Improving hedonic housing price models by integrating optimal accessibility indices into regression and random forest analyses

Location indices are key in explaining variation in house prices. However, the definition of comprehensive indices capturing all locational features, along with their efficient and timely calculation, is usually one of the most complex dimensions of house price modeling. Existing difficulties result in partial location specifications, mostly due to three hurdles: (1) there is not a consensus on the best method to construct these indices, (2) what features (variables) to include: labor, demographic, commuting, etc., and (3) its creation requires granular and updated datasets. We introduce a methodology based on computer algorithms to create car and walk accessibility indices that address the previous concerns and capture location interactions among a wide range of variables. The selection of variables is based on an automated search of the best performing utility bearing gravitational accessibility indices for price prediction. Once these optimal indices are obtained, the method applies principal components analysis to secure their orthogonality. Using a unique dataset from a leading real estate portal in Europe, we illustrate and test for the city of Madrid their applicability in several house asking price models that are estimated using regression analysis and random forests (as a representative family of machine learning techniques). The experimental analysis reveals that using the optimal indices results in significant improvements in accuracy, for both regression-based models (13%) and random forests (21.6%), while achieving a substantial reduction in spatial autocorrelation (around 35%). The generated indices are clearly interpretable, which makes them a valuable tool for urban analyses (planning, transportation, sustainability, etc.). Finally, the methodology can be extended to other types of real state (commercial, industrial, etc.) and location (country, region, etc.).