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.
The foreclosure crisis is among the most significant challenges facing American cities today. It has been difficult as a nation to assess the damage to housing stock, neighborhoods, and communities, let alone decide upon strategies to repair and move forward. As new foreclosures continue to mount, their impact spreads from central cities to places that initially seemed immune.
