Built environments, communities, and housing price: A data-model integration approach

Abstract

<p>The spatially heterogeneous association between built environments and housing prices is crucial for real estate management and urban governance, as it reveals residents' preferences. Despite efforts to refine the factors influencing housing prices, most studies encountered the statistical challenges brought by spatial heterogeneity, and failed to account for a city's internal heterogeneity of potential distinct mechanisms of housing prices by strata. To address this, we developed a comprehensive framework to analyze the relationship between built environments and housing prices in Guangzhou, categorizing the city into three distinct zones: exurban, suburban, and central urban areas, based on multifaceted characteristics variability. The global model shows expected results that distance to the city center and built year are the two most important factors for property prices. However, the influence of environmental visual features outweighs these two features in exurb communities, highlighting the evolving purchasing preferences with people's increasing pursuit of living environment. Additionally, the visual ratio of people and buildings is found significant to housing prices, implying buyers' preferences for “gated communities” characterized by residence complexes and limited external access. Our findings shed light on the contribution of various built environment factors to shaping the spatial pattern of housing prices, which provides potential implications for balancing “livable” built environments and “valuable” land development.<br></p>