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M & D FORUM An Empirical Study on the Relationship Among China’s Real Estate Prices, Money Supply, Bank Loans and Interest Rates WANG Shifen School of Economics, Shanghai University, P.R. China, 200444 [email protected] Abstract: This article selects the monthly data of M2 supply, volume of bank credit, interest rates and the real estate prices between 2007 and 2010, uses the empirical methods of integration test, Granger causality test, VEC Model, impulse response function and variance decomposition, and gets: there are long-time equilibrium relations among money supply, bank credit, interest rates and real estate prices. The real estate prices are positively related with the money supply. In the short run, money supply has the greatest contribution to the changes of real estate prices, however, with the passage of time, the contribution becomes smaller and approaches stationarity. In the long run, bank credit and interest rates have more significant impacts on the changes of real estate prices. With the passage of time they become stationary. Keywords: Real estate prices, Money supply, Bank credit, VECM test 1 Introduction Since the reform of housing policy in 1998, China’s real estate industry achieved great development and became one of the important prop sectors in the economy. At the same time, various speculations in the industry seriously threatened the health and stability of the economy. In order to curb the over-speculation and the bubbles in the market, the central bank carried out a series of austerity monetary policies since 2004, including the frequent increases of legal reserve rates and interest rates, to cool down the over-heated real estate market. However, when the sub-prime crisis in the U.S. was spread to China in 2008, the central bank lowered the interest rates for several times in the second half of the year in concert with the RMB4 trillion expansionary fiscal policy. Under the stimulation of the expansionary policy, the real estate industry began to grow again. The investment in the industry topped RMB4306.45 billion in 2009, an increase of 22.289% year on year; and that in 2010 was 63.4432% more than that in 20081. From 2010 the economy in China began to show overheat, the government increased the strength of control over real estate industry at the time of bringing up with the policies of curbing inflation. First, from the perspective of money supply, the central bank raised the legal reserve rates from time to time to reduce the volume of credit, increased the ratios of the first-payment and the interest rates of mortgages and enhanced the enforcement of differential loans. Second, from the perspective of the house supply, the Central Economic Working Conference in December 2009 called for the increase of the supply of ordinary apartments to cater for people’s demand for self accommodation and improvement of living conditions. In April 2010 the Ministry of Housing and Construction asked to speed up the building of support houses, and the building of the public rent houses was the following priority. Last, from the perspective of house demand, many cities made the purchase-limit decrees. This article takes the above-mentioned economic changes as the background, selects the macroeconomic data between 2007 and 2010, uses the empirical methods of integration test, Granger causality test, VEC Model, impulse response function and variance decomposition to analyze the relations among real estate prices, bank credits, money supply and interest rates. In the end, some This study is kindly supported by the Social Sciences Development Fund of Shanghai University. 1 Source: drcnet http://edu-data.drcnet.com.cn/web/OLAPQuery.aspx?databasename=macro&cubeName=industry_month&channel =1&nodeId=11 678 M & D FORUM policy suggestions are proposed. 