Abstract
The recent surge in property values in China has been similar to the surge in the U.S before the crash in 2007. This raises concerns about whether China is destined to have a crash as well. We estimate similar models of property values for the two countries, in order to compare price dynamics side by side. We find little in common between them. In the U.S. the adjustment process appears prone to “bubbles” in the sense of strong momentum, but Chinese prices have been generally mean reverting, without momentum. This suggests that the recent price rise in China has had more to do with scarcity than with irrational exuberance.
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Notes
“Private label” refers to mortgage-backed securities that are not backed by one of the “Agencies”: Fannie Mae, Freddie Mac or Ginnie Mae.
We do not completely take this for granted. Instead, we checked to see if the model explodes, which is not the case for any of our estimates of residuals.
Because Fannie and Freddie have maximum loan size limits (that are indexed) the data do not have many mortgages backing expensive houses.
For the Price Indices the adjustments come from looking at pairs of prices of the same houses. For the rental indices, the changes come from adjusting actual rent in response to information about changes in property quality, such as from new rooms or aging.
Adjusted from raw data with the Census Bureau’s multiplicative X-12 ARIMA method.
Details of CityRE Data Technology Co., Ltd. can be obtained from http://www.cityre.cn/en/ or http://www.cityhouse.cn. It claims to be operating the biggest real estate data set in China.
See Wu et al. (2012) for a discussion on homogeneity of China housing.
We also group the 80 Chinese cities into Eastern/Central/Western regions because there is a lower ownership rate in the Eastern regions (most of which are also coastal cities) because of more expensive housing. This means that these groups of cities might be subject to different regimes. As a robustness check, we also repeat the tests using the same 35 under-supply and over-supply cities as in Wu et al. (2016), as well as the classification by Wang et al. (2008), namely, Tier-1, Eastern, Middle Southern, Northern, Western. Results are similar, and hence are omitted here.
We tried various auto-regressive models for the Chinese rent series, and both AIC and SC indicate a lag of 7 is the best. Yet, none of the lags is statistically significant. Hence, with the significant constant in this AR(7) process, we can safely conclude that the long term rent growth is constant.
These and other results referred to but not presented in the paper are in a separate appendix, available from the authors upon request.
We used National Bureau of Statistics price indices, but with our own rent series. The calculations turned out to be comparable when multiplied by 3, and were calibrated as such.
Because we are only focusing on the momentum coefficients we are not recalibrating for the new price index, as was done for Table 1.
Appendix 2 provides elasticity estimates for corresponding MSAs.
We do not attempt to find the best fit of residual process by trying different lags for individual MSAs or cities because our goal is not to find the best model for forecasting; rather, results from comparable models will be preferred.
A weak case against China results coming from stabilization policy is that the Tier 1 cities had less mean reversion, and indeed some momentum. One would expect stabilization policy to be more effective for these cities than the others.
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Acknowledgements
We have received expert research assistance from Rui-Hui Xu. We thank Kelvin Wong, Man Cho, participants at the Real Estate Finance and Investment Symposium 2017 organized by the National University of Singapore, University of Cambridge, and University of Florida, and an anonymous referee for their helpful discussions and comments. This research is financially supported by the Research Committee of the University of Macau under the research project number MYRG2015-00085-FBA.
