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Predicting St. Louis Housing Prices with Machine Learning on Market and Assessor Data

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  • Adler, Brian
  • Brown, Anne

Abstract

Housing markets are more complex than a simple supply-demand relationship. Prices are set by complex market and spatial neighborhood dynamics. Certain cities like St. Louis, MO have experienced dramatic population decline marked by extreme vacancy and abandonment. Amidst its population decline, St. Louis simultaneously demonstrates neighborhoods with sharp housing shortages and competition alongside others with entrenched vacancy and disinvestment mere blocks away from one another. We use supervised machine learning models to predict housing prices with a diverse feature set that incorporates spatial aspects of vacancy among other traditional housing amenities in St. Louis. Our results show how proximity to vacancy may impact a home’s value even more than its number of bedrooms. These findings, we expect, may prompt policymakers to combat vacancy even more urgently to maintain neighborhood market stability.

Suggested Citation

  • Adler, Brian & Brown, Anne, 2026. "Predicting St. Louis Housing Prices with Machine Learning on Market and Assessor Data," SocArXiv s9v4u_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:s9v4u_v1
    DOI: 10.31219/osf.io/s9v4u_v1
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    References listed on IDEAS

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    1. Janet Currie & Lucas Davis & Michael Greenstone & Reed Walker, 2015. "Environmental Health Risks and Housing Values: Evidence from 1,600 Toxic Plant Openings and Closings," American Economic Review, American Economic Association, vol. 105(2), pages 678-709, February.
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