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The Predictability of House Prices: "Human Against Machine"

Author

Listed:
  • Kristoffer B. Birkeland

    (Norwegian University of Science and Technology)

  • Allan D. D'Silva

    (Norwegian University of Science and Technology)

  • Roland Füss

    (University of St.Gallen)

  • Are Oust

    (Norwegian University of Science and Technology)

Abstract

We develop an automated valuation model (AVM) for the residential real estate market by leveraging stacked generalization and a comparable market analysis. Specifically, we combine four novel ensemble learning methods with a repeat sales method and tailor the data selection for each value estimate. We calibrate and evaluate the model for the residential real estate market in Oslo by producing out-of-sample estimates for the value of 1,979 dwellings sold in the first quarter of 2018. Our novel approach of using stacked generalization achieves a median absolute percentage error of 5.4%, and more than 96% of the dwellings are estimated within 20% of their actual sales price. A comparison of the valuation accuracy of our AVM to that of the local estate agents in Oslo generally demonstrates its viability as a valuation tool. However, in stable market phases, the machine falls short of human capability.

Suggested Citation

  • Kristoffer B. Birkeland & Allan D. D'Silva & Roland Füss & Are Oust, 2021. "The Predictability of House Prices: "Human Against Machine"," International Real Estate Review, Global Social Science Institute, vol. 24(2), pages 139-183.
    • Handle: RePEc:ire:issued:v:24:n:02:2021:p:139-183
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    AVMs; Housing Market; Machine Learning; Repeat Sales Approach; XGBoost.;
    All these keywords.

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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