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Chapter 8. The Spillover Effects of a Downturn in China’s Real Estate Investment

Author(s):
Anoop Singh, Malhar Nabar, and Papa N'Diaye
Published Date:
November 2013
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Information about Asia and the Pacific Asia y el Pacífico
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Author(s)
Ashvin Ahuja and Alla Myrvoda 

This chapter assesses the impact of a downturn in China’s real estate investment on economic activity in China and among its trading partners. The analysis finds that a 1 percent decline in China’s real estate investment would shave about 0.1 percent off China’s real GDP within the first year, with negative spillovers to China’s G20 trading partners that would cause global output to decline by roughly 0.05 percent from baseline. Japan, the Republic of Korea, and Germany would be among the hardest hit. In that event, commodity prices, especially metal prices, could fall by as much as 0.8–2.2 percent below baseline one year after the shock.

Introduction

Real estate investment accounts for a quarter of total fixed asset investment (FAI) in China (Figure 8.1). It has been growing at about 30 percent per year during 2010–11 (Figure 8.2). The relatively new private property market in China has always been susceptible to excessive price growth, requiring intervention by the authorities over the years. The underlying structural features of the economy, namely low real interest rates in a high-growth environment, the underdeveloped financial system (offering few alternative assets), and a closed capital account, foster overinvestment in real estate and create an inherent tendency toward bubbles in the property market, posing risks to market sustainability and financial stability.

Figure 8.1Fixed Asset Investment by Industry, 2011

(percent of total)

Source: CEIC Data.

Figure 8.2Property Price and Real Estate Investment

(year-over-year growth)

Sources: CEIC Data Company Ltd.; and IMF staff estimates.

To contain real estate bubbles, the authorities rely largely on quantity-based tools, the effectiveness of which tends to erode as more transactions are intermediated outside the banking system, requiring more potent policy responses. In the most recent property boom episode, which started about mid-2009, the authorities escalated their response with restrictions on second and third home purchases in larger cities and credit limits on property developers. Thus far, the authorities appear to have succeeded in curbing market exuberance while maintaining robust investment growth, chiefly through an expansion of social housing programs and a selective easing of financial conditions for first-time home buyers (Figure 8.3). Nevertheless, developers’ financial conditions are deteriorating, and there is a tail risk that policy overtightening could turn near-term price expectation decidedly negative as high inventory-to-sales ratios compress developers’ profitability further, leading to a collapse in real estate investment.

Figure 8.3Property Prices

(year-over-year growth)

Source: CEIC Data; Soufun; and IMF staff calculations.

The risk to growth and financial stability of a collapse in real estate investment in China is high, based on the expected economic repercussion should that event come to pass. The analysis based on China’s input-output data shows that the real-estate-dependent construction industry, which accounts for 7 percent of GDP, creates significant final demand in other domestic sectors; that is, it has among the highest degrees of backward linkage, particularly to mining, manufacturing of construction material, metal and mineral products, machinery and equipment, consumer goods, as well as real estate services (Figure 8.4). Moreover, real estate is the principal source of collateral for external financing of private and state-owned enterprises as well as of local government investment projects, and other economic activities. As a result, a decline in real estate investment has the potential to disrupt the production chain throughout China’s economy and generate spillovers to G20 trading partners.

Figure 8.4The Construction Industry’s Backward Linkages to Selected Contributors

Sources: CEIC Data Company Ltd.; OECD; and IMF staff estimates.

Note: Bars for each industry correspond to data for (starting with lowest) 1995, 2000, 2005, and 2007.