2 Literature Review There are not many literatures on the relations among the money supply, bank credit, real estate prices and interest rates. Most of the articles are discussing the empirical relations between two of them. WU Kangping, PI Shun et al. (2004)2 thought that the increase of the real estate prices led to the increase of bank credit and vice versa. They two had the reciprocal causation mechanism. TU Jiahua et al. (2005)3 used the VAR Model to analyze the various factors to push up the real estate prices of Shanghai, and concluded that there lowering interest rates had insignificant impact on Shanghai’s real estate prices. ZHANG Tao et al. (2006)4 made the empirical studies on the real estate prices and the volume of mortgage, and found the two had strong positive relations. In their empirical research, DUAN Zhongdong et al. (2007)5 used the data between January 2000 and August 2006 to show the long-time reciprocal causations between real estate prices and bank credit. Using Granger causality test, based on the data between 2003 and 2007, XIAO Muhua (2008)6 concluded that credit expansion supported the increase of the real estate prices, while the fast credit expansion was induced by the fast increase of money supply, low real interest rates and the large spread between deposit rates and loan rates. HU Lan (2009)7 built VAR Models for the monthly data (1998—2008) to analyze the impacts of money supply, real estate development loans and mortgages on real estate prices. ZHANG Li (2010)8 got from her empirical analysis that there existed the channeling effect from the interest policies to real estate industry, though, with some lags. 3 Empirical Tests and Analyses 3.1 Sources of the data and their processing This article collects the monthly data of M2, bank credit (LB), real estate sales prices indices (PH) and the loan rates of the bank (I) of China from January 2007 to December 20109. We take the CPI of January 2007 as the basis (100) to build the inflation rates of each month to flatten out the influence of the inflation. Then, we use Census X12 method to do the quarterly adjustments over M2, LB and PH to eliminate the seasonal fluctuations. At last, we take the logarithm for the above 3 variables to cancel the heteroscedastic disturbance. The relevant results are expressed as LM2, LLB, LPH. All the original data come from the database of the People’s Bank of China of various periods10 and the drcnet. 2 WU Kangping, PI Shun, LU Guihua. The General Equilibrium Analyses of the Symbiosis of China’s Real Estate Market and Financial Market[J]. Quantitative Economic and Technical Economic Studies, 2004 (10) (In Chinese) 3 TU Jiahua, ZHANG Jie. What Pushed Up the Real Estate Prices: the Evidences from the Real Estate Market of Shanghai [J]. World Economy, 2005 (5) (In Chinese) 4 ZHANG Tao, GONG Liutang, BU Yongxiang. Return on Assets, Mortgage and Equilibrium Prices of Real Estate[J]. Financial Studies, 2006 (2) (In Chinese) 5 DUAN Zhongdong, ZENG Linghua, HUANG Zexian. An Empirical Study on the Fluctuations of Real Estate Prices and the Increase of Bank Loans[J]. Financial Forum, 2007 (2) (In Chinese) 6 XIAO Benhua. The Credit Expansion and the Real Estate Prices in China[J]. Journal of Shanxi University of Finance and Economics, 2008 (1) (In Chinese) 7 HU Lan. Analyses of the Dynamic Impacts of Money Supply on the Change of Real Estate Prices in China[J]. Statistics and Decision Making, 2009 (23) (In Chinese) 8 ZHANG Li. A Study on the Effectiveness of the Conductibility of China’s Interest Policy to the Real Estate Prices[J]. Academic Forum, 2010 (4) (In Chinese) 9 This article uses the monthly data. There were two loan interest rate changes in October 2008, we took their average as the datum of the month. 10 http://www.pbc.gov.cn/publish/diaochatongjisi/133/index.html. 679 M & D FORUM 3.2 Augmented dickey-fuller tests First, run ADF tests for all the variables to test their planarization. Then decide the length of lag in accordance with AIC Principle. The results are shown as in Table 1: variables Table 1 Form of tests c,t,n The ADF tests for each variable in the time series 1% critical 5% critical DW ADF test statistics values values statistics ( ) H0 LLB (c,t,2) -0.8445 -4.1756 -3.1869 0.9765 Accepted LM2 (c,t,2) -0.7798 -4.1756 -3.5131 0.9212 Accepted LPH (c,t,2) -1.6118 -4.1756 -3.5131 1.0093 Accepted LI (c,0,2) -0.3071 -2.6162 -1.9481 2.0644 Accepted ∆ LLB (c,0,1) -5.4456* -2.4456 -1.6121 1.4075 Rejected ∆2 LM 2 (c,0,1) -5.9089* -2.6186 -1.9485 1.4368 Rejected 2 △LPH (c,0,1) -4.2099* -2.6174 -1.9483 1.0517 Rejected △LI (c,0,1) -4.6509* -2.6162 -1.6123 2.0694 Rejected Note: △LPH、△LI、 ∆ LLB 、 ∆ LM 2 stand for the first order and second order of the original series. 