory business
practices within the insurance industry. Furthermore, a Presidential Executive Order in
January 1994 called for the Department of Housing and Urban Development (HUD) to
establish and develop a special unit to enforce regulations to combat property insurance
discrimination. Following, in May of that same year, The National Fair Housing
Alliance filed formal complaints with HUD against Allstate Insurance Group and

Nationwide Insurance Group. The complaint alleged that these insurers discriminated

against minorities in Chicago, Atlanta, Louisville and Milwaukee (NAIC, 1994).

22

In response to such scrutiny, the association conducted an extensive
investigation with the objective of (NAIC, 1994, p. 8):

understand[ing] the dimensions of the insurance problems urban residents

encounter, to analyze the relevant factors underlying the problems, and [to]

develop for consideration the full range of public policy and regulatory

measures and industry solutions that should be applied to address the problem.

Although the study included both the availability of automobile insurance and
homeowner’s insurance, the focus of this analysis will only discuss the findings related
to the homeowner’s insurance market. The analysis by the N AIC consisted of a
nationwide data call to query insurance agencies’ policies. This data call provided the
raw insurance data used in the statistical analysis of this thesis. The NAIC’s findings
did mirror those of prior studies that illustrate discrepancies in the insurance market.
The findings include (NAIC, 1994, p. 29):

the number of agent appointments in relation to the number of housing units

appears to be strongly and positively correlated with income but there is no

consistent pattern with respect to minority concentration.
This finding confirms an aforementioned study done by Squires & Valez of insurance
redlining in Milwaukee which states “for insurance companies, the primary concern is
profitability” (1987, p. 73).

Furthermore, the N AIC (1994) found that “average premiums are generally
higher in low-income zip codes with greater than 50 percent minorities than in low-

income zip codes with less than 50 percent minorities” (p. 30). In conjunction with

this finding, average premiums also tend to increase with minority concentration,

23

although this pattern does not hold true in all cases. For example, for broad-coverage
policies in low-income zip codes, the average premium increases from $5.53 per
$1,000 in low-minority zip codes to $7.21 per $1,000 in high-minority zip codes
(NAIC, 1994).

Overall, the NAIC’s (1994) conclusions confirm findings done in prior studies.

These simple geographic comparisons tend to support general concerns about

the availability and cost of property insurance coverages in urban areas. The

availability of voluntary market coverage and the extent of insurance coverage
appears to be lower in cities and lower yet within high-minority, low-income
areas within cities. Non-renewal and cancellation rates also are higher in

minority and low-income zip codes (p. 30).

As discussed in this literature review, there is a vast amount of related work on
the subject. This paper proposes another perspective by incorporating part of the
methods used in the Squires & Valez (1987) discourse on insurance redlining and
testing HMDA data, utilizing multiple regression. Through the analysis of the Tri-
County Region, an overview of the current magnitude of racial disparity in mortgage

redlining and its relationship to homeowner’s insurance is examined. Thus, identifying

possible problems within the region that require further attention and analysis.

CHAPTER 3

Legislation and National Trends

In the late 1960’s, the passage of national legislation served as a catalyst for
addressing the scope of the problem of redlining. With the enactment of the Federal
Fair Housing Act (FHA) in 1968, Home Mortgage Disclosure Act (HMDA) in 1975,
and two years later the Community Reinvestment Act (CRA), the US. Government
began to attack the issue of redlining legislatively (Canner & Smith, 1991).

National legislation, Michigan legislation and current problems are discussed in
- this chapter. A historical perspective on federal legislation like the Home Mortgage
Disclosure Act (HMDA), the Community Reinvestment Act (CRA), the Equal Credit
Opportunity Act (ECOA), and the Financial Institutions Reform, Recovery Act
(FIRREA) explains the need for government involvement and the enactment of such
legislation. A series of studies found in the Federal Reserve Bulletin on HMDA over a
period of three years illustrates current problems nationally. This three-year trend
provides information on the nation as a whole and offers a point of reference for the

analysis done on the Tri-County Region.

24

25

Federal Legislation and Current Studies

Mortgage Redlining

 

The enactment of the HMDA, the CRA, and later the ECOA, were to address
the illicit practices of financial institutions. In theory, these proposals seemed
revolutionary; in practice, the acts provided quick-fix solutions to a vast problem.

The Home Mortgage Disclosure Act, introduced by Senator William Proxmire
of Wisconsin and later passed by Congress, was (Canner & Smith, 1991, p. 860)

in response to concerns, that by failing to provide adequate home financing to

qualified applicants on reasonable terms and conditions, some depository

institutions have sometimes contributed to the decline of certain geographic
areas.

By implementing the HMDA, required data on applicant approvals and denials
would be used for (Canner & Smith, 1991, p. 860):

1. Help[ing] determine whether financial institutions are serving the housing

needs of the communities in which they are located.

2. Providing information about the distribution of loan originations.

3. Households could better decide where to invest their savings.

After the HMDA was amended in 1989 by the Financial Institutions Reform,
Recovery and Enforcement Act (FIRREA), data were used to identify a presence of
discriminatory lending practices and help to enforce anti-discriminatory laws. The

enactment of the FIRREA by Congress made more information public, including the

background of the applicant. Another addition made possible by the FIRREA was the

26

inclusion of financial institutions that had not been considered prior to the amendment,
such as non-depository lenders and thrift holding companies. The HMDA, after the
implementation of the FIRREA, now encompassed a wider scope of financial
institutions (Canner & Smith, 1991).

With the development of the Community Reinvestment Act (CRA), the federal
government attempted to address some issues of mortgage redlining that HMDA
ignored. The CRA attempted to further address the problem of mortgage redlining by
(Canner & Smith, 1991, p. 876)

requir[ing] federal agencies to encourage depository institutions to help meet the

needs of their communities, including low- and moderate income

neighborhoods, consistent with safe and sound lending practices.

The CRA established a process using twelve criteria to evaluate the records of
financial institutions to determine the presence of discriminatory practices. In
conjunction with the HMDA, the CRA would serve as measure of performance for
applicable financial institutions by the following criteria (Canner & Smith, 1991, p.
877):

1. The geographic distribution of the institution’s credit applications,

extensions, and denials.

2. The institution’s record of originating or purchasing residential

mortgage loans, housing rehabilitation credit, home improvement loans,

and loans to small businesses and small farms within its community.
3. Evidence of prohibited discriminatory or other illegal credit practices.

27

In 1988, Congress passed the Equal Credit Opportunity Act. The ECOA, in
conjunction with the Fair Housing Act comprises the Fair Lending Laws. These laws
added strength to the HMDA (Taibi, 1994). However, this additional strength was not
felt until FIRREA was enacted and expanded the information needed to be disclosed.
With the addition of race and gender in these data, an in-depth analysis of the financial
institutions’ compliance with the federal regulations could be determined.

Though the development of these acts have aided in data collection, they have
failed to address the entire scope of problem. Instead, legislation functions as a gauge
of the problem through disclosure and additional policy that encompasses the entire
spectrum of redlining and discriminatory lending practices by recognizing the sheer
interdependence of the economy nwds to be incorporated into current legislation.
Although each of the acts do need improvements, they should not be faulted in their
entirety, for they have provided an initial step in the right direction. This
interdependent relationship needs to be further explored especially with insurance
companies’ role within the housing market.