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Appendices
Appendix 1 Average interest rates used (in percentage)
Appendix 2 List of MSAs
Panel A Classifications of MSAs | |||||
|---|---|---|---|---|---|
MSA | Coastal | Supply Elasticity | MSA | Coastal | Supply Elasticity |
Akron, OH | 0 | 2.59 | Mount Vernon-Anacortes, WA | 1 | |
Ann Arbor, MI | 0 | 2.29 | Napa, CA | 1 | 1.14 |
Atlanta-Sandy Springs-Roswell, GA | 0 | 2.55 | New Haven-Milford, CT | 1 | 0.98 |
Atlantic City-Hammonton, NJ | 1 | New York-Newark-Jersey City, NY-NJ-PA | 1 | 1.12 | |
Baltimore-Columbia-Towson, MD | 1 | 1.23 | Olympia-Tumwater, WA | 1 | |
Boston-Cambridge-Newton, MA-NH | 1 | 0.86 | Oxnard-Thousand Oaks-Ventura, CA | 1 | |
Bremerton-Silverdale, WA | 1 | Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 0 | 1.82 | |
Bridgeport-Stamford-Norwalk, CT | 1 | Providence-Warwick, RI-MA | 1 | 1.61 | |
Chicago-Naperville-Elgin, IL-IN-WI | 0 | 0.81 | Reading, PA | 0 | |
Cleveland-Elyria, OH | 0 | 1.02 | Riverside-San Bernardino-Ontario, CA | 1 | 0.94 |
Dallas-Fort Worth-Arlington, TX | 0 | 2.49 | San Francisco-Oakland-Hayward, CA | 1 | 0.68 |
Detroit-Warren-Dearborn, MI | 0 | 1.24 | San Jose-Sunnyvale-Santa Clara, CA | 1 | 0.76 |
Flint, MI | 0 | Santa Cruz-Watsonville, CA | 1 | ||
Gainesville, FL | 1 | Santa Rosa, CA | 1 | ||
Houston-The Woodlands-Sugar Land, TX | 0 | 2.3 | Seattle-Tacoma-Bellevue, WA | 1 | 1.045 |
Kankakee, IL | 0 | Sherman-Denison, TX | 0 | ||
Kingston, NY | 1 | Trenton, NJ | 1 | ||
Los Angeles-Long Beach-Anaheim, CA | 1 | 0.63 | Vallejo-Fairfield, CA | 1 | 1.14 |
Manchester-Nashua, NH | 1 | Vineland-Bridgeton, NJ | 1 | ||
Miami-Fort Lauderdale-West Palm Beach, FL | 1 | 0.69 | Washington-Arlington-Alexandria, DC-VA-MD-WV | 1 | 1.61 |
Michigan City-La Porte, IN | 0 | Winchester, VA-WV | 1 | ||
Monroe, LA | 0 | Worcester, MA-CT | 1 | 0.86 | |
Panel B Cap Rates of MSAs (in percentages)* | |||||||
|---|---|---|---|---|---|---|---|
MSA | 01/1999 – 08/2016 | 01/1999 – 12/2006 | 01/2009 – 08/2016 | MSA | 01/1999 – 08/2016 | 01/1999 – 12/2006 | 01/2009 – 08/2016 |
Akron, OH | 4.092 | 3.746 | 4.477 | Mount Vernon-Anacortes, WA | 3.910 | 3.823 | 4.222 |
Ann Arbor, MI | 4.140 | 3.731 | 4.510 | Napa, CA | 4.306 | 4.170 | 4.646 |
Atlanta-Sandy Springs-Roswell, GA | 3.859 | 3.598 | 4.211 | New Haven-Milford, CT | 4.422 | 4.076 | 4.949 |
Atlantic City-Hammonton, NJ | 3.559 | 3.534 | 3.805 | New York-Newark-Jersey City, NY-NJ-PA | 4.063 | 3.960 | 4.329 |
Baltimore-Columbia-Towson, MD | 1.887 | 1.874 | 1.996 | Olympia-Tumwater, WA | 3.727 | 3.727 | 3.935 |
Boston-Cambridge-Newton, MA-NH | 4.287 | 4.169 | 4.457 | Oxnard-Thousand Oaks-Ventura, CA | 3.448 | 3.223 | 3.764 |
