Measuring the Spatial and Temporal Diffusion of Urban House Prices in East China
Publication: Journal of Urban Planning and Development
Volume 146, Issue 2
Abstract
Based on a spatial vector autoregression (VAR) model and generalized spatiotemporal impulse responses, this paper analyzes urban house price diffusion in East China. The results suggest that a certain amount of spatial and temporal diffusion of neighboring cities exists within the study area. When the central city of the urban agglomeration, Shanghai (SH), and the subcenter city, Hangzhou (HZ), are assumed to be the price diffusion center cities, more than half the surrounding cities are significantly affected by any house price changes in the diffusion center city. Current price changes in SH and HZ have the greatest scope of influence, while SH is the most unlikely city to be affected by house price changes in other cities in the long term. The central city, SH, has a wider radiation range of house price diffusion, while HZ, as a subcenter, has a greater but shorter-lasting impact on the prices of houses within a closer range. However, SH and HZ are not completely dominant in the regional housing market. The house price diffusion mechanism is more significant under conditions of economic connection. Transportation, accessibility, and information dissemination could also explain house price diffusion in East China.
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Data Availability Statement
Some data, models, or code generated or used during the study are available in a repository online, in accordance with funder data retention policies.
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House price data: https://www.creprice.cn/.
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Data about GDP and the population of cities: http://www.tjcn.org/tjgb/.
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Data about land area purchased by real estate developers: http://www.soshoo.com.cn, Soshoo, China Monthly Statistic Report.
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Data about house buyers in Hangzhou: http://www.tmsf.com/index.jsp/.
Some data, models, or code used during the study were provided by a third party, including the Gauss code. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
We thank Professor Sean Holly and Professor Takashi Yamagata for the Gauss code they provided. Thanks also go to the editor and anonymous referees for excellent comments and suggestions. This paper is supported in part by Zhejiang Provincial Natural Science Foundation of China, Grant Number LY18G030002 and the Natural Science Foundation of China (71974169).
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Published In
Journal of Urban Planning and Development
Volume 146 • Issue 2 • June 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Dec 10, 2018
Accepted: Oct 18, 2019
Published online: Apr 8, 2020
Published in print: Jun 1, 2020
Discussion open until: Sep 8, 2020
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