Insurance Redlining

 

Currently, there is no federal legislation that directly regulates the insurance
industry’s role in redlining. Three steps have been taken by the federal government to
regulate insurance underwriting practices, but are all dated. The first of these three

steps is a Supreme Court Ruling in 1944, when the court “reversed its previous ruling

28

and held that the insurance business is engaged in interstate commerce and is subject to

federal regulation” (Dunn v. Midwestern Indemnity, 1979, p. 1106). A year later the

second step followed with the enactment of the McCorran Act. The Act “continued

regulation and taxation by the states are in the public interest, but does not preclude

federal regulation of the business of insurance” (p. 1107).

Later, Congress appointed a National Advisory Panel on Insurance in Riot-

Affected Areas, after the riots of the 1960’s had taken its toll on America’s large cities.

Amongst its findings, the Panel explained (Dunn v. Midwestern Indemnity, 1979, p.

1109):

 

Insurance is essential to revitalize our cities. It is a cornerstone of credit.
Without insurance, banks and other financial institutions will not-and cannot
make loans. New housing cannot be constructed and existing housing cannot be
repaired. New business cannot be opened and existing businesses cannot
expand, or even survive.

The third step, taken by the federal government, followed the Panel’s

recommendations. As a result in 1968, the Urban Property Insmance Protection and

Reinsurance Act (UPIPRA) was enacted.

UPIPRA sets forth the operating structure of the state FAIR plan. It does not
expressly address the issue of discriminatory insurance redlining based on race.
UPIPRA was enacted to protect private insurance companies from the risk of
catastrophic losses which resulted from riots or civil disorders (Dunn v.
Midwestern Indemnity, 1976, p. 1111).

 

Although the National Advisory Panel on Insurance in Riot-Affected Areas did

makeanimportantlinkbetweentheneedforinsumnceinsolidifyingacredit

29

transaction, nothing additional was done to regulate the role of private insurance.
UPIPRA simply set up the framework for state Fair Access to Insurance Requirements
(FAIR) plans which continue in 31 states today.
The Federal Insurance Administration (FIA) was put incharge of running the
riot reinsurance program and maintaining the state FAIR plans and, in 1974, the
agency issued an important report on the performance of residual market
mechanisms in addressing urban insurance availability problems. The FIA
concluded that voluntary market and residual market mechanisms had failed to
provide adequate availability of property and automobile insurance coverages to
all insurable risks and it proposed an alternative “Full Insurance Availability”
system which would address critical insurance needs (NAIC, 1994, p. 14).
The state FAIR plans continue to fulfill a market that is desperately needed.
Plans vary from state to state and are limited in coverage offered. State FAIR plans do
not offer the same types of coverage as private insurance companies, which severely
limit compensation to those affected. As further explained in a 1994 study by the
NAIC, “the FAIR plan market share is positively related to minority concentration and
negatively related to household income” (NAIC, 1994, p. 29). This illustrates that for
the most part, minorities within urban areas are limited to state FAIR plan insurance,

thus limiting compensation.

Michigan Legislation

 

Mortgage Legislation

 

In 197 8, Michigan enacted Public Act 135 which added to the information
lending institutions within Michigan needed to disclose regarding home mortgages.

The Act took federal legislation further by establishing a Financial Institutions Bureau

30

responsible for gathering and publishing these data submitted annually. The Act
"require[d] state chartered institutions to provide a detailed account by census tract of
the number of applications denied and the reasons for such denials” (Kanter & Nystuen,
1982, p. 310).

Michigan Public Act 173, The Mortgage Brokers, Lenders and Servicers
Licensing Act, plays a role in redlining as well. Act 173 details what is required from
such institutions by law. The Act ”define[s] and regulate[s] mortgage brokers,
mortgage lenders and mortgage servicers; prescribels] the powers and duties of the
Financial Institutions Bureau” (Michigan Public Act 173, 1987, p. 1). Both acts
worked in conjunction in regulating financial institutions by requiring further
disclosure, to prevent mortgage redlining.

With the expansion of the HMDA through the FIRREA, Michigan amended the
Act to eliminate the need for financial institutions submitting two reports. The
expansion of the HMDA, to include details on the applicant such as race, gender and
income, nullified the need for state legislation on such disclosure. The last annual
report by the Financial Institutions Bureau was published in 1990. Michigan no longer
saw the nwd for this reporting mechanism because new the HMDA data required

sufficed (Michigan Financial Institutions Bureau, 1990).

31

Insurance Legislation

 

In response to federal state FAIR plans, Michigan enacted its own version
entitled Michigan Basic Property Insurance in the early 1970’s. Michigan Basic is one
of the largest operating state FAIR plans in the country. Basically, Michigan Basic
designed a pool, in which private insurance companies would pay fees according to
their margin of profitability, in order to render services to individual property owners
who were denied private insurance. Michigan Basic is a non-profit corporation that
operates on the pooled resources and premiums charged to clients. There are no agents
employed with Michigan Basic, instead if a client seeks insurance from a private
agency and is turned down, that agent servicing the client applies for Michigan Basic
and is retained as the agent for the property owner under a Basic Plan.

Although there are limitations, Michigan Basic has grown in size and policy
coverage to meet the growing needs of those turned down for private insurance for
various reasons including the simple location of the property owned. Three policies are
offered through Michigan Basic and they are; basic fire, homeowner’s package policy
and fire of commercial buildings. As compared to private policies, Michigan Basic is

severely limited and encourages clients to retain private insurance.

32

Mortgage Redlining As Current Problem in the US.

 

Amendments to both the HMDA and the CRA have made it possible to further
analyze the practices of financial institutions. Additional information now required
from both acts allow for more in-depth study. A series of analysis of 1990, 1991 and
1992 HMDA data found in three separate issues of the Federal Reserve Bulletin,
clearly illustrate a trend of discrimination among financial institutions (Canner & Smith
1991, 1992, 1994).

The study published in the Federal Reserve Bulletin by Canner and Smith
(1991 , 1992,1994), was conducted over a period of three years, and gathered HMDA
data which was analyzed and extrapolated in several issues of the bulletin. For the three
years studied, there were wide disparities between denial rates of whites and minorities.
These data did not vary much over the course of the three year period.

Although the approval rate for loan applications was high for all three years;
72.3 percent in 1990 (Canner & Smith, 1991), 71.2 percent in 1991 (Canner & Smith,
1992) and 81 percent in 1992 (Canner, Passmore & Smith, 1994), a high proportion of
the small percentage of denials were minorities.

In 1990, 16.6 percent of loan applications were denied. As illustrated in Figure
3.1, 16.6 percent were conventional loan denials. Of that 16.6 percent, 14.4 percent

of white applicants were denied, whereas 33.9 percent of blacks and 21.4 percent

33

Hispanics were denied. Only 12.9 percent of their Asian counterparts were denied,
which is approximately a third of the denial rate of their black counterparts (Canner &

Smith, 1991).