2 2 (c,t,n)stands for the intercept, tendency and the number of lags. * means significance at the 1% level. The analyses are based on EVIEWS5.0. From Table 2, we know that none of the variables are stable in the original series. LPH and LI are stable in the first order difference, LM2 and LB are stable after the second order difference. The real estate prices and interest rates are the first order integration I(1), bank credit and money supply are the second order integration I(2). 3.3 Cointegration test This article uses the Johansen Test proposed by Johansen and Juselius to test the cointegration among the variables. JJ Test is a regression coefficient test method based on VAR Model. Consider the following order P VAR Model: (1) Yt = A1Yt −1 + A2 Yt − 2 + LL + A p Yt − p + BX t + ε t Yt is the k-dimension unstable I (1 ) vector, X t is the d-dimension constant exogenous variable. (1) as follows: Rewrite p −1 (2) ∆Yt = ∏ Yt −1 + ∑ Γi ∆Yt −i + BX t + ε t i =1 in which matrix ∏ ∏ P = ∑ Ai − I i =1 ,Γ . If the rank of i ∏ p = − ∑ A j . The basic principle of JJ test is to analyze the rank of the j =i +1 r<k, ∏ can be broken down into ∏ = αβ ' , while β 'Yt ~ I (1) , β is called cointegral vector matrix, α is called parameter adjusting matrix, the rank of matrix r is the number of cointegration. According to the AIC information principle, the length of lags of auto-regression in the VAR Model is 3, and the variables have significant tendency, and set the cointegration equation to include intercepts. The results of the test is shown in Table 2: 680 M & D FORUM 、 、 Table 2 The Johansen (trace) statistics of LM2 LLB LPH and LI (lag interval) Null Hypotheses Trace Statistics 5% critical value P value r=0 110.7869 47.85613 0.0000 r≤1 40.95337 29.79707 0.0018 r≤2 13.41802 15.49471 0.1003 r≤3 1.048233 3.841466 0.3059 We can conclude from above that at the 5% significant level, there are 2 cointegrations among the 4 variables. 3.4 Granger causality tests Johansen cointegration tests demonstrate that there are long run cointegrations among the series LM2, LLB, LPH and LI. Granger 1988 pointed out that if the variables has a cointegration, there would be at least an one-direction Granger causality. Since ∆ 2 LM 2 , ∆ 2 LLB , LPH and LI denoted as DDLM2,DDLLB, DLPH, DLI are all stable, so that we can undergo Granger causality tests for them. The results are shown in Table 3. ( ) ) Table 4 △ △ ( The tests for the long run causality among the variables (lags:2) Null Hypotheses F Statistics P Value DDLB is not Granger Causality for DDLM2 1.35797 DDLM2 is not Granger Causality for DDLB 7.41320* DLPH is not Granger Causality for DDLM2 2.46086** DDLM2 is not Granger Causality for DLPH 5.46513* DLI is not Granger Causality for DDLM2 0.76361 DDLM2 is not Granger Causality for DLI 0.11207 DLPH is not Granger Causality for DDLB 2.44695** DDLB is not Granger Causality for DLPH 0.20482 DLI is not Granger Causality for DDLB 0.32714 DDLB is not Granger Causality for DLI 2.73246** DLI is not Granger Causality for DLPH 0.30134 DLPH is not Granger Causality for DLI 2.77322** Note: *denotes the rejection of H0 at 5% level; **denotes the rejection of H0 at 10% level. 0.26907 0.00187 0.09852 0.00808 0.47282 0.89427 0.09942 0.81564 0.72289 0.07449 0.74149 0.07449 From the above table, at the 5% significant level, money supply is the cause of bank credits; at the 10% level, real state prices are the cause of money supply, and at the 5% level, money supply is the cause of the increase of real estate prices. This can explain that with the increase of money supply, large amount of money will flow into the real estate market for value maintenance or speculation. On the other hand, houses are a type of physical capital, when the prices increase, the value of the house mortgaged assets in the banks will increase to lower the ratio of asset-liability, so that more money can be used as credit to increase money supply. At the 10% level, bank credit is the cause of house prices and interest rates. This is mainly because that the policy adjustments of the banks will certainly exert great impacts on the real economy. The abundance of the credit is the reasons of change of interest rates and the main source of the money used to purchase houses. At the 10% level, house prices is the cause of loan rates. The implementation of the monetary policy has some lags, that is why the change of interest rates is behind the change of house prices. 681 M & D FORUM 3.5 Dynamic VEC test Johansen cointegration test showed that there were cointegration among time series ∆ 2 LM 2 , ∆ 2 LLB , LPH and LI. We can use VEC model to estimate the VAR model within the cointegrations of the variables. The cointegrations among the four variables can be written in the form of VEC model: △ △ 0 . 