Residential Price Modeling using Spatially Validated Machine Learning Methods: A Comparison Across Geographic Contexts

Abstract

Land use and transportation infrastructure are tightly linked systems, and residential property prices play a central role in transportation planning. Transportation investments also influence property values, making accurate forecasting of both systems an interconnected research challenge. This study examines these interactions and evaluates machine learning methods for modeling residential real estate prices across contrasting urban contexts. We use XGBoost and Random Forest models to assess how land use and transportation infrastructure shape dwelling prices, comparing results between the Rawalpindi and Islamabad Metropolitan Area in Pakistan and the City of Toronto in Canada. We also examine the performance of machine learning models on spatial data by comparing nonspatial and spatial cross validation, and use SHAP values to interpret feature impacts. Despite differences in demographics and economic development, both cities show similar effects of transportation infrastructure and local amenities on prices. Proximity to major urban cores increases sale price, while high quality transit such as subway and bus rapid transit raises prices and conventional bus stop proximity lowers them. Nonspatial cross validation overestimates predictive accuracy for spatial datasets. XGBoost performs slightly better than Random Forest. We recommend careful application of machine learning methods when modeling spatially dependent land price data.