1990 Denials For Conventional/Gov. Backed
Loans
In Percentages

Joint(white/mi
non'ty)
White
El Government Backed
I Conventional
Black
American

 

Indian
0 5 10 15 20 25 30 35

Figure 3.1: Note: 1990 Denials for both government hacked and conventional loans
for home purchases in percentages according to race. Source: Glenn G. Canner and
Dolores S. Smith, “Home Mortgage Disclosure Act: Expanded Data on Residential
Lending." Federal Reserve Bulletin. (1991: p. 870)

The figures for 1991 show an increase in overall denials at 18.9 percent which
reflect changes in the economy. However, relative percentages of denial for minority
applicants, as further illustrated in Figure 3.2, did not change drastically. Of the 18.9

percent of the denials, conventional loans, 17.3 percent were whites, but in contrast

37.6 percent were blacks and 26.6 percent were Hispanics. Again, the percent of

Asians remained the lowest at 15 percent (Canner & Smith, 1992).

1991 Denials For Conventional/Gov. Backed
Loans In Percentages

Joint(white/
minority)

White
El Government Backed

I Conventional
Black

American
Indian

 

0 510152025303540

Figure 3.2: Note: 1991 Denials for both government hacked and conventional loans
for home purchases in percentages according to race. Source: Glenn B. Canner and
Dolores Smith. “Expanded HMDA Data on Residential lending: One Year later.”
Federal Reserve Bulletin (1992: p. 808.)

As for 1992, denial figures on the whole dropped to 12.4 percent. Although
there was a decrease in denials for blacks, relative to the decrease in white denials, the
change loses its impact. As Figure 3.3 illustrates, 16 percent of whites were denied
credit, while 36 percent of blacks and 27 percent of Hispanics were denied. Asian

percent of denials did not change from 1991 at 15 percent (Canner, Passmore & Smith,

1994).

35

1992 Denials For Conventional/Gov. Backed Loans
In Percentages

Joint(white/mi
nority)
White
El Government Backed
I Conventional
Black
American

 

Indian
0510152025303540

Figure 3.3: Note: 1992 Denials for both government hacked and conventional loans
for home purchases in percentages according to race. Source: Glenn B. Canner,
William Passmore and Dolores Smith. “Residential lending to Low-Income and

. Minority Families: Evidence for the 1992 HMDA Data.” Federal Reserve Bulletin
(1994: p. 86)

The disparity is found not only in conventional loans but in governmental
backed loans as well. The disparity, though lower in numbers as compared with
conventional loans, are proportional to the conventional loan figures and the racial
characteristics of the applicant.

Although the series of studies in the Federal Reserve Bulletin by Canner and
Smith did not specifically address the problem geographically, the data alone illustrates

a trend of disparity according to racial characteristics. This study illustrates the value

36

of using HMDA data to find a presence of racial disparity. Thus, explaining the use of
HMDA data in this thesis. The statistical analysis of the Tri-County Region’s HMDA
data will indicate if national trends are present locally.

Using HMDA data within a geographic region will help to specifically address
the problems of redlining within an area. The integration of HMDA data and GIS,
Geographical Information Systems' may offer a solution. This integration will enable
the plotting of HMDA data to give a geographical representation of redlining.

There are other factors for denial of candidates, such as income. Generally
speaking, the higher the income, the lower the probability of denial. In conjunction
with this assumption, the argument can be made that minorities on average constitute a
greater proportion of the population in lower income brackets; thus, they would be
expected to be denied more often. However, the income status of applicants is unable
to account for the entire disparity between the denial rates of minorities and whites.
Within lower income brackets, discrimination still occurs. According to the 1991 study
of the 1990 HMDA data found in the Federal Reserve Bulletin by Canner and Smith
(1991), ”among applicants whose incomes place them in the lowest income group, the
denial rates for blacks, Hispanics and Asians were 40.1 percent, 31.1 percent and 17.2

percent respectively, compared with 23.1 percent for white applicants” (p. 870).

. Geographical Information System is a computerized mapping system that has the capability to store data
separate from a drawing and use that data to produce a meaningful map for analysis (Arbeit, 1988).

37

A recent investigative report conducted by Loeb, Cohen and Johnson (1995) in

U.S. News and World Report, further implicates the continual problem of redlining in

 

America’s cities. The investigation took six months and used nine sets of banking and
insurance data which included over 24 million mortgage records.

An overview of the study’s results is as follows (Loeb, Cohen & Johnson,
1995):

First, nearly 50 percent of poor and minority homeowners cannot obtain full
property insurance and the few that do, pay approximately twice as much as their white
counterparts in mostly white, middle-class areas. A ratio of $7 .21 per $1,000 for high-
minority, low-class areas to $3.53 per $1,000 for low-minority, middle-class
neighborhoods. Second, although federal regulations are in place to encourage
community investment, an increasing amount of banks are leaving impoverished areas.
Third, even with current federal regulations, the survey revealed, much like the Federal
Reserve Bulletin’s study, that amongst mortgage applicants, discrimination remains.
The denial rate was 37 percent for blacks while it was only 18 percent for whites.

In addition to these three main discoveries, the study discussed the problems
with homeowner’s insurance, which is a necessity in purchasing a home and the
locations of banks within high minority areas. Taking these two factors into account,
indications of redlining become more defined.

There is no current legislation that governs insurance agencies to guarantee fair

business practices to its customers. The only remedy available is found through federal

38

civil rights violations, which is currently being used in the investigation of three
insurance companies. Allstate, Nationwide Insurance Enterprise and State Farm are all
under investigation by the Department of Housing and Urban Development.
Allegations range from higher costs in high-minority neighborhoods, which are over
double in some cases, canceling insurance policies with little reason and denying those
who have attempted to get insurance because of their location within a high-minority,
low-income area (Loeb, Cohen & Johnson, 1995).

Additionally, the investigation looked at the location of bank branches within
areas of high minority, low-income areas and found a great disproportionment. The
study also focused on this factor in order to see if the financial institutions were
meeting the needs of minorities, because most banks have a history of lending to those

within the area of the branch service. On the following page, Table 3.4 illustrates
cities with the greatest disparity of bank locations between high minority areas and high
white areas. Although Detroit did not rank within the top twenty, access to full-service

bank branches remain a problem in high-minority, low-income areas.

39

Bank Branches Per 10,000 Residents

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

High White High Minority Homes In Share of Loans
Neighborhoods Neighborhoods Ratio Minority In Minority Areas
Areas

Rochester, NY 4.7 0.6] 8 to 1 32% 15%
San Diego 2.6] 0.4] 7 to 1 25% 12%
Baltimore 5.9] 1.2] 5 to 1 56% 28%
Denver 2.9] 0.6] 5 to 1 34% 14%
Long Beach, CA 2.6] 0.6[ 4 to 1 41% 27%
Cincinnati 7] 1.6] 4tol 22% 15%
Atlanta 9] 2.1] 4tol 60% 14%
St. Louis 3.7] 09' 4tol 44% 15%
Santa Ana, CA 2. 0.7 4 to 1 72% 67%
Austin, TX 2 0.5 4 to 1 16% 3%
[Orlando, FL 3.5 1 4 to 1 14% 3%
[Chicago 2.2 0.6- 4tol 50% 22%
Philadelphia 3.2 1 3 to] 45% 20%
Memphis 3.2 1 3 to 1 43% 10%
Miami 16.2 5 3 to 1 88% 66%
Milwaukee 2.2 0.8 3 to 1 27% 11%
New York City 2.7 1 3 to 1 38% 24%
Honolulu 10.1 3.8 3 to l 90% 85%
Sacramento, CA 3.4 1.4 2 to l 35% 23%
Tucson, A2 2.9] 1.3 2 to 1 20% 7%

 

 

 

 

 

 

 

Table 3.1: Note: Cities with the greatest disparities between the number of
bank branch servicing high white areas and high minority areas. Source:
Penny Loeb, Warren Cohen and Constance Johnson. “The New Redlining.”
U.S. News & World Report (1995: p. 53)

 

CHAPTER 4

Data Analysis of the Tri-County Region

The following chapter will detail the geographic perspective of the three
counties within the study area, explain the data and methodology used and discuss the
results of the statistical analysis conducted.