Bremerton-Silverdale, WA | 3.681 | 3.657 | 3.915 | Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 3.714 | 3.735 | 3.837 |
Bridgeport-Stamford-Norwalk, CT | 4.538 | 4.115 | 5.138 | Providence-Warwick, RI-MA | 3.941 | 3.807 | 4.221 |
Chicago-Naperville-Elgin, IL-IN-WI | 4.346 | 3.998 | 4.894 | Reading, PA | 4.100 | 3.994 | 4.358 |
Cleveland-Elyria, OH | 4.159 | 3.710 | 4.657 | Riverside-San Bernardino-Ontario, CA | 3.518 | 3.159 | 4.028 |
Dallas-Fort Worth-Arlington, TX | 3.317 | 3.394 | 3.265 | San Francisco-Oakland-Hayward, CA | 4.517 | 4.450 | 4.735 |
Detroit-Warren-Dearborn, MI | 4.780 | 3.848 | 5.712 | San Jose-Sunnyvale-Santa Clara, CA | 4.833 | 4.899 | 4.905 |
Flint, MI | 4.838 | 3.850 | 5.856 | Santa Cruz-Watsonville, CA | 4.718 | 4.546 | 5.085 |
Gainesville, FL | 3.045 | 3.164 | 3.103 | Santa Rosa, CA | 4.783 | 4.442 | 5.295 |
Houston-The Woodlands-Sugar Land, TX | 2.813 | 2.886 | 2.779 | Seattle-Tacoma-Bellevue, WA | 3.798 | 3.808 | 3.984 |
Kankakee, IL | 4.493 | 4.222 | 4.934 | Sherman-Denison, TX | 3.436 | 3.435 | 3.481 |
Kingston, NY | 3.953 | 3.862 | 4.223 | Trenton, NJ | 4.252 | 4.015 | 4.669 |
Los Angeles-Long Beach-Anaheim, CA | 3.151 | 3.171 | 3.239 | Vallejo-Fairfield, CA | 4.872 | 4.105 | 5.908 |
Manchester-Nashua, NH | 4.329 | 4.098 | 4.676 | Vineland-Bridgeton, NJ | 3.970 | 3.815 | 4.367 |
Miami-Fort Lauderdale-West Palm Beach, FL | 3.019 | 2.762 | 3.431 | Washington-Arlington-Alexandria, DC-VA-MD-WV | 1.813 | 1.821 | 1.869 |
Michigan City-La Porte, IN | 4.542 | 4.301 | 4.873 | Winchester, VA-WV | 2.073 | 1.863 | 2.377 |
Monroe, LA | 3.444 | 3.717 | 3.184 | Worcester, MA-CT | 4.368 | 4.063 | 4.779 |
Panel C cap rates of Case-Shiller 20-City index (in percentages)* | |||||||
|---|---|---|---|---|---|---|---|
City | 01/99–08/16 | 01/99–12/06 | 01/09–08/16 | City | 01/99–08/16 | 01/99–12/06 | 01/09–08/16 |
Atlanta | 3.639 | 3.393 | 3.976 | Los Angeles | 2.952 | 2.903 | 3.109 |
Boston | 3.627 | 3.575 | 3.723 | Miami | 2.812 | 2.552 | 3.230 |
Chicago | 4.027 | 3.693 | 4.556 | New York | 3.667 | 3.567 | 3.927 |
Cleveland | 3.862 | 3.551 | 4.220 | San Francisco | 3.780 | 3.638 | 4.056 |
Dallas | 3.257 | 3.203 | 3.324 | Seattle | 3.619 | 3.635 | 3.788 |
Detroit | 4.389 | 3.563 | 5.280 | Washington | 1.659 | 1.641 | 1.742 |
Appendix 3 List of Chinese Cities
Panel A Classifications of Cities | |||||||||
|---|---|---|---|---|---|---|---|---|---|
City | Province | Tiers | Coastal | Supply Elasticity | City | Province | Tiers | Coastal | Supply Elasticity |
Beijing | Beijing | Tier 1 | 0.53 | Guangzhou | Guangdong | Tier 1 | * | 12.62 | |
Shanghai | Shanghai | Tier 1 | * | 1.52 | Shenzhen | Guangdong | Tier 1 | * | 0.49 |
