This study investigates spatial dependence and neighborhood effects in mortgage lending disparities in the Southern California five-county region. In doing so, it assesses indicators of primary mortgage market activity and their determinants for the region as a whole. The study compiles data from the 2002 HMDA and the 2000 U.S. Census to undertake a variety of analyses, including computation, assessment, and mapping of social-economic characteristics, as well as home mortgage origination, denial rates, and secondary market purchase rates by census tracts among sampled areas and population cohorts. Cluster analyses of social-economic and mortgage parameters show distinctive patterns of spatial clustering among tracts across the region. In observing these blueprints of spatial dependence, the study further undertakes a geographically weighted regression (GWR) to analyze spatial non-stationarity in the determinants of variability in neighborhood primary market loan denial rates for the year 2002.

The modeling results reveal that significant spatial non-stationarity exists between mortgage denial rates and neighborhood-level socioeconomic determinants. Firstly, tracts exhibiting similar neighborhood-denial rate relationship tend to cluster, and the effects and proximity of nearby tracts play significant roles in the determination of the denial outcomes of the underlying tracts. Secondly, the study finds that those locational attributes, including income, population, age, racial composition, housing stock, show significant and varying impacts on mortgage denial rate pattern across space. Specifically, the established relationship between tract denial rates and tract attributes is not necessarily significant everywhere in the region. In particular, among traditionally underserved areas and more affluent areas, there exist spatially varying relationships between denial rate and tract racial composition, which shed lights on the existence and varying causes of redlining across space. The study concludes that mortgage lending patterns are better understood by the geographically-weighted model than traditional Ordinary Least Square (OLS) regression approaches on lending outcome, which ignore the spatial dependence among local determinants.