Detecting bubbles in China’s regional housing markets


This study employs the supremum augmented Dicker–Fuller (SADF) and generalized supremum ADF (GSADF) tests to examine the Chinese housing market for the presence of bubbles from 2005 to 2016. We first employed a conventional right-tailed unit root test, and the results clearly suggest that housing prices showed no explosive behaviour in most cities. We further used SADF and GSADF, and the results suggest that most major cities in China experienced bubbles, with the longest bubble period from 2015 to the present, which is still ongoing. The SADF and GSADF results are robust after considering the effects of interest and mortgage rates. Moreover, the northern and southern regions experienced the largest number of bubbles, with over 1.1 bubbles for each city, while the western region experienced the fewest bubbles over the last decade. The western region had bubbles with the shortest durations, around 4 months, than other regions, where the average duration was at least 5 months. This study provides potentially valuable insights into the Chinese housing market and offers important policy implications.

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  1. 1.

    For full ranking, please visit

  2. 2.

    Engsted (2016) discuss Eugene Fama and Robert Shiller’s views on bubbles.

  3. 3.

    “Appendix” contains the detailed results for the other cities.

  4. 4.

    The minimum bubble lengths for Beijing, Hangzhou, Hohhot, Zhengzhou, and Xinning are 2.02, 2.03, 2.03, 2.03, and 2.00, respectively.

  5. 5.

    We understood there are still some factors, such as tax rates, maintenance costs, which could affect the fundamental value of house markets [see, e.g. the discussion in, e.g. Poterba (1992)]. Nonetheless, unavailability of data in Chinese house markets has restricted us to consider them in our analysis.

  6. 6.

    Following Caspi (2016), we use the difference between expected inflation and the People’s Bank of China rate.

  7. 7.

    We also compute the descriptive statistics for the bubbles in each city and report the results in Table 5 in “Appendix.” Interestingly, Zhengzhou experienced the most bubbles during the study period. This city does not have a major increase in its house prices, and policymakers do not frequently mention it about house price bubbles.

  8. 8.

    The “negative bubble” is defined as the average price during a given bubble period is smaller than the price at bubble start date, while the “positive bubble” is defined as the average price during a given bubble period is larger than the price at bubble start date.


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The author is grateful to the coordinating editor, Robert M. Kunst, and to the two anonymous referees for their helpful comments and insights. Remaining errors, however, are my own.

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Corresponding author

Correspondence to Wei-Fong Pan.

Appendix 1: Bubble periods for each regional city

Appendix 1: Bubble periods for each regional city

See Table 5.

Table 5 Bubble periods for 35 cities

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Pan, W. Detecting bubbles in China’s regional housing markets. Empir Econ 56, 1413–1432 (2019).

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  • Real estate
  • Rational bubbles
  • Housing market
  • Recursive unit root

JEL Classification

  • C32
  • G01
  • R30