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ABSTRACT
We introduce a novel quantitative methodology to detect real estate bubbles and forecast their critical end time, which we apply to the housing markets of China's metropolises. Building on the Log-Periodic Power Law Singularity (LPPLS) model of self-reinforcing feedback loops, we use the quantile regression calibration approach recently introduced by two of us to build confidence intervals and explore possible distinct scenarios. We propose to consolidate the quantile regressions into the arithmetic average of the quantile-based LPPLS Confidence indicator, which accounts for the robustness of the calibration with respect to bootstrapped residuals. We make three main contributions to the literature of real estate bubbles. First, we verify the validity of the arithmetic average of the quantile-based LPPLS Confidence indicator by studying the critical times of historical housing price bubbles in the U.S., Hong Kong, U.K. and Canada. Second, the LPPLS detection methods are applied to provide early warning signals of the housing markets in some metropolises in China. Third, we determine the possible turning points of the markets in Beijing, Shanghai, Shenzhen, Guangzhou, Tianjin and Chengdu and anticipate critical transitions of China's housing markets via our multi-scales and multi-quantiles analyses. Finally, given these projections performed in February 2017, the price trajectories from March 2017 to January 2018 that became available from the time of submission to the time of revision of the present article offer quite unique genuine out-of-sample tests of the performances of our indicators.
Acknowledgements
The authors would like to thank the editor, associate editor, and anonymous referees for their constructive suggestions and helpful comments.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 The data are from the Federal Reserve, Bank of England and Bank of Japan.
2 This is similar to the belief of U.S. households before the 2008 crisis.
3 In China, the first-tier cities include Beijing, Shanghai, Guangzhou and Shenzhen. The second-tier cities include the capital cities of eastern and central provinces and other Municipalities with Independent Planning Status under the National Social and Economic Development. The third-tier cities include the capital cities of western provinces and some developed medium and small-sized cities in eastern and central provinces.
4 The relationship of supply and demand always affects the housing market. Before the housing supply-side structural reform achieved substantial progress in China, due to the rigidity of housing supply, house price has been mainly determined by the demand side. See e.g. Chow and Niu (Citation2015) and Bian and Gete (Citation2015).
5 Just as Wu, Deng, and Liu (Citation2014) said, the official government (housing) price series are of lower quality. Thus, to address the concerns regarding the quality of the Chinese house prices, the data used in our study are the price from the local Real Estate Trading Centers, rather than use ‘the average selling price of new properties in the cities’. This kind of transaction data, which are the total amount of housing sold for the current month, divided by the area of the housing, show the overall level of housing turnover in the city (including the new properties and the repeated housing sale) over a period of time (within 1 month).
6 See posted version on May 20, 2017, on the SSRN archive https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2969801, we are in a position to evaluate this prediction.