Working Paper No. HF-014 

BLACK AND WHITE DISPARITIES IN SUBPRIME
 

MORTGAGE REFINANCE LENDING
 

Randall M. Scheessele 

April 2002 

Office of Policy 

Development 

and Research 

Comments Welcome 

e-mail address: 

Randall_M._Scheessele@hud.gov 

Housing Finance 

W O R K I N G P A P E R S E R I E S
 

U.S. Department of Housing and Urban Development 
Black and White Disparities in Subprime Mortgage Refinance Lending 

ABSTRACT 

This paper examines patterns in Home Mortgage Disclosure Act (HMDA) data in 

an effort to understand the types of neighborhoods with high concentrations of subprime 

refinance lending. The HMDA data clearly demonstrate the growth in subprime 

refinance lending and its disproportionate impact on low-income and predominantly 

black neighborhoods throughout the nation. Since home equity is typically the main 

source of wealth for borrowers in low-income and minority neighborhoods, it is essential 

that creditworthy borrowers in these neighborhoods have access to lower cost prime 

credit and weaker credit borrowers in these neighborhoods have access to subprime credit 

that is priced appropriately to their credit circumstances. 
 

 

 

Black and White Disparities in Subprime Mortgage Refinance Lending1 

I. Introduction 

Over the last decade, subprime mortgage lending has become an important 

component of the overall mortgage market. Subprime mortgage lending increased from 

$90 billion in 1996 to over $173 billion in 2001 and accounted for 8.3 percent of the 

overall mortgage market in 2001.2 (See Figure 1.3

) Subprime mortgage lending serves a 

critical role in the nation’s economy by providing loans to borrowers who do not meet 

the credit standards for borrowers in the prime market. These borrowers may have 

blemishes in their credit record, insufficient credit history, or non-traditional credit 

sources.

4

 Subprime lending allows such borrowers to access credit that they could not 

otherwise obtain in the prime credit market. 

Metropolitan area analyses of subprime lending, however, have shown that 

subprime lending is disproportionately concentrated in low-income and minority 

neighborhoods, particularly black neighborhoods, which may suggest that creditworthy 

borrowers in these neighborhoods pay more for credit than borrowers in other 

neighborhoods.5 Furthermore, because of the concentration of subprime lending in lowincome and black neighborhoods, there has been a growing concern that borrowers in 

these neighborhoods are vulnerable to a subset of subprime lenders, who engage in 

abusive lending practices, strip borrowers’ home equity, and place them at increased risk 

of foreclosure. 

1

 The author appreciates Harold L. Bunce for advice and for editing several drafts of the paper. Errors in 

the paper belong to the author. 

2

 Inside Mortgage Finance produced these estimates of subprime mortgage activity. See “Household Leads 

Lenders to a Banner Origination Year in 2001.” Inside B&C Lending. February 11, 2002; and “Mortgage 

Originations Soared to $2.1 Trillion In 2001, Inside Mortgage Finance Estimates.” Inside Mortgage 

Finance. January 25, 2002. The subprime mortgage share declined in 2001 because of a lower interest rate 

environment that led to an increase in all refinances. 

3

 See Table B.1 in Appendix B for additional information. 

4

 Prime credit is also referred to as “A” credit. Subprime credit includes borrowers with slight blemishes in 

their credit histories (“A-”) as well as borrowers with more serious credit problems (“B-D”). 

5

 The Woodstock Institute report, “Two Steps Back: The Dual Mortgage Market, Predatory Lending, and 

the Undoing of Community Development,” analyzed the growth of subprime lending in Chicago’s 

minority and low-income neighborhoods and found that prime lenders active in white and upper-income 

neighborhoods tend to be much less active in lower-income and minority neighborhoods – effectively 

leaving these neighborhoods to unregulated subprime lenders. See Daniel Immergluck and Marti Wiles. 

Two Steps Back: The Dual Mortgage Market, Predatory Lending, and the Undoing of Community 

Development. Chicago, IL. November 1999. An ACORN report reached the same conclusion in its 

analysis of mortgage lending in a number of metropolitan areas. See Separate and Unequal: Predatory 

Lending in America. ACORN. November 2001. 
 

 

 

 

 

 

The concern over the impact of subprime mortgage lending on low-income and 

black neighborhoods is part of the ongoing debate over whether low-income and minority 

neighborhoods have adequate access to housing credit. This debate has traditionally 

focused on home purchase lending but has taken on a new dimension with the increase in 

home equity in low-income and black neighborhoods and the relatively recent explosion 

of subprime refinance lending. 

In 2000, HUD conducted a number of studies using Home Mortgage Disclosure 

Act (HMDA) data that examined patterns in subprime lending in an effort to understand 

the types of neighborhoods with high concentrations of subprime lending.6 This study 

updates those earlier HUD studies by reexamining neighborhood patterns in subprime 

refinance lending using two additional years of HMDA data.7 The data continue to 

demonstrate the disproportionate concentration of such lending in the nation’s lowincome and minority neighborhoods. 

Main Findings. There were three main findings from this paper: 

1) Subprime refinance lending accounted for larger shares of total refinance 

lending in low-income neighborhoods than in other neighborhoods. In lowincome neighborhoods (neighborhoods where income did not exceed 80 percent 

of the metropolitan area median) subprime refinance mortgages accounted for 

36.3 percent of total refinance mortgages compared to 23.8 percent of total 

refinance lending nationwide. 

2) Subprime lenders were an even larger source of refinance credit in 

predominantly black neighborhoods. Subprime refinance lending accounted 

for 44.8 percent of total refinance loans in neighborhoods where blacks comprised 

between 50 and 80 percent of the population and 53.1 percent of total 83,606 

refinance loans in neighborhoods where blacks comprised at least 80 percent of 

the population. 

3) Borrowers in upper-income black neighborhoods were more likely than 

borrowers in low-income black neighborhoods to use subprime refinance 

6

 In 2000, HUD and Treasury formed a Task Force on Predatory Lending and held a series of forums on 

predatory lending in five cities. The HUD-Treasury Task Force published a report and also published 

reports that described subprime mortgage lending trends nationally and in the forum cities. See 

http://www.huduser.org/publications/hsgfin/curbing.html for the following publications: Curbing Predatory 

Home Mortgage Lending: A Joint Report. HUD and Treasury. June 2000; Unequal Burden: Income and 

Racial Disparities in Subprime Lending in America. HUD. April 2000; Unequal Burden in Atlanta: 

Income and Racial Disparities in Subprime Lending. HUD. April 2000; Unequal Burden in Los Angeles: 

Income and Racial Disparities in Subprime Lending. HUD. May 2000; Unequal Burden in New York: 

Income and Racial Disparities in Subprime Lending. HUD. May 2000; Unequal Burden in Baltimore: 

Income and Racial Disparities in Subprime Lending. HUD. May 2000; and Unequal Burden in Chicago: 

Income and Racial Disparities in Subprime Lending. HUD. May 2000. 

7

 Appendix A describes the data collected under the Home Mortgage Disclosure Act. 

2 
 

 

 

loans. Borrowers in low-income neighborhoods where blacks comprised at least 

80 percent of the population were 1.5 times more likely in 2000 to refinance with 

a subprime loan than borrowers in low-income neighborhoods overall. Borrowers 

in upper income neighborhoods where blacks comprised at least 80 percent of the 

population were 2.9 times more likely to have subprime refinancing as borrowers 

in upper-income neighborhoods overall. 

The outline of the paper is as follows. Section II presents a national picture of 

subprime refinance lending and describes the concentration of subprime lending in lowincome and minority neighborhoods. Appendix C provides similar analyses for 27 

individual metropolitan areas.8 Each individual metropolitan analysis includes data on 

the growth in subprime lending between 1995 and 2000 and 2000 subprime refinance 

shares by neighborhood income and minority composition and a map of the metropolitan 

area that depicts the concentration of subprime refinances in low-income and minority 

neighborhoods. The regression model presented in Section III relates the concentration 

of subprime refinance lending to neighborhood demographic indicators in addition to the 

neighborhood income and racial indicators and complements the descriptive discussion in 

Section II. Section IV concludes the paper. 

II. Neighborhood Disparities in Subprime Refinance Lending 

This section describes the overall increase in subprime refinance lending in the 

1990s and the concentration of subprime refinance lending in low-income and minority 

neighborhoods using Home Mortgage Disclosure Act (HMDA) data.9 We focus on 

subprime refinance lending in this paper because it continues to account for the majority 

of total subprime (purchase and refinance) originations.10 

A. Growth in Subprime Lending 

In the 1990s, subprime mortgage lending became an important component of the 

overall refinance mortgage market. The number of subprime refinance mortgages 

reported under the Home Mortgage Disclosure Act (HMDA) increased over 150 percent 

from 239,509 loans in 1996 to 618,572 loans in 1998.11 (See Figure 2.12) A higher 

8

 The 27 metropolitan areas accounted for 728,925 (or 36.5 percent) of the total 1,998,407 refinance 

mortgages and 184,290 (or 38.8 percent) of the total 475,583 subprime refinance mortgages. 

9

 We use the terms “neighborhood” and “census tract” interchangeably in this paper. 

10 The refinance share of total subprime mortgages, however, decreased in 1999 and 2000. Refinance 

loans accounted for over 75 percent of total subprime purchase and refinance mortgages until 1999 and 

2000 when they accounted for 73 and 66 percent, respectively. Subprime mortgage lending has become an 

important component of the overall home purchase market. In 1996, subprime mortgages accounted for 

2.0 percent of the home purchase market compared with 6.0 percent in 2000. 

11 HUD has compiled an annual list of subprime lenders who report data under HMDA. See Appendix B 

for further information on the list. Total subprime lenders who report to HMDA has increased since 1996 

because of the increased popularity of these loans and because more subprime lenders were required to 

3 
 

 

 

 

 

 

interest rate environment led to lower levels of subprime refinances - and refinances 

overall - in 1999 and 2000.13 However, the 475,583 subprime refinance loans originated 

in 2000 nearly doubled the level of subprime lending in 1996.14 

The subprime share of the refinance market depends on the interest rate 

environment. In years when interest rates were relatively low, subprime loans accounted 

for lower shares of overall refinance lending than in years when interest rates were 

higher.15 The subprime share of all refinances increased from 11.2 percent in 1996 to 

23.8 percent in 2000. (See Figure 3.) The subprime refinance share increased every year 

except in 1998 when lower interest rates led to a substantial increase in overall refinance 

lending. 

The growth in subprime refinance lending may be good for higher-risk borrowers 

because it provides increased access to capital markets but it concerns housing advocates 

and regulators for a number of reasons. First, there is evidence that some borrowers 

obtain subprime loans when their credit would qualify them for conventional loans.16 

report under HMDA. See Appendix A for a further discussion of HMDA data. The paper also focuses on 

HMDA loans originated in metropolitan areas. If loans without metropolitan codes were included, 

subprime refinance loans totaled 69,293 in 1993, 768,233 in 1998, and 581,718 in 2000. 

12 HMDA data on mortgage lending in 2001 will not be available until August 2002. Based on the 

estimates from Inside Mortgage Finance (see Table B.1), HMDA data in 2001 will also reflect an increase 

in subprime lending. See Table B.2 in Appendix B for additional information. 

13 The decline in subprime refinance lending in 2000 may also be attributable to the restructuring of the 

subprime industry. 

14 The growth in the subprime market and its impact on the overall refinancing market in the 1990s can be 

explained by demand and supply factors. Home equity increases associated with home price appreciation 

and households’ desire to consolidate increasing debt burdens were the main reasons for the increased 

demand for subprime lending. Wall Street’s interest in the high returns from subprime loans was the main 

supply factor. Securitization allowed lenders the funds to fuel the rapid growth in subprime lending during 

the 1990s. However, investors became more cautious in 1998 after major subprime lenders had to write 

down earnings because of gain-on-sale accounting practices in the industry that lead to higher than 

projected prepayments. The subprime mortgage industry has since consolidated and large investors and 

lenders - often prime lenders - have added more structure to the industry. 

15 Subprime refinancing is less sensitive to interest rates than prime refinancing. Subprime borrowers are 

more likely to refinance with cash out. Prime borrowers are more likely to refinance to obtain lower 

monthly payments and interest costs. 

16 Freddie Mac has claimed that between 10 and 35 percent of subprime borrowers would qualify for prime 

mortgages. See Automated Underwriting: Making Mortgage Lending Simpler and Fairer for America’s 

Families. Freddie Mac. Publication 259. September 1996. Inside B&C Lending reported findings from a 

survey of 50 of the most active subprime lenders that stated that 50 percent of subprime mortgages could 

qualify as investment grade mortgages. See “Half of Subprime Loans Categorized as ‘A’ Quality.” Inside 

B&C Lending. June 10, 1996. Fannie Mae has stated, “about 50 percent of home buyers in the subprime 

market have credit records that are rated A-minus, just shy of qualifying for a low-cost, conventional 

mortgage.” See “Remarks As Prepared for Franklin Raines – Consumers Union Speech.” Fannie Mae. 

December 8, 1999. 

4 
 

 

Second, there is evidence that the interest rates charged higher-risk borrowers could not 

be fully explained solely as a function of the additional risk.17 Third, since subprime 

lending is largely an unregulated industry, the increase in subprime lending has been 

associated with an increase in predatory practices by unscrupulous subprime lenders. 

Finally, subprime refinance lending has been disproportionately concentrated in 

neighborhoods where home equity is most likely to be the borrower’s primary asset. It is 

important that creditworthy borrowers in these neighborhoods have opportunities in the 

prime mortgage market. It is also important that those borrowers with weaker credit in 

these neighborhoods are able to qualify for loans with rates that reflect the additional risk 

of the loan and not be vulnerable to predatory lending practices. A profile of 

neighborhoods with high concentrations of subprime refinance lending is presented in the 

remainder of the paper. 

B. Disparities by Neighborhood Income 

Subprime refinance lending accounted for larger shares of total refinance lending 

in low-income neighborhoods than in other neighborhoods.18 Nationwide, subprime 

refinance mortgages accounted for 23.8 percent of total refinance lending in 2000. In 

low-income neighborhoods, subprime refinance mortgages accounted for 36.3 percent of 

the total 359,354 refinance mortgages originated in metropolitan areas during 2000. In 

upper-income neighborhoods, subprime refinance mortgages accounted for 16.4 percent 

of the total 581,216 refinance mortgages. (See Figure 4.19) 

The disparity in subprime refinance shares among neighborhoods of different 

income levels has declined since 1996. In 1996, the subprime refinance share in lowincome neighborhoods was double the national subprime refinance share. In 2000, the 

subprime share in low-income neighborhoods was 1.5 times the national subprime 

refinance share. (See Table B.3 in Appendix B.) 

C. Disparities by Neighborhood Race 

Subprime lenders were an even larger source of refinance credit in predominantly 

black neighborhoods.20 Subprime refinance lending accounted for 44.8 percent (or 1.9 

17 See Howard Lax, Michael Manti, Paul Raca, and Peter Zorn. “Subprime Lending: An Investigation of 

Economic Efficiency.” Unpublished Paper. Freddie Mac. February 25, 2000. 

18 The census tract income categories are defined as follows: low-income tracts have median incomes that 

are less than 80 percent of the metropolitan area median income (AMI); middle-income tracts, between 80 

percent and 120 percent AMI, and upper-income tracts, greater than 120 percent AMI. In addition, verylow-income tracts have median family income at or below 50 percent of area median. 

19 The subprime refinance share for middle-income neighborhoods was not depicted in Figure 4 because it 

was approximately equal to the overall subprime refinance share. For additional information, see Tables 

B.3 in Appendix B. 

20 We refer to tracts where blacks comprised at least 50 percent of the population as “predominantly black” 

neighborhoods. 

5 
 

times the national average share) of the total 64,346 refinance loans in neighborhoods 

where blacks comprised between 50 and 80 percent of the population and 53.1 percent 

(or 2.2 times the national average share) of the total 83,606 refinance loans in 

neighborhoods where blacks comprised at least 80 percent of the population. (See Figure 

5.21) 

The disparity in the predominantly black and national subprime refinance shares 

has declined since 1996. In 1996, the subprime refinance share in neighborhoods where 

blacks comprised at least 80 percent of the population was 3.7 times the national 

subprime refinance share (41.1 percent versus 11.2 percent). In 2000, the subprime share 

in these black neighborhoods was 2.2 times the national subprime refinance share (53.1 

percent versus 23.8 percent).22 (See Table B.4a in Appendix B.) 

Middle-income and upper-income predominantly black neighborhoods rely on 

subprime loans for refinancing. In fact, the disparity between the subprime refinance 

share for predominantly black neighborhoods and the national average subprime 

refinance share increases as neighborhood income increases. Borrowers in low-income 

neighborhoods where blacks comprised at least 80 percent of the population were 1.5 

times more likely in 2000 to refinance with a subprime loan than borrowers in lowincome neighborhoods overall (54.0 percent versus 36.3 percent). Borrowers in upper 

income neighborhoods where blacks comprised at least 80 percent of the population were 

2.9 times more likely to have subprime refinancing as borrowers in upper-income 

neighborhoods overall (47.7 percent versus 16.4 percent). (See Figure 6.23) 

Predominantly Hispanic neighborhoods had lower shares of subprime refinances 

than black neighborhoods.24 For example, neighborhoods where Hispanics comprised at 

least 80 percent of the population were 1.5 times more likely than the nation as a whole 

to have a subprime refinance mortgage (36.7 percent versus 23.8 percent). 

Neighborhoods where blacks comprised at least 80 percent of the population, however, 

21 For additional information, see Table B.4a in Appendix B. 

22 However, note that interest rates affect the disparity between the black and national subprime refinance 

shares. In 1998, when interest rates were relatively low, the subprime share in neighborhoods where 

blacks comprised at least 80 percent of the population was 4.8 times the national subprime refinance share. 

The subprime share in these black neighborhoods remained relatively stable in 1998 and 1999 while the 

national subprime refinance share increased from 10.8 percent in 1998 to 16.7 percent in 1999. This 

finding could reflect that the average refinance borrower in 1998 took advantage of lower interest rates to 

lower monthly mortgage costs while the average refinance borrower in predominantly black neighborhoods 

had weaker credit and reacted less to interest rate fluctuations and more to a need for cash or the average 

borrower in predominantly black neighborhoods was creditworthy but did not have conventional prime 

opportunities to refinance and sought out subprime refinancing. 

23 See Tables B.5a in Appendix B for additional information. 

24 We refer to tracts where Hispanics comprised at least 50 percent of the population as “predominantly 

Hispanic” neighborhoods. 

6 
 

 

 

 

were 2.2 times more likely than the nation as a whole to have a subprime refinance 

mortgage (53.1 percent versus 23.8 percent).25 (See Figure 7.26) 

The subprime refinance share did not vary remarkably across predominantly 

Hispanic neighborhoods even after controlling for neighborhood income. In 

neighborhoods where Hispanics comprised at least 80 percent of the population, 

subprime loans accounted for 36.3 percent of refinance loans in low-income 

neighborhoods; 38.0 percent of refinance loans in middle-income neighborhoods; and 

32.3 percent of loans in upper-income neighborhoods. (See Figure 8. 27) 

D. Segregation Patterns 

This section combines information on population segregation patterns with data 

on subprime refinance lending. The data show that Hispanics in general, and Hispanic 

borrowers in particular, are more dispersed spatially than blacks. Differences in the 

segregation of black and Hispanic borrowers require analyzing the data by the racial 

characteristics of the borrower (regardless of the neighborhood) to examine if the impact 

of subprime refinancing on black and Hispanic borrowers can be proxied by examining 

the impact of subprime refinancing by the racial or ethnic characteristics of the 

neighborhood. We conclude that analyzing the data by the racial or ethnic and income 

characteristics of the borrower provides similar patterns to those discussed above for 

neighborhoods except for comparisons between borrowers in upper-income Hispanic 

neighborhoods and upper-income Hispanic borrowers. 

1. Spatial Concentration of Borrowers by Race or Ethnicity 

A Mumford Center report on segregation concluded that the average black lives 

in a neighborhood that is 33.0 percent white, 51.4 percent black, 11.4 percent Hispanic, 

and 3.3 percent Asian. The average Hispanic, however, lives in a neighborhood that is 

36.5 percent white, 10.8 percent black, 45.5 percent Hispanic, and 5.9 percent Asian.28 

25 Hispanics represent the youngest minority in the United States and are entering the mortgage market as 

first-time homebuyers. National Mortgage News reports that in the past, immigrants took about 10 years to 

buy a home but the amount of time has decreased as several families work together to purchase a home. 

See “Immigrants Become Increasingly Important Part of Housing Market.” National Mortgage News. May 

11, 1998. 

26 See Table B.4b in Appendix B for additional information. 

27 See Table B.5b in Appendix B for additional information. 

28 The Mumford Center report compares minority composition using 1980, 1990, and 2000 Census data. 

See http://www.albany.edu/mumford/census for the publication: Ethnic Diversity Grows, Neighborhood 

Integration Lags Behind. Lewis Mumford Center. December 18, 2001. This is also true for borrowers. 

For both subprime refinance borrowers and refinance borrowers in general, there is a greater likelihood 

that black borrowers live in Black neighborhoods than Hispanic borrowers live in Hispanic neighborhoods. 

The Census website, http://www.census.gov/hhes/www/housing/resseg/gettable.html also has information 

on residential segregation by race. 

7 
 

 

Data from the 1990 Census show that 19.7 percent of the nation’s metropolitan 

black population lived in census tracts where blacks comprised between 50 and 80 

percent of the population and 36.0 percent of the nation’s metropolitan black population 

lived in census tracts where blacks comprised at least 80 percent of the population. 

Furthermore, 4.7 percent of the nation’s metropolitan census tracts were tracts where 

blacks comprised between 50 and 80 percent of the population and 6.4 percent of the 

nation’s census tracts were tracts where blacks comprised at least 80 percent of the 

population. (See Table B6.a in Appendix B.) 

The 1990 Census data show that 23.8 percent of the nation’s metropolitan 

Hispanic population live in census tracts where Hispanics comprise between 50 and 80 

percent of the population and 16.0 percent of the nation’s metropolitan Hispanic 

population lives in census tracts where Hispanics comprise at least 80 percent of the 

population. Furthermore, 3.3 percent of the nation’s metropolitan census tracts are tracts 

where Hispanics comprise between 50 and 80 percent of the population and 1.4 percent 

of the nation’s census tracts are tracts where Hispanics comprise at least 80 percent of the 

population. (See Table B6.b in Appendix B.) 