Geographic Perspective of the Regi_on

 

Since the Riots in the 1960’s, Detroit has received a great deal of negative
media attention. A presence of disinvestment is well known in Detroit’s economy.
This presence is one reason behind the designation of the city as an Empowerment
Zone in 1995. This designation was to spark economic activity by providing federal
and state funds for programs aimed at attracting business. Beyond the scope of
matching funds and economic attractors, a large portion of urban disinvestment is
revealed in lending and insurance patterns. This analysis examines the situation in order
to evaluate the extent of mortgage redlining within the Tri-County Region.

The Tri-County Region includes Detroit proper and its surrounding
communities. This geographic representation includes urban areas as well as suburban

communities, which helps to give a broad perspective of redlining. (Map 4.1)

4|

   

Southeast Michigan

 

Map 4.1: Note: Illustrates the geographic area of the Tri-County
' region within Southeast Michigan. Source: Southeast Michigan Council

of Governments (SEMCOG) 1990 Census Tracts: Wayne, Oakland and
Macomb Counties (1992)

42

Wayne County is the most populated of the counties, within the region, with
2,111,687 residents. Of the three counties used in this study, Wayne has the largest
black population of the three counties, with 40 percent of its population being black.
It is the most urban and includes Detroit proper and immediate surrounding
communities. (Map 4.2) The median household income for Wayne County, in 1989,
was $27,997 and is the lowest of the counties within the study area (SEMCOG, 1993).

Oakland County is the second most populated of the Tri-County Region, with
1,083,592 residents. Seven percent of the population is black. The county has
experienced large suburban growth in the last decade and continues to grow. Oakland
County is located to the northwest of Wayne County and includes many popular
southeast Michigan suburbs. (Map 4.3) The median income for Oakland County in
1989 was the highest of the three counties at $43,407 (SEMCOG, 1993).

Macomb County is the least populated of the Tri-County Region, with 717,400
residents. It also has the least black population of the three counties, with 1 percent
black residents. Macomb County is located to the north of Wayne County and has
experienced some suburban growth to the southern fringe and large growth in the

central county region along M-59. For a geographic representation of the area,

43

 

 

 

 

 

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. ...... J .5”; g 75“ ' ‘
m.“ g
N 5"" 3m: . WWI!

 

 

 

 

 

 

 

.... ' ”7:" ”7““ WAYNEyCOUNTY

 

Map 4.2: Note: Illustration of Wayne County geographic area.
Source: Southeast Michigan Council of Governments (SEMCOG)
and Wayne State University. 1990 Census Community Profiles for
Southeast Michigan. (1993, p. 150)

 

 

44

 

 

 

 

 

 

 

 

 

 

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m
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my _ GROVELAND arwvoav
' P..—
nose smmnsw W
may
ANGEUJS
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. svwm LK.
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agent
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FARMMTW
LYGV ‘ HILLS
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I?" g rmuorou
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OAKLAND COUNTY

Map 4.3: Note: Illustrates Oakland County’s geographic region.

Source: Southeast Michigan Council of Governments (SEMCOG)
and Wayne State University. 1990 Census Community Profiles for
Southeast Michtfl. (1993, p. 56)

 

 

45

please refer to Map 4.4. The median income for Macomb County in 1989 was

$39,931, falling right below Oakland County (SEMCOG, 1993).

Data and Methods

 

Data used in this analysis came from three different sources. First, 1992
HMDA data, published in 1993 and is the most recent data available, was gathered for
the three counties. This data set included total number of mortgage loan applicants
who applied for a loan in 1992 within the Tri-County Region, the percentage of black
mortgage loan applicants’ , the percentage of mortgage loan applicant denials by racial
extraction, and income on the applicant. These data are used to find a presence of
racial disparity in lending patterns within the study area. The second data set was
MCD demographic information from the Southeast Michigan Council of Governments.
Data extracted from this source was the median income, percentage of black population
and percentage of owner—occupied households for each minor civil division within the
region. This served as a means of comparison of the average resident profile. lastly,
1992 insurance data from the N AIC, which included number of policies enforce,
number of cancellations and nonrenewals by insurer of voluntary market or private
insmance and the percent of involuntary market or Michigan Basic Insurance by zip

code was used.

46

MEMPHIS

 

 

 

 

 

 

 

 

 

 

mcoua w
BRUCE ARMADA RICHMOND
l E
wmrm 1w LENox qg
SHELBY MACOMB /}
. ur. wens
STERLNG °
W mi“
FRASER
sr. cum SHORES
WARREN
l—I
CENTER LINE
LJ W
I Grosse Potato Shores

 

MACOMB COUNTY

Map 4.4: Note: Illustrates the geographic region of Macomb County.
Source: Southeast Michigan Council of Governments (SEMCOG)
and Wayne State University. 1990 Census Community Profiles for
Southeast Micfigan. (1993, p. 2)

 

 

47

An explanation of the variables used in this analysis is as follows:

PCT 1:

PCT 2:

PCT 3:

PCTOWN:

PCTINVOL:

PCTC:

BLKPCT:

DIFFINC:

DIFFDEN :

REGDEN :

The percentage of white mortgage loan applicants’ denied in

1992 within the Tri-County Region based on 1993 HMDA
data.‘

The percentage of black mortgage loan applicants’ denied in
1992 within the Tri—County Region based on 1993 HMDA data.

The percentage of other mortgage loan applicants’ denied, in
this case includes all other minority extractions other than

black, within the Tri-County Region based on 1993 HMDA
data.

The percentage of owner-occupied households for each
MCD based on data from SEMCOG.

The percentage of involuntary market ( Michigan Basic
Insurance or state FAIR plan insurance) for the region based on
1992 NAIC insurance data.

The percentage of private insurance or voluntary market
insurance cancellations and nonrenewals by the insurer for
the region based on 1992 NAIC data.

The percentage of black population within each MCD based on
data from SEMCOG.

The percent difference between white and black mortgage loan
applicants’ incomes based on 1993 HMDA data.

The percent difference between white and black mortgage
loan applicants’ denied based on 1993 HMDA data.

The mean of all denied applicants for the region based on 1993
HMDA data

 

' Denials in this case included denials by financial institutions, withdrawn applieants and file closures for
both conventioml and govemment-backed loans. Thus leading to the same outcome of applications not

approved.