Hefei | Anhui | Tier 2 | 13.03 | Wuxi | Jiangsu | Tier 2 | |||
Chongqing | Chongqing | Tier 2 | 4.51 | Nanchang | Jiangxi | Tier 2 | 6.78 | ||
Xiamen | Fujian | Tier 2 | * | 3.47 | Changchun | Jilin | Tier 2 | 5.4 | |
Fuzhou | Fujian | Tier 2 | * | 3.85 | Dalian | Liaoning | Tier 2 | * | 4.41 |
Lanzhou | Gansu | Tier 2 | 4.9 | Shenyang | Liaoning | Tier 2 | 5.75 | ||
Beihai | Guangxi | Tier 2 | * | Yinchuan | Ningxia | Tier 2 | 21.98 | ||
Nanning | Guangxi | Tier 2 | 11.45 | Xining | Qinghai | Tier 2 | 37.05 | ||
Guiyang | Guizhou | Tier 2 | 9.71 | Xian | Shaanxi | Tier 2 | 8.04 | ||
Haikou | Hainan | Tier 2 | * | 8.83 | Qingdao | Shandong | Tier 2 | * | 2.89 |
Sanya | Hainan | Tier 2 | * | Jinan | Shandong | Tier 2 | 2.68 | ||
Shijiazhuang | Hebei | Tier 2 | 7.89 | Taiyuan | Shanxi | Tier 2 | 9.16 | ||
Harbin | Heilongjiang | Tier 2 | 6.3 | Chengdu | Sichuan | Tier 2 | 4.36 | ||
Zhengzhou | Henan | Tier 2 | 16.5 | Tianjin | Tianjin | Tier 2 | * | 5.1 | |
Wuhan | Hubei | Tier 2 | 4.66 | Urumqi | Xinjiang | Tier 2 | 16.71 | ||
Changsha | Hunan | Tier 2 | 17.14 | Kunming | Yunnan | Tier 2 | −7.7 | ||
Hohhot | Inner Mongolia | Tier 2 | 9.63 | Ningbo | Zhejiang | Tier 2 | * | 2.27 | |
Suzhou | Jiangsu | Tier 2 | Wenzhou | Zhejiang | Tier 2 | * | |||
Nanjing | Jiangsu | Tier 2 | 3.42 | Hangzhou | Zhejiang | Tier 2 | 2.65 | ||
Bengbu | Anhui | Xuzhou | Jiangsu | ||||||
Anqing | Anhui | Changzhou | Jiangsu | ||||||
Tongling | Anhui | Yancheng | Jiangsu | * | |||||
Wuhu | Anhui | Yangzhou | Jiangsu | ||||||
Quanzhou | Fujian | * | Ganzhou | Jiangxi | |||||
Shantou | Guangdong | * | Jiujiang | Jiangxi | |||||
Dongguan | Guangdong | * | Jilin | Jilin | |||||
Foshan | Guangdong | Weifang | Shandong | ||||||
Huizhou | Guangdong | Yantai | Shandong | * | |||||
Shaoguan | Guangdong | Weihai | Shandong | * | |||||
Zhanjiang | Guangdong | * | Zibo | Shandong | |||||
Zhuhai | Guangdong | * | Linyi | Shandong | |||||
Qinhuangdao | Hebei | * | Rizhao | Shandong | |||||
Tangshan | Hebei | Nanchong | Sichuan | ||||||
Baoding | Hebei | Mianyang | Sichuan | ||||||
Pingdingshan | Henan | Jinhua | Zhejiang | ||||||
Luoyang | Henan | Huzhou | Zhejiang | ||||||
Changde | Hunan | Jiaxing | Zhejiang | ||||||
Yueyang | Hunan | Shaoxing | Zhejiang | ||||||
Nantong | Jiangsu | * | Taizhou | Zhejiang | |||||
List of Province by Regions
Eastern region (11 provinces): Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan;
Central region (8 provinces): Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei and Hunan;
Western region (12 provinces): Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Ningxia, Qinghai, Xinjiang.