The 2000 HMDA data show that both black and Hispanic refinance borrowers are 

less segregated than their populations as a whole but Hispanic borrowers are considerably 

less so. Neighborhoods where blacks comprised between 50 and 80 percent of the 

population accounted for 18.3 percent of all black loans (versus 19.7 percent of the black 

population) and neighborhoods where blacks comprised at least 80 percent of the 

population accounted for 31.2 percent of all black loans (versus 36 percent of the black 

population).29 Neighborhoods where Hispanics comprised between 50 and 80 percent of 

the population accounted for 14.9 percent of all Hispanic loans (versus 23.8 percent of 

the Hispanic population) and neighborhoods where Hispanics comprised at least 80 

percent of the population accounted for 6.7 percent of all Hispanic loans (versus 16.0 

percent of the Hispanic population). (See Tables B.6a-b in Appendix B.) 

2. Subprime Refinance Shares by Borrower Race or Ethnicity 

Subprime lending in 2000 accounted for 54.0 percent of refinance loans 

originated for low-income black borrowers and 32.4 percent of refinance loans originated 

for low-income Hispanic borrowers. The subprime refinance share for all low-income 

borrowers was 34.7 percent. These subprime refinance shares are similar to the shares 

29 Tables 5.a-b indicate that there were a significant number of loans in the “Other/Missing” race category. 

Most of the loans in this category were loans where the applicant did not report his race on a mail or 

telephone application. Subprime loans accounted for a disproportionate number of loans where the 

applicant did not provide race. Subprime refinance loans accounted for 23.8 percent of all refinance loans 

in general but 33.6 percent of loans where the applicant did not provide race. Furthermore, the subprime 

refinance share of these loans increases with the black composition of the neighborhood. The subprime 

refinance share of loans where the applicant did not provide race in neighborhoods where blacks comprised 

no more than 30 percent of the population was 30.6 percent compared to 61.6 percent in neighborhoods 

where blacks comprise at least 80 percent of the population. Therefore, one could conclude that loans 

where the applicant did not provide race are disproportionately loans for black borrowers. 

8 
reported above for low-income predominantly black and low-income predominantly 

Hispanic neighborhoods. The subprime refinance share in low-income neighborhoods 

where blacks (Hispanics) comprised at least 50 percent of the population was 51.0 

percent (34.5 percent). (See Tables B.5a-b and B.7.) 

Subprime loans accounted for 35.1 percent of refinance loans for upper-income 

black borrowers compared to 42.2 percent for borrowers in upper-income predominantly 

black neighborhoods (blacks comprised at least 50 percent of the population). 

Furthermore, upper-income blacks were as likely as low-income borrowers of any race to 

refinance with a subprime loan. (See Tables B.5a-b and B.7.) Subprime loans accounted 

for 16.6 percent of refinance loans for upper-income Hispanic borrowers compared to 

34.0 percent for borrowers in upper-income predominantly Hispanic neighborhoods 

(Hispanics comprised at least 50 percent of the population). As these numbers suggest, 

borrowers in upper-income Hispanic neighborhoods were twice as likely as Hispanic 

borrowers to refinance with a subprime refinance loan. (See Tables B.5b and B.7 in 

Appendix B.) 

The difference between the subprime refinance shares for borrowers in upperincome Hispanic neighborhoods and upper-income Hispanic borrowers was the most 

significant difference found from a comparison of subprime refinancing shares by 

borrower and neighborhood and could be partly explained by the segregation of upperincome Hispanic borrowers in upper-income neighborhoods. That is, upper-income 

Hispanic borrowers were more likely to live in upper-income neighborhoods than upperincome black borrowers. Upper-income neighborhoods accounted for 29.3 percent of 

loans for upper-income black borrowers and 37.5 percent of loans for upper-income 

Hispanic borrowers. (See Table B.8 in Appendix B.) 

E. A Discussion of Results 

The racial and income composition of a neighborhood are correlated with a 

number of factors that may contribute to the observed disparities in subprime refinance 

shares. For example, disparities in the wealth and creditworthiness of a neighborhood’s 

borrowers and the price appreciation of a neighborhood’s properties influence the 

subprime share of a neighborhood’s refinances and are also highly correlated with a 

neighborhood’s income and racial composition. 

Disparities in subprime refinance shares between neighborhoods may be 

exacerbated by the absence of prime lenders in these neighborhoods. Although, lowincome and minority neighborhoods have creditworthy borrowers who would benefit 

from more competition by prime lenders, prime lenders may be more attracted to 

neighborhoods with higher proportions of borrowers who qualify for prime loans and 

who represent profitable cross-selling opportunities for the lenders’ other products. 

Credit History. Research has shown that black borrowers are more likely to 

have a credit blemishes than other racial or ethnic groups and Hispanics and recent 

9 
 

 

 

 

 

 

immigrants have thinner and more non-traditional credit histories.30 Low-income 

borrowers, on the other hand, do not necessarily have worse credit than other 

borrowers.31 Low-income borrowers, however, are more likely to have higher debt 

burdens and lower downpayments, and are more likely to run into credit problems when 

faced with crises like divorce, high medical bills, or unemployment.32 Low-income 

borrowers are also more likely to live paycheck to paycheck, have no savings, and pay 

the minimum on credit cards.33 

Price Appreciation. Borrowers in low-income and minority neighborhoods may 

be less likely than other borrowers to refinance when interest rates fall because they 

typically make smaller initial downpayments and they may live in neighborhoods with 

lower and more house variable price appreciation. To the extent that borrowers in low30 Freddie Mac states that African American borrowers “were about three times as likely to have high-risk 

credit bureau scores – defined as FICO scores below 620 – as were White borrowers, based on Freddie 

Mac’s 1994 mortgage purchases.” Freddie Mac further states that this finding may reflect less about credit 

markets and more about the economic condition of minority families such as higher unemployment rates, 

less security, and lower wealth. See Automated Underwriting: Making Mortgage Lending Simpler and 

Fairer for America’s Families. Freddie Mac. Publication Number: 259. September 1996. HUD 

researchers reached similar conclusions concerning FHA borrowers, “We conclude that the main 

differences between Black, Hispanic, and White borrowers are related to the length of credit history, 

indebtedness, and past credit performance. See Harold L. Bunce, William J. Reeder, and Randall M. 

Scheessele. “Understanding Consumer Credit and Mortgage Scoring: A Work in Progress at HUD.” 

Fannie Mae Research Roundtable Series: Making Fair Lending a Reality in the New Millennium. June 30, 

1999. The HUD PD&R publication, Cityscape (Volume 3, Number 1, March 1997), compiled studies 

related to ethnicity and homeownership. See http://www.dispatch.com/news/special/race/day4/score.html 

for the article: Mark A. Fisher. “Minorities Score lower in ‘Colorblind’ Credit Ratings.” The Columbus 

Dispatch. April 14, 1999. Finally, according to the March 2000 Current Population Survey, black 

households accounted for 8.6 percent of households that owned or were buying a home and according to 

2000 HMDA data, black borrowers accounted for 7.8 percent of refinance loans. According to a 2001 

Fannie Mae, 20 percent of credit-impaired borrowers were African American. Fannie Mae defined a 

credit-impaired borrower as “someone who made a mortgage payment 60 days late, declared bankruptcy, 

or faced foreclosure in the past three years, or had a mortgage and reported any two of the following: they 

had their vehicle repossessed, they had a bill referred to a collection agency, their credit cards were 

charged near their limit, their employer paid them in cash, or they had no credit cards.” Although these 

statistics were bases on different data, they suggest that blacks account for a disproportionate share of 

credit-impaired borrowers. See http://www.census.gov/population/www/socdemo/hh-fam.html for 

information on the Current Population Survey. See http://www.fanniemae.com/media/survey for 

information on Fannie Mae’s National Housing Survey. 

31 Consumers Union reports that low-income borrowers are “notoriously saddled with the stigma of being 

poor credit risks and will continue to pay more, even when they are creditworthy and could qualify for a 

competitively priced loan.” See “Consumers Union Study Blasts Subprime Industry.” Inside B&C 

Lending. August 3, 1998. 

32 The portion of income devoted to servicing debt burden is rising for families that earn less than $50,000 

and falling for families that earn above $50,000. One in five families with incomes below $50,000 

experienced a delinquency greater than 60 days. “While Credit Quality May Improve, Long Term 

Problems Linger.” Page 39. National Mortgage News. September 22, 1997. 

33 “Mortgage Broker Helps Low-Income Buyers.” Origination News. September 1998. 

10 
 

 

 

 

 

 

 

income and minority neighborhoods have lower initial downpayments, lenders may face 

higher default losses on average in these neighborhoods than in other neighborhoods, 

making these neighborhoods less attractive to lenders. 

Financial Services. Borrowers in low-income and minority neighborhoods may 

be more likely than borrowers in other neighborhood to require different financial 

services. First, borrowers in low-income and minority neighborhoods may have lowervalued homes and some prime lenders have a minimum loan amount policy.34 Second, 

low-income and minority neighborhoods may have a lower proportion of borrowers who 

represent profitable cross-selling opportunities for the prime lender.35 Third, borrowers 

in low-income and minority neighborhoods are more likely to experience higher 

insurance prices or be denied coverage, which affects their ability to secure prime 

credit.36 

III. A Regression Model of Subprime Refinance Lending 

The unavailability of individual applicant credit and property characteristics is the 

major limitation of HMDA data and mortgage redlining studies and is also an obvious 

limitation in explaining the neighborhood subprime refinance share. But as in these other 

studies, it is a useful exercise to relate neighborhood indicators to mortgage market 

outcomes to gain further insights on the concentration of subprime refinance lending in 

low-income and minority neighborhoods. This section describes a simple ordinary least 

34 Lenders that have these policies argue that there are fixed costs of origination that preclude them from 

profitably originating these loans without charging higher interest rates and fees. See Ronald E. Wienk. 

“Discrimination in Urban Credit Markets.” Housing Policy Debate. Volume 3, Issue 2, 1992. 

35 In the prime market, lenders typically lose money during the application stage with profits being made 

from servicing rights. The prime mortgage market is a high volume commodity industry where lenders rely 

on servicing rights and cross selling of other products for profits. Subprime lenders are more likely than 

prime lenders to recoup the costs of originating a loan upfront by charging higher rates and fees rather than 

rely on servicing revenue. See “Don’t Bank On It.” City Limits. December 1999. 

36 Borrowers in low-income and minority neighborhoods may also be more likely to purchase less coverage 

and have their insurance canceled. See “The New Redlining,” US News and World Report. April 17, 

1995. There is debate whether higher prices and lower coverage reflect greater risks and lower demand or 

whether they reflect a lack of competition from insurers in these neighborhoods. Insurance industry 

practices that have been criticized for contributing to insurance redlining in low-income and minority 

neighborhoods include: maximum structure age, minimum property values and refusal to provide 

replacement value coverage. Insurance redlining affects the provision of mortgages to low-income and 

minority neighborhoods because prime lenders are more likely to deny mortgages without property 

insurance. That is, there may be qualified prime borrowers in low-income and minority neighborhoods that 

are unable to obtain prime credit because they are unable to obtain property insurance. For recent 

complaints against the insurance industry, see “Insurer Settles Redlining Complaint: Liberty Mutual Will 

Pay DC Housing Group $3.25 Million.” Washington Post. June 10, 1999. This article also mentions 

complaints that have been settled against Nationwide Insurance Company, American Family Insurance 

Group, Allstate Insurance Company, and State Farm Insurance Company. These companies account for 

approximately 40 percent of the homeowners insurance market. 

11 
 

 

squares model, done separately for 1997 through 2000, that describes the relationship of 

neighborhood indicators with the neighborhood subprime refinance share of mortgages.37 

Table B.9 provides definitions and basic descriptive statistics for the subprime 

refinance share and a variety of neighborhood indicators. For example, the mean 

neighborhood subprime share of total refinance mortgages varied between 21.2 percent in 

1998 to a maximum of 29.1 percent in 2000.38 Table B.9 also reports the expected 

effects of the neighborhood indicators on the subprime refinance share. 

The correlation coefficients in Tables B.10 and B.11 provide information on the 

relationships between the subprime refinance share and a variety of neighborhood 

indicators. First, a number of neighborhood indicators were correlated with each other 

and the neighborhood black percentage (BLKPCT).39 For example, the absolute 

correlation coefficient with the black percentage exceeds 0.30 for the capital indicator 

(CAPITAL), the homeownership rate (O_PCT), the share of households that receive 

public assistance (PUBLIC), and median family income (DPMEDINC). Similarly, 

median family income was correlated with the share of households that receive public 

assistance, capital return, and the homeownership rate. Finally, the median age of owneroccupied homes (MEDAGE) was negatively correlated with the percent of owneroccupied households that moved in between 1985 and 1988 (TEN8588) and positively 

correlated with the share of households that received public assistance. 

The correlation of most neighborhood indicators and the subprime refinance share 

was highest in 1998 and 1999, both periods of relatively lower interest rates compared to 

37 We focused on 44,460 metropolitan census tracts with nonzero populations and a nonzero number of 

owners. We also excluded tracts in counties with no more than 30,000 in population and tracts where 

median incomes were suppressed because of confidentiality. For consistency across years, we excluded 

from the analysis areas that OMB designated as metropolitan areas in 1998 (MSA=5140) and 1999 

(MSA=0580, 1890). Total refinance loans include conventional (i.e. prime, subprime, and manufactured 

home) and government-insured (i.e., FHA, VA, and RHS) loans. 

38 The mean tract subprime refinance share is not weighted by the number of loans and therefore differs 

from the mean subprime refinance share reported earlier. Also the unweighted mean tract subprime 

refinance share does not take into account the metropolitan areas that were excluded from this regression 

analysis (see previous footnote). 

39 The indicator for the Hispanic percentage of the tract population was not highly correlated with the tract 

subprime refinance percentage but the correlation was increasing over time from 0.06 in 1997 and 0.14 in 

2000. As discussed above, Hispanics are less concentrated spatially than Hispanics, which likely accounts 

for the lower correlation with the subprime refinance share. Evidence from the Mumford report suggests 

that Hispanics have become slightly more spatially concentrated since 1990. The average Hispanic lived in 

a neighborhood where Hispanics comprised 42.4 percent of the population in 1990 compared to 45.5 

percent in 2000. The regression model uses 1990 Census data and using 2000 Census data would likely 

show a slightly higher correlation between the subprime refinance share and the Hispanic percentage. See 

http://www.albany.edu/mumford/census for the following publication: Ethnic Diversity Grows, 

Neighborhood Integration Lags Behind. Lewis Mumford Center, December 18, 2001. The increase in 

correlation over time may be due to Hispanics becoming more seasoned homeowners with additional home 

equity. 

12 
 

 

 

1997 and 2000. The correlation coefficients between the subprime refinance share and 

the following neighborhood indicators were similar for 1997 and 2000 and similar for 

1998 and 1999: TEN8588, PUBLIC, CAPITAL, DPMEDINC, and BLKPCT. The 

weaker correlation coefficients between the subprime refinance share and the 

neighborhood indicators implies that the model below will explain less of the variance in 

the tract subprime refinance share in 1997 and 2000 than in 1998 and 1999. 

Table B.12 reports the parameter estimates for the ordinary least squares 

regression model. We converted the black percent and median family income percent 

into categorical variables using categories consistent with those used in the descriptive 

analysis in Section II. The coefficients on the other variables and the explanatory power 

of the model as measured by the R-squared statistic were not significantly altered. We 

also used dummy variables for the 328 metropolitan areas.40 We converted the intercept 

into a national average measure so that the intercept can be interpreted as the national 

mean subprime refinance share in middle-income neighborhoods where blacks comprise 

less than 10 percent of the population.41 

The model for 1998 explains the subprime refinance share better than the models 

for 1997, 1999, or 2000. The R-square, a measure of goodness of fit, for 1998 was 

0.6264 compared to 0.4775 for 1997, 0.5807 for 1999, and 0.4284 for 2000. This finding 

is not surprising given that 1998 was a year of high overall refinancing and the subprime 

refinance share could be measured more precisely because of higher refinances volumes 

overall. In the discussion below, we focus on the 1998 model although all models reach 

the same general conclusions. 

Even after controlling for a variety of other neighborhood factors, the black 

composition of the neighborhood was an important indicator of the subprime refinance 

share. For example, the mean subprime percent in middle-income neighborhoods where 

blacks comprised between 50 and 80 percent was 12 percentage points higher than in 

middle-income neighborhoods where blacks comprised less than 10 percent of the 

population. 

The black composition of the neighborhood is highly correlated with a variety of 

individual applicant and property characteristics such as credit history, wealth, mortgage 

downpayment, and house price appreciation that could not be incorporated into the 

analysis.42 As discussed in the previous section, these factors could contribute to prime 

lenders’ decisions to provide loans in these neighborhoods. 

40 The coefficients for the individual metropolitan areas are reported in Table B.13 in Appendix B. 

41 See P. E. Kennedy. “Interpreting Dummy Variables.” Review of Economics and Statistics. Volume, 

68, 1986. 

42 A group of researchers at the Board of Governors of the Federal Reserve System found a statistical 

relationship between credit history scores and the Zip Code minority composition, after controlling for 

other locational characteristics. Their finding suggests that the disparities in the subprime refinance share 

between predominantly black and white neighborhoods reported here would be diminished if a credit score 

measure were included in the model. However, they also concluded that the statistical relationship 

13 
 

 

 

 

 

 

The capital indicator was an important explanatory variable.43 The capital 

indicator is defined as the interest rate that would amortize a 30-year mortgage with an 

initial loan amount equal to the median valued owner occupied home with a mortgage 

payment equal to the median valued rent. Higher values of the capital variable may 

indicate relatively lower future price appreciation and greater volatility in future price 

appreciation estimates.44 

The public assistance indicator was also an important explanatory variable. 

Neighborhoods with higher percentages of households receiving public assistance may 

also require different types of financial services than neighborhoods where few 

households receive public assistance. The share of households that received public 

assistance varied from a minimum of 0.0 percent to 81.8 percent with a mean of 8.3 

percent. In 1998, for approximately every 2-percentage point increase in the share of 

households that receive public assistance, there was a percentage point increase in the 

subprime refinance share. 

The median age of owner-occupied homes varies positively with the subprime 

refinance share. Neighborhood with disproportionately older homes may pose greater 

property risks and be less likely to appreciate in price, suggesting greater credit losses. 

The neighborhood ownership rate varies negatively with the tract subprime refinance 

share. This result is consistent with the idea that prime lenders may concentrate their 

lending efforts in neighborhoods with the most potential business, leaving subprime 

lenders to serve the mortgage needs of other neighborhoods.45 The mobility indicator, 

between minority composition and the credit score could reflect credit-related factors that were omitted 

from the credit scoring models and were correlated with the minority composition of the neighborhood. 

See Robert B. Avery, Raphael W. Bostic, Paul S. Calem, and Glenn B. Canner. “Credit Scoring: Issues 

and Evidence From Credit Bureau Files.” Working Paper. Board of Governors of the Federal Reserve 

System. February 23, 1998. 

43 The beta coefficient is a standardized coefficient estimate calculated by dividing the coefficient estimate 

by the ratio of the standard deviation of the independent variable to the standard deviation of the dependent 

variable. It can be interpreted as the number of standard error changes in the dependent variable resulting 

from a standard error change in the independent variable and is sometimes used as a measure of the relative 

strength of regressors in affecting the dependent variable. See Peter Kennedy. A Guide to Econometrics. 

MIT Press. Fourth Edition. 1998. 

44 On the other hand, neighborhoods with high house prices relative to rents may reflect neighborhoods 

with higher expected future price appreciation than reflected in the current rents. This variable differs 

slightly from the typical median rent-to-median value ratio used in other papers but is essentially the same 

measure of whether a owner-occupied property’s value is over or under-capitalized relative to the return 

from rental properties. See Ying Li and Eric Rosenblatt. “Can Urban Indicators Predict Home Price 

Appreciation? Implications for Redlining Research.” Real Estate Economics. Volume 25, 1997. 

45 The subprime refinance share may be higher in neighborhoods with fewer owner-occupied homes 

because of uncertainty of returns associated with “thin markets”. Lang and Nakamura hypothesize that 

lenders deny more loans in neighborhoods where there is more uncertainty of return. Neighborhoods with 

“thin” housing markets (i.e., less house sales) have higher variances in appraisals that lead lenders to deny 

more loans because of greater uncertainty in return. William W. Lang and Leonard I. Nakamura, “A 

14 
 

 

 

 

which measures the proportion of owner-occupied households that moved between 1985 

and 1988, may be one indicator of households with significant equity in their homes. The 

mobility indicator varies negatively with the subprime refinance share, which is also 

consistent with the idea that prime lenders may concentrate their lending efforts in 

neighborhoods with the most potential business. 

IV. Conclusion 

This study identifies the types of neighborhoods in the nation as a whole where 

borrowers were the most likely to rely on subprime loans when refinancing their 

mortgages. An appendix to this paper presents the same data for 27 specific metropolitan 

areas across the nation. The metropolitan areas were chosen based on previous research 

on subprime lending at HUD and on advice from community advocacy groups 

throughout the nation. The metropolitan areas selected vary regionally and reflect 

differences in the demographic makeup of our nation’s metropolitan areas. 

The national and individual metropolitan area analyses are based on mortgage 

data collected under the Home Mortgage Disclosure Act and clearly demonstrate the 

growth in subprime refinance lending and its disproportionate impact on low-income and 

predominantly black neighborhoods throughout the nation. 

This paper continues HUD’s research on subprime lending. Home equity is 

typically the main source of wealth for borrowers in low-income and minority 

neighborhoods. Creditworthy borrowers in these neighborhoods need access to lower 

cost prime credit and weaker credit borrowers in these neighborhoods should have access 

to subprime credit that is priced appropriately to their credit circumstances. Most 

importantly, borrowers in these neighborhoods should be protected against a subset of 

subprime lenders who engage in predatory lending practices. 

Model of Redlining.” Journal of Urban Economic. Volume 33, 1993. See also Robert B. Avery, Patricia 

E. Beeson, and Mark S. Sniderman, “Neighborhood Information and Home Mortgage Lending,” Journal of 

Urban Economics. Volume 45, 1999; David C. Ling and Susan M. Wachter. “Information Externalities 

and Home Mortgage Underwriting,” Journal of Urban Economics, Volume 44, 1998; Man Cho and Isaac 

F. Megbolugbe. “An Empirical Analysis of Property Appraisal and Mortgage Redlining.” The Journal of 

Real Estate Finance and Economics. Volume 13, 1996; and Paul S. Calem. “Mortgage Credit Availability 

in Low- and Moderate-Income Minority Neighborhoods: Are Information Externalities Critical.” The 

Journal of Real Estate Finance and Economics. Volume 13, 1996. 