48

In order to analyze the data successfully, insurance data by zip code and HMDA
data by census tract was aggregated and put into common geographic units. Minor
Civil Divisions or MCDs were chosen as the best common geographic unit for this
analysis. A list of the 123 MCDs used in this analysis are included in the appendix of
this thesis. Because minor civil divisions are used as a geographic unit by the census
bureau, conversion of the HMDA data to MCDs was done with ease. The conversion
of the insurance data by zip codes posed a slight difficulty. Using a MCD map with
zip code boundary overlays of the three counties done by SEMCOG, these data were
aggregated and converted into MCDs

Once data were aggregated to the same geographic unit, data sets could then be
compared. To test for a significant difference between the means of white and black
mortgage loan applicant denials a T-test was used. Thus, offering a preliminary
indication of a presence of racial disparity. In addition to a T—test, correlation was
used to find if this relationship was positive or negative. In order to identify specific
relationships between the variables, multiple regression was used to test the HMDA

data separately and then correlation was used to test the HMDA and insurance data.

49

HMDA Analysis

 

In order to obtain a schematic of current lending patterns, several statistical tests
were conducted in an order and were used to build upon the results of the former tests
conducted in this analysis. Correlation and T-tests were used to establish and clarify a
relationship between the percent of black and white mortgage loan applicants’ denied.

The correlation, further detailed in Table 4.1, was used to determine whether
there was a positive or negative relationship between the percentage of white mortgage
loan applicants’ denied (PCTl) and the percentage of black mortgage loan applicants’
denied (PCI‘2). The research hypothesis expected a negative correlation. This
negative result would produce an absolute inversion and would indicate evidence of a
totally racist market.

Table 4.1

 

Null Hypothesis Research Hypothesis Result

 

Ho:r=or>0 H1:r<0 r>0

 

 

 

 

 

Note: Correlation conducted on PCT 1 and PCT 2

There was a significant positive correlation between the percentage of white
mortgage loan applicants’ denied and the percentage of black mortgage loan applicants’

denied. The r value in this case was .3057. This means, as the number of white

50

mortgage loan applicants’ denied increases, there is an increase in the number of black
mortgage loan applicants’ denied. This result indicates that there is no absolute
inversion or evidence of a totally racist market but to note any presence of racial
disparity, a T-test was conducted.

A T-test at 95 % confidence level was used to compare the percentage of white
mortgage loan applicants’ denied and the percentage of black mortgage loan applicants’
denied with the regional denial rate. This would compare the means of both
percentages of white and black mortgage loan applicant denials and the regional denial

mean. In the case of PCTl, illustrated in Table 4.2, it was hypothesized that PCTl

would fall bellow the region denial rate.

Table 4.2

 

Null Hypothesis Research Hypothesis Result

 

H0: PCTl Z REGDEN PCT] < REGDEN PCT1< REGDEN

 

 

 

 

 

Note: T-test conducted on PCT l and REGDEN at a 95 % confidence level.

It was expected that the percent white mortgage loan applicants’ denied would
be lower than the regional denial mean. As expected, the percentage of white

mortgage loan applicants’ denied was significantly lower than the regional denial rate.

51

As for PCT2, it was hypothesized that PCT2 would fall above the regional
denial rate. This result was expected based on the aforementioned result of the percent
of white mortgage loan applicants’ denied falling significantly lower than the regional

denial rate.

Table 4.3

 

Null Hypothesis Research Hypothesis Result

 

H0: PCT2 i REGDEN PCT2 > REGDEN PCT2 > REGDEN

 

 

 

 

 

Note: T-test conducted on PCI‘ 2 and REGDEN at a 95 % confidence level.

The results indicate that the percentage of black mortgage loan applicants’
denied was significantly higher than the regional denial rate. This clearly illustrates the
anticipated racial disparity in the denial rates and further illustrates the discrepancies in
lending patterns in the Tri-County Region.

In order to establish whether there was a significant difference between the
denial rates of whites versus blacks within the region, a one-tailed T-test with a 95 %
confidence level was conducted. This test would further clarify any differences
betweenthemeansofPCT 1andPCT2. ItwashypothesizedmatPCleouldbe
significantly lower than PCT 2. Based on the presence of racial disparity thus far, the

mean of the percentage of black mortgage loan applicants’ denied within the Tri-

52

County Region would be expected to be greater than the mean percentage of white

mortgage loan applicants’ denied.

Table 4.4

 

Null Hypothesis Research Hypothesis Result

 

Ho: PCT1=PCT2 H1: PCI‘1<PCI‘2 PCT1<PCT2
orPCT1>PCT2

 

 

 

 

 

Note: One tailed T—test conducted on PCT l and PCT 2 at a 95 % confidence

level.

As anticipated, the result of the T-test illustrates that there is a significant
difference between the denial rates of whites and blacks for the region and that white
denial rates are significantly lower than those of black applicants. The t-value for this
case is -5.27 . In order to establish cause and effect of the discrepancies in the lending
patterns, multiple regression was conducted.

Further analysis was done on the HMDA data for the Tri-County Region in
order to examine the difference of denials between white and black applicants and if
factors such as the difference of income between white and black mortgage loan

applicants, black population, and percent owner-occupied households have an effect in

53

causing the disparities. The following equation illustrates the linear regression model

used:

Yi = a + lel +b2X2 + b3X3 + ei

Yi = indicator of applicant denials
X = independent variable
a = constant
bl = regression coefficient
b2 = regression coefficient
b3 = regression coefficient

ei = error/residuals

54

The following table illustrates the null hypotheses, research hypothesis and

results of the multiple regression of the HMDA data for the Tri-County Region:

 

 

 

 

 

 

 

 

Table 4.5

Dependent Variable Null Hypotheses Research Hypothesis Result
BLKPCT=or<0 BLKPCT>0 BLKPCT>O
PCT] DIFFINC = or>0 DIFFINC < 0 DIFFINC < 0*
PCTOWN = or<0 PCTOWN < 0 PCTOWN < 0
' BLKPCT=or<0 BLKPCT>0 BLKPCT<0*
PCT2 DIFFINC = or >0 DIFFINC < 0 DIFFINC < 0
PCTOWN = or>0 PCTOWN < 0 PCTOWN <0
MEDINC = or>0 MEDINC < 0 MEDINC < 0*
DIFFDEN DIFFINC = or>0 DIFFINC < 0 DIFFINC < 0
BLKPCI‘ = or <0 BLKPCT > 0 BLKPCT < 0
PCTOWN = or<0 PCTOWN <0 PCTOWN < 0

 

Note: *Results with no significance from low t-values at 95 % confidence level.

As illustrated in Table 4.5, there are rather significant results. It was expected

that the lower the percent of white mortgage loan applicants’ denied, the higher

percentage of black population, the lower the difference of income, and the lower the

percentage of owner-occupied households. The test results confirmed what was

expected except for difference of income, which was insignificant. The results clearly

indicate that the percentage of black population and percent owner—occupied households

has an effect on the percent of white mortgage loan applicants’ denied. The residuals

 

55

indicate a normal distribution and no statistical aberrations. Figure 4.1 illustrates the

residuals for the multiple regression analysis on PCT 1.

