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Explainable AI in a Real Estate Context – Exploring the Determinants of Residential Real Estate Values

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  • Bastian Krämer
  • Cathrine Nagl
  • Moritz Stang
  • Wolfgang Schäfers

Abstract

A sound understanding of real estate markets is of economic importance and not simple, as properties are a heterogenous asset and no two are alike. Traditionally, parametric or semi-parametric and, thus, assumption-based hedonic pricing models are used to analyze real estate market fundamentals. These models are characterized by the fact that they require a-priori assumptions regarding their functional form. Usually, the true functional form is unknown and characterized by non-linearities and joint effects, which are hard to fully capture. Therefore, their results should be interpreted with caution. Applying the state-of-the art non-parametric machine learning XGBoost algorithm, in combination with the model-agnostic Accumulated Local Effects Plots, (ALE) enables us to overcome this problem. Using a dataset of 81,166 residential properties for the seven largest German cities, we show how ALE plots enable us to analyze the value-determining effects of several structural, locational and socio-economic hedonic features. Our findings lead to a deeper representation of real estate market fundamentals.

Suggested Citation

  • Bastian Krämer & Cathrine Nagl & Moritz Stang & Wolfgang Schäfers, 2023. "Explainable AI in a Real Estate Context – Exploring the Determinants of Residential Real Estate Values," Journal of Housing Research, Taylor & Francis Journals, vol. 32(2), pages 204-245, July.
    • Handle: RePEc:taf:rjrhxx:v:32:y:2023:i:2:p:204-245
    DOI: 10.1080/10527001.2023.2170769
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