The real estate sector’s extensive industrial and financial linkages make it a special type of economic activity, especially in economies in which the credit creation process relies primarily on collateral, like in China. As a result, the impact on economic activity of a collapse in real estate investment in China—though a low-probability event—would be sizable, with large spillovers to a number of China’s trading partners. Using a two-region factor-augmented vector autoregression (VAR) model that allows for interaction between China and the rest of the G20 economies, the analysis in this chapter finds that a 1 percent decline in China’s real estate investment would result in a decline of about 0.1 percent of China’s real GDP within the first year, with negative impacts on China’s G20 trading partners that would cause global output to decline by roughly 0.05 percent from baseline. Japan, Korea, and Germany would be among the hardest hit. In that event, commodity prices, especially metal prices, could fall by as much as 0.8–2.2 percent below baseline one year after the shock.

Measuring The Spillover Effects

This chapter uses a factor-augmented VAR (FAVAR) approach pioneered by Bernanke, Boivin, and Eliasz (2005) to gauge the domestic and global spillovers of a slowdown in China’s real estate investment in the event of a sharp property market correction. Following Boivin and Giannoni (2008), the FAVAR framework is extended into a two-region model that allows China to interact with the rest of the world (represented in this experiment by the other G20 economies). The analysis captures the feedback from China to the rest of the world, and vice versa, over time. It also captures the spillover effects between the rest of the G20 economies from a specific event originated in China.

The fact that market participants monitor hundreds of economic variables in their decision-making process provides motivation for conditioning the analysis of their decisions on a rich information set. The FAVAR framework extracts information from the rich data set to gauge the impact of particular forces that may not be directly observable. These “forces” are treated as latent common components, which are interrelated, and their impacts on economic variables are traced through impulse response functions. Accounting for unobserved variables provides a better chance that findings based on spurious association can be avoided.1 The data set is a balanced panel of 390 monthly time series from the G20 stretching from January, 2000, to September, 2011, with 68 China variables and 322 from the rest of the world (Table 8.1). The sample contains at least one full cycle of real estate investment in China. It covers the period since China entered the World Trade Organization and became increasingly integrated with the world economy.

Table 8.1Data Set Description
DataChina (68 variables)G20 Excluding China (322 variables)
Real EconomyIndustrial production, Gross value added, Investment, Consumption, Floor space, Land area purchased/developedIndustrial production
LaborUrban employmentEmployment (total/nonfarm)
FinancialM2, Credit, Short-term interest rates, Shanghai Composite, US$/RMBM2, Credit, Short-term interest rates, Treasury bond spreads, Stock market indices, US$/National currency
TradeExports (components), Imports (components), Trade balanceExports, Exports to China, Imports, Imports from China, Trade balance
PricesConsumer price index, Producer price index, House price, Commodity prices (local)Consumer price index, Producer price index, Terms of trade, Commodity prices
Source: IMF staff.Note: M2 = broad money.

The experiment assumes an exogenous, temporary, one standard deviation growth shock to China’s real estate investment. The shock dampens within a few months and dissipates fully after about 36 months. Specifically, this is a one-time, 49 percentage point (seasonally adjusted, annualized) drop in real estate investment growth that largely reverts to trend growth within four to five months.2 Although this is a temporary, negative growth shock, the decline in real estate investment level is permanent. The shock is approximately equivalent to a 2 percent drop from baseline in the real estate investment level after 12 months. The analysis does not assume any policy response beyond that which was already in the sample.

Figures 8.58.15 report 24-month peak impacts (with error bands) following a one standard deviation shock to real estate investment. Impacts on levels 12 months after the shock, in percent below baseline, are also derived and reported for comparison in Tables 8.28.5.

Figure 8.5China: Peak Impact on Investment in Industry

(seasonally adjusted annual rate)

Source: IMF staff estimations.

Note: Impact of a one standard deviation shock to China real estate investment. FAI = Fixed asset investment.

Figure 8.6China: Peak Impact on Exports and Imports

(seasonally adjusted annual rate)

Source: IMF staff estimations.

Note: Impact of a one standard deviation shock to China real estate investment.

Figure 8.7China: Peak Impact on Macroeconomic Indicators

(seasonally adjusted annual rate)

Source: IMF staff estimations.

