Download Research on the Influence of Real Estate Investment on Economic

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Research on the Influence of Real Estate Investment on Economic
Growth in Beijing
LIU Zhe
School of Economics and Management, Beijing University of Technology, P.R.China, 102214
[email protected]
Abstract: By the end of 1980s, reforms of the land system and urban housing system had begun. With
the rapid growth of the real estate investment, the relationship between economic growth and real estate
investment is attracting more and more people’s attention. In this paper, two indexs - real estate
development investment and per capita GDP stand for the level of Beijing’s real estate investment and
economic growth. By adopting the root stationary test, cointegration test and error modification model,
this article analyzes the real estate investment’s influence on Beijing’s economic growth. All of the data
are between 1986-2009. The conclusion shows that real estate investment plays an important and
positive role in the economic growth in Beijing. In addition, the influence chiefly embodies in a
short-term.
Keywords: Real Estate Investment, Economic Growth, Stationary Test, Cointegration Test, Error
Modification Model
1 Introduction
In recent years, the real estate industry in Beijing has gone through a rapid development. The real estate
investment has reached 233.77 billion yuan by the end of 2009, accounting for 19.24% of the GDP and
48.12% of the investment in fixed assets of the period in Beijing. With the importance of the real estate
industry increases continuously, real estate investment becomes the major aspect to push forward
economic growth. High attention has been paid by scholars to this phenomenon.
According to the researches of the relationship between real estate investmen and economic growth, all
of them can be divided into three different kinds:
Firstly, by using cointegration test, error modification model and granger causality test, scholars
analyzed the long-term equilibrium between real estate investment and economic growth. In addition,
they studied on the variables’ short-term fluctuations to the equilibrium and how they affected each other.
The conclusions of the researches were not the same because of the different choices of time periods,
variables and locations. Howerer, it is undeniable that most of the researches admited the positive effect
of the real estate investmen on economic growth. Green(1997) had used granger causality test to study
the relationships among residential, non-residential investment and economic growth. He had found that
there were cointegration relationships among them. Lu Juchun, Jia Ziwu, Tian Hongfang(2008) had
found that: the two-way granger causality relationships existed between real estate investment and
economic growth in both the whole country and the eastern coastal areas, but in the middle and the
western areas only unidirectional causality relationship existed between them. Sun Yafan, Song Jia
(2009) had found that the GDP of Jiangsu Province would increase by 0.22% as long as the real estate
investment increases by 1%.
Secondly, by computing the contribution rate of the real estate investment and using input-output
analysis, scholars studied on the contribution of the real estate investment to economic growth. Bing Jie,
Lu Shichang (2008) had computed the contribution rate of Liaoning Province’s real estate investment to
GDP growth between 1999-2007 and found that real estate investment had a important driving effect on
GDP growth.
Thirdly, by using panel data analysis to study on the relationships between real estate investment and
economic growth. Kong Li (2009) had used panel data analysis to study the relationship between real
estate investment and GDP in eastern, middle and western areas in China. According to the research,
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there were two-way causality relationships between the two variables in above areas, but the effect is
biggest in eastern area, smallest in western area and medium in the middle area.
In this article, by using the data from 1986 to 2009, the relationships between real estate investment and
per capita GDP in Beijing have been analyzed. The methods are root stationary test, cointegration test
and error modification model.
2 Research Frame Work
2.1 Index selection
This paper uses real estate development investmen and per capita GDP to represent Beijing’s real estate
investmen and economic growth respectively. All of the data are got from the Beijing Statistical
Yearbook from 1986 and 2009. In addition, the paper uses per capita GDP index and the secondary
industry index to erase the price level’s influence. Therefore, all of the data are converted into 1986’s
constant price. At last, the article uses real estate investment and per capita GDP’s natural logarithm to
remove heteroskedasticity. The new indexes are recorded as LNRREI and LNRRGDP.
