Predictive Modeling of Surveyed Property Conditions and Vacancy

Abstract

Using the results of a comprehensive in-person survey of properties in Cleveland, Ohio, we fi t 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 fi lings. Using logistic regression and machine learning methods, we are able to make reasonably accurate out-of-sample predictions. Our fi ndings 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.