Chapter 5 Empirical Results
5.5 Granger Causality Test
considers the error correction term to determine the lead/lag relationship between China’s housing bubbles and the variables. The results of Granger causality tests are shown from Table 5-7 to Table 5-9.
In Table 5-7, it shows that LOAN Granger causes BJ while BJ do not Granger cause LOAN. This implies that the total loans of financial institutions lead the housing bubbles in Beijing. Generally speaking, the speculators tend to invest in real estate when they can get mortgages easily, which might lead to housing bubbles.
Since the loan scale is regarded as a significant tool for credit policy, we can infer that the housing bubbles in Beijing are affected by the financial institutions’ credit policy.
However, INCOME_BJ and BJ do not Granger cause each other. Likewise, there is no lead/lag relationship between INT and BJ.
From Table 5-8, we can learn that INT and SH do not Granger cause each other.
And there is also no lead/lag relationship between LOAN and SH. It suggests that the housing bubbles in Shanghai are not controlled by the credit policy. It is possible that the investors in Shanghai’s real estate market have adequate free money. As a result, they are able to purchase houses without bank loans. In addition, SH Granger causes INCOME_SH but INCOME_SH do not Granger causes SH. The development of real estate market has promoted Shanghai’s economy, which can create new jobs and increase people’s income.
As reported in Table 5-9, GZ is Granger caused by INCOME_GZ, INT and LOAN while GZ Granger causes INT and LOAN. The per-capita disposable incomes lead the housing bubbles in Guangzhou, which accords with the normal development of real estate market. As people accumulate more wealth, they tend to invest their money in property. Moreover, INT and GZ have the feedback relationship. The feedback relationship also exists between LOAN and GZ. Similar to the case of Beijing, we can easily learn that the housing bubbles in Guangzhou are under the impact of credit policy.
According to the results of Granger causality tests above, we can find inspiration
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for housing policy. In Beijing and Guangzhou, the financial institutions’ credit policy could be the principal mean for addressing the over-heating real estate market.
Because the housing bubbles in Beijing do not concern per-capita incomes, Beijing’s credit policy should be stricter. In Shanghai, since the credit policy has no effect on real estate market, it’s better to focus on establishing some related tax and administrative policies.
Table 5- 7 Granger Causality Test on INCOME_BJ, LOAN, INT and BJ
Dependent Variable Independent Variable
D(BJ) D(INCOME_BJ) D(INT) D(LOAN)
D(BJ) — 4.77433 5.313607 6.780424*
D(INCOME_BJ) 2.333389 — 6.999474* 8.635452**
D(INT) 1.066239 1.34843
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0.537529D(LOAN) 1.588207 0.799552 3.581872
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Note: 1. This study conducted VEC Granger Causality/Block Exogeneity Tests.
2. Figures in the table are Chi-square statistic.
3. *, ** and *** denote significance at the 10%, 5% and 1% level respectively.
Table 5- 8 Granger Causality Test on INCOME_SH, LOAN, INT and SH
Dependent Variable Independent Variable
D(SH) D(INCOME_SH) D(INT) D(LOAN)
D(SH) — 1.301459 3.327519 5.457886
D(INCOME_SH) 9.702212** — 0.244825 3.53238
D(INT) 3.846077 0.424296
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1.226377D(LOAN) 3.126434 3.840389 5.331905
—
Note: 1. This study conducted VEC Granger Causality/Block Exogeneity Tests.
2. Figures in the table are Chi-square statistic.
3. *, ** and *** denote significance at the 10%, 5% and 1% level respectively.
Table 5- 9 Granger Causality Test on INCOME_GZ, LOAN, INT and GZ
Dependent Variable Independent Variable
D(GZ) D(INCOME_GZ) D(INT) D(LOAN)
D(GZ) — 6.425987* 41.68709*** 8.23999**
D(INCOME_GZ) 1.995909 — 4.243666 3.051406
D(INT) 10.42858** 0.54275
—
0.056404D(LOAN) 6.697625* 3.040764 5.531587
—
Note: 1. This study conducted VEC Granger Causality/Block Exogeneity Tests.
2. Figures in the table are Chi-square statistic.
3. *, ** and *** denote significance at the 10%, 5% and 1% level respectively.
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In recent twenty years, China’s economy develops rapidly. Many cities attract a large number of capital and people. At the same time, the demand and supply of real estate in these cities increase. It pushes up the housing prices and even forms housing bubbles. But the over-heating real estate investments do harm to the development of China’s economy. In order to control the housing bubbles, we must find out the driving factors. Therefore, this study intends to measure China’s housing bubbles and explore the factors that may contribute to China’s housing bubbles.
First, this study evaluates the housing bubbles in Beijing, Shanghai and Guangzhou from 2007 to 2012 by comparing fundamental values with market prices.
The fundamental value of real estate is calculated by annual rents and WACC. The results show that the housing bubbles in Beijing, Shanghai and Guangzhou have risen rapidly before the financial crisis in 2008. After the subprime mortgage crisis burst out, foreign investment in the China’s real estate market was withdrawn. It led to a tide of selling houses and made the housing bubbles decreased sharply. Due to the excellent investment environment in Beijing, Shanghai and Guangzhou, the real estate markets attract the speculators again. The speculators brought a lot of hot money and push up the housing bubbles. Temporarily, the house purchase restrictions can only make the housing prices in three cities increase smoothly. But the housing prices cannot return to the acceptable level.
