Annex 1. Residential Real Estate Data from National Bureau of Statistics and Local Housing Bureaus
The National Bureau of Statistics published the Commodity Building Residential Selling Price index, which is based on aggregate sales value divided by the total floor space sold during a period. The index only consists of provincial level data without individual city information, and therefore tend to understate the increase in house prices. In addition, the NBS also released the price indices for 70 major cities for newly-constructed homes, which are most widely used in analyzing China’s residential real estate markets. However, it tends to under-represent the smaller tier-3 and 4 cities. Similar limitations also exist for the aggregate inventory data (unsold floor space) (Chivakul and others 2015).
In light of these limitations, the note relies more on the data from local housing bureaus, which are more comprehensive and reflective on the underlying cyclical changes across city-tiers. Local housing bureaus are local government divisions in charge of city-level real estate market under the Ministry of Housing and Urban-Rural Development. The bureaus execute administrative functions and are responsible for the registration of real estate sales, leases, mortgages, and transfers. As a result, their data tend to be more accurate based on actual transactions for purchases and sales of newly built residential units, while covering a more balanced sample with about 134 cities, grouped into four tiers based on the official definition (4 tier-1 cities, 36 tier-2 cities, and the rest are small tier-3 and 4 cities).
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Chivakul, M., W.R. Lam, X. Liu, W. Maliszewski, and A. Schipke “Understanding Residential Real Estate in China”, IMF Working Paper No.15/84.
Glaeser, E., W. Huang, Y. Ma, and A. Shleifer, 2016, “A Real Estate Boom with Chinese Characteristics”, NBER Working Paper No. 22789.
Huang, X., T. Jin, and J. Zhang, 2016, “Monetary Policy, Hot Money and Housing Price Growth across Chinese Cities,” Harvard University OpenScholar Working Paper.
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.
IMF, 2008, “The Changing Housing Cycle and the Implications for Monetary Policy,” World Economic Outlook, Chapter 3, October 2008.
Shi, Y., 2017, “Real Estate Booms and Endogenous Productivity Growth”, manuscript. Jacome, L. and S. Mitra, 2015, “LTV and DTI: Going Granular,” IMF Working Paper 15/154.
Vitek, F., 2015, “Macrofinancial Analysis in the World Economy: A Panel Dynamic Stochastic General Equilibrium Approach,” IMF Working Paper 15/227.
Wright K., T. Wang, D. Kwok, E. Lee, and E. Cheung, 2016, “UBS Evidence Lab: China Housing Survey—Borrowing from the Future”, UBS Evidence Lab series.
Wu, J., J. Gyourko, Y.H. Deng, 2016, “Evaluating the Risk of Chinese Housing Markets: What we know and what we need to know?” NBER Working Paper No. 21346.
Ding Ding and W. Raphael Lam work for the International Monetary Fund. Xiaoyu Huang and Tao Jin work for the PBC School of Finance, Tsinghua University. We would like to thank James Daniel, Sonali Jain-Chandra, Markus Rodlauer and participants at the IMF-People’s Bank of China seminar for helpful comments, and Yuchen Wu, Xin Xu, and Tlek Zeinullayev for excellent research assistance. The usual disclaimer applies.
See Huang, Wu, and Du (2008). Real estate has strong linkages with upstream industries such as cement and steel sectors and downstream industries such as autos, electronics and furniture.
The real estate sector usually classifies Chinese cities into four tiers. Tier-1 consists of metropolitan cities including Beijing, Shanghai, Shenzhen, and Guangzhou. Tier-2 cities are usually provincial capitals, while Tiers-3 and 4 include smaller cities.
The empirical study by Huang, Jin and Zhang (2016) reveals the periodical behavior of the housing price growth across 70 major Chinese cities.
In September 2015, the minimum down payment for first-home buyers was lowered from 30 per cent to 25 per cent and a further discretionary cut of 5 percentage points was authorized. Minimum down payments on second properties were reduced from 60–70 per cent to 30 per cent over the same period. Benchmark lending rates have been cut by around 165 basis points since late 2014, and the average effective mortgage rate has fallen by more relative to these benchmarks. Property transaction taxes were reduced and targeted subsidies were provided for certain types of home buyers.
The timing and duration of the cycles are based on levels of peaks and troughs of y/y growth in price, sales, and change of inventory ratios. Some judgment is applied as these series are not necessarily synchronized at the timing of different indicators.
Banks’ mortgage rates are usually determined at a discount or premium of 0-15 percent below or above the benchmark lending rate set by the central bank. Banks have the discretion to adjust the discount or premium based on market conditions. Most mortgage contracts have fixed rates for about 10-20 years based on the benchmark lending rates.
Granular data on household debt are not available across household groups (income and age) or regions to provide finer assessment on the soundness household balance sheets. Other alternative indicators such as household debt to income ratio and service capacity are relatively scare without full time series for crosscountry comparison.
Our estimates are based on down payment ratios and real estate sales from 355 bank lending surveys.
National average income may not be the best indicators given the unusually wide distribution of labor and household income in China (see IMF 2017).
The IMF’s Global Macro-Financial Model (Vitek 2015), a structural macro-econometric framework, estimated that a 10 percent reduction of house prices in China driven by a housing risk premium shock can generate a peak output loss of 0.63 percent, considered in isolation. (This estimated peak output loss ranges from 0.40 to 1.05 percent across economies.)
Data on the distribution of mortgage loans by household income and creditworthiness are not available.
The other frequently used tools include the reserve requirement ratio (RRR) and the different mortgage interest rates for first and second homes. Although they can affect housing market through the credit channel, they are generally viewed as a monetary policy instruments.
The loan to value ratios range from 20-35 percent for first home buyers across cities and provinces (more common for top-tier cities) and have varied moderately by about +/- 3-5 percentage points over real estate cycles, subject to a minimum threshold of 20 percent.
In the panel regression, we included lagged variables to control for endogeneity. The results indicate that the current level of ΔLTV does not significantly affect the current level of house prices. Therefore, we can rule out the simultaneous causality by using the current value of house price as a regressor in the logit regression.
Population density in Chinese cities is typically much lower than major American metropolitan areas. For example, the first tier cities have population density varying from 1,000 to 2,000 people per square kilometer in 2010, whereas the top 100 American MSAs (metropolitan statistical area) all have density above 4,000 people per square kilometer (Glaeser et al 2016).