Does Racial Discrimination Persist in Oakland's Housing Market?
by Cathy Cockrell, Public Affairs
For anyone who thinks that home loan discrimination is a thing of the past, think again.
According to Home Mortgage Lending in Oakland a quantitative analysis of federal mortgage data written by Jesse Blout and published recently by the Institute of Urban and Regional Development (IURD) African Americans in Oakland are 54 percent less likely to be approved for a home mortgage than white applicants. Hispanic, American Indian and Asian applicants are also at a significant disadvantage, the report found.
Blout conducted his research as a graduate thesis under the direction of City and Regional Planning Associate Professor John Landis and lecturer Michael Smith-Heimer. Using 1993-95 data obtained through the Home Mortgage Disclosure Act, Blout studied lending activity in Oakland and the factors influencing loan decisions.
The 1993-95 numbers show that the home loan approval rate in Oakland was lowest for African Americans, at 66.6 percent. Hispanics were next with 75.8 percent, followed by 76.3 percent for American Indians and 81.1 percent for Asians. The approval rate for whites was 86.9 percent.
Often when people talk about discrimination in mortgage lending, Landis said, theres a question of how much of that is (based on) income, and how much is race. Using multivariate regression statistical techniques, the report isolates the impact of race from that of income in a very useful way, he said.
For example, home loan approval rates for whites and African Americans differed by 9.2 percentage points among those in the lowest income quartile, and by 20.7 percentage points in the highest quartile.
Blout, now a real estate and urban development specialist with a local consulting firm, said, The 20 percent point difference in the highest income group is a pretty overwhelming gap, which we believe cant be explained away by credit history. Blout explained that many claim that credit history accounts for racial disparities in approval rates.
While its primary focus is Oakland, the report includes comparative analysis suggesting racial disparities in mortgage lending throughout Alameda County.
Discriminatory trends identified in Oakland, the report says, may be exacerbated in the future by automated underwriting systems endorsed by the two biggest companies in the U.S. mortgage market Freddie Mac and Fannie May and adopted by many lenders nationwide.
To evaluate a loan application using an automated, statistical scoring model, each item is assigned a weighted value. The age of the property, for example, might be a factor but which variables are used in a particular model, and how the variables are weighted, are industry secrets.
In theory, the final score computed from these values can predict if the applicant is likely to default on the loan. Proponents of statistical scoring models say they improve consistency and speed and remove human bias from mortgage lending.
Such systems, however, are only as good as the data on which they were built, the IURD report cautions. Subjecting all applicants to the same set of weighted measures, irrespective of the property or loan type, it warns, may result in unintended bias against certain populations.
Favoring single-family homes over condominiums or townhouses, for example, may put loan applicants from Oaklands poorer neighborhoods, where single-family houses are less common, at a disadvantage. The automated models may also discriminate subtly against applicants who live in extended families or keep their assets in cash instead of in a bank, Blout noted.
In contrast, the report describes an alternative underwriting program offered in Oakland by American Savings Bank (now owned by Washington Mutual, Inc.). Americans Special Purpose Community Lending Program has become a national model and has won numerous awards for its innovative approach to underwriting in low-income communities.
According to Landis, the IURD report documents how close attention to local market conditions is a good business practice as well as good for a community.
But you have to look past simplistic indicators, looking closely at the circumstances of the credit history of an applicant and the local housing market. And you have to want to make home loans in that market, he added.
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