0046 0 . 0047 ∆ LY t = + 0 . 0003 − 0 . 0493 0 . 0521 − 0 . 9968 − 0 . 012 − 0 . 8092 − 0 . 5651 0 . 4918 3 . 6869 2 . 9384 − in which LY t = ( LM 0 . 1440 LPH 1 . 3221 − 0 . 0145 − 0 . 2064 0 . 5469 2 t , LLB t 0 . 3259 0 . 0571 1 . 5422 0 . 2521 3 . 0389 0 . 2396 1 . 6278 − 2 . 9307 0 . 0166 − 0 . 1918 − 0 . 6542 0 . 0009 0 . 0014 − 0 . 0078 3 . 2633 0 . 0311 t , LPH + 0 . 0033 LI t t , LI t ); VECM ∆ LY t−1 t−2 − 0 . 0013 − 0 . 0025 ∆ LY t − 1 + − 0 . 0044 0 . 0408 − 0 . 0012 0 . 0263 ∧ VECM t − 1 + ε + − 0 . 0273 − 0 . 5565 = LM 2 t − 0 . 8847 LLB t t − − 1 . 2193 The degree of fitting of the 4 equations in the VEC model is 0.972089, and the AIC and SI principles are small. The integrating relation curve of the variables is shown in Graph 1: Graph 1 The integrating relation curve of the 4 variables In Graph 1, the 0 average line stands for the long-run equilibrium relation among the variables. The absolute value of the VEC model in early 2007 was big, which meant the short-run fluctuation in that time deviated greatly from the long-run equilibrium. After one year’s adjustment, it returned to the long-run equilibrium in 2008. Then the absolute value of the VEC began to increase again, but with smaller magnitude as 2007. However, the short-run fluctuation started to deviate from the long-run equilibrium again. This exactly reflected that the over-heated economy in 2007 departed from the long-run stability, and the real estate, stock market and concrete economy were overheated. The spread of the sub-prime crisis in 2008 affected the world economy, which led China’s economy into the equilibrium. From 2009, the short-run fluctuation began to diverge from the long-run equilibrium once again. 3.6 Impulse response function and variance decomposition The IRF measures the impacts on the present and future values of the endogenous variables in the VAR model of a standard error shock in a stochastic disturbance from an endogenous variable. This article 682 M & D FORUM discusses the relations among the real estate prices and other variables, so that the following graph shows the present and future responses of the house prices to those variables. Graph 2 The present and future responses of the house prices to various variables In the graph above, the horizontal axes stand for the number of periods, while the vertical axes stand for the degrees of the impulse response functions and the dotted lines stand for the two-time positive and negative standard error deviations ±2S.E . From Pic 2 we can see that when the house prices were shocked by an innovation of the standard error of money supply, the impact effects in Periods 1---13 was positive, the house prices increased to the maximum of some 0.05, which appeared in Period 6. After Period 13, the effect was reinforced to the negative direction, the house prices decreased. This was why the house prices went up with the increase of money supply, as mentioned above. In Pic 3, after a shock from an innovation of the standard error of bank credit, the house prices went up from Period 1. The maximum was 0.1 appearing in Period 11. In Period 17, the response of the house prices was 0, than it increased slowly in the negative direction. From Pic 4 we can see that the house prices responded to the shock of an innovation of the standard error of themselves in Period 1 with a small degree. The impulse response strengthened to reach the maximum about Period 5, and the response at Period 10 was 0. After that the response was negative and returned to 0 at Period 20. A cycle of the fluctuation of the real estate prices is about 10 months. Pic 5 shows that receiving a shock of an innovation of the standard error from the interest rates, the ( ) 683 M & D FORUM response of the house prices was almost 0 in the first 5 periods. There were lags. Then the response increased in the negative direction and reached the minimum at Period. After that the response was reduced gradually, and returned to 0 at Period 20. This explained the situation that real estate prices were negatively related to interest rates. Variance decomposition is used to analyze the contribution of every shock of innovation to the changes of endogenous variables. Pic 6 displays the contributions of LM2, LLB, LI to the changes of