Normal Probability (P-P) Plot
Standardized Residual

 

 

 

1.0 + + .................. + ................... + ................... a

I .*I

I _.. I

I . t I

I new. I

I .* |

I teases I

l ** - I

I . I

I seat I

.75 + *** . +

I tittt I

I eat I

I .. I

I eat I

I n I

I * I

O | at I
b l ‘ I
a | t I
e .5 + * . +
r I tit . I
v I it I
0 I * I
d I I
I at . I

I r I

I u. I

I it. I

I *- |

.25 + . +

I .ee I

I * I

I I

| '* I

| ** l

I ** I

| - I

I . atone I

I ocean I

+* ---+- # --- ¢ +

.25 .5 .75 1.0

Figure 4.1: Note: Illustrates the residuals for the multiple regression
analysis conducted on PCTl.

56

As for PCT2, it was expected that the lower the percent of black mortgage loan
applicants’ denied, the higher the percentage of black population, the lower the
difference of income and the lower the percentage of owner-occupied households. The
results illustrate that the lower the difference of income and the lower the percentage of
owner-occupied units, the lower the black mortgage loan applicants’ denied. As
indicated by the result, the percent difference in income and percent owner—occupied
units has a significant effect on the percent of black mortgage loan applicants’ denied.
An interesting result is that the percentage of black population remained insignificant.
Thus, the percentage of blacks within this region does not factor into black applicant
denial rates. Again, the residuals in this case indicate no statistical aberrations. Figure

4.2 illustrates the residuals for the multiple regression analysis on PCT2.

57

Normal Probability (P-P) Plot:

standardi zed Residual

 

----------__-_-_--+_

A.
T

 

 

*titti

tit

a“
an
n
it
"a
n
ee_
«are
an .
at
as.
as,
it.

tit_

Observed

 

.75

.25

ession

Illustrates the residuals for the multiple regr

Note:

Figure 4.2

analysis on PCT2.

58

In order to further examine the difference between white and black applicant
denials, the difference between the two was taken and the factors of median income,
difference of income, the percentage of black population and the percent of owner-
occupied households were regressed on this difference. It was hypothesized that the
lower the difference between white and black mortgage loan applicants, the lower the
median income, the lower the difference of income, the higher the percentage of black
population and the lower the percent of owner-occupied households.

The results of this test produced expected results. The results indicate that the
percent difference in income between white and black applicants, the percentage of
black population and the percentage of owner-occupied units have a significant effect
on the percent difference between white and black applicants denied. Median income
proved not to be a significant factor. Difference of income and percent owner-
occupied were significant factors and were less than zero as expected. This explains
that the lower the difference of income and percent owner-occupied, the lower the
difference between white and black applicants denied. The percentage of blacks was
expected to be greater than zero, but instead the opposite held true. The result was the
null or the inverse in this case. This could be explained by the low black populations
relative to the white population in both Oakland and Macomb counties. As found with
the last two results, the residuals indicate no statistical aberrations. The residuals for

this multiple regression analysis is found on the next page in Figure 4.3.

59

Normal Probability (P-P) Plot:
standardized bridal

 

1.0 + ------------------- + ------------------- + ------------------- + ------------------- t

I “I

I sentence I

I * I

I H I

I u I

I H I

I t I

I tit I

I . I

.75 + *** +

I t I

I r I

I treat . I

I * I

I test I

I . I

o I * I

b | I. I

I l at I

e .5 + a +

r I to I

V I * I

e I ** . I

d I * I

I II. I

I t. I

I I" I

I * I

I see I

.25 + ** +

I * I

I .. I

I eat I

I - I

I O * I

I * l

I * I

l * I

I . tea I

+***** e t e e
W

.25 I5 .75 1.0

Figure 4.3: Note: Illustrates the residuals for the multiple regression
analysis on DIFFDEN.

60

HMDA & INSURANCE ANALYSIS

 

Two correlation tests were conducted on insurance and HMDA data. The first
correlation is used to find the relationship between involuntary market (state FAIR
plan) and policy cancellations and non—renewals by the private insurer. Thus, the first
hypothesis is that the higher the percentage of the involuntary market, there would be a
higher the percentage of voluntary market (private insurance) cancellations and
nonrenewals by the insurer. Table 4.6 expresses the null hypothesis, research

hypothesis and the result.

Table 4.6

 

Null Hypothesis Research Hypothesis Result

 

Ho:r=or<0 H1:r>0 r>0

 

 

 

 

 

Note: Correlation conducted on PCTINVOL and PCT C

There proved to be a positive correlation between the percent of involuntary
markets or state FAIR plan areas and the percent of voluntary market cancellations and
nonrenewals by the insurer. This confirms the notion that the higher the percent of

involuntary markets, the higher the percent of cancellations and nonrenewals by private

61

insurers. The r value in this case was .578. This association clearly expresses that
there is a problem obtaining and maintaining private insurance within these areas, once
there is an influx of state FAIR plan insurance.

Does the relationship between homeowner’s insurance and mortgage lending
patterns have a significant impact on redlining in urban areas? The second correlation
test used directly addresses this relationship. It was hypothesized that the higher the
percentage of the involuntary market (state FAIR plan), there is a higher the percentage

of black mortgage loan applicant denials.

Table 4.7

 

Null Hypothesis Research Hypothesis Result

 

Ho:r=or<0 H1:r>0 r>0*

 

 

 

 

 

Note: Correlation conducted on PCTINVOL and PCT 2
*Result was not significant.

The final correlation test produced interesting results. Although there was a
positive correlation the results were not significant. The r value in this case was .0396.
There are many factors that could explain this result. Low percentages of black
population within both Oakland and Macomb counties may be a factor and areas of
involuntary markets are high in some rural areas of the region where there is low

percentages of blacks.

62

Summation

Overall, the statistical analysis conducted on the Tri-County Region produced
interesting results. Most of the results were expected and found to be significant in
proving the main hypothesis of this thesis, which is that there is a significant degree of
mortgage redlining. Although there was not a significant relationship between
mortgage redlining and the percent of involuntary market or state FAIR plan, results
indicate problems in obtaining and maintaining private homeowner’s insurance in areas
with a predominance of state FAIR plan insurance. These data clearly illustrate that
there continues to be a disparity between whites and blacks within mortgage lending
patterns. There also remains a significant difference between white applicants denied
and black applicants denied. The residuals clearly indicate no statistical aberrations.
Thus, confirming racial disparities in the Tri-County Region.

Various factors continue to play a role in the denial of blacks. The difference
of income, the percentage of black population and percent owner-occupied households
all significantly effect the denials of blacks. Lending patterns within the Tri-County
area do show problems that are reflected by the nation. Denial rates continue to be
where most of the disparity between white and black applicants are found.

Correlation conducted on the percentage of involuntary markets and voluntary
market cancellations and non-renewals by the insurer clearly show a positive

relationship. This confirms the NAIC’s findings that there is a problem obtaining

63

private insurance in urban areas and voluntary market cancellations and nonrenewals by
the insurer are partly responsible for this problem.

As for the hypothesis, that there is a relationship between homeowner’s
insurance and mortgage lending patterns, the results produced from the correlation
analysis were not conclusive. Although a positive correlation was generated, the r
value was insignificant. Thus, there may be other factors that lend itself to this
relationship. Additionally, the low-black populations in Macomb and Oakland counties

may have been a factor in producing insignificant results.