Note: Impact of a one standard deviation shock to China real estate investment. CPI = Consumer price index.

Figure 8.8China: Impact on Property Market

(seasonally adjusted annual rate)

Source: IMF staff estimations.

Note: Impact of a one standard deviation shock to China real estate investment.

Figure 8.9Peak Impact on Industrial Production

(seasonally adjusted annual rate)

Source: IMF staff estimations.

Note: Impact of a one standard deviation shock to China real estate investment. * Canada’s economic activity is represented by monthly real GDP Index, all industries.

Figure 8.10Implied Peak Impact on Real GDP

(seasonally adjusted annual rate)

Source: IMF staff estimations.

Note: Impact of a one standard deviation shock to China real estate investment.

Figure 8.11Peak Impact on Exports to China

(seasonally adjusted annual rate)

Source: IMF staff estimations.

Note: Impact of a one standard deviation shock to China real estate investment.

Figure 8.12Peak Impact on Stock Market Index

(seasonally adjusted annual rate)

Source: IMF staff estimations.

Note: Impact of a one standard deviation shock to China real estate investment.

Figure 8.13Peak Impact on Sovereign Bond Spreads

(12-month cumulative)

Source: IMF staff estimations.

Note: Impact of a one standard deviation shock to China real estate investment.

Figure 8.14Peak Impact on World Prices

(seasonally adjusted annual rate)

Source: IMF staff estimations.

Note: Impact of a one standard deviation shock to China real estate investment. CPI = Consumer price index.

Figure 8.15Peak Impact on World Metal and Rubber Prices

(seasonally adjusted annual rate)

Source: IMF staff estimations.

Note: Impact of a one standard deviation shock to China real estate investment.

Table 8.2Impacts on Selected China Indicators of a Decline in China’s Real Estate Investment
China IndicatorsImpact

(percent, year-over-year)
Gross value added, real0.1
GDP, real0.1
Retail sales, real0.1
Exports0.7
Imports0.8
Total FAI0.4
Residential property
Price0.7
Floor space sold1.5
Source: IMF staff estimations.Note: Impacts one year after a 1 percent exogenous decline in China’s real estate investment. A one standard deviation decline in growth is equivalent to a 2 percent decline in real estate investment levels from baseline. FAI – Fixed asset investment.
Table 8.3Impacts on Selected Economic Activity Indicators of a Decline in China’s Real Estate Investment(percent, year-over-year)
World IndicatorIndustrial ProductionReal GDP
Argentina0.520.10
Australia10.010.00
Brazil0.280.05
Canada20.060.06
China30.120.10
France0.150.02
Germany0.640.12
India0.270.05
Indonesia0.020.01
Italy0.470.08
Japan0.500.11
Mexico0.320.08
Russian Federation0.230.05
Saudi Arabia0.080.02
South Africa0.290.04
Korea, Rep. of0.190.06
Turkey0.460.10
United Kingdom0.080.01
United States0.200.03
European Union0.170.03
PPP-weighted average0.06
Source: IMF staff estimations.Note: Impacts one year after a 1 percent exogenous decline in China’s real estate investment on selected economic activity indicators. A one standard deviation decline in growth is equivalent to a 2 percent decline in real estate investment levels from baseline. PPP = purchasing-power parity.
Table 8.4Impacts on Trade Indicators of a Decline in China’s Real Estate Investment(percent, year-on-year)
Trade IndicatorTotal ImportsTotal Exports
Argentina2.230.38
Australia0.730.21
Brazil0.970.69
Canada0.900.85
China0.780.68
France0.750.88
Germany0.740.81
India0.510.95
Indonesia0.000.82
Italy0.981.02
Japan0.830.64
Mexico0.910.93
Russian Federation0.810.73
Saudi Arabia0.451.00
South Africa0.840.20
Korea, Rep. of0.650.78
Turkey0.940.47
United Kingdom0.920.94
United States0.900.61
European Union0.830.86
Weighted average0.8210.762
Source: IMF staff estimations.Note: Impacts one year after a 1 percent exogenous decline in China’s real estate investment on selected trade indicators. A one standard deviation decline in growth is equivalent to a 2 percent decline in real estate investment levels from baseline.
Table 8.5Impacts on Selected Commodity Prices of a Decline in China’s Real Estate Investment
World PricesImpact (percent)
Metals2.7
Nonfuel primary commodities1.3
Zinc4.3
Nickel3.7
Lead3.2
Copper3.1
Iron ore1.6
Aluminum2.1
Rubber1.6
Silver1.5
Gold0.4
Source: IMF staff estimations.Note: Impacts one year after a one standard deviation exogenous decline in China’s real estate investment on selected commodity prices. A one standard deviation decline in growth is equivalent to a 2 percent decline in real estate investment levels form baseline.