15 
 

 

Appendix A 

Home Mortgage Disclosure Act Data 

The analysis in this paper is based on Home Mortgage Disclosure Act (HMDA) 

data for 1993 through 2000. HMDA data is the most comprehensive loan level database 

on mortgage lending activity in the United States and includes information on borrower 

income, borrower racial or ethnic group, and census tract location of the property. 

HMDA contains information not only for originations but also for mortgage applications 

that do not end in origination. 

HMDA data does not have a field that identifies subprime mortgages and does not 

contain information that the lender used to underwrite the mortgage such as loan-to-value 

and debt ratios, the annual percentage rate (APR), or the borrower’s credit score.46 HUD 

has annually identified a list of HMDA reporters that specialize in subprime lending. The 

list can be used to distinguish between loans originated by conventional prime and 

subprime lenders and is widely used by mortgage market analysts, policymakers, and 

advocacy groups for their research on subprime lending.47 

HMDA does not cover all subprime mortgage lending. Furthermore, there are 

limitations to identifying subprime loans in HMDA using the list. First, one cannot 

identify prime mortgages originated by subprime lenders or subprime mortgages 

originated by prime lenders in HMDA. For example, large lenders and conduits like 

Chase Manhattan, Residential Funding, and IndyMac often report the mortgages 

originations of their subprime divisions with the mortgage originations of their prime 

divisions. Second, HMDA does not include data on second lien home equity loans 

except possibly for the portion of funds that are used for home improvement. Only loans 

classified as home improvement loans must be reported under HMDA. Third, HMDA 

does not include lenders whose mortgage business accounts for less than 10 percent of 

their overall lending. For example, Household International, a market leader in subprime 

mortgage lending, does not report to HMDA because its mortgage business comprises 

less than 10 percent of its overall lending which includes subprime auto and credit card 

lending. HMDA coverage issues notwithstanding, the mortgages reported by subprime 

lenders in HMDA provide insights on the demographic characteristics of borrowers that 

use subprime mortgages for refinance credit. 

46 There are no publicly available loan level databases that identify subprime loans or information on 

mortgage terms such as loan-to-value or debt ratios or the annual percentage rate (APR) on the loan. Much 

of the available data on subprime lending is aggregated and focuses on the dollar volume of loans 

originated or securitized by particular lenders. These data are usually generated from surveys of subprime 

lenders or collected by ratings agencies for evaluating securitizations. 

47 For a copy of the 2000 list and data on mortgage market trends in the prime and subprime mortgage 

markets, see http://www.huduser.org/datasets/manu.html. For a 1998 analysis of HMDA data, see Randall 

M. Scheessele. “1998 HMDA Highlights.” PD&R Housing Finance Working Paper Series. HUD. 

October 1999. 

16 
Appendix C 

Subprime Refinance Lending in 27 Metropolitan Areas 

This appendix provides information on subprime refinance lending in 27 

metropolitan areas. The metropolitan areas were chosen based on previous analyses at 

HUD and on recommendations by housing advocates. These metropolitan areas 

accounted for 38.8 percent of subprime refinancing and 36.5 percent of all refinances in 

all metropolitan areas. Table C.1 reports the share of subprime and overall refinances 

accounted for by the 27 metropolitan areas. These shares are also broken out by 

borrower race or ethnicity and income. Table C.2 reports the subprime refinance shares 

of loans for the 27 metropolitan areas broken out by tract black and income composition. 

Table C.3 reports the same data for the 27 metropolitan areas but it is broken out by 

borrower race or ethnicity and income. 

The maps give illustrate visually the spatial concentration of subprime refinance 

lending in the 27 metropolitan areas. Predominantly black or Hispanic neighborhoods 

are defined as they were in the paper. That is, a tract is predominantly black (Hispanic) if 

blacks (Hispanics) comprised at least 50 percent of the tract’s population. A subprime 

tract is a tract where subprime refinance mortgages accounted for at least 25 percent of 

all refinance mortgages in the tract. 

17 
Figure 1

Subprime Mortgage Lending Activity 199

(in billions)

$90 

$125 

$160 

$138 

$150 

0 

20 40 60 80 

100 120 140 160 180 200 

1996 1997 1998 1999 2000 

Source: Inside Mortgage Finance
Figure 2 

Subprime Mortgage Loan Activity 1996

239,509 

417,063 

613,880 

618,572 

0 

100,000 200,000 300,000 400,000 500,000 600,000 700,000 

1996 1997 1998 1999 

Source: Home Mortgage Disclosure Act Data
Figure 3 

Subprime Share of All Refinance Loans 

25% 20% 15% 10% 

5% 0% 

1993 1994 1995 1996 1997 1998 1999 
Figure 4 

Subprime Share of All Refinance Loans 

by Neighborhood Income Composition 

0% 5% 

10% 15% 20% 25% 30% 35% 40% 45% 50% 

1993 1994 1995 1996 1997 1998 1999 
Figure 5 

Subprime Share of All Refinance Loans 

by Neighborhood Black Composition 

60% 50% 40% 30% 20% 10% 

0% 

1996 1997 1998 1999 
Figure 6

Subprime Refinance Shares in 2000

by Income and Black Composition

0.0% 

10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 

Low Middle Upper 
Figure 7 

Subprime Share of All Refinance Loans 

by Neighborhood Hispanic Composition 

40% 35% 30% 25% 20% 15% 10% 

5% 0% 

1996 1997 1998 1999 
Figure 8

Subprime Refinance Shares in 2000

by Income and Hispanic Composition

0.0% 5.0% 

10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 

Low Middle Upper 
Table B.1 

Subprime Mortgage Activity 

Subprime Share 

Overall Subprime Growth in of Overall 

Mortgage Mortgage Subprime Mortgage 

Market Market Lending Market 

1996 785 90 NA 11.5% 

1997 859 125 27.7% 14.5% 

1998 1,450 150 17.0% 10.3% 

1999 1,310 160 6.3% 12.2% 

2000 1,048 138 -15.9% 13.2% 

2001 2,100 173 20.3% 8.3% 

Source: Inside Mortgage Finance 
Table B.2
 

HMDA Refinance Mortgages by Product Type
 

1996 

Loans 239,509 

Share of Market 11.2% 

Loans 37,739 

Share of Market 1.8% 

Loans 1,701,250 

Share of Market 79.8% 

Loans 101,560 

Share of Market 4.8% 

Loans 51,232 

Share of Market 2.4% 

Loans 1,158 

Share of Market 0.1% 

Loans 2,132,448 

1997 1998 1999 2000 

Subprime 

417,063 618,572 613,880 475,583 

18.2% 10.8% 16.7% 23.8% 

Manufactured Home 

46,464 69,300 39,834 24,620 

2.0% 1.2% 1.1% 1.2% 

Conventional Prime 

1,682,743 4,538,878 2,786,886 1,438,309 

73.3% 79.2% 75.7% 72.0% 

FHA 

99,090 315,617 170,945 53,547 

4.3% 5.5% 4.6% 2.7% 

VA 

49,952 189,799 67,497 6,077 

2.2% 3.3% 1.8% 0.3% 

RHS 

996 459 430 271 

0.0% 0.0% 0.0% 0.0% 

All Refinance Loans 

2,296,308 5,732,625 3,679,472 1,998,407 
Table B.3 

Subprime Refinance Originations 

by Neighborhood Income Composition
 

1996 1997 1998 1999 2000

Subprime Refinance Loans 

Low 66,679 114,320 165,128 170,413 130,547

Middle 120,402 212,363 317,827 318,341 249,662

Upper 52,067 89,739 135,291 124,947 95,248

Total 239,509 417,063 618,572 613,880 475,583

All Refinance Loans 

Low 294,100 338,420 642,920 539,749 359,354

Middle 1,112,829 1,175,592 2,899,640 1,897,698 1,057,027

Upper 723,956 780,233 2,186,505 1,240,274 581,216

Total 2,132,448 2,296,308 5,732,625 3,679,472 1,998,407

Subprime Share 

Low 22.7% 33.8% 25.7% 31.6% 36.3%

Middle 10.8% 18.1% 11.0% 16.8% 23.6%

Upper 7.2% 11.5% 6.2% 10.1% 16.4%

Total 11.2% 18.2% 10.8% 16.7% 23.8%

Disparity Ratio 

Low 2.0 1.9 2.4 1.9 1.5

Middle 1.0 1.0 1.0 1.0 1.0

Upper 0.6 0.6 0.6 0.6 0.7

Total 1.0 1.0 1.0 1.0 1.0
Table B.4a 

Subprime Refinance Originations 

by Neighborhood Black Composition 

1996 1997 1998 1999 2000 

Subprime Refinance Loans 

Less than 30% 187453 330822 489723 482442 378322 

30-50% 11830 20541 30330 31515 23949 

50-80% 15233 25409 37567 38437 28830 

80-100% 24736 39821 60737 61369 44405 

50-100% 39,969 65,230 98,304 99,806 73,235 

Total 239,509 417,063 618,572 613,880 475,583 

All Refinance Loans 

Less than 30% 1,964,178 2,094,531 5,384,269 3,369,002 1,786,170 

30-50% 55,439 62,265 123,048 100,347 63,739 

50-80% 51,530 61,066 106,345 94,886 64,346 

80-100% 60,177 76,908 116,352 113,964 83,606 

50-100% 111,707 137,974 222,697 208,850 147,952 

Total 2,132,448 2,296,308 5,732,625 3,679,472 1,998,407 

Subprime Share 

Less than 30% 9.5% 15.8% 9.1% 14.3% 21.2% 

30-50% 21.3% 33.0% 24.6% 31.4% 37.6% 

50-80% 29.6% 41.6% 35.3% 40.5% 44.8% 

80-100% 41.1% 51.8% 52.2% 53.8% 53.1% 

50-100% 35.8% 47.3% 44.1% 47.8% 49.5% 

Total 11.2% 18.2% 10.8% 16.7% 23.8% 

Disparity Ratio 

Less than 30% 0.8 0.9 0.8 0.9 0.9 

30-50% 1.9 1.8 2.3 1.9 1.6 

50-80% 2.6 2.3 3.3 2.4 1.9 

80-100% 3.7 2.9 4.8 3.2 2.2 

50-100% 3.2 2.6 4.1 2.9 2.1 

Total 1.0 1.0 1.0 1.0 1.0 
Table B.4b 

Subprime Refinance Originations 

by Neighborhood Hispanic Composition
 

1996 1997 1998 1999 2000 

Subprime Refinance Loans 

Less than 30% 221,390 389,379 576,758 570,617 442,057 

30-50% 8,894 13,586 20,715 21,440 16,652 

50-80% 6,903 10,387 15,868 16,279 12,528 

80-100% 2,065 3,241 5,016 5,427 4,269 

50-100% 8,968 13,628 20,884 21,706 16,797 

Total 239,509 417,063 618,572 613,880 475,583 

All Refinance Loans 

Less than 30% 2,036,743 2,194,790 5,502,090 3,496,531 1,891,315 

30-50% 50,267 53,248 128,950 98,755 56,941 

50-80% 33,463 35,452 76,389 63,293 37,960 

80-100% 10,851 11,280 22,585 19,620 11,645 

50-100% 44,314 46,732 98,974 82,913 49,605 

Total 2,132,448 2,296,308 5,732,625 3,679,472 1,998,407 

Subprime Share 

Less than 30% 10.9% 17.7% 10.5% 16.3% 23.4% 

30-50% 17.7% 25.5% 16.1% 21.7% 29.2% 

50-80% 20.6% 29.3% 20.8% 25.7% 33.0% 

80-100% 19.0% 28.7% 22.2% 27.7% 36.7% 

50-100% 20.2% 29.2% 21.1% 26.2% 33.9% 

Total 11.2% 18.2% 10.8% 16.7% 23.8% 

Disparity Ratio 

Less than 30% 1.0 1.0 1.0 1.0 1.0 

30-50% 1.6 1.4 1.5 1.3 1.2 

50-80% 1.8 1.6 1.9 1.5 1.4 

80-100% 1.7 1.6 2.1 1.7 1.5 

50-100% 1.8 1.6 2.0 1.6 1.4 

Total 1.0 1.0 1.0 1.0 1.0 
Table B.5a 

2000 HMDA Loans 

by Neighborhood Black and Income Composition
 

Low Middle Upper Total 

Subprime 

Less than 30% 67,476 219,682 91,132 378,322 

30-50% 11,645 10,883 1,408 23,949 

50-80% 16,847 10,363 1,616 28,830 

80-100% 34,579 8,734 1,092 44,405 

50-100% 51,426 19,097 2,708 73,235 

Total 130,547 249,662 95,248 475,583 

All Loans 

Less than 30% 229,065 986,289 570,619 1,786,170 

30-50% 29,445 30,073 4,178 63,739 

50-80% 36,768 23,426 4,129 64,346 

80-100% 64,076 17,239 2,290 83,606 

50-100% 100,844 40,665 6,419 147,952 

Total 359,354 1,057,027 581,216 1,998,407 

Subprime Share 

Less than 30% 29.5% 22.3% 16.0% 21.2% 

30-50% 39.5% 36.2% 33.7% 37.6% 

50-80% 45.8% 44.2% 39.1% 44.8% 

80-100% 54.0% 50.7% 47.7% 53.1% 

50-100% 51.0% 47.0% 42.2% 49.5% 

Total 36.3% 23.6% 16.4% 23.8% 

Disparity in Subprime Shares 

Less than 30% 0.8 0.9 1.0 0.9 

30-50% 1.1 1.5 2.1 1.6 

50-80% 1.3 1.9 2.4 1.9 

80-100% 1.5 2.1 2.9 2.2 

50-100% 1.4 2.0 2.6 2.1 

Total 1.0 1.0 1.0 1.0 
Table B.5b 

2000 HMDA Loans 

by Neighborhood Hispanic and Income Composition
 

Low Middle Upper Total 

Subprime 

Less than 30% 111,065 238,142 92,804 442,057 

30-50% 8,510 6,734 1,406 16,652 

50-80% 8,125 3,493 909 12,528 

80-100% 2,847 1,293 129 4,269 

50-100% 10,972 4,786 1,038 16,797 

Total 130,547 249,662 95,248 475,583 

All Loans 

Less than 30% 301,339 1,017,287 572,432 1,891,315 

30-50% 26,197 25,005 5,734 56,941 

50-80% 23,971 11,336 2,651 37,960 

80-100% 7,847 3,399 399 11,645 

50-100% 31,818 14,735 3,050 49,605 

Total 359,354 1,057,027 581,216 1,998,407 

Subprime Share 

Less than 30% 36.9% 23.4% 16.2% 23.4% 

30-50% 32.5% 26.9% 24.5% 29.2% 

50-80% 33.9% 30.8% 34.3% 33.0% 

80-100% 36.3% 38.0% 32.3% 36.7% 

50-100% 34.5% 32.5% 34.0% 33.9% 

Total 36.3% 23.6% 16.4% 23.8% 

Disparity in Subprime Shares 

Less than 30% 1.0 1.0 1.0 1.0 

30-50% 0.9 1.1 1.5 1.2 

50-80% 0.9 1.3 2.1 1.4 

80-100% 1.0 1.6 2.0 1.5 

50-100% 0.9 1.4 2.1 1.4 

Total 1.0 1.0 1.0 1.0 
Table B.6a 

Population and 2000 Mortgage Loans by Black Composition 

Other and 

Tracts Total Black Hispanic White Missing 

Black Composition Population 

Less than 30% 39,239 175,837,539 7,923,931 17,971,746 142,620,170 7,321,692 

30-50% 2,135 8,167,496 3,167,522 1,110,315 3,597,383 292,276 

50-80% 2,199 7,688,811 4,918,478 779,916 1,834,026 156,391 

80-100% 2,970 9,688,671 8,997,457 200,934 430,244 60,036 

50-100% 5,169 17,377,482 13,915,935 980,850 2,264,270 216,427 

All 46,543 201,382,517 25,007,388 20,062,911 148,481,823 7,830,395 

All Loans 

Less than 30% 37,704 1,786,178 60,651 114,107 1,144,012 467,408 

30-50% 1,950 63,739 18,168 3,399 22,870 19,302 

50-80% 2,033 64,346 28,457 2,850 12,984 20,055 

80-100% 2,831 83,607 48,584 1,056 7,672 26,295 

50-100% 4,864 147,953 77,041 3,906 20,656 46,350 

All 44,518 1,998,416 155,884 121,456 1,187,849 533,227 

Subprime Loans 

Less than 30% 35,515 378,322 24,229 26,489 184,481 143,123 

30-50% 1,814 23,949 8,437 1,094 5,084 9,334 

50-80% 1,928 28,830 13,651 964 3,593 10,622 

80-100% 2,742 44,405 25,106 426 2,687 16,186 

50-100% 4,670 73,235 38,757 1,390 6,280 26,808 

All 41,999 475,583 71,432 28,979 195,873 179,299 

Population 

Less than 30% 84.3% 87.3% 31.7% 89.6% 96.1% 93.5% 

30-50% 4.6% 4.1% 12.7% 5.5% 2.4% 3.7% 

50-80% 4.7% 3.8% 19.7% 3.9% 1.2% 2.0% 

80-100% 6.4% 4.8% 36.0% 1.0% 0.3% 0.8% 

50-100% 11.1% 8.6% 55.6% 4.9% 1.5% 2.8% 

All 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

All Loans 

Less than 30% 84.7% 89.4% 38.9% 93.9% 96.3% 87.7% 

30-50% 4.4% 3.2% 11.7% 2.8% 1.9% 3.6% 

50-80% 4.6% 3.2% 18.3% 2.3% 1.1% 3.8% 

80-100% 6.4% 4.2% 31.2% 0.9% 0.6% 4.9% 

50-100% 10.9% 7.4% 49.4% 3.2% 1.7% 8.7% 

All 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

Subprime Loans 

Less than 30% 84.6% 79.5% 33.9% 91.4% 94.2% 79.8% 

30-50% 4.3% 5.0% 11.8% 3.8% 2.6% 5.2% 

50-80% 4.6% 6.1% 19.1% 3.3% 1.8% 5.9% 

80-100% 6.5% 9.3% 35.1% 1.5% 1.4% 9.0% 

50-100% 11.1% 15.4% 54.3% 4.8% 3.2% 15.0% 

All 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

Subprime Share 

Less than 30% 94.2% 21.2% 39.9% 23.2% 16.1% 30.6% 

30-50% 93.0% 37.6% 46.4% 32.2% 22.2% 48.4% 

50-80% 94.8% 44.8% 48.0% 33.8% 27.7% 53.0% 

80-100% 96.9% 53.1% 51.7% 40.3% 35.0% 61.6% 

50-100% 96.0% 49.5% 50.3% 35.6% 30.4% 57.8% 

All 94.3% 23.8% 45.8% 23.9% 16.5% 33.6% 
Table B.6b 

Population and 2000 Mortgage Loans by Hispanic Composition 

Other and 

Tracts Total Black Hispanic White Missing 

Black Composition Population 

Less than 30% 42,466 181,334,553 22,949,044 8,617,619 143,111,380 6,656,510 

30-50% 1,895 8,946,149 1,270,752 3,461,374 3,495,826 718,197 

50-80% 1,534 7,507,425 724,837 4,775,797 1,609,973 396,818 

80-100% 648 3,594,390 62,755 3,208,121 264,644 58,870 

50-100% 2,182 11,101,815 787,592 7,983,918 1,874,617 455,688 

All 46,543 201,382,517 25,007,388 20,062,911 148,481,823 7,830,395 

All Loans 

Less than 30% 40,642 1,891,324 149,269 78,869 1,159,178 504,008 

30-50% 1,819 56,941 4,361 16,361 19,072 17,147 

50-80% 1,450 37,960 2,020 18,062 8,306 9,572 

80-100% 607 11,645 210 8,120 982 2,333 

50-100% 2,057 49,605 2,230 26,182 9,288 11,905 

All 44,518 1,998,416 155,884 121,456 1,187,849 533,227 

Subprime Loans 

Less than 30% 38,358 442,057 68,255 15,748 189,059 168,995 

30-50% 1,710 16,652 1,998 4,798 4,405 5,451 

50-80% 1,364 12,528 1,054 5,701 2,095 3,678 

80-100% 567 4,269 116 2,726 286 1,141 

50-100% 1,931 16,797 1,170 8,427 2,381 4,819 

All 41,999 475,583 71,432 28,979 195,873 179,299 

Population 

Less than 30% 91.2% 90.0% 91.8% 43.0% 96.4% 85.0% 

30-50% 4.1% 4.4% 5.1% 17.3% 2.4% 9.2% 

50-80% 3.3% 3.7% 2.9% 23.8% 1.1% 5.1% 

80-100% 1.4% 1.8% 0.3% 16.0% 0.2% 0.8% 

50-100% 4.7% 5.5% 3.1% 39.8% 1.3% 5.8% 

All 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

All Loans 

Less than 30% 91.3% 94.6% 95.8% 64.9% 97.6% 94.5% 

30-50% 4.1% 2.8% 2.8% 13.5% 1.6% 3.2% 

50-80% 3.3% 1.9% 1.3% 14.9% 0.7% 1.8% 

80-100% 1.4% 0.6% 0.1% 6.7% 0.1% 0.4% 

50-100% 4.6% 2.5% 1.4% 21.6% 0.8% 2.2% 

All 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

Subprime Loans 

Less than 30% 91.3% 93.0% 95.6% 54.3% 96.5% 94.3% 

30-50% 4.1% 3.5% 2.8% 16.6% 2.2% 3.0% 

50-80% 3.2% 2.6% 1.5% 19.7% 1.1% 2.1% 

80-100% 1.4% 0.9% 0.2% 9.4% 0.1% 0.6% 

50-100% 4.6% 3.5% 1.6% 29.1% 1.2% 2.7% 

All 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

Subprime Share 

Less than 30% 94.4% 23.4% 45.7% 20.0% 16.3% 33.5% 

30-50% 94.0% 29.2% 45.8% 29.3% 23.1% 31.8% 

50-80% 94.1% 33.0% 52.2% 31.6% 25.2% 38.4% 

80-100% 93.4% 36.7% 55.2% 33.6% 29.1% 48.9% 

50-100% 93.9% 33.9% 52.5% 32.2% 25.6% 40.5% 

All 94.3% 23.8% 45.8% 23.9% 16.5% 33.6% 
Table B.7 

2000 Refinance Lending by Borrower Race or Ethnicity and Income 

Middle- Upper- Missing 

Low-Income Income Income Income All 

Subprime Refinance 

Black 42,498 16,494 11,572 868 71,432 

Hispanic 12,524 8,474 7,437 544 28,979 

White 78,968 55,162 57,594 4,149 195,873 

Other/Missing 80,043 48,922 47,466 2,868 179,299 

Total 214,033 129,052 124,069 8,429 475,583 

All Refinance Loans 

Black 78,677 37,132 32,927 7,147 155,883 

Hispanic 38,688 30,462 44,934 7,371 121,455 

White 335,229 312,710 493,123 46,781 1,187,843 

Other/Missing 164,745 139,090 197,112 32,279 533,226 

Total 617,339 519,394 768,096 93,578 1,998,407 

Subprime Share 

Black 54.0% 44.4% 35.1% 12.1% 45.8% 

Hispanic 32.4% 27.8% 16.6% 7.4% 23.9% 

White 23.6% 17.6% 11.7% 8.9% 16.5% 

Other/Missing 48.6% 35.2% 24.1% 8.9% 33.6% 

Total 34.7% 24.8% 16.2% 9.0% 23.8% 

Subprime Share 

Black 1.6 1.8 2.2 1.3 1.9 

Hispanic 0.9 1.1 1.0 0.8 1.0 

White 0.7 0.7 0.7 1.0 0.7 

Other/Missing 1.4 1.4 1.5 1.0 1.4 

Total 1.0 1.0 1.0 1.0 1.0 
Table B.8 

2000 Refinance Lending for 

Upper Income Black and Hispanic Borrowers 

by Neighborhood Income 

Low Middle Upper All

Subprime 

Black 3,713 4,806 3,048 11,572

Hispanic 1,730 3,360 2,344 7,437

White 5,989 28,823 22,773 57,594

Black 32.1% 41.5% 26.3% 100.0%

Hispanic 23.3% 45.2% 31.5% 100.0%

White 10.4% 50.0% 39.5% 100.0%

All Loans 

Black 9,505 13,746 9,659 32,928

Hispanic 8,935 19,135 16,839 44,934

White 39,489 231,350 222,061 493,125

Black 28.9% 41.7% 29.3% 100.0%

Hispanic 19.9% 42.6% 37.5% 100.0%

White 8.0% 46.9% 45.0% 100.0%
Table B.9

Definitions of Census Tract Indicators

Expected Actual Sign Standard 

Variable Name Definition Sign 1997-2000 N Mean Deviatio

O_PCT MEDAGE TEN8588 PUBLIC 

Owner-occupancy rate Median age of housing units Percent of owner-occupied units where household moved in between 1985 and 1988 Percent of households with public assistance 

