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Predictive Modeling of Surveyed Property Conditions and Vacancy

Author

Listed:
  • Hal Martin
  • Isaac Oduro
  • Francisca Richter
  • Apirl Hirsh Urban
  • Stephan D. Whitaker

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.

Suggested Citation

  • Hal Martin & Isaac Oduro & Francisca Richter & Apirl Hirsh Urban & Stephan D. Whitaker, 2016. "Predictive Modeling of Surveyed Property Conditions and Vacancy," Working Papers (Old Series) 1637, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwp:1637
    DOI: 10.26509/frbc-wp-201637
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    More about this item

    Keywords

    Vacancy; distressed properties; machine learning; predictive models; property surveys;
    All these keywords.

    JEL classification:

    • 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

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