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Working Paper

Predictive Modeling of Surveyed Property Conditions and Vacancy

Using the results of a comprehensive in-person survey of properties in Cleveland, Ohio, we fit predictive models of vacancy and property conditions. We draw predictor variables from administrative data that is available in most jurisdictions such as deed recordings, tax assessor's property characteristics, and foreclosure filings. Using logistic regression and machine learning methods, we are able to make reasonably accurate out-of-sample predictions. Our findings indicate that housing professionals could use administrative data and predictive models to identify distressed properties between surveys or among non-surveyed properties in an area subject to a random sample survey.

Suggested Citation

Martin, Hal, Isaac Oduro, Francisca García-Cobián Richter, April Hirsh Urban, and Stephan D. Whitaker. 2016. “Predictive Modeling of Surveyed Property Conditions and Vacancy.” Federal Reserve Bank of Cleveland, Working Paper No. 16-37.