Unveiling the spatially varied nonlinear effects of urban built environment on housing prices using an interpretable ensemble learning model

Abstract

The relationship between urban built environment (UBE) and housing prices manifests as complex, exhibiting significant nonlinearities and spatial heterogeneity that remain inadequately understood. Taking Shanghai as a testbed, this study employs a novel ensemble learning approach, augmented by Bayesian optimization and Monte Carlo simulation, to decipher the intricate and nonlinear impacts of UBE factors on housing markets across diverse urban geographies. Our analysis unveils substantial spatial variations in how transit accessibility, amenities, residential density, and green/blue spaces influence real estate values. Proximity to metro stations and bike-sharing facilities exerts a more pronounced positive effect than bus stops. Moreover, residents in central areas demonstrate a higher willingness-to-pay for public service amenities, while those in outer suburbs prioritize access to public transportation infrastructure. Intriguingly, it invokes an optimal threshold range of urban density for properties in new cities, thereby increasing the vitality and dense socio-economic networks. Furthermore, the sprawling suburbs have identified an adverse economic impact of large conservation green/blue spaces. These insights can guide policymakers in crafting spatially-tailored strategies that harness localized built environment drivers to catalyse equitable and prosperous urban development. Tailored policies informed by this spatially explicit understanding of nonlinear built environment-housing interactions can foster more sustainable, liveable, and inclusive cities.