Chapter 3 Research Method and Data Information
3.2 Data Information
interest rates of mortgage and per-capita disposable income in the three cities.
First of all, the real estate industry is capital-intensive. Both the developers and investors are deeply dependent on financing. When the central bank expands the credit scale, the home buyers can get more money from financial institutions easily.
Then the demand for houses increases significantly and the housing prices rise accordingly. As long as the easy credit policies continue, the housing bubbles would constantly accumulate. Thus, this study expects that total loans of financial institutions are positively correlated with the housing bubbles.
Secondly, interest rate is one of the indispensable macro-control means in China.
And in the real estate investment, interest rates of mortgage affect the capital costs which cannot be ignored. If interest rates rise, the carrying costs of houses will rise proportionately. Then the speculators change their minds to sell houses at the appropriate price, instead of hoarding properties. It is likely to reduce housing bubbles.
Hence, it is considered that the increase of mortgage rates might lead to declines in housing bubbles.
Lastly, personal income is one of the indicators of economic development. When the economy develops, people's income should increase, which in turn promotes economic development. Furthermore, the domestic economy and real estate industry should also be complementary to each other. Therefore, this study anticipates that personal incomes have a positive relationship with housing bubbles. However, there are obvious income disparities in different cities. Consequently, we select per-capita disposable incomes in Beijing, Shanghai and Guangzhou respectively.
3.2.2 Data Source
The data employed in this study range from January 2007 to November 2012, 71 samples in total. Because the status of housing bubbles is not published data, we
‧
repayment term of home loan, the lending rate of over 5 years is usually employed as the mortgage rate. The per-capita disposable incomes in the three cities are provided by GTA CSMAR database. Total loans of financial institutions and interest rates of mortgage are reported by People’s Bank of China monthly. However, the original data of disposable incomes is quarterly. Thus, we need to convert the quarterly data into monthly data by Eviews statistical software. Table 3-1 shows the brief information of the variables used in this study.Table 3- 1 The Variable Descriptions
Variable Code Source Time Period Direction
Housing Bubbles in
Income in Beijing INCOME_BJ CSMAR 2007Q1-2012Q4 +
Per-capita Disposable
Income in Shanghai INCOME_SH CSMAR 2007Q1-2012Q4 +
Per-capita Disposable
Income in Guangzhou INCOME_GZ CSMAR 2007Q1-2012Q4 +
Total Loans of
Financial Institutions LOAN People's Bank
of China 2007.1-2012.11 +
Interest Rates of
Mortgage INT People's Bank
of China 2007.1-2012.11 −
3.2.3 Data Analysis
It is helpful for further study to understand the characteristics of the data. The descriptive statistics of variables are shown in Table 3-2, including the mean, median, maximum, minimum and standard deviation. However, the characteristics of the housing bubbles in the three cities will be discussed later because the status of the housing bubbles is going to be calculated in the next chapter. From Table 3-2, INCOME_BJ, INCOME_SH, INCOME_GZ and LOAN have increased a lot during
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
the research period, which accords with China’s economic growth situation. In addition, since interest rates are controlled by the central bank strictly, interest rates of mortgage do not change much certainly.
Table 3- 2 Descriptive Statistics of Variables
Variable INCOME_BJ INCOME_SH INCOME_GZ LOAN INT
Mean 2502.652 2591.438 2472.467 432881.8 0.067
Median 2473.880 2537.386 2424.446 425622.6 0.068
Maximum 3328.044 3818.363 3778.754 666868.3 0.078
Minimum 1767.888 1767.024 1644.325 243944.1 0.059
Std. Dev. 438.053 513.065 516.104 130845.4 0.007
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
Chapter 4
Measure of Housing Bubbles
This chapter consists of three parts. The housing prices and rents in Beijing, Shanghai and Guangzhou from 2007 to 2012 are presented in the first part. The second part introduces the parameters used for calculating fundamental values. And the last part describes the situation of housing bubbles in three cities and analyzes the causes of changes.
