Housing Markets in China and Policy Implications: Comovement or Ripple Effect

Chiang, Shu-hen

doi:10.1111/cwe.12094

Cited by 39 publications

(28 citation statements)

References 46 publications

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“…using quarterly housing price data from 2000 to 2012, they found that most of the provinces have bubbles and significant spillover effects exist among geographically adjacent regions. Another recent work by Chiang (2014) detected ripple effects among China's six first-tier cities from 2003 to 2013 by using the Toda-yamamoto (Ty) causality test. The results show that the ripple effect is characterized by a lead-lag relationship and Beijing is the main source of housing price appreciation, which should be targeted as the regulatory object.…”

Section: Literature Reviewmentioning

confidence: 99%

On Price Co-Movement and Volatility Spillover Effects in China’s Housing Markets

Weng

Gong

2017

International Journal of Strategic Property Management

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The soaring property prices in many Chinese cities have recently attracted increasing attention. This study uses the data on housing price indices from January 2005 to December 2014 in 10 large Chinese cities to analyze volatility spillover effects and to identify the determinants of price co-movement across the China’s regional housing markets. This research proposes a novel dynamic spatial panel data model that accounts for multivariate asymmetrical generalized autoregressive conditional heteroskedasticity components in disturbances to address these issues empirically. Results reveal that housing prices in cities are significantly influenced by population, income, mortgage rates, policy factors, and the national macroeconomic situation. The analysis further indicates that the housing returns of regions in China that are in close geographic and economic proximities exhibit strong co-movement and volatility spillovers. Evidence of significantly positive leverage effects in regional housing markets is also determined. This study’s findings have significant implications for academic researchers, financial experts, and policy makers.

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Section: Literature Reviewmentioning

confidence: 99%

On Price Co-Movement and Volatility Spillover Effects in China’s Housing Markets

Weng

Gong

2017

International Journal of Strategic Property Management

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“…If the ripple effect is valid, there should be long-run relativities between the regions. As such, the government can identify the original shock of local housing prices and intervene with the real estate market in the source region, rather than that all regions (Chiang 2014). Therefore, the intervention policies could improve efficiency by using the right cure for the disease.…”

Section: Introductionmentioning

confidence: 99%

Ripple Effect and Regional House Prices Dynamics in China

Lee

Chiang

2016

International Journal of Strategic Property Management

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This paper examines the stationarity properties, the long-run equilibrium and the leadlag relationship among the regional house prices in China from December 2000 to July 2013. Unlike traditional unit-root tests, the panel seemingly unrelated regressions augmented Dickey-Fuller (SURADF) unit-root test reveals that the regional house prices in China are a mixture of I(0) and I(1) processes. There is concrete evidence in favor of the hypothesis of a long-run equilibrium relationship among all regions, except for Shanghai region, and supporting the price diffusion or ripple effect among these Chinese cities. Finally, we determine that these regional house prices exhibit uni-directional causalities running from Beijing, Chongqing, and Shenzhen to Guangzhou and Tianjin, respectively.

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“…Moreover, when spillovers from other cities are increasingly close to spillovers to other cities after 2014, this not only reveals the rise in total spillovers, but it also reflects the near-zero net spillovers across the six cities. This situation implies that spillover behaviour is a bilateral interaction among cities that have remarkable and equal spillovers ‘from’ and ‘to’ other cities – that is, there is co-movement among the six cities’ housing returns (Chiang, 2014). In fact, the co-movement among all urban housing markets fully indicates that very high system risk appears and the overheated housing market is now a national and not a local debate.…”

Section: Estimation Results and Policy Implicationsmentioning

confidence: 99%

“…Similarly, by dividing subsample periods in the presence of structural change, Brady (2014) showed that spillovers among states in the US have become stronger since the late-1990s. In the case of China, Chiang (2014) first applied the causality test to search for the source city of spillovers among six mega cities, finding the principal source city to be Beijing. Gong et al (2016) utilised the cointegration approach and causality with spatial correlations to indicate that there is little evidence of spillovers among these cities within the Pan-Pearl River Delta region.…”

Section: Literature Reviewmentioning

confidence: 99%

Time-varying spillovers among first-tier housing markets in China

Chen

Chiang

2019

Urban Studies

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Numerous efforts have over the last few years been devoted to studying spillovers (ripple effects) among cities as a means of evaluating overheated housing markets. What seems to be lacking, however, is the application of a rolling-window approach to further explore time-varying spillovers in a timely manner in order to look more closely at a housing market with Chinese characteristics; for example, a market with rapidly increasing prices and a sequence of policy recommendations. By focusing on total, directional and net spillovers, and using 2000–2017 monthly housing price data across six Chinese cities, this study’s results indicate that time-varying spillovers provide a better understanding of the interactions among first-tier cities. It is interesting to note that, following the downside risk faced by the economy in 2014, the spillovers among cities have been abruptly transformed into those exhibiting bilateral co-movements based on high total spillovers and low net spillovers, and these results are also confirmed by the frequency dynamics of spillovers. Based on the above, there is sufficient evidence to conclude that the housing frenzies in China, which have become a national-level issue, deserve a more explicit macro-control policy in relation to real estate assets.

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Housing Markets in China and Policy Implications: Comovement or Ripple Effect

Cited by 39 publications

References 46 publications

On Price Co-Movement and Volatility Spillover Effects in China’s Housing Markets

On Price Co-Movement and Volatility Spillover Effects in China’s Housing Markets

Ripple Effect and Regional House Prices Dynamics in China

Time-varying spillovers among first-tier housing markets in China

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