Chapter 5 Empirical Results
5.4 Vector Error Correction Model
results of VECM analysis are reported in Table 5-4, Table 5-5 and Table 5-6.
From Table 5-4, we can learn that the current BJ is affected by the first and second lag of itself. Furthermore, BJ is also influenced by the first lag of INT and the second lag of LOAN. Moreover, BJ is negatively related to itself. With the increase of the housing bubbles in Beijing, the investors predict that the bubbles are in the danger of collapse and prefer to resale the houses. Therefore, the housing bubbles in Beijing can curb themselves when they keep rising. And INT is positively related to BJ at the first lag, which is inconsistent with the general economic theory. This is because the influence of interest rates is reflected more rapidly in fundamental value than in market price. The impact of LOAN on BJ is positive. Once the banks tighten the credit quota which makes the financial market liquidity weakened, the investors’
capital chains are affected. It should be noted that the impact of INCOME_BJ on BJ is not significant. Combined with the influence of LOAN, we can infer that the housing bubbles in Beijing rely more on the financial activities than real economy.
In the adjusting speed, the significant parameter denoted as CointEq1 or CointEqe2 represents the short-run deviations from equilibrium. When the series deviates the long-run equilibrium, they will be adjusted towards the equilibrium by a specific rate. However, as the coefficient of CointEq1 in the VECM analysis on BJ is not significant, it implies the deviation will not be adjusted immediately. Nevertheless, the deviation will be finally adjusted.
As reported in Table 5-5, the current SH is strongly affected by not only the first lag of itself but also the first lag of LOAN. Similar with BJ, SH is negatively related to itself. The rational investors in Shanghai are also afraid that the ballooning housing bubbles are going to collapse. In addition, the impact of LOAN on SH is positive. So it seems that the easy credit policy also encourages overinvestment in Shanghai’s real estate market. The coefficients of INCOME_SH are not significant. Clearly, the
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growth rate of INCOME_SH was far lower than that of housing bubbles. Although the coefficients of INT are also not significant, the directions of INT in Shanghai are the same as the ones in Beijing. In all probability, the interest rates are pretty low for the investors in Shanghai, which leads to a large-scale financing campaign and the inflating housing bubbles. At last, the CointEq1 is also not significant in the VECM analysis on SH. But over time, SH will return to the long-run equilibrium value.
As shown in Table 5-6, the current GZ is strongly affected by the first lag of itself as well as the first lag of INCOME_GZ. Besides, the current GZ is also affected by not only the first and second lag of INT but also the second lag of LOAN. The impact of GZ on itself is negative, which is the same situation as BJ and SH. In Guangzhou’s real estate market, the investors would also intend to exit the market when the housing bubbles keep increasing. And INCOME_GZ is positively related to GZ. It implies that Guangzhou’s income growth can chase the housing bubbles’. People can probably afford the houses in Guangzhou. Specially, the impact of INT on GZ is positive first and then turns negative. When the interest rates increase, the fundamental values drop immediately and the market prices give no response temporarily. Afterwards, the increase of investment cost depresses the speculators from holding houses. LOAN is positively related to GZ, too. Thus, the tight credit policies have a positive effect on curbing the housing bubbles. Moreover, the coefficients of CointEq1 and CointEq2 are significant in the VECM analysis on GZ. And the adjusting speeds are 24% and 0.13% respectively.
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Table 5- 4 VECM Analysis on INCOME_BJ, LOAN, INT and BJ
Variables D(BJ)
Note: *, ** and *** denote significance at the 10%, 5% and 1% level respectively.
Table 5- 5 VECM Analysis on INCOME_SH, LOAN, INT and SH
Variables D(SH)
Note: *, ** and *** denote significance at the 10%, 5% and 1% level respectively.
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Table 5- 6 VECM Analysis on INCOME_GZ, LOAN, INT and GZ
Variables D(GZ)
D(INCOME_GZ(-1)) 0.001204 ** 2.50782
D(INCOME_GZ(-2)) 0.000263 0.71503
Note: *, ** and *** denote significance at the 10%, 5% and 1% level respectively.
As has been shown, the three factors above contribute to the housing bubbles. It is noteworthy that the low interest rates and high total loans encourage many speculators to invest real estate in Beijing, Shanghai and Guangzhou. Besides, the economic disparities between different cities also lead to the present situation of real estate market in China. numbers. From 2007 to 2012, the population in Beijing had increased by 810,000. In Shanghai and Guangzhou, the population had both expanded by 480,000. The employed population in Beijing increased from 7,981,000 to 10,744,000, expanding by 34.61%. In Shanghai, the employed people rose from 6,578,000 to 9,486,000, an increase of 44.21%. The employed population has expanded by 46.12%, which increased from 2,236,900 to 3,268,500. Since these three large cities attract plenty of extraneous people, we can reasonably infer that there are huge house demands for
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non-investment in Beijing, Shanghai and Guangzhou. In addition, the house purchase restrictions make many potential buyers take a wait-and-see attitude and postpone buying houses. So the house demands for non-investment are accumulated constantly, which also stimulates greater investment demands. But the housing supply which is limited by the urban land cannot meet the vast house demand. In a short time, housing prices would not decline and so do housing bubbles. It is essential to achieve a better distribution of resources and the balanced regional economic developments, rather thanconcentrate resource in the big cities.
Figure 5- 4 The population in Beijing, Shanghai and Guangzhou Source: National Bureau of Statistics of China
Figure 5- 5 The employed population in Beijing, Shanghai and Guangzhou Source: National Bureau of Statistics of China
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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