Several researchers from the Poverty Center co-authored a new report on “Predictive Modeling of Surveyed Property Conditions and Vacancy” published in the proceedings of the 18th Annual International Conference on Digital Government Research by the Digital Government Society.
Using the results of a comprehensive in-person survey of properties in Cleveland, Ohio, the researchers fit predictive models of vacancy and property conditions. Data from the Poverty Center’s NEO CANDO was used to establish variables to consider as predictors for the model.
Their 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.
Center researchers who contributed on the article include recent doctoral assistant Isaac Oduro, post-doctoral scholar Éamon Johnson, faculty associate Francisca García-Cobián Richter, and research associate April Urban. Hal Martin and Stephan D. Whitaker of the Federal Reserve Bank of Cleveland are also authors of the report.
Also see "Exploring the Relationship Between Vacant and Distressed Properties and Community Health and Safety", a paper released earlier this month prepared by the Center.