the impact of property market developments on the real economy of malaysia

International Research Journal of Finance and Economics
ISSN 1450-2887 Issue 30 (2009)
© EuroJournals Publishing, Inc. 2009


The Impact of Property Market Developments on the Real
Economy of Malaysia


Hon-Chung Hui
Nottingham University Business School, University of Nottingham Malaysia
Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
E-mail:
Tel: 603-89248268; Fax: 603-89248019


Abstract

This paper examines the effects of property market developments on the real
economy in Malaysia. Our findings suggest that in the long-run, domestic demand and
GDP are neutral to fluctuations in property prices. The reason is that while property booms
drive higher gross investments, this is always accompanied by an offsetting decline in
private consumption. In the short-run however, the neutrality of demand and GDP to
property price fluctuations is less certain. It is conceivable that property booms can
reinforce real economic booms since property prices do seem to exert temporary pro-
cyclical effects on both consumption and investment. These findings imply that stimulating
property market activities is not an effective way to drive sustained growth in the real
economy. Nonetheless, there may be room to consider the property market as a policy tool
for short-term macroeconomic management.

Keywords: Wealth effect, investment channel, cointegration, property market
developments
JEL Classification Codes: C32, E20

1. Introduction
The effects of property market developments on economic activities have received ample attention in
recent years.
1
This interest is partly motivated by observations of strong asset prices in the US and
other industrialised economies, which many believed had contributed to the robust macroeconomic
performance before the sub-prime mortgage crisis. Debates on the property-economy linkages continue
to remain relevant as the crisis unfolds because they offer important lessons for other developing
economies.
Recent literatures on the impact of property markets on macroeconomic performance include
Ho and Wong (2008) who assessed the impact of house prices on domestic private demand in Hong
Kong and found that housing market booms significantly augment domestic demand. Other studies
modelled the transmission channels of property market shocks (i.e. the investment channel and the
wealth effect on consumption). For instance, Ludwig and Slok (2004) and Case, Quigley and Shiller
(2005) reported significant positive links between property prices and consumption in the US and a
number of OECD economies. However, studies on some Asian economies such as Singapore did not
confirm such positive links (see among others, Phang, 2004, Edelstein and Lum, 2004). Peng, Cheung

1
See, among others, BIS (2005) and Hunter et al (2003)
International Research Journal of Finance and Economics - Issue 30 (2009) 67
and Leung (2001) and Peng, Tam and Yiu (2008) examined both investment and consumption
channels in Hong Kong and China respectively. The results for Hong Kong suggest that both channels
respond positively and significantly to property prices. However, in China only the investment channel
was positive and significant while the wealth effect on consumption was negative and statistically
insignificant. The ambiguity of these findings implies that observed relationships between the property

sector and macroeconomic performance are far from being conclusive and cannot be generalised across
various countries and regions with diverse institutional structures.
In this paper, we extend the assessment of the property market-economic performance analysis
to the case of Malaysia. Our objectives are to (1) assess the real effects of property price fluctuations
by considering how property prices affect consumption and investment spending, which are the two
known transmission channels of property market shocks widely discussed in the extant literature and
(2) in the light of the findings from the first objective, to assess the importance of property prices as a
driving factor for fluctuations in domestic demand and real GDP over the short-run and long-run. Our
study covers the period of 1991Q1-2006Q2.
This research makes sense for several reasons. Despite garnering plentiful attention in other
economies, debates on the importance of property markets in economic development have received
scant attention in Malaysia. Given that the property market and the real economy seem closely
intertwined (see Figure 1), there is still very little understanding of the importance of the former in
affecting the latter. Next, promoting and managing growth in property markets has always been one of
the important policy objectives of the government because of claims that such growth would have
spillover effects on other sectors of the economy. Nonetheless, since there is no empirical evidence to
confirm or refute such claims, there is no way for policy-makers to know if promoting property market
booms necessarily create the intended effects.

Figure 1: Annual growth (%) in real GDP and property prices and in Malaysia


-15
-10
-5
0
5
10
15
20

25
30
1992 Q1
1992 Q4
1993 Q3
1994 Q2
1995 Q1
1995 Q4
1996 Q3
1997 Q2
1998 Q1
1998 Q4
1999 Q3
2000 Q2
2001 Q1
2001 Q4
2002 Q3
2003 Q2
2004 Q1
2004 Q4
2005 Q3
2006 Q2
Growth real GDP Growth property prices

Source: Bank Negara Malaysia Monthly Statistical Bulletin (various issues)

A preview of our findings is as follows. First, in the long-run, domestic demand and GDP are
neutral to fluctuations in property prices. We rationalise this outcome by the observation that while
property booms drive higher gross investments, there is an offsetting decline in private consumption. In
the short-run however, the neutrality of demand and GDP to property price fluctuations is less certain.

