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