Analytical Limitations

 

There are limitations to the statistical analysis of the Tri-County Region. These
limitations are not formidable but do need to be recognized. First, homeowner’s
insurance data used in this analysis was from a nationwide data call by the National
Association of Insurance Commissioners, NAIC, as opposed to a neutral party. This
was the only insurance data available because insurance companies are not required by
law to disclose information. Furthermore, data from the NAIC were aggregated by zip
codes and in order to be statistically analyzed it needed to be converted into Minor
Civil Divisions or MCDs, which may have introduced a small percentage of error.

Another problem encountered in the analysis of the Tri-County Region, is that

HMDA data does not include credit worthiness because it is difficult to quantify.

However, the auditing technique does allow for the inclusion of credit worthiness in its
analysis. It is suggested that statistical analysis, like the one conducted in this thesis, is
used to give a schematic of problem areas and then mapping residuals will give a
geographic scope the problem. After the generation of maps, spot check audits could
be conducted to target questionable business practices prevalent in an area.

Lastly, this study does not separate loan types. FHA/VA loans were factored
into the total number of denials. It is suspected that the greatest disparities lie in
conventional loan denials. Further study that builds upon this analysis using a

separation of loan types could detail the disparity between them.

Chapter 5

Discussion and Conclusions

Visualize a beautiful home and the hopes of a young couple as they enter a
lending institution to secure an investment. Results of this analysis clearly indicate that
their opportunity for denial in the Tri-County Region increases because they are black.

Despite legislation designed to combat mortgage redlining, this study clearly
illustrates a presence of racial disparity in mortgage lending denial rates in the Tri-
County Region. There is a significant degree of redlining present and difficulty in
obtaining and maintain homeowner’s insurance parallels. Tests proved a significant
difference between the percentage of white mortgage loan applicants’ denied and the
percentage of black mortgage loan applicants’ denied. Black mortgage loan applicants
were significantly denied more often than their white counterparts. These results
confirm discriminatory practices by the private sector.

Racial disparity in mortgage lending patterns and insurance redlining affects the
entire economy.

Although the racial dimension is among the most pressing and morally

repugnant aspects of community disinvestment, the eroding job base, the small

business credit crunch and the globalization of capital are problems that affect

all Americans. These issues are racial but they require more than racial
solutions (Taibi, 1994, p. 1470).

65

66

This is key in addressing the problem of redlining. Reformulating the problem based
on geography in relation to racial characteristics cannot be the only remedy in this case
because of the sheer interdependence of the economy.
Additionally, problems keeping with the spirit of the law become apparent when
analyzing the mechanisms used in the CRA rating process. Currently,
banks are rated on 12 factors. Only three have anything to do with where loans
are actually made [and] whether banks locate branches in poor neighborhoods.
Three other rating factors relate only to paperwork. Banks are thus assessed,
for example, on the quality of their brochures they publish about their lending
philosophy (Loeb, Cohen & Johnson, 1995, p. 58).
Based on these factors, the CRA has become a measure of marketing to a target
audience, instead of measuring true indicators of redlining. Eugene Ludwig,
comptroller of the currency, concedes the point: ”What the law asks us to evaluate is
‘Are you meeting with the community? Are you advertising to the community’ "
(Loeb, Cohen & Johnson, 1995, p. 58).
This further explains how financial institutions continue practices of redlining
and maintain satisfactory CRA ratings.
One bank asked the FDIC for a favorable CRA rating because some of its employees
participated in a community window-washing project; the FDIC approved the request.
Another bank asked the Federal Reserve for high CRA rating, in part because it
established a $60,000 line of credit for local businesses to purchase Girl Scout cookies.
The Fed commended the bank for the effort (Loeb, Cohen & Johnson, 1995, p. 58).

These are only a few examples of how financial institutions can elude the requirements

of the CRA.

67

This interdependence of the economy is why the current acts have not
effectively addressed the problem. ”These two prevailing civil rights paradigms cannot
address the structural nature of disinvestment, because they implicitly accept a
neoclassical economic ideology that is incompatible with genuine reform” (Taibi,
1994, p. 1467). It is recognized that any forced acceptance of investment does not
help the economic well being of the community as a whole. This specifically addresses
the affirmative action argument as relating to lending practices of institutions. To what
extent are lending institutions socially responsible to their community?

Social responsibility is an important factor in harboring the essence behind the
CRA. ' There is always an expected risk in lending, thus the use of variable interest
rates. To what extent are such risks "real” or 'inculterated"? This extent varies
. according to the philosophy of the lending institution as well as society’s expectations.

However, the problem with this argument is that discrimination is found even
when the loan may be risky. Data found in the series in the Federal Reserve Bulletin
clearly indicates that when applicants fall into the lowest income levels, a greater
percentage of whites are approved, whereas their minority counterparts are not. The
results, in this analysis, of the T-test between white mortgage loan applicants’ denied
and black mortgage loan applicants’ denied for the Tri-County Region clearly show a

significant difference between the two with white denial rates significantly lower than

68

black denial rates. Again, this is where subjective criteria interplays with real or
imagined risks associated with race.

The study by Squires and O’Connor (1993) confirmed that lenders who do
redline are not any more profitable than those who do not practice redlining. This is an
important conclusion because it directly addresses this subjective criteria interplayed
with real or imagined risks associated with race. This conclusion clearly illustrates the
perceived risks of lenders, who avoid lending in predominately minority urban areas,
are greater than reality.

The problem that has occurred with using civil rights as a focal point in solving
the problem of redlining is that it is easy to lose sight of the big picture. If an
individualistic approach is not a viable solution, because of the interdependence of the
economy, then a civil rights approach, in conjunction with policy that addresses the
economic vitality of the community, is the best alternative.

The effects of these acts on small lending institutions may be detrimental.
Although banks offset the risk of loans through interest rates, small lending institutions
cannot afford to take high risks on limited resources. What has been found with the
recent expansion of the HMDA is that many small banking institutions have suffered
because of the lack of resources to meet the requirements of these acts. Large lending
institutions have absorbed the impact and have also taken advantage of these small

lending institutions closing down by either buying them out or eliminating the

69

competition. Therefore, keeping in mind that one of the most efficient ways of
harboring the CRA within the community is to promote the vitality of small lending
institutions in the community. Provisions in the development of policy that specifically
addresses these small lending institutions is necessary.

In addition to current financial institutions, homeowner’s insurance companies
should be held responsible for fair business practices. Currently, there is no legislation
regulating the homeowner’s insurance industry and its affects on redlining. Insurance
companies are directly involved because in order for a home mortgage to be processed
homeowner’s insurance is required. As proved in the correlation analysis between the
percentage of involuntary markets and the percentage of voluntary market cancellations
and nonrenewals by the insurer there is a problem with the availability of private
homeowner’s insurance. This availability problem is present in obtaining and
maintaining homeowner’s insurance which directly affects the financial secmity of the
applicant and his/her community.

