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Erschienen in: Empirical Economics 4/2019

21.12.2017

Detecting bubbles in China’s regional housing markets

verfasst von: Wei-Fong Pan

Erschienen in: Empirical Economics | Ausgabe 4/2019

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Abstract

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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Fußnoten
2
Engsted (2016) discuss Eugene Fama and Robert Shiller’s views on bubbles.
 
3
“Appendix” contains the detailed results for the other cities.
 
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
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
Following Caspi (2016), we use the difference between expected inflation and the People’s Bank of China rate.
 
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
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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Metadaten
Titel
Detecting bubbles in China’s regional housing markets
verfasst von
Wei-Fong Pan
Publikationsdatum
21.12.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Empirical Economics / Ausgabe 4/2019
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-017-1394-3

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