Residential Price Modeling Using Spatially Validated Machine Learning Methods: A Comparison Across Geographical Contexts

Land use (i.e., buildings) and transportation infrastructure are tightly coupled systems, such that residential property prices play a critical role in transportation planning. In a similar manner, transportation infrastructure influences property prices, such that accurate forecasts of both systems are related research problems. The objective of this study is to examine these interactions and evaluate the effectiveness of machine learning methods in modeling residential real estate prices across different urban contexts. Specifically, we first examine the impact of land and transportation infrastructure on residential real estate prices using Extreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning methods, and compare results between two cities representing diverse geographic and socioeconomic contexts. Second, we investigate the application of machine learning methods on spatial data and provide a comparison of non-spatial and spatial cross-validation on the performance of machine learning methods. We use SHapley Additive exPlanations (SHAP) values to study the impact of land use and transportation infrastructure on real estate prices. The models are applied, and results are compared between the Rawalpindi and Islamabad Metropolitan Area in Pakistan and the City of Toronto in Canada. We find that, despite differences in demographics and economic development, the two cities exhibit similarities in the effect of transportation infrastructure and local amenities on dwelling prices. Proximity to the major central city cores (i.e., downtowns) increases sale price. The effect of transportation infrastructure is differentiated, with high quality transit (e.g., subway and BRT) increasing and conventional bus stop proximity decreasing sale price, respectively. We confirm the previous finding in other fields that non-spatial cross-validation over-estimates the prediction accuracy of machine learning algorithms on spatially referenced datasets. We find that the XGBoost model has slightly higher performance than the RF model. We recommend careful use of machine learning methods in the case of spatial data specifically in modeling of land prices.