Spatially Optimized Multi-Level Modeling of Housing Price Differentiation and Resource Allocation Strategies

China’s rapid urbanization has intensified its intra-urban differentiation, with housing prices increasingly reflecting the uneven distribution of public resources and development opportunities. Taking Nanchang as a case study, this study examines the spatial structure of housing prices and the heterogeneity of their driving mechanisms. By comparing ordinary least squares and geographically weighted regression models, we identify a strong spatial non-stationarity in the determinants of housing prices, with key factors exhibiting location-dependent effects and, in some cases, directional reversals. To enhance spatial interpretation, Kriging interpolation is applied to local coefficients, revealing continuous spatial gradients in the factors’ influence. Building on these findings, a capitalization potential index is simulated under standardized resource-improvement scenarios to diagnose potential mismatches between market price responsiveness and spatial equity. The results indicate that areas with high capitalization potential often coincide with relatively low housing prices, suggesting a structural misalignment between market efficiency and spatial equity. This study contributes to a deeper understanding of housing price spatial heterogeneity and provides insights for promoting spatial equity and sustainable urban development.