4.1 Trends of Housing Prices and Rents
The data of housing prices in three selected cities comes from the monthly market report of China Real Estate Information Corporation (CRIC), as reported in Figure 4-1. As we all see, from January 2007 to November 2012, the housing prices had kept increasing in Beijing, Shanghai and Guangzhou. Beijing’s housing price rose from
￥9446/m2 to ￥21447/m2 while the housing price in Shanghai increased from
￥9548/m2 to ￥21863/m2. Also Guangzhou’s housing price rose from ￥8622/m2 to
￥12821/m2. In 2008, China’s real estate market was influenced by the subprime mortgage crisis and the housing prices fell a little bit. However, after several months, the housing prices perked up again, which meant that the real estate market had got out of the depression.
The housing rents in China’s real estate market are not transparent. So it is quite difficult to obtain the data of housing rents as long as we need. Under this circumstance, we decide to convert the housing rental index into the actual rents.
Firstly, we get the housing rents in November 2012 by CityRE Data. Secondly, the trend of the housing rental index is acquired in the China Real Estate Index System.
At last, according to the data above, we figure out the housing rents in three selected cities which range from January 2007 to November 2012 (as shown in Figure 4-2).
Compared with the housing prices, the rents grew at a slow rate.
‧
Figure 4- 1 The Housing Prices in Beijing, Shanghai and Guangzhou Source: CRIC
Figure 4- 2 The Housing Rents in Beijing, Shanghai and Guangzhou Source: CityRE and CREIS
According to the direct capitalization method, the market capitalization rates in three selected cities are calculated by the housing prices and rents, as reported in
2007/1 2007/4 2007/7 2007/10 2008/1 2008/4 2008/7 2008/10 2009/1 2009/4 2009/7 2009/10 2010/1 2010/4 2010/7 2010/10 2011/1 2011/4 2011/7 2011/10 2012/1 2012/4 2012/7 2012/10
(￥/m2)
2007/1 2007/4 2007/7 2007/10 2008/1 2008/4 2008/7 2008/10 2009/1 2009/4 2009/7 2009/10 2010/1 2010/4 2010/7 2010/10 2011/1 2011/4 2011/7 2011/10 2012/1 2012/4 2012/7 2012/10
(￥/m2)
Beijing Shanghai Guangzhou
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
annual rents and do not take off the operating expenses. In other words, these capitalization rates are higher than the actual values. Even so, in the research period, the capitalization rates are rather low in Beijing, Shanghai and Guangzhou. The capitalization rate in Beijing decreased from 3.98% to 3.40% while the one in Guangzhou increased from 3.34% to 3.66%. They didn’t change much. In contrast, the capitalization rate in Shanghai underwent a big change, which dropped from 5.42%
to 2.91%. In light of this, there was a chasm between the housing prices and rents in Beijing, Shanghai and Guangzhou. It implies the housing bubbles might exist in real estate market of these selected cities.
Figure 4- 3 The Market Capitalization Rates in Beijing, Shanghai and Guangzhou
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
2007/1 2007/4 2007/7 2007/10 2008/1 2008/4 2008/7 2008/10 2009/1 2009/4 2009/7 2009/10 2010/1 2010/4 2010/7 2010/10 2011/1 2011/4 2011/7 2011/10 2012/1 2012/4 2012/7 2012/10
Beijing Shanghai Guangzhou
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
4.2 Fundamental Value
In order to obtain the fundamental values, the reasonable capitalization rates should be estimated through the use of the WACC approach first. Based on the formula of WACC, we need to make a decision about the costs of equity and loan.
Generally speaking, the interest rate for time deposit is regarded as the cost of equity for property investors. At the moment, time deposits in China offer 6 kinds of investment maturity terms: 3 months, 6 months, 1 year, 2 years, 3 years and 5 years.
The owners of houses usually hold the properties for a relatively long period.