-

+ 

-

+ 

-

+ 

-

+ 

44,460 44,460 44,460 44,460 

57.51 29.41 28.08 

8.26 

2313

8

9

CAPITAL DPMEDINC BLKPCT 

The rate of return that amortizes a mortgage amount equal to the median valued home with a mortgage payment equal to the median rent The ratio of tract median family income to metropolitan family income 

The black percentage of the population 

+ 

-

+ 

+ 

-

+ 

43,637 44,460 44,460 

5.61 

100.21 

14.59 

4

4025

SUB_PCT_97 SUB_PCT_98 SUB_PCT_99 SUB_PCT_00 

1997 subprime share of refinance applications 1998 subprime share of refinance applications 1999 subprime share of refinance applications 2000 subprime share of refinance applications 

43,739 43,814 43,737 43,483 

24.10 21.19 24.18 29.09 

17161516
Table B.10 

Correlation Coefficients for Subprime Refinance Share and Neighborhood Indicators 

1997 1998 1999 2000

O_PCT -0.20 -0.23 -0.21 -0.18

MEDAGE 0.28 0.32 0.26 0.20

TEN8588 -0.18 -0.25 -0.23 -0.19

PUBLIC 0.46 0.59 0.55 0.41

CAPITAL 0.32 0.46 0.47 0.36

DPMEDINC -0.37 -0.45 -0.46 -0.38

BLKPCT 0.47 0.60 0.58 0.46
Table B.11 

Correlation Coefficients for Neighborhood Indicators 

O_PCT MEDAGE TEN8588 PUBLIC CAPITAL DPMEDINC BLKPCT

O_PCT 1.00 -0.27 -0.12 -0.50 0.01 0.53 -0.34

MEDAGE -0.27 1.00 -0.41 0.34 0.12 -0.29 0.22

TEN8588 -0.12 -0.41 1.00 -0.13 -0.14 0.11 -0.16

PUBLIC -0.50 0.34 -0.13 1.00 0.31 -0.61 0.59

CAPITAL 0.01 0.12 -0.14 0.31 1.00 -0.42 0.31

DPMEDINC 0.53 -0.29 0.11 -0.61 -0.42 1.00 -0.42

BLKPCT -0.34 0.22 -0.16 0.59 0.31 -0.42 1.00
Table B.12
 

Ordinary Least Squares Model
 

Parameter Standard 

Estimate Error T Prob > T Beta 

1997 

INTERCEP 13.0131 0.5942 43.7720 0.0001 0.0000 

O_PCT 0.0335 0.0040 8.3860 0.0001 0.0432 

MEDAGE 0.0486 0.0064 7.6160 0.0001 0.0382 

TEN8588 -0.0490 0.0100 -4.8800 0.0001 -0.0215 

PUBLIC 0.3356 0.0118 28.3750 0.0001 0.1648 

CAPITAL 0.6833 0.0201 34.0800 0.0001 0.1792 

LOW 2.7011 0.2009 13.4430 0.0001 0.0698 

UPP -2.3307 0.1703 -13.6870 0.0001 -0.0572 

BLK1030 3.8742 0.2078 18.6470 0.0001 0.0736 

BLK3050 7.9871 0.3284 24.3210 0.0001 0.0927 

BLK5080 12.0249 0.3322 36.1990 0.0001 0.1435 

BLK8000 16.7033 0.3343 49.9670 0.0001 0.2333 

R-Squared 0.4775 

Adj R-Squared 0.4733 

1998 

INTERCEP 6.9959 0.4573 40.3960 0.0001 0.0000 

O_PCT 0.0549 0.0031 17.8300 0.0001 0.0780 

MEDAGE 0.0488 0.0049 9.8920 0.0001 0.0418 

TEN8588 -0.0537 0.0077 -6.9320 0.0001 -0.0258 

PUBLIC 0.4886 0.0090 54.1940 0.0001 0.2653 

CAPITAL 0.7841 0.0154 51.0440 0.0001 0.2261 

LOW 2.9144 0.1564 18.6390 0.0001 0.0823 

UPP -1.8356 0.1321 -13.8940 0.0001 -0.0491 

BLK1030 2.6769 0.1610 16.6290 0.0001 0.0555 

BLK3050 6.8521 0.2535 27.0340 0.0001 0.0873 

BLK5080 12.0878 0.2563 47.1580 0.0001 0.1583 

BLK8000 19.3082 0.2575 74.9760 0.0001 0.2968 

R-Squared 0.6264 

Adj R-Squared 0.6234 
Table B.12
 

Ordinary Least Squares Model
 

Parameter Standard 

Estimate Error T Prob > T Beta 

1999 

INTERCEP 10.5128 0.4709 35.9340 0.0001 0.0000 

O_PCT 0.0699 0.0032 22.0120 0.0001 0.1021 

MEDAGE 0.0316 0.0051 6.2220 0.0001 0.0279 

TEN8588 -0.0545 0.0080 -6.8300 0.0001 -0.0269 

PUBLIC 0.4299 0.0093 46.2590 0.0001 0.2403 

CAPITAL 0.7381 0.0158 46.7160 0.0001 0.2194 

LOW 3.0550 0.1609 18.9870 0.0001 0.0888 

UPP -2.8280 0.1359 -20.8160 0.0001 -0.0780 

BLK1030 3.3142 0.1658 19.9930 0.0001 0.0707 

BLK3050 7.4211 0.2603 28.5050 0.0001 0.0976 

BLK5080 12.2001 0.2640 46.2120 0.0001 0.1644 

BLK8000 18.3031 0.2649 69.1000 0.0001 0.2900 

R-Squared 0.5807 

Adj R-Squared 0.5774 

2000 

INTERCEP 17.6658 0.5845 50.5750 0.0001 0.0000 

O_PCT 0.0606 0.0039 15.4060 0.0001 0.0836 

MEDAGE 0.0133 0.0063 2.1130 0.0346 0.0111 

TEN8588 -0.0328 0.0099 -3.3040 0.0010 -0.0153 

PUBLIC 0.2630 0.0116 22.6870 0.0001 0.1385 

CAPITAL 0.7801 0.0196 39.7440 0.0001 0.2192 

LOW 2.1806 0.1986 10.9820 0.0001 0.0601 

UPP -3.8278 0.1674 -22.8650 0.0001 -0.1003 

BLK1030 4.2456 0.2043 20.7780 0.0001 0.0860 

BLK3050 7.9712 0.3222 24.7410 0.0001 0.0989 

BLK5080 11.7982 0.3256 36.2350 0.0001 0.1510 

BLK8000 15.2915 0.3270 46.7600 0.0001 0.2297 

R-Squared 0.4284 

Adj R-Squared 0.4239 
Table B.13 

Metropolitan Dummy Coefficients 

1997 Sum of 1998 Sum of 1999 Sum of 

Share of Variable Parameter Weighted Adjusted Parameter Weighted Adjusted Parameter Weighted 

MSA Name MSA # Frequency Tracts Name Estimate Coefficients Coefficient Estimate Coefficients Coefficient Estimate Coefficients NATIONAL 44,460 INTERCEPT 26.0090 12.9959 13.0131 18.4728 11.4769 6.9959 16.9210 6.4082 ABILENE, TX, (MSA) 40 33 0.1% V001 -36.1470 -0.0268 -23.1510 -19.1115 -0.0142 -7.6346 -13.5809 -0.0101 

AKRON, OH, (PMSA) 80 144 0.3% V002 -9.6110 -0.0311 3.3850 -6.2017 -0.0201 5.2752 -5.1787 -0.0168 

ALBANY, GA, (MSA) 120 31 0.1% V003 -23.7786 -0.0166 -10.7827 -21.3997 -0.0149 -9.9228 -8.3197 -0.0058 

ALBANY-SCHENECTADY-TROY, NY, (MSA) 160 213 0.5% V004 -11.6650 -0.0559 1.3309 -9.9462 -0.0477 1.5307 -5.2233 -0.0250 

ALBUQUERQUE, NM, (MSA) 200 135 0.3% V005 -9.1718 -0.0278 3.8241 -10.8675 -0.0330 0.6094 -3.6813 -0.0112 

ALEXANDRIA, LA, (MSA) 220 33 0.1% V006 -17.5060 -0.0130 -4.5101 -8.7393 -0.0065 2.7376 -16.0155 -0.0119 

ALLENTOWN-BETHLEHEM-EASTON, PA-NJ, (MSA) 240 140 0.3% V007 -9.3890 -0.0296 3.6069 -10.1361 -0.0319 1.3408 -6.1413 -0.0193 

ALTOONA, PA, (MSA) 280 36 0.1% V008 -7.8178 -0.0063 5.1781 -8.6576 -0.0070 2.8193 -2.7602 -0.0022 

AMARILLO, TX, (MSA) 320 67 0.2% V009 -30.4851 -0.0459 -17.4892 -19.6311 -0.0296 -8.1542 -4.4563 -0.0067 

ANCHORAGE, AK, (MSA) 380 54 0.1% V010 -26.0390 -0.0316 -13.0431 -23.9805 -0.0291 -12.5036 -18.7072 -0.0227 

ANN ARBOR, MI, (PMSA) 440 118 0.3% V011 -20.2759 -0.0538 -7.2800 -16.1130 -0.0428 -4.6361 -12.2948 -0.0326 

ANNISTON, AL, (MSA) 450 26 0.1% V012 -27.5832 -0.0161 -14.5873 -12.6645 -0.0074 -1.1876 -10.6330 -0.0062 APPLETON-OSHKOSH-NEENAH, WI, (MSA) 460 81 0.2% V013 -20.7161 -0.0377 -7.7202 -19.0219 -0.0347 -7.5450 -15.2138 -0.0277 

ASHEVILLE, NC, (MSA) 480 38 0.1% V014 -0.6046 -0.0005 12.3913 -3.2200 -0.0028 8.2569 -1.9767 -0.0017 

ATHENS, GA, (MSA) 500 19 0.0% V015 -15.9290 -0.0068 -2.9331 -16.1236 -0.0069 -4.6467 -15.0562 -0.0064 

ATLANTA, GA, (MSA) 520 489 1.1% V016 -12.9372 -0.1423 0.0587 -14.0613 -0.1547 -2.5844 -8.2535 -0.0908 ATLANTIC CITY, NJ, (MSA) 560 91 0.2% V017 -13.6089 -0.0279 -0.6130 -11.9556 -0.0245 -0.4787 -7.3613 -0.0151 

AUGUSTA, GA-SC, (MSA) 600 77 0.2% V018 -12.0794 -0.0209 0.9166 -12.4788 -0.0216 -1.0019 -7.0751 -0.0123 

AUSTIN, TX, (MSA) 640 205 0.5% V019 -28.8222 -0.1329 -15.8263 -14.7183 -0.0679 -3.2414 -5.8189 -0.0268 

BAKERSFIELD, CA, (MSA) 680 108 0.2% V020 -11.3828 -0.0277 1.6131 -15.1908 -0.0369 -3.7139 -8.6901 -0.0211 BALTIMORE, MD, (MSA) 720 570 1.3% V021 -10.7650 -0.1380 2.2309 -13.0260 -0.1670 -1.5491 -8.5775 -0.1100 

BANGOR, ME, (MSA) 730 28 0.1% V022 -13.0109 -0.0082 -0.0150 -16.8641 -0.0106 -5.3872 -12.6042 -0.0079 

BARNSTABLE-YARMOUTH, MA, (MSA) 740 36 0.1% V023 -17.3529 -0.0141 -4.3570 -14.9123 -0.0121 -3.4354 -6.3904 -0.0052 BATON ROUGE, LA, (MSA) 760 106 0.2% V024 -15.9477 -0.0380 -2.9518 -7.1677 -0.0171 4.3092 -5.9756 -0.0142 

BEAUMONT-PORT ARTHUR, TX, (MSA) 840 104 0.2% V025 -23.2018 -0.0543 -10.2059 -10.1420 -0.0237 1.3349 -1.8362 -0.0043 BELLINGHAM, WA, (MSA) 860 23 0.1% V026 -16.2135 -0.0084 -3.2176 -14.2414 -0.0074 -2.7645 -9.4894 -0.0049 BENTON HARBOR, MI, (MSA) 870 52 0.1% V027 -11.3394 -0.0133 1.6565 -11.9587 -0.0140 -0.4818 -13.3999 -0.0157 

BERGIN-PASSAIC, NJ, (PMSA) 875 260 0.6% V028 -11.1035 -0.0649 1.8924 -12.5431 -0.0734 -1.0662 -7.4491 -0.0436 BILLINGS, MT, (MSA) 880 27 0.1% V029 -17.0615 -0.0104 -4.0656 -18.5352 -0.0113 -7.0583 -14.3288 -0.0087 

BILOXI-GULFPORT, MS, (MSA) 920 69 0.2% V030 -11.3118 -0.0176 1.6841 -7.6705 -0.0119 3.8065 -6.1592 -0.0096 

BINGHAMTON, NY, (MSA) 960 65 0.1% V031 -8.4268 -0.0123 4.5691 -10.2328 -0.0150 1.2441 -3.4345 -0.0050 

BIRMINGHAM, AL, (MSA) 1000 189 0.4% V032 -26.9106 -0.1144 -13.9147 -17.1719 -0.0730 -5.6950 -5.0926 -0.0216 

BISMARCK, ND, (MSA) 1010 15 0.0% V033 -29.9047 -0.0101 -16.9088 -23.2185 -0.0078 -11.7416 -22.8535 -0.0077 

BLOOMINGTON, IN, (MSA) 1020 20 0.0% V034 -16.8201 -0.0076 -3.8242 -13.9913 -0.0063 -2.5144 -10.2832 -0.0046 

BLOOMINGTON-NORMAL, IL, (MSA) 1040 29 0.1% V035 -18.3762 -0.0120 -5.3803 -17.6199 -0.0115 -6.1430 -14.4967 -0.0095 

BOISE CITY, ID, (MSA) 1080 64 0.1% V036 -9.1030 -0.0131 3.8929 -12.2675 -0.0177 -0.7906 -7.6839 -0.0111 

BOSTON, MA, (PMSA) 1120 687 1.5% V037 -20.3971 -0.3152 -7.4012 -16.7215 -0.2584 -5.2446 -8.8192 -0.1363 

BOULDER-LONGMONT, CO, (PMSA) 1125 56 0.1% V038 -16.8365 -0.0212 -3.8406 -15.6297 -0.0197 -4.1528 -12.1423 -0.0153 

BRAZORIA, TX, (PMSA) 1145 50 0.1% V039 -28.1525 -0.0317 -15.1566 -20.1186 -0.0226 -8.6417 -9.0747 -0.0102 

BREMERTON, WA, (MSA) 1150 44 0.1% V040 -12.5725 -0.0124 0.4234 -15.0141 -0.0149 -3.5372 -7.3646 -0.0073 

BRIDGEPORT-MILFORD, CT, (PMSA) 1160 112 0.3% V041 -12.8475 -0.0324 0.1484 -5.5718 -0.0140 5.9051 -1.1563 -0.0029 

BROCKTON, MA, (PMSA) 1200 51 0.1% V042 -17.1612 -0.0197 -4.1653 -15.7244 -0.0180 -4.2475 -4.7626 -0.0055 

BROWNSVILLE-HARLINGEN, TX, (MSA) 1240 62 0.1% V043 -33.9398 -0.0473 -20.9439 -17.1085 -0.0239 -5.6316 -1.4506 -0.0020 

BRYAN-COLLEGE STATION, TX, (MSA) 1260 22 0.0% V044 -32.8338 -0.0162 -19.8379 -20.9524 -0.0104 -9.4755 -12.9391 -0.0064 BUFFALO, NY, (PMSA) 1280 285 0.6% V045 -8.7025 -0.0558 4.2934 -7.8074 -0.0500 3.6695 -1.7831 -0.0114 

BURLINGTON, VT, (MSA) 1305 31 0.1% V046 -24.7603 -0.0173 -11.7644 -19.7720 -0.0138 -8.2951 -14.3236 -0.0100 

CANTON, OH, (MSA) 1320 75 0.2% V047 -6.0492 -0.0102 6.9467 -2.5500 -0.0043 8.9269 -0.6294 -0.0011 

CASPER, WY, (MSA) 1350 19 0.0% V048 -19.0831 -0.0082 -6.0872 -18.4680 -0.0079 -6.9911 -8.4102 -0.0036 

CEDAR RAPIDS, IA, (MSA) 1360 39 0.1% V049 -24.4316 -0.0214 -11.4357 -13.5630 -0.0119 -2.0861 -13.4209 -0.0118 

CHAMPAIGN-URBANA-RANTOUL, IL, (MSA) 1400 37 0.1% V050 -22.6144 -0.0188 -9.6185 -18.6772 -0.0155 -7.2003 -18.0903 -0.0151 CHARLESTON, SC, (MSA) 1440 107 0.2% V051 -10.2285 -0.0246 2.7674 -10.0990 -0.0243 1.3779 -3.2396 -0.0078 

CHARLESTON, WV, (MSA) 1480 61 0.1% V052 -12.4862 -0.0171 0.5097 -10.2962 -0.0141 1.1807 -0.7236 -0.0010 

CHARLOTTE-GASTONIA-ROCK HILL, NC-SC, (MSA) 1520 257 0.6% V053 -5.4735 -0.0316 7.5224 -6.1002 -0.0353 5.3767 -2.5476 -0.0147 CHARLOTTESVILLE, VA, (MSA) 1540 29 0.1% V054 -22.7507 -0.0148 -9.7548 -21.9254 -0.0143 -10.4485 -11.4938 -0.0075 

CHATTANOOGA, TN-GA, (MSA) 1560 84 0.2% V055 -12.1978 -0.0230 0.7982 -6.6191 -0.0125 4.8578 2.1146 0.0040 CHEYENNE, WY, (MSA) 1580 18 0.0% V056 -17.5301 -0.0071 -4.5342 -18.0770 -0.0073 -6.6001 -11.9143 -0.0048 

CHICAGO, IL, (PMSA) 1600 1719 3.9% V057 -15.4889 -0.5989 -2.4930 -14.8869 -0.5756 -3.4100 -9.6772 -0.3742 CHICO, CA, (MSA) 1620 37 0.1% V058 -13.7450 -0.0114 -0.7491 -16.4669 -0.0137 -4.9900 -9.5623 -0.0080 CINCINNATI, OH-KY-IN, (PMSA) 1640 361 0.8% V059 -8.5348 -0.0693 4.4611 -10.7964 -0.0877 0.6805 -6.6601 -0.0541 

CLARKSVILLE-HOPKINSVILLE, TN-KY, (MSA) 1660 42 0.1% V060 -15.0417 -0.0142 -2.0457 -17.4239 -0.0165 -5.9470 -8.4931 -0.0080 

CLEVELAND, OH, (PMSA) 1680 691 1.6% V061 -5.7760 -0.0898 7.2199 -5.4394 -0.0845 6.0375 -2.5548 -0.0397 

COLORADO SPRINGS, CO, (MSA) 1720 82 0.2% V062 -13.2292 -0.0244 -0.2333 -13.5367 -0.0250 -2.0598 -8.1140 -0.0150 

COLUMBIA, MO, (MSA) 1740 28 0.1% V063 -25.6648 -0.0162 -12.6689 -19.3475 -0.0122 -7.8706 -16.7801 -0.0106 COLUMBIA, SC, (MSA) 1760 102 0.2% V064 -5.8912 -0.0135 7.1047 -9.9479 -0.0228 1.5290 -3.8883 -0.0089 

COLUMBUS, GA-AL, (MSA) 1800 65 0.1% V065 -12.7541 -0.0186 0.2418 -13.3594 -0.0195 -1.8825 3.4958 0.0051 

COLUMBUS, OH, (MSA) 1840 337 0.8% V066 -10.4841 -0.0795 2.5118 -9.4612 -0.0717 2.0157 -5.6905 -0.0431 

CORPUS CHRISTI, TX, (MSA) 1880 77 0.2% V067 -25.7147 -0.0445 -12.7188 -13.2855 -0.0230 -1.8086 -0.4788 -0.0008 

CUMBERLAND, MD-WV, (MSA) 1900 25 0.1% V068 -10.9996 -0.0062 1.9963 -11.0293 -0.0062 0.4476 -3.8424 -0.0022 DALLAS, TX, (PMSA) 1920 559 1.3% V069 -23.9244 -0.3008 -10.9285 -13.5194 -0.1700 -2.0425 -5.1370 -0.0646 

