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
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.26509/frbc-wp-201637 Full text (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fedcwp:1637

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in Working Papers (Old Series) from Federal Reserve Bank of Cleveland Contact information at EDIRC.
Bibliographic data for series maintained by 4D Library ().