Abstract
In the past decades, the booming growth of housing markets in China triggers the urgent need to explore how the rapid urban spatial expansion, large-scale urban infrastructural development, and fast-changing urban planning determine the housing price changes and spatial differentiation. It is of great significance to promote the existing governing policy and mechanism of housing market and the reform of real-estate system. At the level of city, an empirical analysis is implemented with the traditional econometric models of regressive analysis and GIS-based spatial autocorrelation models, focusing in examining and characterizing the spatial homogeneity and nonstationarity of housing prices in Guangzhou, China. There are 141 neigborhoods in Guangzhou identified as the independent individuals (named as area units), and their values of the average annual housing prices (AAHP) in (2009–2015) are clarified as the dependent variables in regressing analysis models used in this paper. Simultaneously, the factors including geographical location, transportation accessibility, commercial service intensity, and public service intensity are identified as independent variables in the context of urban development and planning. The integration and comparative analysis of multiple linear regression models, spatial autocorrelation models, and geographically weighted regressing (GWR) models are implemented, focusing on exploring the influencing factors of house prices, especially characterizing the spatial heterogeneity and nonstationarity of housing prices oriented towards the spatial differences of urban spatial development, infrastructure layout, land use, and planning. This has the potential to enrich the current approaches to the complex quantitative analysis modelling of housing prices. Particularly, it is favorable to examine and characterize what and how to determine the spatial homogeneity and nonstationarity of housing prices oriented towards a microscale geospatial perspective. Therefore, this study should be significant to drive essential changes to develop a more efficient, sustainable, and competitive real-estate system at the level of city, especially for the emerging and dynamic housing markets in the megacities in China.
Highlights
Since the 1990s, the reform of real-estate system and land-use system in China have been implemented, leading to a continuous booming growth of the real-estate market, which has grown into one of the pillar industries in the national economic development
At the level of city, this paper implements an empirical analysis with the using of the traditional econometric models of regressive analysis and GIS-based spatial autocorrelation analysis tools, focusing in exploring and characterizing the spatial homogeneity and nonstationarity of the housing prices in 2009–2015 in the Complexity context of the neighborhoods in Guangzhou, China
In Guangzhou, there are total 141 neighborhoods identified as area units, and their average annual housing prices (AAHP) in 2009–2015 are represented as dependent variables
Summary
Since the 1990s, the reform of real-estate system and land-use system in China have been implemented, leading to a continuous booming growth of the real-estate market, which has grown into one of the pillar industries in the national economic development. The factors influencing on the changes in housing prices generally include monetary, fiscal, and housing policies [3,4,5,6,7], while the specific factors affecting the housing prices at the level of city mainly involve with urban development, land use, population, housing supply and demand, and urban planning [8,9,10,11,12]. At the level of city, the study on the fluctuations of housing prices and the correlations to the influencing factors has been a hot topic in the fields of governing policy of housing market and the reform of real-estate and related urban systems, e.g., land-use system, as well as the hotspots in urban spatial layout, urban planning, and sustainability development [10]. Hedonic price models define that the price of a specific commodity should be constituted by several different elements in which their number and combination are discrepant, leading to unequal prices for different commodities
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