Therefore, this study chooses the interest rate of the 5-year deposit as the cost of equity. In China, the residential mortgage includes housing fund loan and commercial loan. But the application of housing fund loan is constrained by many regulations and not all citizens have housing fund. The interest rate for commercial loan meets the actual circs better. The items of commercial loans consist of 6 months, 1 year, 3years, 5 years and over 5 years. Since the mortgages usually have long repayment terms, the loan rate of over 5years is employed in home loan2. Although the loan rates are specified by the central bank, the merchant banks could float the rates within a certain range. Because the houses are used as the collaterals for the mortgages, the banks would offer the prime lending rate. This study selects the interest rate of the 5-year loan as the cost of debt, which is close to the fact. The data of interest rate are shown in Figure 4-4, whichis collected from People’s Bank of China.
2 There is no specific mortgage rate in China. In practice, the interest rate of commercial loan is regarded as the mortgage rate directly.
‧
Figure 4- 4 The 5-year deposit rates and the 5-year loan rates Source: People's Bank of China
It is also very important to clarify the weights in the formula. Due to the differences of housing policies, the loan-to-value ratios of residential mortgage vary all over China. According to the newest housing restriction policy, the highest loan-to-value ratio of the first suite loan is 70% and the highest one of the second suite loan is 50%. For convenience, we calculate the fundamental values in three chosen cities under the circumstances of 50% and 70% loan-to-value ratio, as reported in Figure 4-5 and Figure 4-6.
Figure 4- 5 The Fundamental Values in Beijing, Shanghai and Guangzhou (50% LTV)
0.00%
2007/1 2007/4 2007/7 2007/10 2008/1 2008/4 2008/7 2008/10 2009/1 2009/4 2009/7 2009/10 2010/1 2010/4 2010/7 2010/10 2011/1 2011/4 2011/7 2011/10 2012/1 2012/4 2012/7 2012/10
rE
2007/1 2007/4 2007/7 2007/10 2008/1 2008/4 2008/7 2008/10 2009/1 2009/4 2009/7 2009/10 2010/1 2010/4 2010/7 2010/10 2011/1 2011/4 2011/7 2011/10 2012/1 2012/4 2012/7 2012/10
（￥/m2 ）
Beijing Shanghai Guangzhou
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
Figure 4- 6 The Fundamental Values in Beijing, Shanghai and Guangzhou (70% LTV)
0.00 2000.00 4000.00 6000.00 8000.00 10000.00 12000.00 14000.00
2007/1 2007/4 2007/7 2007/10 2008/1 2008/4 2008/7 2008/10 2009/1 2009/4 2009/7 2009/10 2010/1 2010/4 2010/7 2010/10 2011/1 2011/4 2011/7 2011/10 2012/1 2012/4 2012/7 2012/10
（￥/m2 ）
Beijing Shanghai Guangzhou
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
4.3 Status of Housing Bubbles
As the literature review implies, the definition of housing bubbles can be in a number of ways. From the point of fundamental value, this study defines housing bubbles as the discrepancy between housing fundamental values and market prices.
The formula of housing bubbles is as follows:
Bubble = (𝑀𝑃𝐹𝑉 − 1) × 100% (4.1)
In this equation, MP represents the market price of property and FV represents the housing fundamental value. The results are reported in Figure 4-7 and Figure 4-8.
However, the fundamental values in this study do not consider the tax shield. It would make the calculated housing bubbles become bigger than the actuals but do not affect the trends.
