The Neighborhood Distribution of Subprime Mortgage Lending
Subprime lending in the residential mortgage market, characterized by relatively high credit risk and high interest rates or fees, has become a prominent segment of the market in the last ten years. Research indicates there is geographical concentration of subprime mortgages in Census tracts comprising high concentrations of low-income and minority households. The growth in subprime lending represents an expansion of mortgage credit among households who do not meet prime market underwriting standards. Nonetheless, its apparent concentration in minority and lower-income neighborhoods has generated concerns that households in these areas may not be obtaining equal opportunity in the prime mortgage market, hence undermining efforts to revitalize minority and lower-income areas. This paper examines the robustness of previous findings of minority and low-income concentration, but also adds to the existing literature. We selected Chicago and Philadelphia as the subjects of this study. The city-level analysis allows us to identify factors associated with within-city concentrations of subprime loans and eliminates the need to control for systematic differences in lending patterns across cities. Second, we investigate the spatial concentration of subprime lending across Census tracts within each city more closely than previous studies, by examining its spatial association with risk measures along with tract demographic variables. The incorporated tract-level risk variables include the proportion of individuals (of borrowing age) that have low credit ratings and the proportion without ratings, based on data from a major national credit bureau. Third, we supplement the analysis of subprime distribution across Census tracts with a logit regression analysis at the borrower level, where we relate whether the loan obtained was subprime to both tract and borrower characteristics. Our goal is to provide a broad overview of within-city, cross-neighborhood subprime lending patterns, making the best use of available data.
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