Panel B Cap Rates (in percentages) | |||||||
|---|---|---|---|---|---|---|---|
City | Tier | City_RE# | NBS* | City | Tier | City_RE# | NBS* |
Anqing | 2.499 | 3.294 | Ningbo | Tier 2 | 2.613 | 2.548 | |
Baoding | 2.709 | Pingdingshan | 3.268 | 3.563 | |||
Beihai | Tier 2 | 5.173 | 1.730 | Qingdao | Tier 2 | 2.695 | 2.558 |
Beijing | Tier 1 | 2.313 | 2.798 | Qinhuangdao | 2.692 | 3.340 | |
Bengbu | 3.108 | 3.090 | Quanzhou | 3.570 | 3.012 | ||
Changchun | Tier 2 | 4.098 | 3.033 | Rizhao | 2.318 | ||
Changde | 4.055 | 3.061 | Sanya | Tier 2 | 2.759 | 1.762 | |
Changsha | Tier 2 | 3.956 | 2.369 | Shanghai | Tier 1 | 2.335 | 2.709 |
Changzhou | 3.258 | 2.841 | Shantou | 4.622 | |||
Chengdu | Tier 2 | 3.390 | Shaoguan | 4.587 | 2.542 | ||
Chongqing | Tier 2 | 3.619 | 2.968 | Shaoxing | 2.536 | ||
Dalian | Tier 2 | 3.512 | 2.531 | Shenyang | Tier 2 | 3.617 | 2.584 |
Dongguan | 3.936 | Shenzhen | Tier 1 | 2.564 | 3.087 | ||
Foshan | 3.839 | Shijiazhuang | Tier 2 | 2.868 | 2.922 | ||
Fuzhou | Tier 2 | 2.727 | 3.132 | Suzhou | Tier 2 | 2.853 | |
Ganzhou | 3.652 | 4.896 | Taiyuan | Tier 2 | 3.503 | 3.173 | |
Guangzhou | Tier 1 | 2.855 | 2.995 | Taizhou | 2.367 | ||
Guiyang | Tier 2 | 4.589 | 2.431 | Tangshan | 3.674 | 2.634 | |
Haikou | Tier 2 | 4.014 | 1.930 | Tianjin | Tier 2 | 2.640 | 2.412 |
Hangzhou | Tier 2 | 2.405 | 2.448 | Tongling | 2.860 | ||
Harbin | Tier 2 | 4.518 | 2.768 | Urumqi | Tier 2 | 3.461 | 3.049 |
Hefei | Tier 2 | 2.946 | 3.385 | Weifang | 2.949 | ||
Hohhot | Tier 2 | 3.594 | 3.820 | Weihai | 3.052 | ||
Huizhou | 4.296 | 2.494 | Wenzhou | Tier 2 | 1.902 | 2.595 | |
Huzhou | 3.039 | Wuhan | Tier 2 | 3.385 | 3.203 | ||
Jiaxing | 3.145 | Wuhu | 3.037 | ||||
Jilin | 3.874 | 2.891 | Wuxi | Tier 2 | 3.147 | 2.607 | |
Jinan | Tier 2 | 2.879 | 3.199 | Xiamen | Tier 2 | 2.269 | 3.244 |
Jinhua | 2.065 | 2.767 | Xian | Tier 2 | 3.942 | 2.583 | |
Jiujiang | 3.402 | 3.820 | Xining | Tier 2 | 4.364 | 2.919 | |
Kunming | Tier 2 | 3.272 | 3.132 | Xuzhou | 2.820 | 3.291 | |
Lanzhou | Tier 2 | 3.480 | 3.233 | Yancheng | 3.084 | ||
Linyi | 3.232 | Yangzhou | 2.733 | ||||
Luoyang | 3.719 | 2.705 | Yantai | 3.105 | 2.860 | ||
Mianyang | 3.404 | Yinchuan | Tier 2 | 3.592 | 2.978 | ||
Nanchang | Tier 2 | 2.797 | 2.748 | Yueyang | 4.082 | 2.810 | |
Nanchong | 3.045 | 15.469 | Zhanjiang | 4.323 | 2.451 | ||
Nanjing | Tier 2 | 2.361 | 2.877 | Zhengzhou | Tier 2 | 3.364 | 3.267 |
Nanning | Tier 2 | 4.084 | 2.381 | Zhuhai | 2.967 | ||
Nantong | 2.614 | Zibo | 2.776 | ||||
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Lai, R., Van Order, R.A. A Tale of Two Countries: Comparing the US and Chinese Housing Markets. J Real Estate Finan Econ 61, 505–547 (2020). https://doi.org/10.1007/s11146-018-9670-3
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DOI: https://doi.org/10.1007/s11146-018-9670-3