Annex: Background on China’s Residential Real Estate Statistics
China produces a wide range of data on the residential real estate sector, both from government sources—such as the NBS, the National Development Research Center (NDRC), and local housing bureaus (FangGuanJu)—and private entities such as SouFun that conduct surveys or collect data themselves.20
Bian, T.Y., and P. Gete, 2015, “What Drives Housing Dynamics in China? A Sign Restrictions VAR Approach”, NBER Working Paper 2015 (Cambridge, Massachusetts: MIT Press).
Berkelmans, L. and H. Wang, 2012, “Chinese Urban Residential Construction to 2040,” Reserve Bank of Australia, Research Discussion Paper 2012–04.
Gao, B., W. Wang, and X. Li, 2013, Expectation, Income Inequality and the Puzzle of City’s Housing Price to Rent Scissors in China, Economic Research Journal, Vol. 6.
Grenadier, S. R., 1996, “The Strategic Exercise of Options: Development Sascades and Overbuilding in Real Estate Markets,” Journal of Finance, Vol. 51, No. 5, pp. 1653–79.
Huang, Z., C. Wu, and X. Du, 2008, “Real Estate Investment and Economic Growth: An Analysis of National and Regional Panel Data,” Finance & Trade Economics, Vol. 8.
Li X., G. Hong, L. Huang, 2013, “The Mystery of Land Finance Growth in China: Tax-Sharing Reform, Strategic Interaction of Land Finance,” China Economic Quarterly, Vol. 12, No. 4.
Liang, Y., T. Gao, and S. He, 2006, “An Empirical Analysis of Harmonious Development between the Real Estate Industry and the National Economy in Transitional China,” Social Sciences in China, Vol. 3.
Lv W., and Liu C., 2012, “Fiscal Expenditure, Land Finance and Housing Price Bubble: Measurement and Empirical Study Based on Provincial Panel Data,” Finance & Trade Economics, Vol. 12.
Shen, Y., and H. Liu, 2004, “Housing Prices and Economic Fundamentals: A Cross City Analysis of China for 1995–2002,” Economic Research Journal, Vol. 6.
Wu, J., J. Gyourko, and Y. Deng, 2012, “Evaluating Conditions in Major Chinese Housing Market,” Regional Science and Urban Economics, Vol. 42, pp. 531–43.
Zheng, S., and Z. Shi, 2011, “The Land and Housing Markets in the Context of ‘Land Finance’: an Analysis of Local Government Behaviors,” Social Sciences in Guangdong, Vol. 2.
We thank Soufun for excellent data provision and assistance. We are also grateful for comments from Steven Barnett, Jun Ma, Bin Zhang, Zhongli Yin, and seminar participants at the International Monetary Fund, People’s Bank of China, Chinese Academy of Social Sciences, and Shanghai Economics and Finance University.
Real estate investment here is based on real estate investment published by the National Bureau of Statistics. See the annex for a description. Authors’ estimates of real estate gross fixed capital formation (GFCF) are also described in the annex. Hung, Wu, and Du (2008) also estimate that the growth contribution of real estate investment is more than 10 percent.
See detailed data source descriptions in the annex.
Chinese cities are generally grouped into four categories: Tier I cities include Beijing, Shanghai, Guangzhou, and Shenzhen; Tier II cities include Beihai, Changchun, Changsha, Chengdu, Chongqing, Dalian, Fuzhou, Guiyang, Haikou, Hangzhou, Harbin, Hefei, Huhhot, Jinan, Kunming, Lanzhou, Nanchang, Nanjing, Nanning, Ningbo, Qingdao, Sanya, Shenyang, Shijiazhuang, Suzhou, Taiyuan, Tianjin, Urumqi, Wenzhou, Wuhan, Wuxi, Xiamen, Xi’an, Yinchuan, and Zhengzhou; other small and medium cities are grouped into Tier III or IV cities.
Based on FangGuanJu data, which contain all Tier I cities, most Tier II cities, but only 50 to 60 Tier III/IV cities.
FangGuanJu data in general are better indicators but have some shortcomings as well. Developers need to register first at FangGuanJu for property sale. Developers may have incentives to arrange registrations to suggest higher sales to boost property price.
An estimate of the per capita housing stock across cities is constructed using the stock data available from NBS and the flow of floor space sold from SouFun for the past few years after NBS data were discontinued.
A caveat is in order. The denominator represents the population holding a household residency (hukou), such that the ratio is likely to be overestimated for larger coastal cities (that attract migrants) and underestimated for the rest (from which migrants typically originate). Therefore, the actual oversupply in some Tier III and IV cities may be even more severe than the results suggest.
Only national mortgage rates are available without city-level data. We have used it as an explanatory variable and do not find the coefficient statistically significant in the demand equation.
Data consist of 255 prefecture-level cities with close to 2,000 observations from both Tier I and Tier III/IV cities.
Potential endogeneity may arise given that property prices are included as an explanatory variable, which itself may be driven by the per capita measure of the housing stock. However, estimates will still be consistent as long as the regression represents a long-term equilibrium relationship among nonstationary series. We do not formally test for stationarity and cointegration among the series given short time series and small power of the tests.
Other specifications (not shown in Table 1) include interaction terms of city-tier dummy with residential prices and urbanization rate. Relative to Tier II cities, smaller cities tend to have a greater negative impact on demand from higher residential prices, while higher prices tend to increase demand in Tier I cities, largely reflecting an expectation that future residential prices will increase. The effects of the urbanization rate on housing are less monotonic, but fall within the expectation that the effects vary across city tiers. Higher urbanization rates tend to depress demand in smaller cities, while increasing demand in larger ones.
A robustness check was also performed to forecast demand using a regression with the floor space sold indicator as the dependent variable. Though the indicator is more volatile, the regression based on floor space sold directly provides a flow measure that is comparable with floor space starts. It is also a general specification. Results are fairly similar given the common use of explanatory variables such as household income.
The scenario analysis in the following section provides an illustrative scenario of stronger real estate demand, which could also be interpreted as more accommodative policies.
The demolition rate is similar to depreciation. The forecast of excess supply is not sensitive to a moderate change in the assumption. Considering the current housing stock, the demolition pace could decelerate in the future, which will reduce the upgrading housing demand.
The cumulative floor space completed was higher than floor space sold before the global financial crisis, partly reflecting the opening up of residential real estate markets. Discussion with real estate developers suggest that the measure of floor space completed does not fully reflect supply conditions.
The local housing administrative bureaus (Fangguanju) are city-level government agencies in charge of the real estate market in the city, under the Ministry of Housing and Urban-Rural Development. The bureaus execute and take charge of the registration of all real estate sales, leases, mortgages, and transfers.