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Different automated valuation modelling techniques evaluated over time

Author

Listed:
  • Michael Mayer
  • Steven C. Bourassa
  • Martin Hoesli
  • Donato Scognamiglio

Abstract

We use a rich data set consisting of 123,000 houses sold in Switzerland between 2004 and 2017 to investigate different automated valuation techniques in settings where the models are updated regularly. We apply six methods (linear regression, robust regression, mixed effects regression, gradient boosting, random forests, and neural networks) to both moving window and extending window models. With respect to the criteria of appraisal accuracy and stability, the preferred methods are robust regression using moving windows, gradient boosting using extending windows, or mixed effects regression for either strategy.

Suggested Citation

  • Michael Mayer & Steven C. Bourassa & Martin Hoesli & Donato Scognamiglio, 2018. "Different automated valuation modelling techniques evaluated over time," ERES eres2018_40, European Real Estate Society (ERES).
    • Handle: RePEc:arz:wpaper:eres2018_40
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    File URL: https://eres.architexturez.net/doc/oai-eres-id-eres2018-40
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    More about this item

    Keywords

    automated valuation; Machine Learning; Statistics;
    All these keywords.

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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