How Does Urban Public Transit Accessibility Affect Housing Prices? A Comprehensive Analysis with Geographical Detector Combined and Geographically Weighted Regression

The accessibility of urban public transit directly influences residents’ quality of life, travel behavior, and social equity. Its correlation with housing prices has garnered significant attention across disciplines such as geography, economics, and urban planning. Although much existing research focuses on the impact of individual transportation facilities on housing prices, there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics. This study selected the main urban area of Hefei, China, as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022. Employing techniques such as the optimized parameter geographical detector and local spatial regression models, the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices. The findings revealed that: 1) housing prices in Hefei exhibited a clustered spatial pattern, with high prices concentrated in the city center and lower prices in peripheral areas, forming three distinct high-price hotspots with a ‘belt-like’ distribution; 2) public transit accessibility showed a ‘core-periphery’ structure, with accessibility declining in a ‘circumferential’ pattern around the city center. Based on the ‘housing price-accessibility’ dimension, four categories were identified: high price-high accessibility (37.25%), high price-low accessibility (19.07%), low price-high accessibility (21.95%), and low price-low accessibility (21.73%); 3) the impact of transit accessibility on housing prices was spatially heterogeneous, with bus travel showing the strongest explanatory power (0.692), followed by automobile, subway, and bicycle travel. The interaction of these transportation modes generated a synergistic effect on housing price differentiation, with most influencing factors contributing more than 25%. These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’ living standards.