Michigan has one of the largest state FAIR plan insurance programs in the
counuy, yet it still has problems as illustrated in this analysis of the Tri-County
Region. Insurance companies should be held responsible for fair business practices.
Although increased governmental regulation is not popular, disclosure should be
mandated to obtain a perspective of the problem. The NAIC has admitted there is a

problem with the availability of insurance within urban areas and has confirmed it

70

through independent statistical tests. However, they do not recommend disclosure
because of costs. Large financial institutions have had no problems meeting the cost of
disclosure, the insurance industry should not. Furthermore, at the cost of those unable
to obtain insurance, profitability remains the industries primary focus. This is
confirmed by the study done by the NAIC (1994) and a study done by Squires and
Valez (1987).

Current legislation does require reform and change is needed in order to
perpetuate economic vitality in needed areas. Current legislation has been useful in
gathering extensive raw data to gauge the problem of mortgage lending. As seen in the
data found in the 1991, 1992 and 1994 issues of the Federal Reserve Bulletin, little has
changed over the course of 1990, 1991 and 1992 (Canner & Smith, 1991, 1992, 1994).
. Due to the value of the HMDA data and the CRA ratings, it becomes clear that
proposed legislation that would eliminate 88 percent of financial institutions currently
regulated by the CRA would be detrimental.

The issue of redlining is problematic because of the wide variety of elements
that create the interdependence of the economy. Financial institutions do have a social
responsibility to their community because they are federally mm by tax dollars.
However, in order to stay in business, they cannot be expected to practice ”altruistic"
lending in every case proposed. Incentives for small lending institutions within the

community to invest and remain in their area will help to off-set some of the costs of

71

these acts. Policy that considers how to curtail the extensive denial rates of minority
applicants will also help to address the problem holistically.

Administrative reactions to financial institutions’ compliance with these acts
have proven to be the most acceptable process by both the federal government and the
courts. However, to expedite the compliance of lending institutions, the process needs
to be accelerated through implementing advanced technological systems that can handle
the volume of data generated. In addition, the acts themselves need to be amended to
clarify when legal action should be taken and if damages are appropriate to further
compliance of lending institutions. Furthermore, integrating current HMDA data and
geographic databases, like GIS, will help to incorporate a clear geographical picture of
affected areas. Administering the same requirements to insurance companies and
mandating data be gathered by census tracts and blocks, as HMDA data is required,
will help to measure current problems and implement regulation.

Further study which includes a larger sample of data and a separation of loan
types would help to specify problem areas. This separation of loan types will illustrate
if problems lie in conventional loans or in FHA/VA loans. It is expected that there are
significant problems with minorities obtaining conventional loans. Auditing bank
procedures and analyzing bank locations relative to urban areas would show if financial
institutions within the region are living up to their CRA commitment and to clarify

applicant credit worthiness. Future study should also include mapping the residuals

72

from the multiple regression analysis to give a geographic representation of problem
areas to target.

Additionally, analyzing bank branch ratios and homeowner’s insurance patterns
will further reveal the effects of qualitative factors on redlining. It has been shown that
these factors have had an effect but more research needs to be conducted in order to
expose the extent of the problem. Homeowner’s insurance providers have been ignored
in this respect and there is no current legislative remedy. Legislators and the public
need to be aware of the adverse effects of homeowner’s insurance companies’ dubious
practices involving redlining.

Legislation that promotes disclosure would make it possible to conduct larger
studies on insurance redlining. The data disclosed should also conform to census tract
geography which would make it easier for larger studies to take place. Currently, the
industry uses zip code boundaries to aggregate information. This makes it very
difficult to transfer the data if the study area is any larger than used in this analysis.

The positive association found between the percentage of involuntary markets
(state FAIR plan) and the percentage of voluntary cancellations and nonrenewals by the
private insurer, clearly illustrate a lack of availability of insmance for those within
involuntary markets. These results confirm studies done by the NAIC (1994) and

Squires and Valez (1987). This lack of availability of insurance for those within urban

73

areas confirmed by such studies should promote the need for disclosure and further
study.

Subsequent hypotheses further proved there is a significant degree of redlining
within urban areas. There is a significant disparity between the denial rates of white
and blacks. Residuals in all cases show no statistical aberrations. Thus, the results are
conclusive and preclude the need for further study and reform. However, complete
elimination of government involvement is not the answer. As seen by the abuse of
insurance companies, government regulation is nwded to ensure fair business practices.
Regulations that address the entire spectrum of the problem would be best suited.
Although government involvement has lost popularity, this proposes to be the best
method of curtailing redlining.

Awareness is the key. Discrimination has not disappeared with the 1960’s. The
results confirm the need for constant monitoring and disclosure by both the mortgage
and insurance industries. Holding the private sector responsible for dubious practices
should be consistent.

Historically, the U.S. government has reacted to the problem of redlining
through legislation. In order to further address the problem holistically, proactive
measures will prove to be best in clarifying current standards. Due to the

interdependence of the economy, redlining has proven to deteriorate the economic

74

vitality of some areas. This deterioration effects the entire economy. Therefore,

continued efforts at curtailing this social enigma will prove to benefit all.

APPENDIX A

75

Allen Park
Armada

Auburn Hills
Belleville
Berkley

Beverly Hills
Birmingham .
Bloomfield Hills
Bloomfield Twp
Brandon Twp
Brownstown Twp
Bruce Twp
Canton Twp
Centerline
Chesterfield Twp
Clarkston
Clawson

Clinton Twp
Commerce Twp
Dearbom
Dearbom Hts.
Detroit

East Detroit
Ecorse
Farmington
Pennington Hills
Femdale
Flatrock

Franklin

Fraser

Garden City
Gibraltar

Grosse IIIe
Grosse Pointe
Grosse Pointe Farms
Grosse Pointe Park
Grosse Pointe Shores
Grosse Pointe Woods
Groveland Twp
Hamtramck
Harper Woods

76

Minor Civil Divisions Used in this Analysis

Harrison Twp
Hazel Park
Highland Park
Highland Twp
Holly

Holly Twp
Huntington Woods
Huron Twp
Independence Twp
lnkster

Keego Harbor
Lake AngeIus
Lake Orion
Lathrup \frllage
Lenox Twp
Leonard
Lincoln Park
Livonia

Lyon Twp
Macomb Twp
Madison Hts
Melvindale
Milford

Milford Twp
Mt. Clemens
New Baltimore
New Haven
Northville
Northville Twp
Novi

Oak Park
Oakland Twp
Orchard Lake VIIIage
On'on Twp
Ortonville
Oxford

Oxford Twp
Pleasant Ridge
Plymouth
Plymouth Twp
Pontiac

Ray Twp
Redford Twp
Richmond
Richmond Twp
River Rouge
Riverview
Rochester
Rochester Hills
Rockwood
Romeo
Romulus

Rose Twp
Roseville
Royal Oak
Royal Oak Twp
Shelby Twp
South Lyon
Southfield
Southfield Twp
Southgate

St Clair Shores
Sterling Hts
Sumpter Twp
Sylvan Lake
Taylor

Trenton

Troy

Utica

Van Buren Twp
Walled Lake
Warren
Washington Twp
Waterford
Wayne

West Bloomfield
Westland
White Lake Twp
Wrxom
Wolverine Lake
Wood Haven
Wyandotte

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