Abstract
This study aims to investigate the spatial and temporal dynamics of housing prices associated with the urban infrastructure in Wuhan, China. The relationship between urban infrastructure and housing prices during rapid urbanization has drawn popular concerns. This article takes 619 residential communities during the period 2010 to 2018 in Wuhan’s main urban area as research units, and uses the geographically and temporally weighted regression (GTWR) model to study the spatial-temporal differentiation in the effects of urban infrastructure on housing prices. The results show that: 1) From 2010 to 2018, housing prices in Wuhan’s main urban area were generally on the rise, but the increment speed has shown an obvious periodic characteristic, the spatial distribution of housing prices has shown an obvious core and periphery distribution and the peak value area shifted from Hankou to Wuchang. 2) The influential factors of housing prices have significant spatiotemporal non-stationarity, while the impact, direction and intensity of the influential factors varies in time and space. Spatially, the influence factors show different differentiation rules for spatial distribution, and the influencing direction and strength of the urban infrastructure on housing prices are closely related to the spatial location, distribution density and the type of urban infrastructure. Temporally, the influencing strength of various urban facilities varies. This research will benefit both urban planners for optimizing urban facilities and policy-makers for formulating more specific housing policies, which ultimately contributes to urban sustainability.
Highlights
Housing prices have been a broad area of concern due to their importance to public welfare and urban vitality
The macro perspective mainly focuses on the impact of macro economy, demography, policies and regulations on regional housing prices [1,2,3,4] based on the supply and demand theory, while the micro perspective mainly focuses on the effects of architecture characteristics and spatial location of residential buildings, such as structure, location and neighborhood on housing prices with the theory of hedonic price [5,6,7,8]
To examine the effects of urban facilities on housing prices at the community level, we extended the hedonic price model with geographically and temporally weighted regression (GTWR), which enabled us to explore various mechanisms operating across space and time
Summary
Housing prices have been a broad area of concern due to their importance to public welfare and urban vitality. Researchers have conducted in-depth and extensive studies on urban housing prices from different perspectives. Recent housing prices studies with a micro perspective have increasingly paid attention to the impact of urban infrastructure on housing prices around the world caused by both urban planners and policy-makers [9,10,11]. Numerous examples of empirical research have been conducted on the impact of accessibility and amenity/disamentity of various urban facilities on housing prices. In terms of public transport facilities, Song, Cao, Han and Hickman [14] used London’s Docklands Light Railway as a case study and found that public transport infrastructure has a positive impact on housing prices, especially in areas with poor public transport accessibility. With the in-depth research of the impact of urban facilities on housing prices, researchers became more aware of the differences in the spatial distribution of urban housing prices in different periods due to the spatio-temporal imbalance of urban development
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