Figure 4- 7 The Housing Bubbles in Beijing, Shanghai and Guangzhou (50% LTV)
-50.00%
0.00%
50.00%
100.00%
150.00%
200.00%
250.00%
2007/1 2007/4 2007/7 2007/10 2008/1 2008/4 2008/7 2008/10 2009/1 2009/4 2009/7 2009/10 2010/1 2010/4 2010/7 2010/10 2011/1 2011/4 2011/7 2011/10 2012/1 2012/4 2012/7 2012/10
Beijing Shanghai Guangzhou
‧
Figure 4- 8 The Housing Bubbles in Beijing, Shanghai and Guangzhou (70% LTV)
As shown in Figure 4-7, from 2007 to 2009, the housing bubbles in Guangzhou were in the first place and the ones in Beijing come second, while the bubbles in Shanghai were smallest. In this period, although the housing prices in Beijing and Shanghai were higher than the ones in Guangzhou, the housing bubbles in Guangzhou were most serious. It might relate to the seriously low housing rents in Guangzhou. In 2008, the subprime mortgage crisis burst out. The housing bubbles in three cities all reduced. By early 2009, the housing bubbles in three cities reached their lowest levels and even the ones in Shanghai were negative briefly. After that, the real estate markets in three cities shook off the influence of the subprime mortgage crisis and began to recover gradually. The housing bubbles were going up again. However, in April 2010, a series of house purchase restrictions were announced. At that time, the housing bubbles in Beijing and Shanghai suddenly dropped and then ballooned. When the restrictions were officially implemented, the trends of the bubbles in Beijing and Shanghai merely changed a little. It is thus clear that the house purchase restrictions can curb over-heating property markets in a short time. At the same time, the restrictions also gave confidence to the people who would buy houses to live in. They purchased a large number of properties and made the real estate markets take a good turn. On the other hand, the housing bubbles in Guangzhou edged up after 2009. It is possible that the home buyers in Guangzhou took a wait-and-see attitude towards the
0.00%
2007/1 2007/4 2007/7 2007/10 2008/1 2008/4 2008/7 2008/10 2009/1 2009/4 2009/7 2009/10 2010/1 2010/4 2010/7 2010/10 2011/1 2011/4 2011/7 2011/10 2012/1 2012/4 2012/7 2012/10
Beijing Shanghai Guangzhou
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
real estate market.
In Figure 4-8, we can see that the housing bubbles which are calculated under the circumstances of 70% loan-to-value ratio are higher than the ones under the circumstances of 50% loan-to-value ratio. It is more consistent with the practice.
From January 2007 to November 2012, there are 21 months in which the housing bubbles in Beijing were more than 100%. And the highest point (135.8%) arose in February 2011. In Shanghai, the housing bubbles exceeded 100% for 27 times and their peak (144.44%) appeared in January 2012. When it comes to Guangzhou, there are 21 months that the housing bubbles were higher than 100%. And the bubbles in Guangzhou peaked at 214.40% in October 2007. Thus it can be seen that the housing market prices deviated from the fundamental values seriously in Beijing, Shanghai and Guangzhou. The problem of high housing bubbles cannot be ignored any more.
‧
This chapter mainly presents the relationship between housing bubbles and the selected variables. First, the existence of structural change and the unit root are checked. Then the results of cointegration test are interpreted. Finally, the results of Granger causality tests provide suggestions for housing polices.
Monthly data or quarterly data usually exhibits some form of seasonality (Enders, 1995). The seasonal variation of time series may cover up the actual trends of economic processes. So we should deseasonalize the time series before the econometric analysis. The variables used in this study, except interest rates, have obvious seasonal patterns. Accordingly, they are seasonally adjusted by using Census X-12 method3.
5.1 Structural Change
Before performing unit root test, it is necessary to confirm that there are no structural changes during the research period. If there are structural breaks, the results of the following econometric analysis would be biased. In order to avoid the deviated estimation, we would identify whether the time series of housing bubbles has structural changes or not. In this study, we apply the Cumulative Sum of the recursive residuals test (CUSUM test).
The results of CUSUM test are shown respectively in Figure 5-1, Figure 5-2 and Figure 5-3. Apparently, from Figure 5-1 to Figure 5-3, the housing bubbles in three cities do not exceed the critical value at the 5% significance level (dashed line). In other words, there are no structural changes in these series of housing bubbles during the research period. Therefore, these time series are appropriate for the following tests, and the results would be authentic.