DANBURY, CT, (PMSA) 1930 47 0.1% V070 -17.5762 -0.0186 -4.5803 -14.4797 -0.0153 -3.0028 -10.6677 -0.0113 
Table B.13 

Metropolitan Dummy Coefficients 

1997 Sum of 1998 Sum of 1999 Sum of 

Share of Variable Parameter Weighted Adjusted Parameter Weighted Adjusted Parameter Weighted 

MSA Name MSA # Frequency Tracts Name Estimate Coefficients Coefficient Estimate Coefficients Coefficient Estimate Coefficients DANVILLE, VA, (MSA) 1950 30 0.1% V071 -18.2593 -0.0123 -5.2634 -17.3195 -0.0117 -5.8426 -6.5478 -0.0044 

DAVENPORT-ROCK ISLAND-MOLINE, IA-IL, (MSA) 1960 95 0.2% V072 -15.9109 -0.0340 -2.9149 -13.8889 -0.0297 -2.4120 -12.9264 -0.0276 

DAYTON-SPRINGFIELD, OH, (MSA) 2000 240 0.5% V073 -9.8827 -0.0533 3.1132 -9.8764 -0.0533 1.6005 -4.7085 -0.0254 

DAYTONA BEACH, FL, (MSA) 2020 71 0.2% V074 -15.9068 -0.0254 -2.9109 -11.3262 -0.0181 0.1507 -2.6500 -0.0042 

DECATUR, AL, (MSA) 2030 34 0.1% V075 -31.8045 -0.0243 -18.8086 -20.6273 -0.0158 -9.1504 -8.7102 -0.0067 

DECATUR, IL, (MSA) 2040 37 0.1% V076 -6.8795 -0.0057 6.1164 -7.6499 -0.0064 3.8270 -9.1467 -0.0076 

DENVER, CO, (PMSA) 2080 408 0.9% V077 -15.2143 -0.1396 -2.2184 -14.4253 -0.1324 -2.9484 -7.9414 -0.0729 

DES MOINES, IA, (MSA) 2120 90 0.2% V078 -12.1323 -0.0246 0.8636 -12.2277 -0.0248 -0.7508 -8.7986 -0.0178 

DETROIT, MI, (PMSA) 2160 1152 2.6% V079 -21.0516 -0.5455 -8.0557 -14.2238 -0.3686 -2.7469 -11.3052 -0.2929 

DOTHAN, AL, (MSA) 2180 34 0.1% V080 -35.2271 -0.0269 -22.2312 -27.4831 -0.0210 -16.0062 -18.5900 -0.0142 

DOVER, DE, (MSA) 2190 31 0.1% V081 -11.5681 -0.0081 1.4278 -9.0775 -0.0063 2.3994 -4.0243 -0.0028 

DUBUQUE, IA, (MSA) 2200 25 0.1% V082 -27.6179 -0.0155 -14.6220 -21.8202 -0.0123 -10.3433 -18.4223 -0.0104 DULUTH, MN-WI, (MSA) 2240 85 0.2% V083 -23.7211 -0.0454 -10.7252 -20.4847 -0.0392 -9.0078 -16.5070 -0.0316 DUTCHESS COUNTY, NY, (PMSA) 2281 66 0.1% V084 -10.4102 -0.0155 2.5858 -9.5974 -0.0142 1.8795 -4.4936 -0.0067 

EAU CLAIRE, WI, (MSA) 2290 31 0.1% V085 -27.8159 -0.0194 -14.8200 -21.5296 -0.0150 -10.0527 -17.7266 -0.0124 

EL PASO, TX, (MSA) 2320 94 0.2% V086 -18.6830 -0.0395 -5.6871 -10.4286 -0.0220 1.0483 9.1389 0.0193 

ELKHART-GOSHEN, IN, (MSA) 2330 33 0.1% V087 -4.5746 -0.0034 8.4213 -8.1464 -0.0060 3.3305 -4.2919 -0.0032 

ELMIRA, NY, (MSA) 2335 22 0.0% V088 -15.3713 -0.0076 -2.3754 -2.6979 -0.0013 8.7790 -2.0590 -0.0010 

ENID, OK, (MSA) 2340 12 0.0% V089 -6.0324 -0.0016 6.9635 -5.3476 -0.0014 6.1293 -1.3745 -0.0004 

ERIE, PA, (MSA) 2360 68 0.2% V090 -15.4213 -0.0236 -2.4254 -8.6624 -0.0132 2.8145 -3.5340 -0.0054 

EUGENE-SPRINGFIELD, OR, (MSA) 2400 69 0.2% V091 -13.2146 -0.0205 -0.2187 -15.3502 -0.0238 -3.8733 -9.4755 -0.0147 

EVANSVILLE, IN-KY, (MSA) 2440 73 0.2% V092 -1.0556 -0.0017 11.9403 -5.6333 -0.0092 5.8436 0.1953 0.0003 

FARGO-MOORHEAD, ND-MN, (MSA) 2520 36 0.1% V093 -27.2231 -0.0220 -14.2271 -23.8831 -0.0193 -12.4062 -18.2594 -0.0148 

FAYETTEVILLE, NC, (MSA) 2560 49 0.1% V094 -5.3161 -0.0059 7.6798 -10.8764 -0.0120 0.6005 -3.0927 -0.0034 FAYETTEVILLE-SPRINGDALE, AR, (MSA) 2580 54 0.1% V095 -24.2918 -0.0295 -11.2959 -17.3772 -0.0211 -5.9003 -14.8612 -0.0181 

FITCHBURG-LEOMINSTER, MA, (MSA) 2600 29 0.1% V096 -18.4865 -0.0121 -5.4906 -15.4496 -0.0101 -3.9727 -3.2238 -0.0021 

FLAGSTAFF, AZ-UT (MSA) 2620 25 0.1% V097 -7.0697 -0.0040 5.9263 -14.8279 -0.0083 -3.3510 -9.4328 -0.0053 FLINT, MI, (MSA) 2640 101 0.2% V098 -13.9176 -0.0316 -0.9217 -15.0100 -0.0341 -3.5331 -7.8038 -0.0177 

FLORENCE, AL, (MSA) 2650 28 0.1% V099 -33.8616 -0.0213 -20.8656 -23.6554 -0.0149 -12.1785 -11.3532 -0.0072 

FLORENCE, SC, (MSA) 2655 29 0.1% V100 -6.6579 -0.0043 6.3380 -8.3480 -0.0054 3.1289 -3.1305 -0.0020 

FORT COLLINS-LOVELAND, CO, (MSA) 2670 43 0.1% V101 -17.0670 -0.0165 -4.0711 -15.9561 -0.0154 -4.4792 -9.7166 -0.0094 

FORT LAUDERDALE-HOLLYWOOD-POMPANO BEACH 2680 160 0.4% V102 -11.8795 -0.0428 1.1164 -9.2683 -0.0334 2.2086 -4.0643 -0.0146 

FORT MYERS-CAPE CORAL, FL, (MSA) 2700 91 0.2% V103 -12.3927 -0.0254 0.6032 -7.6293 -0.0156 3.8476 -1.4216 -0.0029 

FORT PIERCE, FL, (MSA) 2710 55 0.1% V104 -19.7434 -0.0244 -6.7475 -12.5482 -0.0155 -1.0713 -6.3376 -0.0078 FORT SMITH, AR-OK, (MSA) 2720 41 0.1% V105 -21.9461 -0.0202 -8.9502 -15.1256 -0.0139 -3.6487 -8.8090 -0.0081 FORT WALTON BEACH, FL, (MSA) 2750 32 0.1% V106 -19.5062 -0.0140 -6.5103 -18.3246 -0.0132 -6.8477 -11.4457 -0.0082 

FORT WAYNE, IN, (MSA) 2760 110 0.2% V107 -7.7858 -0.0193 5.2101 -6.3250 -0.0156 5.1519 -2.9340 -0.0073 FORT WORTH-ARLINGTON, TX, (PMSA) 2800 289 0.7% V108 -23.3548 -0.1518 -10.3588 -15.0593 -0.0979 -3.5824 -5.6304 -0.0366 

FRESNO, CA, (MSA) 2840 141 0.3% V109 -8.2642 -0.0262 4.7317 -13.3185 -0.0422 -1.8416 -6.6626 -0.0211 

GADSDEN, AL, (MSA) 2880 29 0.1% V110 -27.9291 -0.0182 -14.9332 -18.5050 -0.0121 -7.0281 -9.5997 -0.0063 

GAINESVILLE, FL, (MSA) 2900 28 0.1% V111 -16.4602 -0.0104 -3.4642 -17.3469 -0.0109 -5.8699 -10.7020 -0.0067 

GALVESTON-TEXAS CITY, TX, (PMSA) 2920 63 0.1% V112 -29.3754 -0.0416 -16.3795 -12.4197 -0.0176 -0.9428 -1.0027 -0.0014 

GARY-HAMMOND, IN, (PMSA) 2960 117 0.3% V113 -5.5294 -0.0146 7.4665 -4.4390 -0.0117 7.0379 -1.4783 -0.0039 

GLENS FALLS, NY, (MSA) 2975 35 0.1% V114 -8.2372 -0.0065 4.7587 -3.8220 -0.0030 7.6549 -1.8907 -0.0015 

GOLDSBORO, NC, (MSA) 2980 19 0.0% V115 -4.7654 -0.0020 8.2305 -12.7166 -0.0054 -1.2397 2.3435 0.0010 GRAND FORKS, ND, (MSA) 2985 29 0.1% V116 -28.0383 -0.0183 -15.0424 -21.6811 -0.0141 -10.2041 -18.7380 -0.0122 

GRAND JUNCTION, CO (MSA) 2995 21 0.0% V117 -18.7116 -0.0088 -5.7157 -16.5758 -0.0078 -5.0989 -13.5791 -0.0064 

GRAND RAPIDS, MI, (MSA) 3000 208 0.5% V118 -14.0845 -0.0659 -1.0886 -16.1018 -0.0753 -4.6249 -13.4445 -0.0629 

GREAT FALLS, MT, (MSA) 3040 24 0.1% V119 -15.6162 -0.0084 -2.6203 -15.0702 -0.0081 -3.5933 -14.2957 -0.0077 

GREELEY, CO, (MSA) 3060 32 0.1% V120 -12.4905 -0.0090 0.5054 -10.7295 -0.0077 0.7474 -6.3641 -0.0046 

GREEN BAY, WI, (MSA) 3080 48 0.1% V121 -24.2147 -0.0261 -11.2188 -20.4489 -0.0221 -8.9720 -17.6478 -0.0191 GREENSBORO--WINSTON-SALEM--HIGH POINT, NC, 3120 251 0.6% V122 -6.0081 -0.0339 6.9878 -6.9919 -0.0395 4.4850 -2.1881 -0.0124 

GREENVILLE, NC, (MSA) 3150 20 0.0% V123 -16.3079 -0.0073 -3.3120 -13.9498 -0.0063 -2.4729 -6.7449 -0.0030 

GREENVILLE-SPARTANBURG, SC, (MSA) 3160 205 0.5% V124 -8.9277 -0.0412 4.0682 -6.8363 -0.0315 4.6406 0.3834 0.0018 

HAGERSTOWN, MD, (MSA) 3180 28 0.1% V125 -12.3313 -0.0078 0.6646 -10.0091 -0.0063 1.4678 -1.6944 -0.0011 

HAMILTON-MIDDLETOWN, OH, (PMSA) 3200 72 0.2% V126 -8.5219 -0.0138 4.4740 -8.5997 -0.0139 2.8772 -5.9334 -0.0096 

HARRISBURG-LEBANON-CARLISLE, PA, (MSA) 3240 126 0.3% V127 -10.7862 -0.0306 2.2097 -9.2929 -0.0263 2.1840 -5.9591 -0.0169 

HARTFORD, CT, (PMSA) 3280 288 0.6% V128 -15.8867 -0.1029 -2.8908 -10.5571 -0.0684 0.9198 -4.4053 -0.0285 

HATTIESBURG MS, (MSA) 3285 23 0.1% V129 -28.7226 -0.0149 -15.7267 -10.0604 -0.0052 1.4165 -6.1511 -0.0032 HICKORY-MORGANTON, NC, (MSA) 3290 45 0.1% V130 -2.5666 -0.0026 10.4293 1.2330 0.0012 12.7099 3.3160 0.0034 

HONOLULU, HI, (MSA) 3320 175 0.4% V131 -13.8410 -0.0545 -0.8451 -11.9432 -0.0470 -0.4662 -10.6970 -0.0421 

HOUMA-THIBODAUX, LA, (MSA) 3350 36 0.1% V132 -14.6326 -0.0118 -1.6367 -7.8229 -0.0063 3.6540 -9.3232 -0.0075 

HOUSTON, TX, (PMSA) 3360 668 1.5% V133 -21.1542 -0.3178 -8.1583 -10.6407 -0.1599 0.8362 0.9140 0.0137 

HUNTINGTON-ASHLAND, WV-KY-OH, (MSA) 3400 75 0.2% V134 -14.4173 -0.0243 -1.4214 -9.9468 -0.0168 1.5301 -2.8466 -0.0048 

HUNTSVILLE, AL, (MSA) 3440 75 0.2% V135 -31.4863 -0.0531 -18.4904 -23.6129 -0.0398 -12.1360 -10.7666 -0.0182 

INDIANAPOLIS, IN, (MSA) 3480 328 0.7% V136 -3.5707 -0.0263 9.4253 -7.7036 -0.0568 3.7733 -3.1542 -0.0233 

IOWA CITY, IA, (MSA) 3500 23 0.1% V137 -29.6512 -0.0153 -16.6553 -23.3745 -0.0121 -11.8975 -20.6055 -0.0107 

JACKSON, MI, (MSA) 3520 31 0.1% V138 -1.6196 -0.0011 11.3763 -9.2623 -0.0065 2.2146 -2.3646 -0.0016 

JACKSON, MS, (MSA) 3560 88 0.2% V139 -14.5930 -0.0289 -1.5971 -8.7974 -0.0174 2.6795 -8.5417 -0.0169 

JACKSON, TN, (MSA) 3580 21 0.0% V140 -22.5328 -0.0106 -9.5369 -10.7269 -0.0051 0.7500 0.3598 0.0002 

JACKSONVILLE, FL, (MSA) 3600 165 0.4% V141 -11.7946 -0.0438 1.2013 -8.5482 -0.0317 2.9287 -1.1574 -0.0043 
Table B.13 

Metropolitan Dummy Coefficients 

1997 Sum of 1998 Sum of 1999 Sum of 

Share of Variable Parameter Weighted Adjusted Parameter Weighted Adjusted Parameter Weighted 

MSA Name MSA # Frequency Tracts Name Estimate Coefficients Coefficient Estimate Coefficients Coefficient Estimate Coefficients JACKSONVILLE, NC, (MSA) 3605 21 0.0% V142 -7.8246 -0.0037 5.1713 -11.5671 -0.0055 -0.0902 -10.0127 -0.0047 JAMESTOWN-DUNKIRK, NY, (MSA) 3610 33 0.1% V143 -10.3361 -0.0077 2.6599 -1.4252 -0.0011 10.0518 -0.0982 -0.0001 

JANESVILLE-BELOIT, WI, (MSA) 3620 34 0.1% V144 -20.4526 -0.0156 -7.4567 -17.3225 -0.0132 -5.8456 -15.7113 -0.0120 

JERSEY CITY, NJ (PMSA) 3640 156 0.4% V145 -1.8313 -0.0064 11.1646 -9.7096 -0.0341 1.7673 -4.8225 -0.0169 

JOHNSON CITY-KINGSPORT-BRISTOL, TN-VA, (MSA) 3660 91 0.2% V146 -18.8523 -0.0386 -5.8563 -9.4586 -0.0194 2.0183 -3.9648 -0.0081 

JOHNSTOWN, PA, (MSA) 3680 67 0.2% V147 -9.2819 -0.0140 3.7140 -5.5555 -0.0084 5.9214 -6.0918 -0.0092 

JONESBORO, AR, (MSA) 3700 12 0.0% V148 -33.5231 -0.0090 -20.5271 -21.7745 -0.0059 -10.2976 -18.0086 -0.0049 

JOPLIN, MO, (MSA) 3710 32 0.1% V149 -18.6151 -0.0134 -5.6192 -11.3649 -0.0082 0.1120 -9.9810 -0.0072 KALAMAZOO, MI, (MSA) 3720 111 0.2% V150 -9.6541 -0.0241 3.3418 -11.7823 -0.0294 -0.3054 -9.5434 -0.0238 

KANKAKEE, IL, (MSA) 3740 26 0.1% V151 -15.2288 -0.0089 -2.2329 -9.3992 -0.0055 2.0778 -4.7715 -0.0028 

KANSAS CITY, MO-KS, (MSA) 3760 418 0.9% V152 3.4762 0.0327 16.4721 -2.3620 -0.0222 9.1149 -3.3284 -0.0313 

KENOSHA, WI, (PMSA) 3800 31 0.1% V153 -16.1018 -0.0112 -3.1059 -13.7846 -0.0096 -2.3077 -13.3430 -0.0093 

KILLEEN-TEMPLE, TX, (MSA) 3810 54 0.1% V154 -33.7423 -0.0410 -20.7463 -21.1887 -0.0257 -9.7118 -5.3518 -0.0065 

KNOXVILLE, TN, (MSA) 3840 130 0.3% V155 -9.1782 -0.0268 3.8178 -3.9279 -0.0115 7.5490 0.0585 0.0002 KOKOMO, IN, (MSA) 3850 21 0.0% V156 -4.9739 -0.0023 8.0220 -9.5783 -0.0045 1.8986 -6.0798 -0.0029 

LA CROSSE, WI, (MSA) 3870 25 0.1% V157 -29.1143 -0.0164 -16.1184 -24.2973 -0.0137 -12.8204 -19.2544 -0.0108 

LAFAYETTE, LA, (MSA) 3880 84 0.2% V158 -26.7729 -0.0506 -13.7770 -9.0357 -0.0171 2.4412 -10.7517 -0.0203 

LAFAYETTE-WEST LAFAYETTE, IN, (MSA) 3920 41 0.1% V159 -11.3806 -0.0105 1.6153 -14.4066 -0.0133 -2.9297 -10.7082 -0.0099 

LAKE CHARLES, LA, (MSA) 3960 40 0.1% V160 -18.6131 -0.0167 -5.6172 -7.0403 -0.0063 4.4366 -10.1322 -0.0091 

LAKELAND-WINTER HAVEN, FL, (MSA) 3980 75 0.2% V161 -12.4316 -0.0210 0.5643 -5.8275 -0.0098 5.6494 0.7918 0.0013 LANCASTER, PA, (MSA) 4000 94 0.2% V162 -17.2727 -0.0365 -4.2768 -13.1683 -0.0278 -1.6914 -7.3483 -0.0155 

LANSING-EAST LANSING, MI, (MSA) 4040 114 0.3% V163 -7.4759 -0.0192 5.5200 -14.1711 -0.0363 -2.6942 -9.7581 -0.0250 LAREDO, TX, (MSA) 4080 24 0.1% V164 -30.5190 -0.0165 -17.5231 -9.8282 -0.0053 1.6487 -1.0677 -0.0006 

LAS CRUCES, NM, (MSA) 4100 22 0.0% V165 -18.2306 -0.0090 -5.2347 -15.6445 -0.0077 -4.1676 -8.1767 -0.0040 LAS VEGAS, NV, (MSA) 4120 155 0.3% V166 -10.3515 -0.0361 2.6444 -11.9973 -0.0418 -0.5204 -4.0762 -0.0142 

LAWRENCE, KS, (MSA) 4150 14 0.0% V167 -14.2778 -0.0045 -1.2819 -15.8848 -0.0050 -4.4079 -13.2551 -0.0042 

LAWRENCE-HAVERHILL, MA-NH (PMSA) 4160 73 0.2% V168 -19.4470 -0.0319 -6.4511 -14.2259 -0.0234 -2.7490 -2.6471 -0.0043 

LAWTON, OK, (MSA) 4200 30 0.1% V169 -1.8694 -0.0013 11.1265 0.5768 0.0004 12.0538 -7.7314 -0.0052 

LEWISTON-AUBURN, ME, (MSA) 4240 24 0.1% V170 -17.3971 -0.0094 -4.4012 -14.9989 -0.0081 -3.5220 -5.1497 -0.0028 LEXINGTON-FAYETTE, KY, (MSA) 4280 74 0.2% V171 -10.5989 -0.0176 2.3971 -14.9342 -0.0249 -3.4573 -9.1094 -0.0152 

LIMA, OH, (MSA) 4320 46 0.1% V172 -11.0709 -0.0115 1.9251 -10.0577 -0.0104 1.4192 -6.5102 -0.0067 LINCOLN, NE, (MSA) 4360 48 0.1% V173 -18.2239 -0.0197 -5.2280 -16.7329 -0.0181 -5.2560 -12.8761 -0.0139 LITTLE ROCK-NORTH LITTLE ROCK, AR, (MSA) 4400 109 0.2% V174 -15.2899 -0.0375 -2.2939 -10.3325 -0.0253 1.1445 -9.2160 -0.0226 

LONGVIEW-MARSHALL, TX, (MSA) 4420 41 0.1% V175 -35.8706 -0.0331 -22.8747 -15.4170 -0.0142 -3.9401 -8.8497 -0.0082 

LOS ANGELES-LONG BEACH, CA, (PMSA) 4480 1622 3.6% V176 -9.2642 -0.3380 3.7317 -11.6007 -0.4232 -0.1238 -8.1355 -0.2968 

LOUISVILLE, KY-IN, (MSA) 4520 238 0.5% V177 -10.6092 -0.0568 2.3867 -12.0735 -0.0646 -0.5966 -10.2265 -0.0547 LOWELL, MA-NH, (PMSA) 4560 51 0.1% V178 -21.1512 -0.0243 -8.1553 -15.9134 -0.0183 -4.4365 -5.8651 -0.0067 

LUBBOCK, TX, (MSA) 4600 61 0.1% V179 -30.7408 -0.0422 -17.7449 -18.4280 -0.0253 -6.9511 -3.9090 -0.0054 

LYNCHBURG, VA, (MSA) 4640 38 0.1% V180 -17.0309 -0.0146 -4.0350 -14.6222 -0.0125 -3.1452 -5.6152 -0.0048 

MACON-WARNER ROBINS, GA, (MSA) 4680 60 0.1% V181 -21.6730 -0.0292 -8.6771 -13.8655 -0.0187 -2.3886 -10.5117 -0.0142 MADISON, WI, (MSA) 4720 85 0.2% V182 -25.1555 -0.0481 -12.1596 -22.4582 -0.0429 -10.9813 -19.0171 -0.0364 

