Explainable AI in a Real Estate Context - Exploring the Determinants of Residential Real Estate Values
48 Pages Posted: 20 Dec 2021 Last revised: 21 Jun 2022
Date Written: December 20, 2021
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 the ALE plots enables 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.
Keywords: Housing Market, Machine Learning, Feature Importance, Explainable AI, ALE Plots
JEL Classification: R15, R30, R31
Suggested Citation: Suggested Citation