LPH Graph 3 The effectiveness of the variance decomposition As Graph 3 (Pic 6) shows, in Period 1, the estimated variance of LPH was 45.39%, that of LM2 was 54.39%, that of LLB was 0.22%, and that of LI was 0. The estimated variances were the contributions of each variable to the changes of LPH. Then the estimated LPH decreased fast, and approached stationary after 10 periods. The estimated variance of LM2 reached its maximum of 59% during periods 3 to 4, then decreased gradually, and became stationary at Period 13. The estimated variance of LLB rose quickly and touched the maximum at Period 11, then went down gradually. Last, the contribution of LI to the changes of LPH was 0 in the first 4 periods. After that it rose steadily and maintained at 40% after Period 17. 4 Conclusions and Suggestions Through the theoretic and empirical analyses of money supply, volume of bank credit, interest rates and the real estate prices, using the empirical methods of cointegration test, VAR model, Granger causality test, VEC Model, impulse response function and variance decomposition, we got the following conclusions: There were positive relations among real estate prices LPH, bank credit LLB and money supply LM2. LPH had strong negative relation with interest rates LI. In order to adjust the high real estate prices, the government has to control the volume of bank loans, promote the financial innovation of the industry and change the situation of over-reliance on the bank credit. There are long-time equilibrium relations among money supply, bank credit, interest rates and real estate prices. At the same time, the real state prices and money supply have reciprocal causalities. The government can control the money supply, together with other measures such as interest rates and legal reserve rates to adjust the real estate prices. After the sub-prime crisis, China’s economy tried to quicken recovery. The GDP growth rates from 2008 to 2010 were 9.6%, 9.2% and 10.3% respectively. However, the VEC model and the cointegration curve showed that the short-run fluctuation degrees of the absolute value of error correction terms began to deviate from the long-run equilibrium stability. Thus, the government should continue to reinforce macroeconomic control, over real estate and stock market in special, to guarantee the healthy and steady development of the economy. From the impulse response function and ① ② ③ ④ 684 M & D FORUM variance decomposition we know: the shocks from money supply and bank credit to real estate prices led to positive response, while the shocks from interest rates to real estate prices had negative response. In addition, money supply and house prices themselves had the largest contributions to the house prices changes in the early stages, but with the passage of time, the contribution decreased gradually, and reached stationary in the end; bank loans and interest rates had increasing contributions to the changes of real estate prices with the passage of time, and approached stationary. This means that in order to achieve the goals of the policies, the government shall choose the relevant variables to adjust the house prices according to the different time spans. References [1]. WU Kangping, PI Shun, LU Guihua. The General Equilibrium Analyses of the Symbiosis of China’s Real Estate Market and Financial Market [J]. Quantitative Economic and Technical Economic Studies, 2004 (10) (In Chinese) [2]. TU Jiahua, ZHANG Jie. What Pushed Up the Real Estate Prices: the Evidences from the Real Estate Market of Shanghai [J]. World Economy, 2005 (5) (In Chinese) [3]. ZHANG Tao, GONG Liutang, BU Yongxiang. Return on Assets, Mortgage and Equilibrium Prices of Real Estate [J]. Financial Studies, 2006 (2) (In Chinese) [4]. DUAN Zhongdong, ZENG Linghua, HUANG Zexian. An Empirical Study on the Fluctuations of Real Estate Prices and the Increase of Bank Loans [J]. Financial Forum, 2007 (2) (In Chinese) [5]. XIAO Benhua. The Credit Expansion and the Real Estate Prices in China [J]. Journal of Shanxi University of Finance and Economics, 2008 (1) (In Chinese) [6]. HU Lan. Analyses of the Dynamic Impacts of Money Supply on the Change of Real Estate Prices in China [J]. Statistics and Decision Making, 2009 (23) (In Chinese) [7]. ZHANG Li. A Study on the Effectiveness of the Conductibility of China’s Interest Policy to the Real Estate Prices [J]. Academic Forum, 2010 (4) (In Chinese) 685