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Federal Reserve Bank of Cleveland
Working Papers (Old Series)
Predictive Modeling of Surveyed Property Conditions and Vacancy
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.
Cite this item
Hal Martin & Isaac Oduro & Francisca Richter & Apirl Hirsh Urban & Stephan Whitaker, Predictive Modeling of Surveyed Property Conditions and Vacancy, Federal Reserve Bank of Cleveland, Working Papers (Old Series) 1637, 23 Dec 2016.
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
Keywords: Vacancy; distressed properties; machine learning; predictive models; property surveys
This item with handle RePEc:fip:fedcwp:1637
is also listed on EconPapers
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