Housing price diffusions in mainland China: evidence from a spatially penalized graphical VAR model

Xiandeng Jiang, Le Chang*, Yanlin Shi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Although China has a large territory, few studies consider the factor of geographical distance when studying spatial interactions among city-level prices. We study housing price diffusions among seventy medium- and large-sized Chinese cities between July 2005 and August 2017 by using a spatially penalized graphical vector autoregression method that incorporates the intercity geographical distance. Our study reveals that the cities with the largest positive net centrality in each region are Nanchang (Central), Nanjing (Southeast), Changchun (Northeast), Beijing (North), Xiamen (South), Chongqing (Southwest) and Xining (Northwest). Furthermore, our results suggest that the four largest cities do not present the largest net centrality. Instead, many non-top-developed and non-major cities can play a significant role in affecting the regional housing markets. Our findings demonstrate that neglecting the influences of non-major cities may reduce the effectiveness and efficiency of relevant policies.
Original languageEnglish
Pages (from-to)765-795
Number of pages31
JournalEmpirical Economics
Volume64
Issue number2
Early online date1 Jul 2022
DOIs
Publication statusPublished - Feb 2023

Keywords

  • Centrality
  • Geographical distance
  • Graphical models
  • Housing prices
  • Spatial spillovers

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