3 Census X-12 method is a seasonal adjusted procedure established by Bureau of Census. This procedure is revised for many times and becomes one of the most usual seasonal adjusted methods. Much statistical software can perform the Census X-12 procedure.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
Figure 5- 1 Result of CUSUM Test for BJ
Figure 5- 2 Result of CUSUM Test for SH
-30 -20 -10 0 10 20 30
II III IV I II III IV I II III IV I II III IV I II III IV I II III IV
2007 2008 2009 2010 2011 2012
CUSUM 5% Significance
-30 -20 -10 0 10 20 30
II III IV I II III IV I II III IV I II III IV I II III IV I II III IV
2007 2008 2009 2010 2011 2012
CUSUM 5% Significance
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
Figure 5- 3 Result of CUSUM Test for GZ
-30 -20 -10 0 10 20 30
II III IV I II III IV I II III IV I II III IV I II III IV I II III IV
2007 2008 2009 2010 2011 2012
CUSUM 5% Significance
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
5.2 Unit Root Test
In the time series data analysis, it is essential to confirm whether the series are stationary or not. With the non-stationary variables, the estimates of the classic regression model might be spurious regression4 results. Thus, we employ Augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test to examine all the variables used in this study.
The result of unit root tests is reported in Table 5-1. We can learn that housing bubbles in the three cities (BJ, SH and GZ), per-capita disposable incomes in these cities (INCOME_BJ, INCOME_SH and INCOME_GZ), total loans of financial institutions (LOAN), and interest rates of mortgage (INT) accept the null hypothesis at the level order, i.e., these time series are non-stationary. However, they reject the null hypothesis at the first difference order, becoming stationary series. Hence, all the variables used in this study are I(1) series.
Table 5- 1 Result of Unit Root Test
ADF test PP test
Level 1st difference Level 1st difference
BJ -2.544 -8.952 *** -2.593 * -8.970 ***
SH -1.796 -9.567 *** -1.813 -9.472 ***
GZ -1.080 -7.028 *** -1.546 -7.236 ***
INCOME_BJ 0.966 -6.284 *** 1.707 -6.652 ***
INCOME_SH 1.546 -4.160 *** 1.719 -11.027 ***
INCOME_GZ 0.189 -9.427 *** 1.646 -11.985 ***
LOAN 3.043 -6.207 *** 2.228 -6.529 ***
INT -2.086 -2.875 * -1.448 -3.018 **
Note: 1. Null hypothesis: The series has a unit root.
2. *, ** and *** denote significance at the 10%, 5% and 1% level respectively.
‧
In order to investigate the long-run equilibrium relationship between the housing bubbles and other variables, we need to detect whether cointegrations exist in the variables first. Since all the variables are I(1) series, Johansen cointegration test is available in this study. If cointegrations exist, it means that there are long-run relationships in the variables. So we can directly employ the standard values to estimate VECM. However, if there is no cointegration in the variables, we would apply the VAR model to examine the short-run equilibrium relationship between the variables in the first difference.
5.3.1 The Optimal Lag Length
The result of Johansen cointegration test based on the VAR approach is sensitive to the lag length. To ensure the accuracy of the following analysis, it is very important to determine the lag order of an unrestricted VAR. In this study, the optimal lag length is selected by Akaike information criterion (AIC) and Likelihood ratio (LR) test statistic. The results are shown in Table 5-2. As we can see, the third lag is suitable for these three VAR models.
Table 5- 2 The Optimal Lag Order
INCOME_BJ, LOAN, INT and BJ
INCOME_SH, LOAN, INT and SH
Note: * denotes the lag order selected by LR and AIC.
‧
cointegration relationships. The results of cointegration test are reported in Table 5-3.At the 5% significance, there is a cointegration relationship between BJ and other variables. Similarly, under the Trace and Maximum Eigenvalue tests, the results of cointegration between INCOME_SH, LOAN, INT and SH reject the null hypothesis of no cointegration at the 5% significance. Last but not least, the test statistics also show that two cointegration relationships exist between INCOME_GZ, LOAN, INT and GZ. In other words, it is appropriate to employ VECM to discuss the housing bubbles in three cities respectively.
Table 5- 3 Results of Cointegration Test
INCOME_BJ, LOAN, INT and BJ
Note: ** denotes that the null hypothesis can be rejected at the 5% significance.‧
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