Panel Paper: Predictive Modeling of Surveyed Property Conditions and Vacancy

Saturday, November 4, 2017
McCormick (Hyatt Regency Chicago)

*Names in bold indicate Presenter

Stephan Whitaker1, Hal Martin1, Isaac Oduro2, Francisca G.-C. Richter2 and April Urban2, (1)Federal Reserve Bank of Cleveland, (2)Case Western Reserve University


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 nonsurveyed properties in an area subject to a random sample survey.