2.2 Research methods
2.2.1 Stationary test
To avoid spurious regression and make sure the result is unbiased and validated, the sequences’
stationary test must be hold firstly. Up to now the widely used methods are Dickey-Fuller(DF) test and
Augmented Dickey-Fuller(ADF) test. DF test are only validated when sequences are AR(1), so we will
choose ADF test. If the t-test statistics is smaller than the critical value, then we can reject the null
hypothesis, otherwise we accept it. We have to pay attention to the three kinds of regression model when
using ADF test. By checking the graphs of the sequences, we can decide the proper model.
2.2.2 Cointegration test
By using cointegration analysis, we can find the steady equilibrium relationships among two or more
non-stationary variables. Although these variables have their own rules of long-term fluctuations, the
linear combination of them can get a stationary residual sequence as long as they are in the same order.
It also means that they are cointegrated. There are two kinds of cointegration test: Johansen test and EG
test, this paper will use the EG test.
When testing the cointegration relationship between real estate investment and per capita GDP, we can
use OLS to make a regression estimation on equation(1) as long as the two variables are in the same
order. Then we can get the residual sequence ε t . If ε t is a stationary sequence (which can be known
by ADF test), a long-term steady equilibrium relationship may exist between the real estate investment
and per capita GDP, otherwise there is not.
LNRRGDPt = c0 + c1LNRREI t + ε t
(1)
2.2.3 Error modification model
Cointegration test can only demonstrate the long-term steady equilibrium relationships among variables,
it can not reveal the short-term dynamic relationships among them. However, error modification model
can remedy the defect of it. Error modification model explains the dependent variables’ short-term
fluctuations from two aspects: on the one hand, the influence of the independent variables’ short-term
fluctuations; on the other hand, the influence of the variables’ short-term deviation from the long-term
steady equrlibrium condition.
We can mark the residual sequence ε t as ecmt . The error modification model can be writen as
follow:
∆LNRRGDPt = c0 + c1∆LNRREIt + c2ecmt −1 + ε t
(2)
∆ represents the first order difference. c2 is always negative, it reflects the adjustment of the deviation
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from the long-term steady equilibrium. The bigger is the absolute value of c2 , the faster is the speed to
recover to the steady equilibrium condition.
3 Empirical Analysis
3.1 ADF test
In order to choose the proper regression equation, we have to make a preliminary judgement of the two
sequences - LNRRGDP and LNRREI. According to the graphs of the two sequences, we can find that
the mean value of LNRRGDP and LNRREI are far from zero. In addition, the sequences have an
obvious time trend. Therefore we choose the regression model with a intercept and the time trend. Based
on the ADF test, we can get table1. From table 1, we know that sequences of LNRRGDP and LNRREI
are not stationary. But their 1 difference sequences have passed the test under 1% of the significance
levels. It means that LNRRGDP and LNRREI are I(1). We can mark them as LNRRGDP I(1),
LNRREI I(1). Then we can have the cointegration test on them.
～
～
Table 1 The result of ADF Test
1%
5%
10%
Test Type
ADF
P-value
Critical
Critical
Critical
Conclusion
Variable
C,T,K
Test Value
Value
Value
Value
LNRRGDP
-3.494937
(C,T,1)
0.0648
-4.440739
-3.632896
-3.254671 Non-stationary
C,T,0
-3.622033
-3.248592 Non-stationary
LNRREI
-2.548441
0.3043
-4.416345
C,0,3
-3.029969
-2.655194
Stationary
∆LNRRGDP -4.709491
0.0016
-3.831511
C,0,3
-3.029969
-2.655194
Stationary
∆LNRREI
-4.222637
0.0044
-3.831511
Notes: (C,T,T) reprents whether the equation of ADF test has constant item, time trend and lag time.
（
）
（
（
（
）
）
）
3.2 Cointegration analysis
Because that sequences of LNRRGDP and LNRREI are all integrated of 1, maybe between them there
are a long-term steady equilibrium relationship. It can be demonstrated by the cointegration test. This
article uses per capita GDP (LNRRGDP) as dependent variable, real estate development investmen
(LNRREI) as independent variable. According to the EG test: firstly, by using OLS method we are able
to formulate a one-dimensional linear regression model; secondly, by using ADF test we are able to
check whether the residual sequence is stationary or not. If it is stationary, there must be a cointegration
relationship between them, otherwise there is not.
