Although credit history scoring offers benefits to lenders and borrowers, failure to consider situational circumstances raises important statistical issues that may affect the ability of scoring systems to accurately quantify an individual’s credit risk. Evidence from a national sample of credit reporting agency records suggests that failure to consider measures of local economic circumstances and individual trigger events when developing credit history scores can diminish the potential effectiveness of such models. There are practical difficulties, however, associated with developing scoring models that incorporate situational data, arising largely because of inherent limitations of the credit reporting agency databases used to build scoring models.

This study tests for the presence of prejudicial or “noneconomic” discrimination on the part of mortgage lenders by evaluating the performance of home mortgage loans. The approach differs from that of previous studies of loan performance in that it is based on the proposition that noneconomic discrimination should be more pronounced in less competitive lending environments, while statistical discrimination should not. Using a rich set of FHA-insured loan records and measures of local market concentration to proxy the competitive environment, we test for the prediction of better loan performance by minority borrowers relative to white borrowers in more concentrated markets. We argue that this approach substantially reduces the potential for omitted-variable bias that has cast a shadow on previous studies of lending discrimination. Results fail to reject the null hypothesis of no noneconomic discrimination.

This paper examines the association between consolidation and changes in levels of bank branching as measured by changes in the number of bank branches per capita. Using a specially-constructed data set, we address this issue as well as how this relationship varies with the type of consolidation and initial regulatory, competitive, and market conditions. We find that merges where merging institutions have branch networks which overlap within a ZIP code (within-ZIP merger) are strongly associated with a reduction in offices per capita in that ZIP code. This result is robust across time and holds in both rural and urban areas. The findings also suggest that, contrary to popularly held views, consolidation is not unambiguously negatively associated with changes in the number of banking offices per capita. Neither within-market-but-not-within-ZIP mergers nor out-of-market mergers consistently show such a relationship. We also find that the relationships between within-ZIP and within-market-but-not-within-ZIP mergers and changes in the number of bank offices per capita are more negative in low-income neighborhoods than in other neighborhoods. However, because most states now have unrestricted branching and because savings associations are less prevalent and financially healthier than in the past, these findings may not be indicative of future branching patterns.