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Credit scores, race, and residential sorting

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  • Ashlyn Aiko Nelson

    (Assistant Professor, School of Public and Environmental Affairs, Indiana University)

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.

Suggested Citation

  • Ashlyn Aiko Nelson, 2010. "Credit scores, race, and residential sorting," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 39-68.
  • Handle: RePEc:wly:jpamgt:v:29:y:2010:i:1:p:39-68
    DOI: 10.1002/pam.20478
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    File URL: http://hdl.handle.net/10.1002/pam.20478
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    References listed on IDEAS

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    Cited by:

    1. Darolia, Rajeev, 2014. "Working (and studying) day and night: Heterogeneous effects of working on the academic performance of full-time and part-time students," Economics of Education Review, Elsevier, vol. 38(C), pages 38-50.

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