Explainable spatial machine learning for hedonic real estate modeling

Abstract Accurately modeling rents and prices is a key challenge in real estate analysis. Traditional linear models may fail to capture complex non‐linear relationships, and spatial dependencies are often ignored in existing machine‐learning approaches. This article introduces a novel hybrid statistical machine‐learning model for modeling real estate rents and prices. The proposed approach combines a spatial Gaussian process with tree boosting. In so doing, spatial correlations are explicitly accounted for, and the tree‐boosting part can handle complex non‐linear relationships and interactions. We compare the proposed model against established benchmarks using a large‐scale dataset consisting of more than 1.5 million rental apartment listings across Germany and also a smaller condominium price listings dataset. Our findings demonstrate that the proposed model yields superior prediction accuracy due to accounting for both nonlinear patterns and spatial dependencies. We further use machine‐learning explainability techniques to better understand the nonlinear relationships present among rents and predictor variables, and we conduct a detailed analysis of the impact of locational characteristics on rents.