The Nonlinear Association between the Metro and Housing Prices in Chengdu: A Perspective of Residents' Activity Spaces Reshaping

The construction and development of the metro have reshaped the activity spaces of residents and provided them with abundant opportunities to access various resources. However, existing literature often overlooks the impact of increased access to resources due to the metro system on housing prices. This study, based on residential transaction data from 2017 to 2019 obtained from Beike and metro line and station data from Chengdu, employed network analysis to define a 15-minute activity space for 56,999 residential samples in 2,609 neighborhoods around metro stations, serving as the basis for decomposing resource allocation. Subsequently, an XGBoost model was developed to explore the nonlinear impacts of accessibility and resource allocation at the neighborhood and cross-regional levels on housing prices. The results show that 1) the closeness centrality of the nearest metro station primarily affected housing prices. 2) Additionally, the relative importance of resource allocation on housing prices at the neighborhood and cross-regional levels was 34.60% and 19.55%, respectively, highlighting the significance of resource allocation at the cross-regional level. The development of the metro has reshaped residents' activity spaces, increasing access to various resources. The value of resources obtained through the metro has significantly affected residents' willingness to pay. Furthermore, resource allocation at the two levels reflected a different impact on housing prices. At the neighborhood level, the relative importance of factories and comprehensive hospital facilities was 8.02% and 7.47%, respectively. Meanwhile, the relative importance of parks and comprehensive hospital facilities were 4.85% and 3.86%, respectively, at the cross-regional level. 3) The relationship between accessibility, resource allocation characteristics, and housing prices is complex and nonlinear. Regarding accessibility, the travel time to the nearest metro station and housing prices exhibited a roughly U-shaped relationship, whereas the closeness centrality of the nearest metro station positively affected housing prices. In terms of resource allocation characteristics, different facilities had varying degrees of impact on housing prices at the neighborhood and cross-regional levels. Specifically, parks and primary and secondary schools at the neighborhood level showed an overall positive impact on housing prices, while factories, comprehensive hospitals, and shopping services at the neighborhood level generally suppressed housing prices. Additionally, business and financial service facilities at the neighborhood level showed an inverted U-shaped relationship with housing prices. At the cross-regional level, comprehensive hospital facilities and primary and secondary school facilities had opposite effects on housing prices compared to park facilities and shopping services. The impact of factories at the cross-regional level on housing prices was unstable. Business and financial service facilities at the cross-regional level demonstrated an inverted U-shaped relationship with housing prices, with a suppressive effect when the count of these facilities exceeded 423. The study findings provide valuable insights into sustainable metro development and rational resource allocation.