MANCHESTER, NH, (MSA) 4760 42 0.1% V183 -16.6553 -0.0157 -3.6594 -14.8939 -0.0141 -3.4170 -6.6055 -0.0062 

MANSFIELD, OH, (MSA) 4800 44 0.1% V184 -5.1103 -0.0051 7.8856 -3.1382 -0.0031 8.3387 -1.7052 -0.0017 

MCALLEN-EDINBURG-MISSION, TX, (MSA) 4880 63 0.1% V185 -31.4955 -0.0446 -18.4996 -16.9179 -0.0240 -5.4410 -0.8038 -0.0011 MEDFORD, OR, (MSA) 4890 30 0.1% V186 -10.9504 -0.0074 2.0455 -11.4256 -0.0077 0.0513 -7.4922 -0.0051 

MELBOURNE-TITUSVILLE-PALM BAY, FL, (MSA) 4900 85 0.2% V187 -17.8204 -0.0341 -4.8245 -14.2229 -0.0272 -2.7460 -7.5384 -0.0144 

MEMPHIS, TN-AR-MS, (MSA) 4920 212 0.5% V188 -12.6177 -0.0602 0.3782 -7.4451 -0.0355 4.0318 -1.0364 -0.0049 MERCED, CA, (MSA) 4940 29 0.1% V189 -10.5739 -0.0069 2.4220 -15.5510 -0.0101 -4.0741 -10.1925 -0.0066 

MIAMI-HIALEAH, FL, (PMSA) 5000 261 0.6% V190 -7.3609 -0.0432 5.6350 -4.9316 -0.0290 6.5453 -0.7532 -0.0044 

MIDDLESEX-SOMERSET-HUNTERDON, NJ, (PMSA) 5015 250 0.6% V191 -13.3504 -0.0751 -0.3545 -14.2490 -0.0801 -2.7720 -9.9648 -0.0560 MILWAUKEE, WI, (PMSA) 5080 384 0.9% V192 -17.7597 -0.1534 -4.7638 -17.4681 -0.1509 -5.9912 -15.9287 -0.1376 

MINNEAPOLIS-ST. PAUL, MN-WI, (MSA) 5120 639 1.4% V193 -15.3698 -0.2209 -2.3739 -15.6147 -0.2244 -4.1378 -11.4762 -0.1649 MOBILE, AL, (MSA) 5160 138 0.3% V194 -26.1367 -0.0811 -13.1408 -16.1216 -0.0500 -4.6447 -0.7297 -0.0023 MODESTO, CA, (MSA) 5170 72 0.2% V195 -7.1230 -0.0115 5.8729 -14.6313 -0.0237 -3.1544 -11.6127 -0.0188 

MONMOUTH-OCEAN, NJ, (PMSA) 5190 224 0.5% V196 -10.5281 -0.0530 2.4678 -12.2150 -0.0615 -0.7381 -6.8343 -0.0344 

MONROE, LA, (MSA) 5200 42 0.1% V197 -15.9301 -0.0150 -2.9342 -14.1382 -0.0134 -2.6613 -17.0595 -0.0161 

MONTGOMERY, AL, (MSA) 5240 76 0.2% V198 -34.1839 -0.0584 -21.1880 -28.5565 -0.0488 -17.0796 -15.4547 -0.0264 

MUNCIE, IN, (MSA) 5280 31 0.1% V199 1.5945 0.0011 14.5904 -3.6111 -0.0025 7.8658 -2.6198 -0.0018 

MYRTLE BEACH, SC, (MSA) 5330 39 0.1% V200 -18.3450 -0.0161 -5.3491 -12.7344 -0.0112 -1.2575 -8.3033 -0.0073 

NAPLES, FL, (MSA) 5345 31 0.1% V201 -17.8077 -0.0124 -4.8118 -9.7275 -0.0068 1.7494 -5.5411 -0.0039 NASHUA, NH (PMSA) 5350 32 0.1% V202 -22.7466 -0.0164 -9.7506 -15.8655 -0.0114 -4.3886 -10.3777 -0.0075 

NASHVILLE, TN, (MSA) 5360 197 0.4% V203 -7.4424 -0.0330 5.5535 -8.3884 -0.0372 3.0885 -1.6271 -0.0072 

NASSAU-SUFFOLK, NY, (PMSA) 5380 569 1.3% V204 -6.2575 -0.0801 6.7384 -6.0090 -0.0769 5.4679 -1.0806 -0.0138 

NEW BEDFORD, MA, (MSA) 5400 44 0.1% V205 -20.2232 -0.0200 -7.2273 -15.9441 -0.0158 -4.4672 -4.3406 -0.0043 

NEW HAVEN-MERIDEN, CT, (MSA) 5480 123 0.3% V206 -8.3298 -0.0230 4.6661 -6.0899 -0.0168 5.3870 -0.0801 -0.0002 NEW LONDON-NORWICH, CT-RI, (MSA) 5520 66 0.1% V207 -13.4430 -0.0200 -0.4471 -11.6958 -0.0174 -0.2189 -5.3088 -0.0079 

NEW ORLEANS, LA, (MSA) 5560 359 0.8% V208 -14.5137 -0.1172 -1.5178 -10.7111 -0.0865 0.7658 -6.8893 -0.0556 

NEW YORK, NY, (PMSA) 5600 2351 5.3% V209 0.0000 12.9959 0.0000 11.4769 0.0000 

NEWARK, NJ, (PMSA) 5640 464 1.0% V210 -10.8546 -0.1133 2.1413 -10.8891 -0.1136 0.5878 -6.2091 -0.0648 

ORANGE COUNTY, NY, (PMSA) 5660 64 0.1% V211 -4.6501 -0.0067 8.3458 -4.2710 -0.0061 7.2059 0.2192 0.0003 

NORFOLK-VIRGINIA BEACH-NEWPORT NEWS, VA, (M 5720 291 0.7% V212 -17.1128 -0.1120 -4.1169 -18.7649 -0.1228 -7.2880 -12.2780 -0.0804 
Table B.13 

Metropolitan Dummy Coefficients 

1997 Sum of 1998 Sum of 1999 Sum of 

Share of Variable Parameter Weighted Adjusted Parameter Weighted Adjusted Parameter Weighted 

MSA Name MSA # Frequency Tracts Name Estimate Coefficients Coefficient Estimate Coefficients Coefficient Estimate Coefficients OAKLAND, CA, (PMSA) 5775 451 1.0% V213 -15.7058 -0.1593 -2.7099 -16.8623 -0.1711 -5.3854 -10.1562 -0.1030 

OCALA, FL, (MSA) 5790 45 0.1% V214 -16.8120 -0.0170 -3.8161 -10.7779 -0.0109 0.6990 -3.3859 -0.0034 

ODESSA, TX, (MSA) 5800 55 0.1% V215 -33.6979 -0.0417 -20.7020 -14.0060 -0.0173 -2.5291 -3.1701 -0.0039 

OKLAHOMA CITY, OK, (MSA) 5880 293 0.7% V216 -4.9033 -0.0323 8.0926 -7.1855 -0.0474 4.2914 -4.6400 -0.0306 

OLYMPIA, WA, (MSA) 5910 30 0.1% V217 -13.5907 -0.0092 -0.5948 -14.1981 -0.0096 -2.7212 -8.8132 -0.0059 

OMAHA, NE-IA, (MSA) 5920 154 0.3% V218 -10.5338 -0.0365 2.4621 -11.3668 -0.0394 0.1101 -6.8318 -0.0237 

ORANGE COUNTY, CA, (PMSA) 5945 475 1.1% V219 -14.7392 -0.1575 -1.7433 -13.8199 -0.1476 -2.3429 -8.7499 -0.0935 

ORLANDO, FL, (MSA) 5960 220 0.5% V220 -13.0052 -0.0644 -0.0093 -13.0665 -0.0647 -1.5896 -5.5798 -0.0276 

OWENSBORO, KY, (MSA) 5990 20 0.0% V221 -15.5136 -0.0070 -2.5177 -13.4545 -0.0061 -1.9776 -12.9055 -0.0058 

PANAMA CITY, FL, (MSA) 6015 25 0.1% V222 -15.5348 -0.0087 -2.5389 -11.1984 -0.0063 0.2785 -5.1346 -0.0029 

PARKERSBURG-MARIETTA, WV-OH, (MSA) 6020 45 0.1% V223 -18.4888 -0.0187 -5.4929 -4.8687 -0.0049 6.6082 -5.3527 -0.0054 PENSACOLA, FL, (MSA) 6080 62 0.1% V224 -15.6559 -0.0218 -2.6600 -13.4668 -0.0188 -1.9899 -5.9389 -0.0083 PEORIA, IL, (MSA) 6120 92 0.2% V225 -14.4551 -0.0299 -1.4592 -10.1510 -0.0210 1.3259 -9.3381 -0.0193 PHILADELPHIA, PA-NJ, (PMSA) 6160 1237 2.8% V226 -13.0790 -0.3639 -0.0831 -11.6790 -0.3249 -0.2021 -6.2265 -0.1732 

PHOENIX, AZ, (MSA) 6200 485 1.1% V227 -5.3500 -0.0584 7.6459 -11.7312 -0.1280 -0.2543 -2.5116 -0.0274 

PINE BLUFF, AR, (MSA) 6240 28 0.1% V228 -7.9129 -0.0050 5.0831 -6.9924 -0.0044 4.4845 -5.7851 -0.0036 

PITTSBURGH, PA, (PMSA) 6280 732 1.6% V229 1.1242 0.0185 14.1201 -0.2486 -0.0041 11.2283 2.8900 0.0476 

PITTSFIELD, MA, (MSA) 6320 23 0.1% V230 -22.1649 -0.0115 -9.1690 -20.0667 -0.0104 -8.5898 -12.5503 -0.0065 POCATELLO, ID, (MSA) 6340 22 0.0% V231 -14.2556 -0.0071 -1.2597 -16.6578 -0.0082 -5.1809 -9.2557 -0.0046 PORTLAND, ME, (MSA) 6400 60 0.1% V232 -15.7437 -0.0212 -2.7478 -17.2464 -0.0233 -5.7694 -11.3666 -0.0153 PORTLAND, OR, (PMSA) 6440 348 0.8% V233 -12.8849 -0.1009 0.1110 -14.7552 -0.1155 -3.2783 -10.1797 -0.0797 PORTSMOUTH-DOVER-ROCHESTER, NH-ME, (MSA) 6450 47 0.1% V234 -20.3657 -0.0215 -7.3697 -16.1153 -0.0170 -4.6384 -7.1655 -0.0076 

PROVIDENCE, RI, (PMSA) 6480 250 0.6% V235 -11.3410 -0.0638 1.6550 -9.5417 -0.0537 1.9352 -0.9464 -0.0053 

PROVO-OREM, UT, (MSA) 6520 43 0.1% V236 -12.5712 -0.0122 0.4247 -11.9159 -0.0115 -0.4390 -9.1509 -0.0089 

PUEBLO, CO, (MSA) 6560 46 0.1% V237 -10.4827 -0.0108 2.5132 -14.1588 -0.0146 -2.6819 -1.5939 -0.0016 PUNTA GORDA, FL, (MSA) 6580 22 0.0% V238 -14.3179 -0.0071 -1.3220 -10.5518 -0.0052 0.9251 -4.5167 -0.0022 

RACINE, WI, (PMSA) 6600 37 0.1% V239 -19.1253 -0.0159 -6.1293 -16.3500 -0.0136 -4.8731 -14.2331 -0.0118 

RALEIGH-DURHAM, NC, (MSA) 6640 187 0.4% V240 -14.4487 -0.0608 -1.4528 -12.1734 -0.0512 -0.6965 -9.1798 -0.0386 

RAPID CITY, SD, (MSA) 6660 21 0.0% V241 -26.1011 -0.0123 -13.1052 -21.0280 -0.0099 -9.5511 -17.0341 -0.0080 

READING, PA, (MSA) 6680 75 0.2% V242 -16.4207 -0.0277 -3.4248 -11.1367 -0.0188 0.3402 -8.1567 -0.0138 

REDDING, CA, (MSA) 6690 27 0.1% V243 -15.0353 -0.0091 -2.0394 -16.5617 -0.0101 -5.0848 -8.4459 -0.0051 

RENO, NV, (MSA) 6720 48 0.1% V244 -13.6843 -0.0148 -0.6884 -13.5358 -0.0146 -2.0589 -9.7703 -0.0105 

RICHLAND-KENNEWICK-PASCO, WA, (MSA) 6740 34 0.1% V245 -12.2863 -0.0094 0.7096 -15.3098 -0.0117 -3.8328 -9.1593 -0.0070 

RICHMOND-PETERSBURG, VA, (MSA) 6760 198 0.4% V246 -12.7512 -0.0568 0.2447 -16.5388 -0.0737 -5.0619 -8.9588 -0.0399 

RIVERSIDE-SAN BERNARDINO, CA, (PMSA) 6780 291 0.7% V247 -10.2733 -0.0672 2.7226 -14.1082 -0.0923 -2.6313 -8.4920 -0.0556 

ROANOKE, VA, (MSA) 6800 36 0.1% V248 -10.6103 -0.0086 2.3856 -9.7503 -0.0079 1.7266 -3.2612 -0.0026 

ROCHESTER, MN, (MSA) 6820 21 0.0% V249 -21.4159 -0.0101 -8.4200 -18.6600 -0.0088 -7.1831 -11.9115 -0.0056 

ROCHESTER, NY, (MSA) 6840 255 0.6% V250 -13.5201 -0.0775 -0.5242 -11.9718 -0.0687 -0.4949 -2.8005 -0.0161 

ROCKFORD, IL, (MSA) 6880 84 0.2% V251 -16.2992 -0.0308 -3.3033 -11.8871 -0.0225 -0.4102 -9.4879 -0.0179 

ROCKY MOUNT, NC, (MSA) 6895 31 0.1% V252 -9.8677 -0.0069 3.1282 -7.4724 -0.0052 4.0045 3.2454 0.0023 

SACRAMENTO, CA, (MSA) 6920 267 0.6% V253 -16.0067 -0.0961 -3.0108 -17.6988 -0.1063 -6.2219 -10.7769 -0.0647 SAGINAW-BAY CITY-MIDLAND, MI, (MSA) 6960 103 0.2% V254 -10.5577 -0.0245 2.4382 -13.7750 -0.0319 -2.2981 -10.9611 -0.0254 

ST. CLOUD, MN, (MSA) 6980 36 0.1% V255 -17.5236 -0.0142 -4.5276 -18.1785 -0.0147 -6.7016 -16.9629 -0.0137 

ST. JOSEPH, MO, (MSA) 7000 30 0.1% V256 0.5385 0.0004 13.5344 -0.4066 -0.0003 11.0703 -2.3010 -0.0016 

ST. LOUIS, MO-IL, (MSA) 7040 442 1.0% V257 -6.8552 -0.0682 6.1408 -8.4145 -0.0837 3.0624 -3.8962 -0.0387 

SALEM, OR, (MSA) 7080 45 0.1% V258 -10.3924 -0.0105 2.6035 -11.9111 -0.0121 -0.4342 -6.2473 -0.0063 

SALINAS-SEASIDE-MONTEREY, CA, (MSA) 7120 66 0.1% V259 -11.0767 -0.0164 1.9192 -12.7400 -0.0189 -1.2631 -5.9036 -0.0088 SALT LAKE CITY-OGDEN, UT, (MSA) 7160 228 0.5% V260 -9.7379 -0.0499 3.2580 -12.0332 -0.0617 -0.5563 -7.8857 -0.0404 

SAN ANGELO, TX, (MSA) 7200 23 0.1% V261 -34.7952 -0.0180 -21.7993 -18.6277 -0.0096 -7.1508 -5.9917 -0.0031 

SAN ANTONIO, TX, (MSA) 7240 244 0.5% V262 -24.5656 -0.1348 -11.5697 -5.2734 -0.0289 6.2036 2.0144 0.0111 SAN DIEGO, CA, (MSA) 7320 427 1.0% V263 -12.4521 -0.1196 0.5438 -12.8822 -0.1237 -1.4053 -8.0056 -0.0769 SAN FRANCISCO, CA, (PMSA) 7360 344 0.8% V264 -16.5866 -0.1283 -3.5907 -14.5898 -0.1129 -3.1129 -11.1095 -0.0860 SAN JOSE, CA, (PMSA) 7400 294 0.7% V265 -16.0637 -0.1062 -3.0678 -13.1343 -0.0869 -1.6573 -10.5866 -0.0700 

SAN LUIS OBISPO-ATASCADERO-PASO ROBLES, CA, 7460 31 0.1% V266 -14.4448 -0.0101 -1.4489 -13.0442 -0.0091 -1.5673 -10.0372 -0.0070 

SANTA BARBARA-SANTA MARIA-LOMPOC, CA, (MSA 7480 78 0.2% V267 -15.2286 -0.0267 -2.2327 -15.6493 -0.0275 -4.1724 -11.9999 -0.0211 

SANTA CRUZ, CA, (PMSA) 7485 45 0.1% V268 -14.4339 -0.0146 -1.4380 -12.1719 -0.0123 -0.6950 -8.2213 -0.0083 

SANTA FE, NM, (MSA) 7490 26 0.1% V269 -11.0339 -0.0065 1.9620 -12.3184 -0.0072 -0.8415 -6.2598 -0.0037 SANTA ROSA-PETALUMA, CA, (PMSA) 7500 58 0.1% V270 -15.4998 -0.0202 -2.5039 -14.5328 -0.0190 -3.0559 -9.1143 -0.0119 

SARASOTA, FL, (MSA) 7510 85 0.2% V271 -17.8743 -0.0342 -4.8784 -13.3696 -0.0256 -1.8927 -6.7056 -0.0128 

SAVANNAH, GA, (MSA) 7520 64 0.1% V272 -11.0883 -0.0160 1.9077 -12.9982 -0.0187 -1.5213 -2.4730 -0.0036 

SCRANTON-WILKES-BARRE, PA, (MSA) 7560 182 0.4% V273 -4.6032 -0.0188 8.3927 -3.1121 -0.0127 8.3648 -0.5656 -0.0023 

SEATTLE, WA, (PMSA) 7600 423 1.0% V274 -15.2238 -0.1448 -2.2279 -14.4695 -0.1377 -2.9926 -8.5785 -0.0816 

SHARON, PA, (MSA) 7610 31 0.1% V275 -6.3425 -0.0044 6.6534 -1.2496 -0.0009 10.2273 10.2914 0.0072 

SHEBOYGAN, WI, (MSA) 7620 24 0.1% V276 -24.3636 -0.0132 -11.3677 -20.5917 -0.0111 -9.1148 -18.7190 -0.0101 

SHERMAN-DENISON, TX, (MSA) 7640 22 0.0% V277 -29.9627 -0.0148 -16.9667 -16.6135 -0.0082 -5.1366 -6.9664 -0.0034 

SHREVEPORT, LA, (MSA) 7680 85 0.2% V278 -22.2489 -0.0425 -9.2530 -12.2637 -0.0234 -0.7868 -14.0456 -0.0269 SIOUX CITY, IA-NE, (MSA) 7720 25 0.1% V279 -15.8375 -0.0089 -2.8416 -13.2152 -0.0074 -1.7383 -12.2657 -0.0069 

SIOUX FALLS, SD, (MSA) 7760 29 0.1% V280 -25.3589 -0.0165 -12.3629 -21.5363 -0.0140 -10.0594 -18.6920 -0.0122 

SOUTH BEND-MISHAWAKA, IN, (MSA) 7800 69 0.2% V281 -10.7629 -0.0167 2.2330 -5.7413 -0.0089 5.7356 -1.8740 -0.0029 SPOKANE, WA, (MSA) 7840 97 0.2% V282 -10.1886 -0.0222 2.8073 -15.1674 -0.0331 -3.6905 -9.6604 -0.0211 

SPRINGFIELD, IL, (MSA) 7880 41 0.1% V283 -19.4402 -0.0179 -6.4442 -12.8257 -0.0118 -1.3488 -12.0184 -0.0111 
Table B.13

Metropolitan Dummy Coefficients

1997 Sum of 1998 Sum of 1999 Sum of 

Share of Variable Parameter Weighted Adjusted Parameter Weighted Adjusted Parameter Weighted 

MSA Name MSA # Frequency Tracts Name Estimate Coefficients Coefficient Estimate Coefficients Coefficient Estimate Coefficients SPRINGFIELD, MO, (MSA) 7920 61 0.1% V284 -14.3153 -0.0196 -1.3193 -13.6904 -0.0188 -2.2135 -11.5071 -0.0158 

SPRINGFIELD, MA, (MSA) 8000 113 0.3% V285 -11.8807 -0.0302 1.1153 -13.6501 -0.0347 -2.1732 -5.1602 -0.0131 

STAMFORD, CT (PMSA) 8040 82 0.2% V286 -17.8978 -0.0330 -4.9019 -16.2666 -0.0300 -4.7897 -11.7335 -0.0216 

STATE COLLEGE, PA, (MSA) 8050 26 0.1% V287 -16.3271 -0.0095 -3.3312 -11.9231 -0.0070 -0.4462 -9.7934 -0.0057 

STEUBENVILLE-WEIRTON, OH-WV, (MSA) 8080 36 0.1% V288 -4.3727 -0.0035 8.6232 1.5144 0.0012 12.9913 3.6947 0.0030 

STOCKTON, CA, (MSA) 8120 109 0.2% V289 -8.7358 -0.0214 4.2601 -16.3301 -0.0400 -4.8531 -10.2272 -0.0251 

SUMTER, SC, (MSA) 8140 22 0.0% V290 -4.2038 -0.0021 8.7922 -6.5299 -0.0032 4.9470 5.2605 0.0026 

SYRACUSE, NY, (MSA) 8160 203 0.5% V291 -17.2469 -0.0787 -4.2510 -12.4586 -0.0569 -0.9817 -3.6350 -0.0166 TACOMA, WA, (PMSA) 8200 107 0.2% V292 -12.4741 -0.0300 0.5218 -12.1513 -0.0292 -0.6744 -6.2864 -0.0151 

TALLAHASSEE, FL, (MSA) 8240 49 0.1% V293 -15.2972 -0.0169 -2.3013 -15.9148 -0.0175 -4.4379 -7.2909 -0.0080 TAMPA-ST. PETERSBURG-CLEARWATER, FL, (MSA) 8280 406 0.9% V294 -12.7335 -0.1163 0.2624 -10.3060 -0.0941 1.1709 -3.8386 -0.0351 