Domestic Feedback

A rapid slowdown in growth in real estate investment would reverberate across the economy, lowering investment in a broad range of sectors. Given the strong backward linkages to other industries, especially manufacturing of construction material, metal and mineral products, and machinery and equipment, a temporary, one standard deviation decline in real estate investment growth would cause investment in the manufacturing and heavy secondary industries to drop by as much as 1½ percentage points within the first year. (Whether a slowdown would occur in investment growth in primary industry, which contains mining, is unclear [Figure 8.5].) This result translates into a total FAI decline of approximately 0.8 percent from the baseline level 12 months after the shock (see Table 8.2).

Other components of demand respond in a consistent fashion. Export growth, particularly of manufacturing exports, would fall by about 2¼ percentage points mainly from diminishing trading partners’ demand (Figure 8.6). The deterioration in domestic demand and weaker export growth would bring import growth down by roughly 5¾ percentage points at peak impact. Equivalently, exports and imports would fall by about 1.4 and 1.6 percent, respectively, below baseline levels 12 months after the shock (see Table 8.2). A large fall in imports also results from a significant share of processing trade in total trade. More important, the strong import responses reflect robust linkages of real estate activity to domestic industries that require inputs from abroad, namely manufacturing of construction material, mineral and metal products, as well as machinery and equipment.3 China’s real effective exchange rate (REER) and the bilateral RMB/US$ exchange rate do not seem to help cushion exports in a meaningful way even though the rate of appreciation (depreciation) appears to slow down (accelerate) slightly and lasts about two or three quarters.

Consumption would be dampened as income and wealth expansion (including house price appreciation and stock market valuation) slow down. Real retail sales would dip by 0.5 percent at peak impact (Figure 8.7). The end result would be a drop in total industrial value added and output. All in all, industrial gross value added growth would fall by about 0.4 percentage point at peak, which is consistent with a 0.3 percentage point decline in real GDP on an annualized basis.4 The impact would be felt almost immediately and would start to dissipate after four quarters. These events would translate into a decline of about 0.3 and 0.2 percent below baseline levels for gross value added and GDP, respectively, one year out (Table 8.2).

Consumer price index inflation would fall slightly, reflecting modest easing of price pressures as excess capacity diminishes along with demand growth.5 The overall growth slowdown is reflected in the stock market as well as in labor market conditions as employment growth slows in the urban areas of China.

