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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
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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.
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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:
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、 、
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.
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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
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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
（
）
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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
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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