Credit scores, race, and residential sorting

Abstract

Credit scores have a profound impact on home purchasing power and mortgage pricing, yet little is known about how credit scores influence households' residential location decisions. This study estimates the effects of credit scores on residential sorting behavior using a novel mortgage industry data set combining household demographic, credit, and financial data with property location information and detailed community attribute data. I employ the data set to estimate a discrete-choice residential sorting model. I find that credit scores significantly predict residential sorting behavior and models that do not account for credit score provide biased estimates of housing utilities for black households in particular. Simulation results show that increases in credit score are associated with increases in the consumption of higher-priced homes in more expensive school districts, higher-quality public schools, and proximity to urban|metropolitan areas. © 2010 by the Association for Public Policy Analysis and Management.

Who has Housing Affordability Problems? Disparities in Housing Cost Burden by Race, Nativity, and Legal Status in Los Angeles

“…Consequently, housing affordability problems continue to expand to more sectors of U.S. households, with especially powerful impacts on low income households (Joint Center for Housing Studies 2012; Williams 2012). The current financial and housing crisis has particular relevance for Latinos and African Americans, given the concentration of subprime loans and foreclosures to minority borrowers and minority neighborhoods and their higher unemployment rates relative to Whites (Hinojosa Ojeda, Jacquez et al 2009; Nelson 2010). Since 2000, local and state-level legislation increasingly focused on unauthorized immigrants (Chavez and Provine 2009).…”

Empirical Innovations in Policy Analysis

“…The residential sorting model used in this study requires that individual household and property data be linked to local neighborhood attributes. Nelson () accesses a mortgage data set of 16,805 observations from a Southern California bank. Traditionally, Nelson might have used the census tract code associated with each mortgage and simply linked observations to tract‐level attributes.…”

State Landlord–Tenant Policy and Eviction Rates in Majority-Minority Neighborhoods

“…Similarly, landlords use tenant screening companies to assess criminal history, credit scores, and eviction records (Kleysteuber, 2007;Thacher, 2008). This access allows tenants to be screened for criminal records and creditworthiness-both of which are factors that negatively and disproportionately affect Black and Hispanic individuals (Greenlee, 2014;Nelson, 2010). Additionally, landlords are permitted to evict tenants for drug-related criminal activity, whether convicted or not (Truman, 2003), leading to greater housing instability, an inability to initiate another rental contract, or otherwise being charged an inflated security deposit (Alexander, 2016;Desmond, 2016).…”