Abstract
This chapter investigates evidence of housing bubbles in different locations of China by looking at data at the provincial and city levels from late 1990s to 2016, using the price-to-rent analysis and the generalized sup-Augumented Dickey-Fuller Test (GSADF) test. The price-to-rent ratio analysis indicates that housing bubbles began to develop in Shenzhen and Xiamen as early as 2007, while the bubble in Beijing, Hangzhou, Ningbo, Hefei, and Wenzhou started later in 2009. Those findings are largely consistent with results of the GSADF test. We also observe that the government intervention has been quite effective in maintaining a relatively stable upward trend in housing prices by timely interventions either to revive a depressed market or dampen an overheating market. Even though it has been propping up the upward trend in housing prices, even allowing it to rise in an explosive pace at times, it also does not hesitate to use policy intervention to cause moderate downward adjustments to avoid spectacular burst.
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Notes
- 1.
The housing price/income ratio is set to equal (housing price per m2 ∗ area per person)/average disposable income .
- 2.
The right-tail unit root test is a test of the null hypothesis of a unit root against the alternative of a root that is greater than unity. In comparison, a typical unit root test of a time series is a left-tail test of the null hypothesis of a unit root against the alternative of a root that is lower than unity.
- 3.
For example, see Campbell and Shiller (1988).
- 4.
In the SADF method, the beginning of the bubble is stamped on the first date on which the ADF t-statistic is greater than the corresponding critical value of the right-tail unit root test. The end of the speculative bubble is stamped on the first day when the ADF t-statistic is below the aforementioned critical value. In such a way, the SADF can detect the formation and collapse of bubbles. The authors argue that their tests have a greater power of test because they are sensitive to changes that occur when a process transits from a unit root to a mildly explosive root and vice versa.
- 5.
Rent data at the provincial level are unavailable on a monthly or quarterly basis. Even though quarterly rent indexes are available for 30 capital cities, they are available only for 52 quarters (from Q1 1998 to Q4 2010), not enough for conducting credible GSADF tests.
- 6.
The authors suggest 1% of total number of observations (T) as an ideal choice of the initial window size when the sample size is large enough, while T*(0.01 + 1.8/√T) is a better alternative rule for setting window size when the sample size is limited. Since our sample size is modest (224 months), we follow the alternative rule of thumb.
- 7.
We conducted simulations with reiterations of 1000, 2000, 3000, 5000, 7500, 10,000, 15,000, 25,000, and 50,000 times.
- 8.
To make the picture clearer and avoid the disturbance of outliers, we used HP filter to signal the trend. To prevent losing too much information due to the HP filter, we choose a low λ (=500) instead of what is typically used for monthly data (λ = 14,400).
- 9.
For example, “Notice on Enhancing Credit Regulation on Commercial Real Estate” (关于加强商业性房地产信贷管理的通知) was issued by People’s Bank of China, in 2007 (No. 359). This act was aimed to control the ownership of second houses and set the down-payment for them as 40% of the property value.
- 10.
The three north-east provinces are Heilongjiang, Jilin, and Liaoning. The 15 inland provinces are Anhui, Gansu, Guizhou, Henan, Hubei, Hunan, Inner Mongolia, Jiangxi, Ningxia, Qinghai, Shanxi(山西), Shanxi(陕西), Sichuan, Xinjiang and Yunnan.
- 11.
We remove outliers by not counting “bubbles” that lasted longer than 20 months or shorter than 6 months. For example, there are six periods in which Guangxi’s GSADF statistics exceeded the critical values; however, when we check the data, we found all six periods scatter in different years. So we did not count those six periods as bubble periods for Guangxi.
- 12.
Those with 18 points are colored blue, those points ≥36 are red (because the average total points for all provinces are about 25.5 and standard deviation is 10.5), and the rest with points 19–35 are yellow.
- 13.
After experimenting with different window sizes, we found that bubble episodes identified by three different window sizes (20, 30, and 40) have little differences. Consequently, we only present the results obtained from setting the window size to 30 months.
