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

Real estate is a heterogeneous commodity where no two are alike. Therefore, making assumptions about determinants and the way they influence the value of a property is difficult. Traditionally, parametric and, thus, assumption-based regression techniques are used to identify those dependencies. However, recent studies show that these relationships can only be mapped to a limited extent by those approaches. On the contrary, modern Machine Learning (ML) approaches are less restrictive and able to identify complex patterns hidden in the data. Nevertheless, these algorithms are less transparent to human beings. An ML approach may be the best solution to predict the value of a property, but it fails at determining the factors driving that value. To overcome this limitation, explainable artificial intelligence (XAI) has come forward as a new important direction of research. So far, there has been almost no research applying XAI in the field of real estate. Therefore, we introduce two different state-of-the-art XAI approaches, namely Permutation Feature Importance (PFI) and Accumulated Local Effects Plots (ALE) in the context of real estate valuation. Focusing on the residential market, we use a dataset consisting of around 1.2 million observations in Germany. Our findings show that using XAI methods enables us to open the “black box” of ML models. In addition, we find several unexpected non-linear dependencies between real estate values and their hedonic characteristics and therefore deliver important insights to better understand the fundamental functioning of residential real estate markets.