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

  • S.I.: Regression Methods based on OR techniques
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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.

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Authors and Affiliations

  1. School of Production Engineering and Management, Financial Engineering Laboratory, Technical University of Crete, University Campus, 73100, Chania, Greece

    Michalis Doumpos & Constantin Zopounidis

  2. Cerved Property Services, Eslin & 20, Amaliados Str., 11523, Athens, Greece

    Dimitrios Papastamos & Dimitrios Andritsos

  3. Audencia Business School, Finance Department, 8 route de la Jonelière, 44312, Nantes, France

    Constantin Zopounidis

Authors
  1. Michalis Doumpos
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  2. Dimitrios Papastamos
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  3. Dimitrios Andritsos
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  4. Constantin Zopounidis
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Correspondence to Michalis Doumpos.

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Doumpos, M., Papastamos, D., Andritsos, D. et al. Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches. Ann Oper Res 306, 415–433 (2021). https://doi.org/10.1007/s10479-020-03556-1

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  • DOI: https://doi.org/10.1007/s10479-020-03556-1

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