- 14.
The disposable income data are from the National Bureau of Statistics.
- 15.
See Kuang (2016)—and Ma and Sun (2008) for more discussion on the Chinese and international standards of the P/R ratio.
- 16.
- 17.
They refer to Wenzhou housing speculators (温州炒房团), words often referred to in newspapers.
- 18.
The one-child policy was introduced in 1979 as part of China’s population planning. The policy began to be phased out since 2015. The policy allowed exceptions for many special groups, including ethnic minorities.
- 19.
Of course, this extremely high homeownership rate (the share of households that are owners) is partly attributable to the fact that many young and elderly adults live with their parents in China. When young adults live with their parents, or elder people live with their grown children, or people live with their housemates, they count as part of someone else’s household. Moreover, poor people in China tend to share a small room in cities to save rent. Both factors will reduce the number of households counting as renters in the data, thereby raising the Homeownership rate.
- 20.
See Wei and Zhang (2011).
- 21.
See “Notice about enhancing credit regulation on commercial real estate”《关于加强商业性房地产信贷管理的通知》, issued by People’s Bank of China, Year 2007 No. 359.
- 22.
See “Additional notice about credit regulation on commercial real estate”《关于加强商业性房地产信贷管理补充通知》, issued by People’s Bank of China, Year 2007 No. 452.
- 23.
See “Notice about further decrease of commercial private mortgage rate”《关于扩大商业性个人住房贷款利率下浮幅度等有关问题的通知》, issued by People’s Bank of China, Year 2008 No. 302.
- 24.
See “Notice about curbing rapid rise of housing prices in some cities”《关于坚决遏制部分城市房价过快上涨的通知》, issued by the State Council (国务院), Year 2010 No. 10.
- 25.
See “Notice about further enhancing macro regulation on real estate market”, issued by the State Council, Year 2011 No. 1.
- 26.
See “Notice about further improvement in housing financial service”, issued by People’s Bank of China, Year 2014 No. 287.
- 27.
The writer, Guolian Wu, is the chairperson of PBoC’s Wenzhou branch.
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Appendix
Appendix
1.1 Appendix 1: GSADF Critical Values Under Different Replications
Repeating times | 1000 | 3000 | 5000 | 7500 | 10,000 | 15,000 | 25,000 | 50,000 |
Mean | 1.259 | 1.248 | 1.244 | 1.241 | 1.24 | 1.232 | 1.231 | 1.229 |
Median | 1.262 | 1.272 | 1.269 | 1.273 | 1.272 | 1.266 | 1.264 | 1.266 |
Maximum | 1.738 | 1.435 | 1.435 | 1.411 | 1.377 | 1.373 | 1.355 | 1.35 |
Minimum | 0.614 | 0.711 | 0.748 | 0.74 | 0.763 | 0.755 | 0.756 | 0.75 |
Std. dev. | 0.167 | 0.12 | 0.117 | 0.113 | 0.108 | 0.109 | 0.111 | 0.108 |
Skewness | −0.608 | −1.807 | −1.747 | −1.855 | −1.882 | −1.83 | −1.815 | −1.939 |
Kurtosis | 4.56 | 7.436 | 6.959 | 7.138 | 7.083 | 6.882 | 6.472 | 7.054 |
1.2 Appendix 2: GSADF Test on China’s Average Housing Prices
Note: In this and the subsequent appendices, naming rule for critical values is “GSADF confidence level.”
1.3 Appendix 3: GSADF Test on Real Housing Price of Bubble Provinces
1.4 Appendix 4: GSADF Test on Real Housing Price of Slight-Bubble Provinces
1.5 Appendix 5: GSADF Test on Real Housing Price of No-Bubble Provinces
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Zhang, X., Hung, J.H. (2018). China’s Housing Price: Where Are the Bubbles?. In: Hung, J., Chen, Y. (eds) The State of China’s State Capitalism. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-13-0983-0_3
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