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Article

Spatiotemporal Evolution and Driving Forces of Housing Price Differentiation in Qingdao, China: Insights from LISA Path and GTWR Models

College of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao 266590, China
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Author to whom correspondence should be addressed.
Buildings 2025, 15(16), 2941; https://doi.org/10.3390/buildings15162941
Submission received: 27 July 2025 / Revised: 13 August 2025 / Accepted: 18 August 2025 / Published: 19 August 2025
(This article belongs to the Topic Architectures, Materials and Urban Design, 2nd Edition)

Abstract

As China’s urbanization deepens, the spatial structure of residential areas and land use patterns has undergone profound transformations, with the differentiation of housing prices emerging as a key indicator of urban spatial dynamics and socioeconomic stratification. This study examines the spatial and temporal evolution of residential housing prices in Qingdao’s main urban area over a 20-year period, using data from three representative years (2003, 2013, and 2023) to capture key stages of change. It employs Local Indicators of Spatial Association (LISA) spatial and temporal path and leap analyses, as well as Geographically and Temporally Weighted Regression (GTWR) modeling. The results show that Qingdao’s housing price patterns exhibit distinct spatiotemporal heterogeneity, characterized by multi-level transitions, leapfrog dynamics and strong spatial dependence. The urban center and coastal zones demonstrate positive synergistic growth, while some inland and peripheral areas show negative spatial coupling. Evident is the spatial restructuring from a monocentric to a polycentric pattern, driven by shifts in industrial layout, policy incentives, and transportation infrastructure. Key driving factors, such as community attributes, locational conditions, and amenity support, show differentiated impacts across regions and over time. Business agglomeration and educational resources are primary positive drivers in central districts, whereas natural environments and commercial density play a more complex role in peripheral areas. These findings provide empirical evidence to inform our understanding of housing market dynamics and offer insights into urban planning and the design of equitable policies in transitional urban systems.
Keywords: housing prices; influencing factors; LISA spatiotemporal leap; spatiotemporal geographically weighted regression models housing prices; influencing factors; LISA spatiotemporal leap; spatiotemporal geographically weighted regression models

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MDPI and ACS Style

Feng, Y.; Wang, Y. Spatiotemporal Evolution and Driving Forces of Housing Price Differentiation in Qingdao, China: Insights from LISA Path and GTWR Models. Buildings 2025, 15, 2941. https://doi.org/10.3390/buildings15162941

AMA Style

Feng Y, Wang Y. Spatiotemporal Evolution and Driving Forces of Housing Price Differentiation in Qingdao, China: Insights from LISA Path and GTWR Models. Buildings. 2025; 15(16):2941. https://doi.org/10.3390/buildings15162941

Chicago/Turabian Style

Feng, Yin, and Yanjun Wang. 2025. "Spatiotemporal Evolution and Driving Forces of Housing Price Differentiation in Qingdao, China: Insights from LISA Path and GTWR Models" Buildings 15, no. 16: 2941. https://doi.org/10.3390/buildings15162941

APA Style

Feng, Y., & Wang, Y. (2025). Spatiotemporal Evolution and Driving Forces of Housing Price Differentiation in Qingdao, China: Insights from LISA Path and GTWR Models. Buildings, 15(16), 2941. https://doi.org/10.3390/buildings15162941

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