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Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches

Author

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  • Michalis Doumpos

    (Technical University of Crete)

  • Dimitrios Papastamos

    (Cerved Property Services)

  • Dimitrios Andritsos

    (Cerved Property Services)

  • Constantin Zopounidis

    (Technical University of Crete
    Audencia Business School, Finance Department)

Abstract

Automated valuation models are widely used in real estate to provide estimates for property prices. Such models are typically developed through regression approaches. This study presents a comparative analysis about the performance of parametric and non-parametric regression techniques for developing reliable automated valuation models for residential properties. Different approaches are explored to incorporate spatial effects into the valuation process, covering both global and locally weighted models. The analysis is based on a large sample of properties from Greece during the period 2012–2016. The results demonstrate that linear regression models developed with a weighted spatial (local) scheme provide the best results, outperforming machine learning approaches and models that do not consider spatial effects.

Suggested Citation

  • Michalis Doumpos & Dimitrios Papastamos & Dimitrios Andritsos & Constantin Zopounidis, 2021. "Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches," Annals of Operations Research, Springer, vol. 306(1), pages 415-433, November.
    • Handle: RePEc:spr:annopr:v:306:y:2021:i:1:d:10.1007_s10479-020-03556-1
    DOI: 10.1007/s10479-020-03556-1
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    References listed on IDEAS

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