TERRE HAUTE, IN, (MSA) 8320 28 0.1% V295 -6.0326 -0.0038 6.9633 -4.0968 -0.0026 7.3801 1.1434 0.0007 

TEXARKANA, TX-TEXARKANA, AR, (MSA) 8360 28 0.1% V296 -36.3398 -0.0229 -23.3439 -24.1272 -0.0152 -12.6503 -12.2232 -0.0077 TOLEDO, OH, (MSA) 8400 160 0.4% V297 -9.4706 -0.0341 3.5253 -6.1096 -0.0220 5.3673 -3.9043 -0.0141 

TOPEKA, KS, (MSA) 8440 42 0.1% V298 -5.3297 -0.0050 7.6662 -8.6944 -0.0082 2.7825 -11.1301 -0.0105 

TRENTON, NJ, (PMSA) 8480 61 0.1% V299 -12.8462 -0.0176 0.1498 -13.0540 -0.0179 -1.5771 -7.4794 -0.0103 

TUCSON, AZ, (MSA) 8520 115 0.3% V300 -4.8475 -0.0125 8.1484 -10.9471 -0.0283 0.5298 -5.0310 -0.0130 

TULSA, OK, (MSA) 8560 205 0.5% V301 -8.9164 -0.0411 4.0795 -9.4576 -0.0436 2.0193 -5.5540 -0.0256 

TUSCALOOSA, AL, (MSA) 8600 29 0.1% V302 -35.7171 -0.0233 -22.7212 -23.9871 -0.0156 -12.5102 -9.3916 -0.0061 

TYLER, TX, (MSA) 8640 35 0.1% V303 -31.6974 -0.0250 -18.7015 -21.5131 -0.0169 -10.0362 -2.9521 -0.0023 

UTICA-ROME, NY, (MSA) 8680 95 0.2% V304 -18.0511 -0.0386 -5.0552 -9.0637 -0.0194 2.4132 1.8934 0.0040 VALLEJO-FAIRFIELD-NAPA, CA, (PMSA) 8720 88 0.2% V305 -16.5698 -0.0328 -3.5739 -18.5064 -0.0366 -7.0295 -11.3480 -0.0225 VENTURA, CA, (PMSA) 8735 128 0.3% V306 -14.7868 -0.0426 -1.7909 -14.3981 -0.0415 -2.9212 -9.0401 -0.0260 

VICTORIA, TX, (MSA) 8750 21 0.0% V307 -24.7685 -0.0117 -11.7726 -21.4158 -0.0101 -9.9388 -1.8585 -0.0009 VINELAND-MILLVILLE-BRIDGETON, NJ, (PMSA) 8760 29 0.1% V308 -15.6215 -0.0102 -2.6256 -13.9207 -0.0091 -2.4438 -4.5651 -0.0030 

VISALIA-TULARE-PORTERVILLE, CA, (MSA) 8780 53 0.1% V309 -10.5258 -0.0125 2.4701 -14.0860 -0.0168 -2.6091 -7.6909 -0.0092 

WACO, TX, (MSA) 8800 52 0.1% V310 -35.1424 -0.0411 -22.1465 -17.1767 -0.0201 -5.6998 -9.5550 -0.0112 

WASHINGTON, DC-MD-VA, (PMSA) 8840 907 2.0% V311 -20.4512 -0.4172 -7.4553 -19.8857 -0.4057 -8.4088 -14.9845 -0.3057 

WATERBURY, CT, (MSA) 8880 51 0.1% V312 -8.8424 -0.0101 4.1535 -6.7966 -0.0078 4.6803 -0.3109 -0.0004 

WATERLOO-CEDAR FALLS, IA, (MSA) 8920 36 0.1% V313 -21.2248 -0.0172 -8.2289 -15.7095 -0.0127 -4.2326 -12.2740 -0.0099 

WAUSAU, WI, (MSA) 8940 27 0.1% V314 -28.1257 -0.0171 -15.1298 -23.1878 -0.0141 -11.7109 -18.6321 -0.0113 

WEST PALM BEACH-BOCA RATON-DELRAY BEACH, 8960 205 0.5% V315 -11.5797 -0.0534 1.4162 -12.4529 -0.0574 -0.9760 -6.0212 -0.0278 

WHEELING, WV-OH, (MSA) 9000 49 0.1% V316 -13.2123 -0.0146 -0.2164 -6.9991 -0.0077 4.4778 1.0709 0.0012 

WICHITA, KS, (MSA) 9040 118 0.3% V317 -11.5516 -0.0307 1.4443 -12.4009 -0.0329 -0.9240 -10.1269 -0.0269 

WICHITA FALLS, TX, (MSA) 9080 38 0.1% V318 -37.3779 -0.0319 -24.3820 -19.2020 -0.0164 -7.7251 -3.7412 -0.0032 

WILLIAMSPORT, PA, (MSA) 9140 29 0.1% V319 -15.9482 -0.0104 -2.9523 -9.1404 -0.0060 2.3365 -6.7493 -0.0044 

WILMINGTON, DE-NJ-MD, (PMSA) 9160 133 0.3% V320 -10.4036 -0.0311 2.5923 -11.6594 -0.0349 -0.1825 -8.1876 -0.0245 

WILMINGTON, NC, (MSA) 9200 39 0.1% V321 -11.8909 -0.0104 1.1050 -11.4667 -0.0101 0.0102 -7.4029 -0.0065 

WORCHESTER, MA, (MSA) 9240 109 0.2% V322 -17.3957 -0.0426 -4.3998 -16.4906 -0.0404 -5.0137 -7.0779 -0.0174 

YAKIMA, WA, (MSA) 9260 33 0.1% V323 -10.9566 -0.0081 2.0393 -11.6580 -0.0087 -0.1811 -6.5703 -0.0049 

YOLO, CA, (PMSA) 9270 30 0.1% V324 -17.1529 -0.0116 -4.1569 -18.0970 -0.0122 -6.6201 -12.2931 -0.0083 

YORK, PA, (MSA) 9280 76 0.2% V325 -16.2325 -0.0277 -3.2366 -12.3944 -0.0212 -0.9175 -8.8790 -0.0152 

YOUNGSTOWN-WARREN, OH, (MSA) 9320 152 0.3% V326 -8.1855 -0.0280 4.8104 -6.8176 -0.0233 4.6593 -0.1427 -0.0005 YUBA CITY, CA, (MSA) 9340 26 0.1% V327 -17.8241 -0.0104 -4.8282 -19.5882 -0.0115 -8.1113 -7.0849 -0.0041 

YUMA, AZ, (MSA) 9360 21 0.0% V328 -1.7539 -0.0008 11.2420 -5.4915 -0.0026 5.9854 -1.9547 -0.0009 
Table C.1 

Share of Loans in 27 Metropolitan Areas
 

by Borrower Race or Ethnicity and Income
 

Low-Income Middle-Income Upper-Income Missing Income All 

Subprime Refinance Loans in 27 Metropolitan Areas 

Black 24,255 9,019 6,261 535 40,070 

Hispanic 4,887 3,504 2,890 298 11,579 

White 25,567 17,609 17,896 1,627 62,699 

Other/Missing 31,418 18,899 18,371 1,254 69,942 

Total 86,127 49,031 45,418 3,714 184,290 

All Refinance Loans in 27 Metropolitan Areas 

Black 44,794 20,115 17,111 4,660 86,680 

Hispanic 16,793 12,574 11,358 3,129 43,854 

White 113,494 104,361 160,563 18,101 396,519 

Other/Missing 63,774 52,404 73,019 12,675 201,872 

Total 238,855 189,454 262,051 38,565 728,925 

Subprime Refinance Loans in All Metropolitan Areas 

Black 42,498 16,494 11,572 868 71,432 

Hispanic 12,524 8,474 7,437 544 28,979 

White 78,968 55,162 57,594 4,149 195,873 

Other/Missing 80,043 48,922 47,466 2,868 179,299 

Total 214,033 129,052 124,069 8,429 475,583 

All Refinance Loans in All Metropolitan Areas 

Black 78,677 37,132 32,927 7,147 155,883 

Hispanic 38,688 30,462 44,934 7,371 121,455 

White 335,229 312,710 493,123 46,781 1,187,843 

Other/Missing 164,745 139,090 197,112 32,279 533,226 

Total 617,339 519,394 768,096 93,578 1,998,407 

Share of Subprime Refinance Loans in 27 Metropolitan Areas 

Black 57.1% 54.7% 54.1% 61.6% 56.1% 

Hispanic 39.0% 41.4% 38.9% 54.8% 40.0% 

White 32.4% 31.9% 31.1% 39.2% 32.0% 

Other/Missing 39.3% 38.6% 38.7% 43.7% 39.0% 

Total 40.2% 38.0% 36.6% 44.1% 38.8% 

Share of All Refinance Loans in 27 Metropolitan Areas 

Black 56.9% 54.2% 52.0% 65.2% 55.6% 

Hispanic 43.4% 41.3% 25.3% 42.5% 36.1% 

White 33.9% 33.4% 32.6% 38.7% 33.4% 

Other/Missing 38.7% 37.7% 37.0% 39.3% 37.9% 

Total 38.7% 36.5% 34.1% 41.2% 36.5% 
Table C.2 

Subprime Refinance Lending by Tract Black and Income Composition 

Subprime Refinances Subprime Share All Refinances 

Low Middle Upper All Low Middle Upper All Low Middle Upper All 

Less than 30% 86 317 114 517 34.7% 30.1% 22.8% 28.7% 248 1,053 500 1,801 

30-50% 13 30 0 43 44.8% 34.1% NA 36.8% 29 88 0 117 

50-80% 20 46 0 66 55.6% 56.8% NA 56.4% 36 81 0 117 

80-100% 6 71 0 77 40.0% 59.7% NA 57.5% 15 119 0 134 

Total 125 464 114 703 38.1% 34.6% 22.8% 32.4% 328 1,341 500 2,169 

Kansas City (MSA=3760) 

Less than 30% 762 3,390 1,173 5,325 40.3% 30.4% 19.3% 27.8% 1,893 11,162 6,070 19,125 

30-50% 204 14 0 218 53.7% 43.8% NA 52.9% 380 32 0 412 

50-80% 131 110 0 241 59.0% 58.2% NA 58.6% 222 189 0 411 

80-100% 997 23 0 1,020 65.5% 48.9% NA 65.1% 1,521 47 0 1,568 

Total 2,094 3,537 1,173 6,804 52.1% 30.9% 19.3% 31.6% 4,016 11,430 6,070 21,516 

Los Angeles-Long Beach (PMSA=4480) 

Less than 30% 1,628 4,284 4,818 10,730 24.2% 21.8% 15.1% 18.4% 6,724 19,667 31,987 58,378 

30-50% 502 148 26 676 36.2% 27.6% 21.5% 33.1% 1,385 536 121 2,042 

50-80% 1,068 304 290 1,662 38.7% 38.2% 33.2% 37.5% 2,760 796 873 4,429 

80-100% 133 319 116 568 38.9% 37.4% 32.9% 36.7% 342 853 353 1,548 

Total 3,331 5,055 5,250 13,636 29.7% 23.1% 15.7% 20.5% 11,211 21,852 33,334 66,397 

Memphis (MSA=4920) 

Less than 30% 14 511 825 1,350 29.2% 27.8% 25.8% 26.5% 48 1,841 3,198 5,087 

30-50% 190 314 8 512 46.2% 36.1% 34.8% 39.3% 411 869 23 1,303 

50-80% 114 206 0 320 48.7% 53.2% NA 51.5% 234 387 0 621 

80-100% 999 198 4 1,201 64.5% 68.3% 80.0% 65.1% 1,550 290 5 1,845 

Total 1,317 1,229 837 3,383 58.7% 36.3% 25.9% 38.2% 2,243 3,387 3,226 8,856 

Miami (PMSA=5000) 

Less than 30% 410 1,212 1,356 2,978 38.7% 39.1% 29.5% 34.0% 1,059 3,096 4,596 8,751 

30-50% 55 332 71 458 39.3% 54.5% 55.9% 52.3% 140 609 127 876 

50-80% 280 457 53 790 59.3% 59.6% 51.5% 58.9% 472 767 103 1,342 

80-100% 403 203 33 639 60.3% 60.6% 55.0% 60.1% 668 335 60 1,063 

Total 1,148 2,204 1,513 4,865 49.1% 45.8% 31.0% 40.4% 2,339 4,807 4,886 12,032 

Milwaukee-Waukesha (PMSA=5080) 

Less than 30% 295 1,214 363 1,872 27.1% 18.0% 8.5% 15.5% 1,090 6,728 4,263 12,081 

30-50% 114 12 0 126 40.1% 29.3% NA 38.8% 284 41 0 325 

50-80% 214 15 0 229 42.7% 39.5% NA 42.5% 501 38 0 539 

80-100% 419 0 0 419 50.6% NA NA 50.6% 828 0 0 828 

Total 1,042 1,241 363 2,646 38.5% 18.2% 8.5% 19.2% 2,703 6,807 4,263 13,773 

Minneapolis-St. Paul (MSA=5120) 

Less than 30% 1,510 4,270 940 6,720 31.8% 16.5% 11.3% 17.2% 4,741 25,895 8,350 38,986 

30-50% 195 27 0 222 47.0% 31.8% NA 44.4% 415 85 0 500 

50-80% 125 13 0 138 49.2% 31.7% NA 46.8% 254 41 0 295 

80-100% 31 0 0 31 53.4% NA NA 53.4% 58 0 0 58 

Total 1,861 4,310 940 7,111 34.0% 16.6% 11.3% 17.8% 5,468 26,021 8,350 39,839 

Nashville (MSA=5360) 

Less than 30% 429 1,948 452 2,829 30.4% 27.6% 18.3% 25.9% 1,409 7,062 2,468 10,939 

30-50% 138 28 0 166 43.1% 40.0% NA 42.6% 320 70 0 390 

50-80% 151 130 0 281 49.0% 49.2% NA 49.1% 308 264 0 572 

80-100% 209 0 0 209 40.3% NA NA 40.3% 518 0 0 518 

Total 927 2,106 452 3,485 36.3% 28.5% 18.3% 28.1% 2,555 7,396 2,468 12,419 

Nassau-Suffolk (PMSA=5380) 

Less than 30% 995 4,737 769 6,501 36.4% 32.1% 24.8% 31.6% 2,730 14,736 3,102 20,568 

30-50% 87 229 23 339 52.4% 48.8% 47.9% 49.6% 166 469 48 683 

50-80% 172 205 0 377 56.8% 55.7% NA 56.2% 303 368 0 671 

80-100% 145 156 0 301 62.5% 58.6% NA 60.4% 232 266 0 498 

Total 1,399 5,327 792 7,518 40.8% 33.6% 25.1% 33.5% 3,431 15,839 3,150 22,420 

New Orleans (MSA=5560) 

Less than 30% 129 862 760 1,751 27.6% 24.7% 19.6% 22.4% 467 3,494 3,870 7,831 

30-50% 101 253 53 407 34.7% 31.8% 36.6% 33.1% 291 795 145 1,231 

50-80% 225 216 34 475 40.8% 44.0% 55.7% 43.1% 551 491 61 1,103 

80-100% 613 45 38 696 54.0% 53.6% 45.2% 53.4% 1,135 84 84 1,303 

Total 1,068 1,376 885 3,329 43.7% 28.3% 21.3% 29.0% 2,444 4,864 4,160 11,468 

New York (PMSA=5600) 

Less than 30% 652 1,582 3,032 5,266 44.7% 33.9% 24.7% 28.6% 1,460 4,673 12,294 18,427 
Table C.2 

Subprime Refinance Lending by Tract Black and Income Composition 

Subprime Refinances Subprime Share All Refinances 

Low Middle Upper All Low Middle Upper All Low Middle Upper All 

Atlanta (MSA=0520) 

Less than 30% 1,300 3,928 2,300 7,528 25.2% 26.4% 20.9% 24.3% 5,158 14,893 10,981 31,032 

30-50% 329 216 74 619 31.6% 39.7% 44.6% 35.3% 1,042 544 166 1,752 

50-80% 430 683 89 1,202 37.2% 47.4% 50.0% 43.3% 1,156 1,441 178 2,775 

80-100% 1,293 670 125 2,088 42.1% 50.3% 50.8% 44.9% 3,072 1,333 246 4,651 

Total 3,352 5,497 2,588 11,437 32.1% 30.2% 22.4% 28.4% 10,428 18,211 11,571 40,210 

Baltimore (PMSA=0720) 

Less than 30% 513 1,716 1,081 3,310 34.1% 21.6% 16.1% 20.5% 1,504 7,935 6,700 16,139 

30-50% 57 239 0 296 35.6% 27.0% 0.0% 28.2% 160 885 3 1,048 

50-80% 131 202 33 366 38.6% 34.2% 35.1% 35.7% 339 591 94 1,024 

80-100% 753 134 0 887 55.4% 39.9% NA 52.3% 1,360 336 0 1,696 

Total 1,454 2,291 1,114 4,859 43.2% 23.5% 16.4% 24.4% 3,363 9,747 6,797 19,907 

Bergen-Passaic (PMSA=0875) 

Less than 30% 253 965 410 1,628 27.9% 23.1% 16.6% 21.6% 906 4,171 2,473 7,550 

30-50% 91 33 0 124 40.3% 34.7% NA 38.6% 226 95 0 321 

50-80% 60 66 0 126 46.2% 29.6% NA 35.7% 130 223 0 353 

80-100% 29 0 29 0.0% 40.8% NA 39.2% 3 71 0 74 

Total 404 1,093 410 1,907 31.9% 24.0% 16.6% 23.0% 1,265 4,560 2,473 8,298 

Boston (PMSA=1120) 

Less than 30% 1,250 3,497 1,004 5,751 26.2% 20.0% 12.1% 18.8% 4,777 17,473 8,282 30,532 

30-50% 64 64 0 128 35.2% 33.5% NA 34.3% 182 191 0 373 

50-80% 187 61 0 248 43.7% 39.1% NA 42.5% 428 156 0 584 

80-100% 275 0 0 275 49.9% NA NA 49.9% 551 0 0 551 

Total 1,776 3,622 1,004 6,402 29.9% 20.3% 12.1% 20.0% 5,938 17,820 8,282 32,040 

Chicago (PMSA=1600) 

Less than 30% 1,950 5,902 3,113 10,965 23.1% 18.1% 10.9% 15.7% 8,449 32,602 28,617 69,668 

30-50% 343 394 104 841 35.1% 41.2% 34.7% 37.6% 978 957 300 2,235 

50-80% 590 631 49 1,270 49.2% 40.7% 44.1% 44.4% 1,199 1,551 111 2,861 

80-100% 3,922 1,237 8 5,167 52.8% 50.3% 38.1% 52.1% 7,429 2,461 21 9,911 

Total 6,805 8,164 3,274 18,243 37.7% 21.7% 11.3% 21.5% 18,055 37,571 29,049 84,675 

Cleveland-Lorain-Elyria (PMSA=1680) 

Less than 30% 1,483 2,705 932 5,120 41.4% 26.3% 18.1% 27.0% 3,578 10,270 5,143 18,991 

30-50% 87 87 52 226 45.8% 37.7% 42.3% 41.5% 190 231 123 544 

50-80% 105 194 36 335 48.8% 49.9% 52.2% 49.8% 215 389 69 673 

80-100% 1,738 396 18 2,152 61.2% 54.6% 54.5% 59.8% 2,842 725 33 3,600 

Total 3,413 3,382 1,038 7,833 50.0% 29.1% 19.3% 32.9% 6,825 11,615 5,368 23,808 

Dallas (PMSA=1920) 

Less than 30% 698 1,559 1,177 3,434 31.8% 30.0% 18.1% 24.7% 2,196 5,196 6,488 13,880 

30-50% 145 91 6 242 44.9% 46.2% 35.3% 45.1% 323 197 17 537 

50-80% 162 51 27 240 54.5% 62.2% 79.4% 58.1% 297 82 34 413 

80-100% 337 38 19 394 67.3% 61.3% 55.9% 66.0% 501 62 34 597 

Total 1,342 1,739 1,229 4,310 40.5% 31.4% 18.7% 27.9% 3,317 5,537 6,573 15,427 

Denver (PMSA=2080) 

Less than 30% 2,090 3,121 1,288 6,499 27.7% 18.7% 12.7% 18.9% 7,551 16,674 10,169 34,394 

30-50% 205 0 0 205 34.2% NA NA 34.2% 600 0 0 600 

50-80% 117 282 0 399 33.6% 35.2% NA 34.7% 348 801 0 1,149 

80-100% 170 0 0 170 38.9% NA NA 38.9% 437 0 0 437 

Total 2,582 3,403 1,288 7,273 28.9% 19.5% 12.7% 19.9% 8,936 17,475 10,169 36,580 

Detroit (PMSA=2160) 

Less than 30% 2,126 5,739 2,022 9,887 34.1% 19.7% 11.1% 18.5% 6,235 29,088 18,172 53,495 

30-50% 378 372 40 790 45.8% 37.9% 26.1% 40.3% 826 981 153 1,960 

50-80% 1,116 273 232 1,621 50.4% 42.3% 35.9% 46.2% 2,216 645 647 3,508 

80-100% 4,609 1,347 58 6,014 55.8% 49.0% 34.3% 53.8% 8,263 2,749 169 11,181 

Total 8,229 7,731 2,352 18,312 46.9% 23.1% 12.3% 26.1% 17,540 33,463 19,141 70,144 

Houston (PMSA=3360) 

Less than 30% 896 1,819 1,457 4,172 41.0% 34.5% 23.1% 30.3% 2,185 5,274 6,318 13,777 

30-50% 175 93 41 309 40.6% 47.7% 48.8% 43.5% 431 195 84 710 

50-80% 184 223 16 423 62.2% 57.8% 53.3% 59.4% 296 386 30 712 

80-100% 471 153 31 655 67.4% 59.1% 59.6% 64.9% 699 259 52 1,010 

Total 1,726 2,288 1,545 5,559 47.8% 37.4% 23.8% 34.3% 3,611 6,114 6,484 16,209 

Jersey City (PMSA=3640) 
Table C.2
 

Subprime Refinance Lending by Tract Black and Income Composition
 

Subprime Refinances Subprime Share All Refinances 

Low Middle Upper All Low Middle Upper All Low Middle Upper All 

30-50% 246 270 188 704 50.8% 48.0% 43.4% 47.6% 484 563 433 1,480 

50-80% 470 419 258 1,147 59.8% 48.1% 51.4% 53.1% 786 871 502 2,159 

80-100% 673 866 591 2,130 59.9% 58.6% 53.9% 57.6% 1,123 1,477 1,097 3,697 

Total 2,041 3,137 4,069 9,247 53.0% 41.4% 28.4% 35.9% 3,853 7,584 14,326 25,763 

Oakland (PMSA=5775) 

