Street Credit: Neighborhood Level Predictors of Financial Inclusion in Four U.S. Metropolitan Areas

Abstract

Financial inclusion has gained recognition as both a domestic and international governance objective. However, full participation in the financial sector remains an elusive goal, and a number of significant questions present themselves regarding defining the scope of financial inclusion and formulating efficacious policy to ensure access to and promoting the usage of financial services. Paramount among these questions is the relationship between the geographic aspects of retail financial markets and consumer outcomes including rates of savings and indebtedness, the types of consumer credit utilized, and levels of unbanked and underbanked populations. The central aim of this research is to address this lack of understanding by using quantitative analytical tools including geographic information systems (GIS) and spatial regression analysis to examine relationships between the uneven geography of retail financial services, mortgage lending activity, and sociodemographic variables. Four metropolitan study areas in the United States—Las Vegas, Nevada; Los Angeles, California; Miami, Florida; and Philadelphia, Pennsylvania—are examined in order to address a range of question related to the neighborhood level determinants of financial inclusion. This study will provide a foundation for improving policy solutions through contributing to the understanding of how data-driven and analytical approaches can be applied to this problem. Specifically, the following research questions are addressed: 1) How does the spatial distribution of mainstream financial institutions (banks and credit unions) and alternative financial service providers (AFSPs) contribute to financial inclusion at the neighborhood level? What is the geographic relationship between these services; and how does access to these services interact with neighborhood demographic variables and mortgage lending activity? 2) How can traditional approaches to spatial analysis of mortgage lending be improved and expanded to incorporate new spatial analysis methods and better understand how mortgage credit denial and subprime lending interact with one another, as well as with neighborhood demographic variables? Building on scholarship in the academic areas of community reinvestment, asset building, and economic geography, this research contributes a number of new insights and refinements in methodology. The results of spatial regression analyses reveal significant predictive relationships, even after controlling for sociodemographic variables and spatial clustering by using simultaneous autoregressive (SAR) models. This research is unique in its examination of the relationship between the landscape of financial services in neighborhoods and mortgage lending activity, and finds that increasing levels of subprime mortgage lending in neighborhoods is predictive of nearer distance to AFSPs. Another finding is that higher percentages of black and Latino populations in neighborhoods are predictive of nearer proximity to AFSPs and greater distances to mainstream brick-and-mortar financial institution locations. A new method is developed to address the spatial void hypothesis, the spatial relationship between mainstream financial institutions and AFSPs. The results of binary logistic regression models indicate that neighborhoods where alternative service providers are more prevalent comparatively feature lower average income levels, higher percentages of minority residents, lower levels of educational attainment, and higher levels of both mortgage application denial and subprime mortgage lending. Advances are also made in developing regression models to address relationships between sociodemographic variables and mortgage lending activity. Using SAR modeling, this study finds that mortgage purchase denial is a strong predictor of subprime lending for home purchase and refinance loans. Confirming prior research findings with a new method, the percentage of the population that is black and Latino is found to be a statistically significant predictor of mortgage purchase denial, as well as rates of subprime mortgage purchase lending.