Based on the evaluated housing bubbles situations, this study then applies Cointegration analysis to further explore the factors that may contribute to the housing bubbles. The empirical results show that per-capita disposable income, interest rates of mortgage and total loans of financial institutions are related to China’s housing bubbles.
With the rapid economic development, people’s disposable income increase and
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purchase houses. However, in Beijing and Shanghai, the impact of personal incomes on housing bubbles is not obvious. This demonstrates that the growth of housing bubbles goes far beyond that of personal disposable incomes. Perhaps it is because the home buyers in Beijing and Shanghai are driven more by investment need or chasing demand. In short, the growths of housing bubbles in Beijing and Shanghai depend on the financial perspective，instead of incomes.As long as the central bank tightens credit volume, the housing bubbles in Beijing, Shanghai and Guangzhou begin to reduce. Since 2007 People’s Bank of China and China Banking Regulatory Commission have managed the house mortgage loans. The loan-to-value ratio of the second house cannot exceed 60%. In 2010, the housing policies became stricter. The loan-to-value ratio of the second house cannot be high than 50%. Even in some big cities the house mortgage loan cannot be applied for the third house. This series of house purchase restrictions dampen the speculators' initiatives seriously. The decrease of credit volume makes market liquidity cut down and ultimately affects the investors’ capital chains through a series of transmission mechanism. Then the speculators sell their house to maintain their capital chains, which makes housing prices start to go down. Therefore, the housing bubbles reduce correspondingly.
Since the interest rates of mortgage are raised, the housing bubbles in Beijing and Guangzhou rise at first. This happens because the acceptable capitalization rates increase accordingly when the interest rates become higher. As a result, the fundamental value decrease correspondingly and then the housing bubbles expand.
Afterwards, Guangzhou’s property bubbles drop. The holding cost of houses raise due to the increase of interest rates. However, the housing prices are not influenced by the interest rate temporarily. It is the best time for the speculators to sell their houses.
Once a large number of home owners are selling their houses, the housing prices will be depressed. On the other hand, many people prefer to rent houses due to high cost of loan. Over time, the housing rents increase gradually which results in augmenting the
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fundamental value. The changes of market prices and rents lead to the decrease of housing bubbles. Nevertheless, Shanghai’s housing bubbles are not influenced by the fluctuation of interest rates. Given the characters of Shanghai’s investors, the costs of mortgage might not be a major consideration.
According to Granger causality tests, total loans can serve as a leading indicator of housing bubbles in Beijing. And there is no variable that leads Shanghai’s property bubbles. In Guangzhou, personal incomes, interest rates and total loans lead the volatility of housing bubbles. These conclusions provide marginal contributions in policy making.
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property speculation. The results of Granger causality tests provide some profound suggestions for housing policies to address the over-heating real estate market. The combination of financial policies, fiscal policies and administrative policies works perfect for controlling housing prices.(1) Financial Policies
The low interest rates and easily accessible mortgages encourage speculator to get into real estate market. Thus, the Chinese central bank should implement a prudent monetary policy. It means that money supply and credit volume should be prevented to grow excessively. Furthermore, the banks must carry on the differentiated mortgage policy strictly. Based on the central bank’s benchmark interest rate, the banks should roll out more effective mortgage rates and loan-to-value ratios for different home buyers. Meanwhile, bank loans for property speculation or non-owner occupied housing purchase must be suspended.
(2) Fiscal Policies
The current imperfection of property tax system contributes to the rapidly rising house prices. So the property tax system cries out for reform. The related taxes in real estate transaction might be increased appropriately, especially for speculators. Besides, the property tax should be gradually introduced across the country. The property tax rates should be progressive: Effective tax rate rises with the amount of houses. And further, each city can set its own tax benefit to encourage the housing consumptions for non-investment.
(3) Administrative Policies
The current supply of social houses obviously cannot meet the huge demand. The government should speed up the construction of social housing project. In addition, the sufficient construction funds of social houses must be ensured. It is an effective method to increase social house investment in the fiscal budget. And the related
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authorities should supervise the use of funds and the project schedule so as not to cause any problems. The government should lease social houses instead of selling them, which would make rental cost become lower than that of house purchase. It could transfer redundant demand of house purchase to rental demand, which might promote the healthy development of real estate market.
In addition, the government should reduce the economic disparities between the cities. Big cities could share the resources with small cities. In this way, some metropolitan functions can be moved to minor cities. And the construction of transport facilities between the cities should be accelerated. It is favorable for the resources and population to circulate in the cities, which contributes to the balanced development between the cities.
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6.3 Limitations
According to the literatures above, there could be many factors that affect China’s housing bubbles. Under the specification of selected empirical model, all the data used in this study must have the same frequency. The housing prices and rents that can be obtained are monthly data. But the statistics issued as monthly data are really limited. As a result, this study can only construct the model with limited variables. If the research could be conducted through more variables, the empirical results might be more informative and provide more directions for housing polices.
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