Worsened income and wealth would have an important bearing on the overall and residential property markets. As demand conditions deteriorate, property market transaction volume and prices would drop. For example, residential transactions volume growth would drop by about 7 percentage points at peak (Figure 8.8). One year out, residential real estate transaction volume would fall 3 percent below baseline (Table 8.2). House prices, however, would be cushioned by dwindling current and future housing supply (caused by shrinking housing starts). Measured using official house price statistics, which may understate residential property price inflation and deflation, house price growth would decline by about 3 percentage points at peak, or 1.5 percent below baseline 12 months after impact (Table 8.2). Meanwhile, inflation in domestic prices of metal required for construction activity, such as aluminum, electrolyzed copper, and zinc, would be shaved by 1¼, 5, and 7⅓ percentage points, respectively. Deterioration in the property market climate is expected to have implications for financial institutions’ balance sheets and financial stability as well. Nevertheless, without sufficient financial indicators at monthly frequency, the model cannot uncover the relationships between a property market slowdown and financial stability indicators.6

Global Spillover

A temporary shock to China’s real estate investment growth would have spillover implications around the world, with the impacts on G20 economies lasting approximately four to five quarters. In this exercise, the approximate impact on GDP growth would vary with the size of the industrial-production-to-GDP ratio in each economy (Figures 8.9 and 8.10).7 The implied peak impact on purchasing-power-parity-weighted G20 GDP growth is −0.2 percentage point, which translates to about 0.1 percent below baseline 12 months after the shock originated in China (Table 8.3). Overall, capital goods manufacturers that have sizable direct exposures to China through exports to China as a percentage of own GDP and that are highly integrated with the rest of the G20—therefore sharing adverse feedback from a negative shock in China with other trading partners, such as Germany, Japan, and Korea—would see more of the impact to industrial production and GDP. The results also show that global trade activity would decline (total exports and total imports for every G20 economy would weaken), which suggests that economies, such as Germany and Japan, that derive significant benefit from global trade expansion and have deepened links via supply chain countries during the past decade, should be more hard hit in the second round (Table 8.4). The impact on Korea’s GDP peaks within the first two quarters and fades away more quickly, which is consistent with Korea’s large direct exposure to China, but second-round effects through supply chain countries are smaller than in Japan and Germany (also see Riad, Asmundson, and Saito, 2012).

Trade expansion with China and overall global trade would also slow as global and China demand growth weakens (Table 8.4). For the United Kingdom and India, exports to China would bear the brunt of the impact, but because they are not important components of final demand in these economies, the impact on economic activity would be relatively moderate.8 Commodities exporters to China, such as Australia, Brazil, and Canada, would also experience nonnegligible spillover effects on export growth (Figure 8.11).9 Australia’s relatively large direct exposure to China would suggest a larger direct impact, but other forces seem to blunt the effect on Australia’s industrial production, for example, the Australian dollar exchange rate works as a shock absorber. Nevertheless, other indicators, such as employment growth and total import growth (not shown here), point to a slowdown in Australia’s economic activity. The impact on Indonesia’s exports would likely come through China’s coal demand. Because coal exports to China have risen sharply since late in the first decade of the 2000s, the impact on Indonesia’s real GDP could be larger today than shown in Table 8.3.

The growth spillover effects are also reflected in asset prices and valuation. Specifically, the impact on financial wealth generation, as represented by the expansion of stock market indexes in the G20 economies, would be tangible and would remain for as long as four to five quarters (Figure 8.12). Related to this, a general decline in sovereign bond spreads (cumulative over the first 12 months after impact; Figure 8.13) seems to signal concerns about future global growth, in accord with the immediate impacts on industrial production shown earlier. In the United States’ case, the initial flattening of the yield curve is reversed about two quarters after the shock, which suggests that U.S. recovery prospects could be faster than those of other G20 economies. The result for Australia is consistent with the estimated impact on that country’s industrial production.

A global growth slowdown and a drop in China’s demand for base metal imports, initiated by a China real estate investment decline, could lead to a drop in iron ore, aluminum, copper, lead, nickel, and zinc price growth of between 2¾ and 8 percentage points (Figures 8.14 and 8.15). The impact on overall metal prices could last four quarters—up to five or six quarters for lead and zinc, possibly as the result of a weaker supply response. This result is equivalent to a decline in price levels of about ½–4½ percent below baseline levels, one year out (Table 8.5). How crude oil prices would be affected is unclear in this exercise—the impulse responses show a drop in crude price growth, with a peak about three quarters after impact, but they are not statistically significant. Even as nonfuel primary commodity price inflation retreats, the impact on global inflation appears modest.