Less than 30% 927 3,207 1,424 5,558 28.0% 20.2% 12.6% 18.2% 3,316 15,853 11,320 30,489 

30-50% 233 67 36 336 30.8% 30.9% 14.9% 27.6% 757 217 242 1,216 

50-80% 668 175 22 865 38.2% 35.0% 22.0% 36.8% 1,750 500 100 2,350 

80-100% 363 2 0 365 43.8% 40.0% NA 43.8% 829 5 0 834 

Total 2,191 3,451 1,482 7,124 32.9% 20.8% 12.7% 20.4% 6,652 16,575 11,662 34,889 

Philadelphia (PMSA=6160) 

Less than 30% 1,064 3,629 1,953 6,646 40.4% 23.8% 16.1% 22.1% 2,632 15,237 12,147 30,016 

30-50% 395 203 35 633 51.6% 37.4% 34.7% 44.9% 766 543 101 1,410 

50-80% 416 266 38 720 49.8% 48.5% 46.9% 49.1% 835 549 81 1,465 

80-100% 2,066 546 0 2,612 71.4% 62.0% NA 69.2% 2,893 881 0 3,774 

Total 3,941 4,644 2,026 10,611 55.3% 27.0% 16.4% 28.9% 7,126 17,210 12,329 36,665 

St. Louis (MSA=7040) 

Less than 30% 1,069 4,673 2,137 7,879 36.9% 27.7% 21.9% 26.7% 2,896 16,891 9,765 29,552 

30-50% 152 250 6 408 37.1% 35.1% 18.2% 35.3% 410 713 33 1,156 

50-80% 404 402 98 904 46.8% 48.1% 49.2% 47.6% 864 836 199 1,899 

80-100% 1,336 98 0 1,434 54.1% 40.3% NA 52.9% 2,468 243 0 2,711 

Total 2,961 5,423 2,241 10,625 44.6% 29.0% 22.4% 30.1% 6,638 18,683 9,997 35,318 

Washington, DC (PMSA=8840) 

Less than 30% 933 2,427 1,191 4,551 21.6% 16.7% 11.1% 15.4% 4,314 14,557 10,701 29,572 

30-50% 105 325 55 485 19.3% 22.7% 25.5% 22.1% 545 1,430 216 2,191 

50-80% 169 375 152 696 27.8% 30.0% 26.2% 28.5% 608 1,250 580 2,438 

80-100% 937 302 17 1,256 36.6% 35.8% 26.6% 36.2% 2,560 844 64 3,468 

Total 2,144 3,429 1,415 6,988 26.7% 19.0% 12.2% 18.6% 8,027 18,081 11,561 37,669 

All 27 Metropolitan Areas 

Less than 30% 23,462 69,214 36,091 128,767 30.2% 22.7% 15.8% 21.1% 77,566 305,521 227,974 611,061 

30-50% 4,604 4,091 818 9,513 39.2% 36.1% 35.0% 37.4% 11,741 11,336 2,335 25,412 

50-80% 7,709 6,005 1,427 15,141 45.1% 43.9% 39.0% 43.9% 17,108 13,693 3,662 34,463 

80-100% 22,898 6,833 1,058 30,789 54.7% 50.8% 47.7% 53.5% 41,897 13,440 2,218 57,555 

Total 58,673 86,143 39,394 184,210 39.6% 25.0% 16.7% 25.3% 148,312 343,990 236,189 728,491 
Table C.3

Subprime Refinance Lending by Borrower Black and Income Composition

Subprime Refinances Subprime Share 

Low Middle Upper Missing All Low Middle Upper Missing All Low Mid

Atlanta (MSA=0520) 

Black 1,674 653 408 44 2,779 46.6% 36.2% 28.6% 9.5% 38.2% 3,589 

Hispanic 42 24 10 11 87 23.9% 23.3% 10.5% 20.8% 20.4% 176 

White 1,281 687 676 70 2,714 18.4% 13.1% 9.1% 8.1% 13.2% 6,945 5Other/Missing 2,823 1,646 1,327 61 5,857 60.0% 50.9% 37.8% 11.4% 48.9% 4,702 3

Total 5,820 3,010 2,421 186 11,437 37.8% 29.0% 19.4% 9.7% 28.4% 15,412 10

Baltimore (PMSA=0720) 

Black 700 220 168 11 1,099 49.1% 38.4% 32.6% 2.3% 36.8% 1,427 

Hispanic 16 8 11 2 37 34.8% 28.6% 31.4% 7.7% 27.4% 46 

White 748 469 486 50 1,753 22.1% 16.6% 12.3% 4.5% 15.6% 3,379 2

Other/Missing 1,075 456 416 23 1,970 50.9% 32.6% 24.3% 7.3% 35.6% 2,113 1

Total 2,539 1,153 1,081 86 4,859 36.5% 23.9% 17.4% 4.5% 24.4% 6,965 4

Bergen-Passaic (PMSA=0875) 

Black 83 44 34 2 163 48.8% 40.4% 35.4% 6.1% 40.0% 170 

Hispanic 66 54 32 12 164 28.6% 27.7% 23.7% 15.6% 25.7% 231 

White 241 231 290 16 778 24.7% 19.1% 15.2% 6.0% 17.8% 974 1

Other/Missing 250 259 274 21 804 39.9% 33.5% 24.0% 6.0% 27.8% 627 

Total 640 588 630 51 1,909 32.0% 25.7% 19.2% 7.0% 23.0% 2,002 2

Boston (PMSA=1120) 

Black 208 174 99 8 489 46.3% 43.7% 34.7% 16.3% 41.4% 449 

Hispanic 66 56 31 8 161 29.3% 29.2% 29.8% 17.0% 28.3% 225 

White 1,103 1,118 1,068 103 3,392 19.4% 17.5% 11.2% 10.8% 15.1% 5,681 6

Other/Missing 833 792 689 56 2,370 42.4% 38.1% 24.6% 5.4% 30.1% 1,964 2

Total 2,210 2,140 1,887 175 6,412 26.6% 23.7% 14.9% 8.4% 19.9% 8,319 9

Chicago (PMSA=1600) 

Black 4,085 1,237 568 75 5,965 55.2% 44.7% 34.7% 15.0% 48.5% 7,406 2

Hispanic 1,089 464 152 30 1,735 23.6% 20.2% 16.5% 7.2% 21.0% 4,620 2

White 2,595 1,810 1,367 117 5,889 20.0% 13.9% 7.4% 6.0% 12.7% 12,943 13Other/Missing 2,428 1,201 967 67 4,663 39.7% 24.3% 16.8% 7.0% 26.2% 6,119 4

Total 10,197 4,712 3,054 289 18,252 32.8% 20.5% 11.4% 7.6% 21.5% 31,088 23

Cleveland-Lorain-Elyria (PMSA=1680) 

Black 1,304 392 224 8 1,928 60.2% 50.5% 45.0% 12.3% 55.0% 2,165 

Hispanic 107 18 10 3 138 45.3% 22.2% 27.8% 37.5% 38.2% 236 

White 1,536 763 537 26 2,862 30.3% 20.6% 14.1% 6.6% 22.1% 5,070 3

Other/Missing 1,714 719 443 31 2,907 53.8% 38.3% 25.6% 18.1% 41.7% 3,186 1

Total 4,661 1,892 1,214 68 7,835 43.7% 29.3% 20.0% 10.7% 32.9% 10,657 6
Table C.3

Subprime Refinance Lending by Borrower Black and Income Composition

Subprime Refinances Subprime Share 

Low Middle Upper Missing All Low Middle Upper Missing All Low Mid

Dallas (PMSA=1920) 

Black 466 174 137 11 788 69.9% 54.9% 36.2% 9.9% 53.5% 667 

Hispanic 223 100 52 5 380 32.0% 30.3% 24.1% 8.6% 29.2% 697 

White 706 474 585 41 1,806 34.0% 24.0% 13.0% 7.8% 19.9% 2,076 

Other/Missing 701 347 276 13 1,337 56.9% 40.7% 21.1% 7.3% 37.4% 1,233 

Total 2,096 1,095 1,050 70 4,311 44.9% 31.5% 16.4% 8.0% 27.9% 4,673 3

Denver (PMSA=2080) 

Black 349 190 128 5 672 44.2% 40.9% 38.4% 8.3% 40.8% 790 

Hispanic 645 333 135 10 1,123 34.4% 32.6% 27.1% 5.3% 31.3% 1,876 

White 1,399 1,172 951 64 3,586 20.4% 17.6% 11.7% 5.7% 15.8% 6,847 6

Other/Missing 740 597 514 52 1,903 28.7% 21.9% 17.6% 11.4% 21.9% 2,575 2

Total 3,133 2,292 1,728 131 7,284 25.9% 21.1% 14.6% 7.2% 19.9% 12,088 10

Detroit (PMSA=2160) 

Black 4,588 1,164 719 71 6,542 54.7% 42.8% 37.4% 22.3% 49.0% 8,391 2

Hispanic 98 42 21 4 165 31.2% 30.7% 25.3% 14.8% 29.4% 314 White 3,065 1,615 1,289 109 6,078 21.4% 14.0% 9.2% 7.5% 14.7% 14,355 1

Other/Missing 3,039 1,331 1,079 83 5,532 47.6% 35.6% 24.9% 18.4% 37.1% 6,389 3

Total 10,790 4,152 3,108 267 18,317 36.6% 22.9% 15.3% 11.8% 26.1% 29,449 18

Houston (PMSA=3360) 

Black 655 317 272 3 1,247 73.7% 62.0% 50.1% 4.3% 62.0% 889 

Hispanic 461 223 148 2 834 39.0% 36.7% 29.0% 2.2% 34.9% 1,183 

White 723 551 730 14 2,018 43.8% 31.1% 17.7% 3.2% 25.3% 1,650 

Other/Missing 679 414 360 8 1,461 56.8% 43.8% 24.4% 3.8% 38.2% 1,196 

Total 2,518 1,505 1,510 27 5,560 51.2% 39.2% 22.7% 3.3% 34.3% 4,918 3

Jersey City (PMSA=3640) 

Black 43 30 28 1 102 54.4% 52.6% 45.9% 5.9% 47.7% 79 

Hispanic 17 31 41 13 102 26.6% 29.8% 22.9% 27.1% 25.8% 64 

White 50 35 49 13 147 49.0% 27.1% 13.1% 19.4% 21.9% 102 

Other/Missing 99 77 152 24 352 56.9% 39.9% 37.8% 19.5% 39.5% 174 Total 209 173 270 51 703 49.9% 35.8% 26.6% 20.0% 32.4% 419 

Kansas City (MSA=3760) 

Black 781 197 128 6 1,112 65.8% 54.1% 42.7% 12.5% 58.6% 1,187 

Hispanic 66 18 11 1 96 42.9% 36.0% 24.4% 9.1% 36.9% 154 

White 1,292 721 552 22 2,587 27.3% 19.8% 11.4% 5.6% 19.1% 4,730 3

Other/Missing 1,574 826 594 18 3,012 64.5% 53.0% 37.4% 8.7% 52.0% 2,442 1

Total 3,713 1,762 1,285 47 6,807 43.6% 31.4% 19.0% 7.2% 31.6% 8,513 5
Table C.3

Subprime Refinance Lending by Borrower Black and Income Composition

Subprime Refinances Subprime Share 

Low Middle Upper Missing All Low Middle Upper Missing All Low Mid

Los Angeles-Long Beach (PMSA=4480) 

Black 684 683 823 24 2,214 45.5% 45.8% 36.0% 9.4% 40.0% 1,504 

Hispanic 713 1,017 1,085 45 2,860 23.3% 25.6% 25.5% 3.6% 22.8% 3,062 3

White 687 1,016 2,668 113 4,484 22.9% 21.9% 14.6% 8.6% 16.5% 2,994 4

Other/Missing 661 997 2,343 77 4,078 29.1% 23.0% 17.8% 5.8% 19.3% 2,273 4

Total 2,745 3,713 6,919 259 13,636 27.9% 25.7% 18.2% 6.3% 20.5% 9,833 14

Memphis (MSA=4920) 

Black 963 320 185 10 1,478 65.9% 55.0% 40.3% 16.7% 57.7% 1,462 Hispanic 6 5 4 15 60.0% 62.5% 33.3% 0.0% 44.1% 10 

White 280 175 168 8 631 28.3% 19.2% 9.0% 6.5% 16.2% 988 

Other/Missing 664 299 274 22 1,259 66.4% 51.6% 39.7% 21.6% 53.1% 1,000 

Total 1,913 799 631 40 3,383 55.3% 38.4% 20.9% 13.8% 38.2% 3,460 2

Miami (PMSA=5000) 

Black 495 364 310 8 1,177 64.0% 56.9% 48.1% 17.8% 56.0% 773 

Hispanic 429 515 676 30 1,650 40.5% 37.8% 28.9% 13.9% 33.2% 1,060 

White 107 162 243 4 516 38.5% 34.2% 18.6% 4.9% 24.2% 278 

Other/Missing 461 471 578 12 1,522 73.8% 64.2% 44.2% 8.0% 54.0% 625 

Total 1,492 1,512 1,807 54 4,865 54.5% 47.1% 32.3% 11.0% 40.4% 2,736 3

Milwaukee-Waukesha (PMSA=5080) 

Black 413 99 53 2 567 51.8% 38.2% 29.9% 10.5% 45.3% 797 

Hispanic 51 9 4 64 27.9% 13.8% 12.5% 0.0% 22.0% 183 

White 514 308 171 9 1,002 17.0% 11.0% 5.0% 3.5% 10.5% 3,025 2

Other/Missing 555 261 193 4 1,013 50.8% 35.6% 25.8% 3.0% 37.4% 1,092 

Total 1,533 677 421 15 2,646 30.1% 17.5% 9.6% 3.6% 19.2% 5,097 3

Minneapolis-St. Paul (MSA=5120) 

Black 411 107 46 4 568 59.7% 46.1% 36.8% 20.0% 53.3% 688 

Hispanic 47 15 9 71 27.3% 19.7% 18.8% 0.0% 23.4% 172 

White 2,409 1,323 707 30 4,469 20.7% 13.9% 8.9% 3.9% 15.0% 11,614 9

Other/Missing 1,099 575 307 22 2,003 32.8% 20.7% 14.2% 7.5% 23.3% 3,349 2

Total 3,966 2,020 1,069 56 7,111 25.1% 16.0% 10.4% 5.1% 17.8% 15,823 12

Nashville (MSA=5360) 

Black 415 114 48 3 580 53.7% 39.7% 30.0% 4.8% 45.2% 773 

Hispanic 11 1 5 17 30.6% 11.1% 31.3% 0.0% 26.2% 36 

White 877 418 275 13 1,583 26.9% 19.3% 11.7% 3.9% 19.5% 3,260 2

Other/Missing 729 332 237 7 1,305 54.3% 41.1% 33.9% 6.9% 44.2% 1,342 

Total 2,032 865 565 23 3,485 37.6% 26.4% 17.5% 4.6% 28.1% 5,411 3
Table C.3

Subprime Refinance Lending by Borrower Black and Income Composition

Subprime Refinances Subprime Share 

Low Middle Upper Missing All Low Middle Upper Missing All Low Mid

Nassau-Suffolk (PMSA=5380) 

Black 290 186 88 34 598 55.2% 54.5% 40.9% 41.0% 51.4% 525 

Hispanic 102 60 29 23 214 34.8% 28.4% 28.4% 25.8% 30.8% 293 

White 1,049 928 817 269 3,063 31.0% 25.6% 18.6% 27.7% 24.7% 3,387 3

Other/Missing 1,504 1,149 856 137 3,646 61.3% 50.0% 38.9% 11.1% 44.6% 2,453 2

Total 2,945 2,323 1,790 463 7,521 44.2% 35.8% 25.9% 19.5% 33.5% 6,658 6

New Orleans (MSA=5560) 

Black 534 302 305 11 1,152 57.9% 49.0% 39.4% 24.4% 48.8% 923 

Hispanic 33 17 13 2 65 44.6% 25.4% 18.1% 33.3% 29.7% 74 

White 319 243 335 7 904 24.2% 16.6% 10.2% 3.7% 14.5% 1,318 

Other/Missing 489 348 370 1 1,208 61.4% 50.1% 36.2% 0.8% 45.8% 797 

Total 1,375 910 1,023 21 3,329 44.2% 32.0% 19.8% 5.8% 29.0% 3,112 2

New York (PMSA=5600) 

Black 543 628 533 114 1,818 55.9% 51.2% 42.2% 36.3% 48.1% 972 

Hispanic 114 139 161 82 496 37.5% 35.2% 30.0% 45.1% 35.0% 304 

White 301 465 998 331 2,095 30.0% 25.0% 16.7% 33.5% 21.3% 1,003 

Other/Missing 1,046 1,486 2,012 310 4,854 63.4% 55.4% 40.8% 20.8% 45.1% 1,651 2

Total 2,004 2,718 3,704 837 9,263 51.0% 44.1% 29.1% 28.1% 35.9% 3,930 6

Oakland (PMSA=5775) 

Black 573 395 336 10 1,314 48.3% 41.4% 33.9% 11.1% 40.8% 1,186 Hispanic 246 276 213 8 743 25.4% 29.2% 25.8% 8.5% 26.2% 968 

White 585 753 1,042 38 2,418 20.6% 18.6% 12.2% 8.2% 15.2% 2,836 4

Other/Missing 702 830 1,078 49 2,659 31.5% 23.7% 16.2% 8.0% 20.5% 2,230 3

Total 2,106 2,254 2,669 105 7,134 29.2% 23.8% 15.7% 8.3% 20.4% 7,220 9

Philadelphia (PMSA=6160) 

Black 1,348 303 167 13 1,831 65.1% 49.9% 33.4% 8.6% 55.0% 2,072 

Hispanic 129 36 22 2 189 45.3% 33.0% 23.9% 7.1% 36.8% 285 

White 1,387 828 908 44 3,167 26.4% 17.1% 10.9% 4.6% 16.3% 5,250 4

Other/Missing 3,151 1,177 1,026 75 5,429 59.7% 37.3% 23.8% 10.5% 40.4% 5,281 3

Total 6,015 2,344 2,123 134 10,616 46.7% 26.9% 16.0% 7.2% 28.9% 12,888 8

St. Louis (MSA=7040) 

Black 1,502 301 187 18 2,008 54.7% 40.2% 33.8% 17.6% 48.4% 2,744 

Hispanic 22 4 2 28 32.8% 18.2% 7.4% 0.0% 23.9% 67 

White 1,341 823 550 29 2,743 19.5% 14.9% 7.3% 4.5% 13.3% 6,890 5

Other/Missing 2,766 1,609 1,446 26 5,847 62.8% 59.2% 52.2% 4.9% 56.1% 4,402 2

Total 5,631 2,737 2,185 73 10,626 39.9% 30.4% 20.0% 5.7% 30.1% 14,103 9
Table C.3

Subprime Refinance Lending by Borrower Black and Income Composition

Subprime Refinances Subprime Share 

Low Middle Upper Missing All Low Middle Upper Missing All Low Mid

Washington, DC (PMSA=8840) 

Black 1,148 425 267 39 1,879 36.3% 33.5% 27.5% 3.2% 28.4% 3,166 

Hispanic 88 39 13 5 145 19.3% 21.3% 9.4% 2.5% 14.9% 457 

White 972 521 434 87 2,014 16.5% 11.9% 7.1% 5.9% 11.2% 5,899 4Other/Missing 1,636 700 560 55 2,951 35.9% 22.7% 15.2% 6.4% 24.2% 4,559 3

Total 3,844 1,685 1,274 186 6,989 27.3% 18.9% 11.7% 5.0% 18.6% 14,081 8

All 27 Metropolitan Areas 

Black 24,255 9,019 6,261 535 40,070 54.1% 44.8% 36.6% 11.5% 46.2% 44,794 20

Hispanic 4,887 3,504 2,890 298 11,579 29.1% 27.9% 25.4% 9.5% 26.4% 16,793 12

White 25,567 17,609 17,896 1,627 62,699 22.5% 16.9% 11.1% 9.0% 15.8% 113,494 104

Other/Missing 31,418 18,899 18,371 1,254 69,942 49.3% 36.1% 25.2% 9.9% 34.6% 63,774 52

Total 86,127 49,031 45,418 3,714 184,290 36.1% 25.9% 17.3% 9.6% 25.3% 238,855 189
Subprime Refinance Market in Atlanta Metropo

Other Black Tracts Other Hispanic Tracts Other Subprime Tracts Hispanic and Subprime Tracts Black and Subprime Tracts 
Subprime Refinance Market in Baltimore Metrop

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Subprime Refinance Market in Bergen-Passaic Met

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Subprime Refinance Market in Boston Metropo

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Subprime Refinance Market in Chicago Metropo

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Subprime Refinance Market in Cleveland-Lorain-Elyria Me

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Subprime Refinance Market in Dallas Metropo

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Subprime Refinance Market in Denver Metropo

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Subprime Refinance Market in Detroit Metropol

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Subprime Refinance Market in Houston Metropo

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Subprime Refinance Market in Jersey City Metro

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Subprime Refinance Market in Kansas City Metro

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Subprime Refinance Market in Los Angeles-Long Beach Me

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Subprime Refinance Market in Memphis Metrop

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Subprime Refinance Market in Miami Metropo

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Subprime Refinance Market in Milwaukee-Waukesha Metr

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Subprime Refinance Market in Minneapolis-St. Paul Metr

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Subprime Refinance Market in Nashville Metrop

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Subprime Refinance Market in Nassau-Suffolk Met

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Subprime Refinance Market in New Orleans Metro

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Subprime Refinance Market in New York Metrop

Other Black Tracts Other Hispanic Tracts Other Subprime Tracts Hispanic and Subprime Tracts Black and Subprime Tracts 
Subprime Refinance Market in Newark Metropo

Other Black Tracts Other Hispanic Tracts Other Subprime Tracts Hispanic and Subprime Tracts Black and Subprime Tracts 
Subprime Refinance Market in Oakland Metropo

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Subprime Refinance Market in Philadelphia Metro

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Subprime Refinance Market in St. Louis Metrop

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Subprime Refinance Market in Trenton Metropo

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Subprime Refinance Market in Washington, DC Me

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