According to equation(1), by using OLS to make regression estimation, we can get:
LNRRGDPt = 6.098865 + 0.859772 LNRREI t + ε t
(3)
(281.5116)
2
R = 0.9987
(129.2306)
， DW= 1.046，AIC= -4.7718，SC= -4.6736，F= 16700.5467
2
According to T-test, all parameters’ t statistics are large enough to reject the null hypothesis. R is close
to 1 which means the goodness of fit of the model is good.
According to equation(3), we can get residual sequence ε t ’s expression:
ε t = LNRRGDPt − 6.098865 − 0.859772 LNRREIt
(4)
Based on the ADF test of ε t , we can get the results as table2. From table2, we know that sequence ε t
is stationary. It means there is a long-term steady equilibrium relationship between LNRRGDP and
LNRREI.
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Variable
ADF
Test Value
εt
-2.914395
Tble2 The result of Cointegration Test
1%
Test Type
5%
P-value
Critical
C,T,K
Critical Value
Value
（ ）
（0,0,0）
0.0055
-2.669359
-1.956406
10%
Critical
Value
Conclusion
-1.608495
Stationary
Notes: (C,T,T) reprents whether the equation of ADF test has constant item, time trend and lag time.
3.3 Error modification model
From the above analysis, there is a long-term equilibrium relationship between LNRRGDP and
LNRREI. But in the real world, variables are seldom on the long-term equilibrium point. The
equilibrium condition can only be got through the short-term dynamic adjustments. We mark the
stationary sequence ε t as ecm. According to equation (2), by using OLS estimation, we can get:
∆LNRRGDPt = 0.021566 + 0.632976∆LNRREIt − 0.378697ecmt −1
(1.842150)
2
，
(5.207307)
，
(-1.903895)
，
(5)
，
R = 0.576925
DW= 1.847504 AIC= -5.079165 SC= -4.931057 F= 13.63649
2
Although R is a little small, the model is significicant as a whole. The parameters’ t statistics are also
significiant. From equation(5), we know the short-term elasticity of per capita GDP with respect to real
estate investment is 0.63%. It means that in short-term period, per capital GDP could increase by 0.63%
as long as real estate investment increasesby 1%. The coefficient of ecmt −1 represents the adjustment
of the deviation from the long-term equilibrium condition, from the equation we know it is -0.378697. It
means that when the short-term fluctuations depart from the long-term equilibrium, the economic
system will return to the equilibrium condition by -0.378697.
4 Conclusion
In this paper, we use real estate investment as the variable which stimulates the economic growth.
According to the cointegration test and error modification model, we can get the long-term equilibrium
relationship between real estate investment and economic growth. Conclusions are as follows:
4.1 Real estate investmen has a grate effect on economic growth in Beijing, there is a long-term
equilibrium relationship between the two variables
Real estate investment has an obvious positive effect on Beijing’s economic growth. From the long-term
equilibrium equation we can find that: the elasticity of per capita GDP with respect to real estate
investment is 0.859772. That is to say, if real estate investmen increases by 1%, per capita GDP could
increase by 0.859772%.
4.2 The influence of real estate investment on Beijing’s economic growth mainly appears in a
short-term
Real estate investment’s positive influence on Beijing’s economic growth not only appears in a
long-term period, in short-term the real estate investmen’s deviation from the equilibrium condition can
also directly result in the fluctuations of per capita GDP. From the error modification model, we can find:
in short-term,when real estate investmen increases by 1%, the real per capita GDP could increases by
0.632976%;in the long run, when real estate investmen increases by 1%, the per capita GDP can be
stimulated by 0.859772%; when the short-term fluctuations depart from the long-term equilibrium, the
economic system could adjust itself to the equilibrium condition. From the research we can find that the
real estate investment’s short-term effect on Beijing’s economic growth is more important than the
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long-term effect. The cause of this phenomenon can attribute to the rapid development of Beijing’s real
estate industry and the high return rate of real estate investment. People are confident with Beijing’s real
estate industry, so the market is capable of quickly response in a short-term. As a result, real estate
investment generates huge demand effect and supply effect which promotes the rapid development of
Beijing’s economy.
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