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

Bastian Krämer et al.

Journal of Housing Research2023https://doi.org/10.1080/10527001.2023.2170769article
ABDC B
Weight
0.75

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.

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https://doi.org/https://doi.org/10.1080/10527001.2023.2170769

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@article{bastian2023,
  title        = {{Explainable AI in a Real Estate Context – Exploring the Determinants of Residential Real Estate Values}},
  author       = {Bastian Krämer et al.},
  journal      = {Journal of Housing Research},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.1080/10527001.2023.2170769},
}

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0.75

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact1.00 × 0.4 = 0.40
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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