Analysis of the diffusion effect of urban housing prices in China based on the spatial-temporal model. (February 2021)

• Record Type: Journal Article
• Authors: Zhu, Linlin; Zhang, Hui
• Is Part Of: Cities. Volume 109(2021)
• Publication Date: 2021-02
• Subjects: Urban housing prices -- Spatial-temporal model -- Diffusion effect -- Regional differences; City planning -- Periodicals; Urban policy -- Periodicals; 711.4
• DOI: 10.1016/j.cities.2020.103015
• Languages: English
• ISSNs: 0264-2751

Abstract

Abstract: This article contributes to the recent literature in the spatial diffusion pattern of house prices by constructing spatial-temporal model to test the spatial relationships of 70 housing markets in China. Drawing on the spatial-temporal method proposed by Holly, a multiregional spatial-temporal model suitable for China is formulated which are applied to study the spatial-temporal diffusion effect of urban housing prices in China. Firstly, the diffusion mechanism of urban housing prices is analyzed from theoretical perspective, and four cities are assumed as dominant cities, namely, Beijing, Shanghai, Shenzhen and Sanya. Secondly, the spatial linkage of the housing prices of 70 large and medium-sized cities from 2007 to 2017 is examined by calculating the global Moran's I index. Thirdly, a spatial-temporal model is constructed to empirically analyze the diffusion effects of the housing prices of 70 sample cites. The study concludes that 1) significant global spatial autocorrelation is observed among the housing prices in the 70 cites; 2) empirical analysis shows that Beijing, Shanghai, Shenzhen and Sanya are the dominant cities of the 70 cities; and 3) the spatial effects of the four dominant cities on other cities differ in areas, degrees and duration. Highlights: This paper takes 70 large and medium-sized cities as research sample to study the temporal and spatial diffusion mechanisms of housing prices, analyzes the fluctuation rules of regional housing prices and provides a reference for the governments to implement differentiated regulation. This study makes several major contributions: (1) We use the global Moran's I to verify whether housing prices in 70 large and medium-sized cities from 2007 to 2017 are spatially linked. (2) Based on the theory of "regional growth poles" and "ripple effect, " we portray the spatial and temporal diffusion mechanism of regional housing prices and verify the existence of four dominant cities in China, they are Beijing, Shanghai, Shenzhen and Sanya. (3) Taking Beijing, Shanghai, Shenzhen and Sanya as the dominant cities, we construct a temporal and spatial diffusion model, validate the theoretical hypothesis, analyze and compare the diffusion characteristics of housing price in different dominant cities using spatial and temporal impulse response functions. The main findings of this paper include: (1) Based on spatial autocorrelation analysis, there is a significant global spatial autocorrelation between housing prices in 70 large and medium cities and there is a spatial linkage between different cities. (2) The estimated results of the spatial and temporal diffusion model show that Beijing, Shanghai, Shenzhen and Sanya are the dominant cities in China, which has verified the theoretical hypothesis. (3) The diffusion characteristics of the housing prices in these four dominant cities differ in the aspects of area, degree and duration. This paper can propose the following suggestions for the relevant entities in the real estate market at home and abroad: (1) For governments, focus on the dominant cities of each country, and formulate real estate control policies in a specific way by identifying the differences in the impact of changes in housing prices in each dominant city on itself and other regions in terms of area, degree and duration. Furthermore, manage the real estate market by combining the actual conditions of each country or region, such as population mobility, economic development level, and the degree of marketization. (2) For investor and housing demanders, according to the similarities and differences of housing price diffusion mechanisms in various countries, predict the future development trend of housing prices in a certain region, adjust investment portfolios, and make housing consumption decisions scientifically.