What Are the Pivotal Factors Influencing Housing Prices? A Spatiotemporal Dynamic Analysis Across Market Cycles from Upturn to Downturn in Wuhan

Abstract

With the new phase of urbanization in China, enhancing urban spatial quality has become a key task in urban development. As an important indicator of residents’ willingness to live, housing prices provide valuable feedback from their perspective for improving spatial quality. Taking Wuhan as a case study, this paper constructs an indicator system with 12 explanatory variables, including a subjective evaluation of buildings generated using deep learning techniques. Using OLS and GWR models, the study analyzes the factors influencing housing prices and their spatiotemporal dynamics in Wuhan’s core urban areas from 2016 to 2024, encompassing the full cycle of housing price fluctuations from an upward to a downward trend. The findings reveal that, as housing prices return to more rational levels, the impact of location factors diminishes, while the influence of community quality factors—such as property fees, green space ratio, and building quality—significantly increases. Factors such as proximity to hospitals also exhibit a certain degree of spatiotemporal complexity. This trend highlights residents’ growing attention to housing quality and living environments, marking a fundamental shift in the behavior of homebuyers. The results of this study provide crucial insights into the evolution of residential preferences and the spatiotemporal dynamics of the housing market. They offer significant theoretical and practical references for understanding residents’ housing needs from their perspective, thereby promoting the healthy development of the real estate market and improving urban spatial quality.

Keywords

• housing price determinant
• urban spatial quality
• perceptual assessment of buildings
• spatiotemporal dynamic analysis
• fluctuation cycle
• OLS