Predictive Modeling of Surveyed Property Conditions and Vacancy
Hal Martin,
Isaac Oduro,
Francisca Richter,
Apirl Hirsh Urban and
Stephan Whitaker
No 1637, Working Papers (Old Series) from Federal Reserve Bank of Cleveland
Abstract:
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.
Keywords: Vacancy; distressed properties; machine learning; predictive models; property surveys (search for similar items in EconPapers)
JEL-codes: C25 C53 R31 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2016-12-23
New Economics Papers: this item is included in nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedcwp:1637
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