Conclusion

Real estate investment accounts for a quarter of total FAI in China. The impact on economic activity of a hypothetical collapse in real estate investment in China is sizable, with large spillovers to a number of China’s trading partners. A 1 percent decline in China’s real estate investment would shave about 0.1 percent off China’s real GDP within the first year, with negative spillovers on China’s G20 trading partners that would cause global output to decline by roughly 0.06 percent from baseline. Japan, Korea, and Germany would be among the hardest hit. In that event, commodity prices, especially metal prices, could fall by as much as 0.8–2.2 percent below baseline one year after the shock.

Overall, capital goods manufacturers that have sizable direct exposures to China (especially Japan and Korea) and are highly integrated with the rest of the G20, therefore sharing the adverse feedback from a negative shock in China with other trading partners, would experience larger declines in industrial production and GDP. Worsened global growth prospects would be reflected in asset prices and sovereign bond spreads. In that event, commodity prices, especially construction-related metal prices, would also fall.

The sample contains at least one full cycle of real estate investment and property market data in China, and covers the period of China’s increasing integration with the world economy. Strictly from a statistical point of view, this relatively short sample is expected to make statistical relationships harder to detect and will be an important constraint on the richness of the model. Nevertheless, as the results suggest, there is still sufficient statistical information in the sample to deliver useful insights about China’s interaction with the world in the recent past. It must be stressed, however, that China is more important to the global economy today than the sample would suggest, and a real estate investment bust in China is not likely to be a linear event as measured by the model. The impact on G20 trading partners, and therefore global growth today, could be larger than reported here.

References

    Ahuja, A., and A.Myrvoda,2012, “The Spillover Effects of a Downturn in China’s Real Estate Investment,IMF Working Paper No. 12/266 (Washington: International Monetary Fund).

    Bernanke, B., J.Boivin, and P.Eliasz,2005, “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach,Quarterly Journal of Economics, Vol. 120, No.1, pp. 387422.

    Boivin, J., and M.Giannoni,2008, “Global Forces and Monetary Policy Effectiveness,NBER Working Paper No. 13736 (Cambridge, Massachusetts: National Bureau of Economic Research).

    International Monetary Fund, 2012, “People’s Republic of China: Staff Report for the 2012 Article IV Consultation,IMF Country Report No. 12/195 (Washington).

    Riad, N., I.Asmundson, and M.Saito,2012, “China’s Trade Balance Adjustment: Spillover Effects,2012 Spillover Report–Background Paper (Washington: International Monetary Fund).

A more detailed description of the model and estimation strategy can be found in Ahuja and Myrvoda (2012).

A one standard deviation shock is equivalent to 3 percentage points in month-over-month, seasonally adjusted, growth rates.

The results are consistent with the input-output analysis, not shown in this chapter, that shows that machinery and equipment manufacturing as well as mining have the highest import coefficients, followed by chemical industry.

A 1 percentage point decline in real industrial value added growth is consistent with a decline in real GDP growth for China of about 0.8 percentage point.

For further discussion on excess capacity issues and their relationship with the investment drive in China, see IMF (2012).

Data availability aside, financial exposures to the property sector are likely to be larger than official data suggest, considering the increasing prominence of off-balance-sheet activities at banks, trust company lending, the shadow banking system, and unobserved intercompany lending, which could be property related.

Industrial production is defined differently from country to country. The Organization for Economic Cooperation and Development definition includes production in mining, manufacturing, and public utilities (electricity, gas, and water), but excludes construction.

Exports to China are mostly in machinery, equipment, and industrial supplies for the United Kingdom and mineral commodities and primary metal products for India.

Canada’s exports to China are more diversified in mineral and manufactured commodities.

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