It is conceivable that property booms can reinforce real economic booms since property prices do seem
to exert temporary pro-cyclical effects on both consumption and investment. These findings imply that
stimulating property market activities is not an effective way to drive sustained growth in the real
economy. Nonetheless, there may be room to consider the property market as a policy tool for short-
term macroeconomic management.
68 International Research Journal of Finance and Economics - Issue 30 (2009)
2. Overview of Macroeconomic Developments in Malaysia
Before we proceed to our formal analysis, some background macroeconomic developments are
presented to provide the proper context. Malaysia underwent one major and several more minor
property boom-bust cycles over the period 1991-2006 (see Figure 1). The major boom episode
occurred in the early 1990s and consisted of ‘twin peaks’ in the growth rate of property prices, with the
first peak occurring in 1990-91 and the second in 1994-97. This expansionary phase came abruptly to
an end during the financial crisis in 1997-1998. A sharp recovery followed in 1999-2000 and
culminated in another round of modest real estate boom in 2001-06.
Closely in line with developments in the property markets, a construction and spending boom
had also picked up in the early 1990s as financial institutions accelerated lending activities (Figures 3-
5), enabling GDP to grow in the range of 9-10%. Notably, the pre-crisis economic expansion was
driven mainly by gross investments rather than private consumption, fuelled by demand for more
residential, industrial and commercial building space. Economic growth was briefly interrupted by the
1997-98 financial crisis, during which construction and gross investment experienced sharp
contractions in response to a property market collapse. Expansion of the economy resumed in 1999
albeit at more modest rates.
An issue which arises from all these statistics is whether and how property markets reinforce
real economic activities. The next few sections attempt to examine the effects of property markets on
real economic activities.

Figure 2: Growth (%) in GDP


-40

-30
-20
-10
0
10
20
30
19
9
1 Q1
19
9
1 Q4
19
9
2 Q3
19
9
3 Q2
1994 Q1
1994 Q4
1995 Q3
1996 Q2
1997 Q1
1997 Q4
19
9
8 Q3
19
9

9 Q2
20
0
0 Q1
2000 Q4
2001 Q3
2002 Q2
2003 Q1
2003 Q4
2004 Q3
2005 Q2
Real GDP growth Real construction output growth

Source: Bank Negara Malaysia Monthly Statistical Bulletin (various issues)

Figure 3: Growth (%) in commercial banks lending


-5
0
5
10
15
20
25
30
35
40
19
9

1 Q1
1
99
1 Q4
1
992
Q3
1
993 Q
2
1
994 Q
1
1
994 Q
4
19
9
5 Q3
1
99
6 Q2
1
99
7 Q1
1
997

Q
4

1
998 Q
3
1
999 Q
2
2000 Q
1
20
0
0 Q4
2
00
1 Q3
2
002
Q2
2
003

Q
1
2
003 Q
4
2
004 Q
3
2005 Q2

Source: Bank Negara Malaysia Monthly Statistical Bulletin (various issues)
International Research Journal of Finance and Economics - Issue 30 (2009) 69
Figure 4: Growth (%) in spending


-80
-60
-40
-20
0
20
40
60
1
99
1
Q
1
1991

Q4
1
9
92
Q
3
1
993

Q

2
1994 Q1
1
99
4
Q
4
1995

Q3
1
99
6
Q
2
1997

Q
1
1
9
97
Q
4
1
99
8
Q
3
1999

Q2
2
00
0
Q
1
2000

Q4
2
0
01
Q
3
2
002

Q
2
2
0
03
Q
1
2
00
3
Q
4

2004

Q3
2
00
5
Q
2
growth in gross fixed capital formation (1987 prices)
growth in consumption (1987 prices)

Source: Bank Negara Malaysia Monthly Statistical Bulletin (various issues)


3. Theoretical Frameworks
The property and macroeconomy nexus is a relatively new but important research area (Leung, 2003).
Among the salient issues that deserve attention is how property market fluctuations affect the
macroeconomy. Property market shocks induce fluctuations in real macroeconomic activities via two
known channels, namely investment and consumption spending (Zhu, 2003). We elaborate each
channel in greater detail in the following illustration.

(i). Investment channel
Higher property prices relative to replacement/construction cost of property assets raise the
profitability of building construction activities, according to Tobin’s q-ratio theory of investment.
Hence, developers and non-financial firms would engage in more residential and non-residential
building construction. This building boom would in turn boost demand and employment in property-
related sectors. Moreover, construction activities are not the only beneficiaries of a property market
boom. Higher property prices also provide incentives for firms in other sectors (i.e. non-property firms
and financial institutions) to increase their investment spending via the liquidity effect. In particular,
rising property prices tend to strengthen the balance sheet positions of property owners irrespective of

the line of business, enabling them to secure external funds more easily and at lower cost to finance
new investment projects. This effect is what Bernanke et al (1996) referred to as the financial
accelerator principle.

(ii). Wealth effect channel of consumption
According to the life-cycle hypothesis (Ando and Modigliani, 1963), household consumption spending
is affected by wealth, of which housing is an important constituent. Thus, changes in house prices
would, via changes in housing wealth, affect consumption expenditure. In contrast to the more
straightforward investment channel, the existence and magnitude of the wealth effect is harder to
rationalise. There exist various transmission mechanisms linking property prices with private
consumption.
First, changes in housing prices would have little effects on the welfare of owner-occupiers.
Since housing is very much an asset as it is a consumption good, higher house prices also implies a
higher cost of consuming housing services. An owner-occupier would not be richer in any sense and
hence a rise in consumption on other goods would not follow (Edelstein and Lum, 2004). Notably, if
the owner-occupier measures the implicit cost of consuming housing in terms of the rental rate for
similar types of homes in the same neighbourhood, there could be different short-run and long-run
70 International Research Journal of Finance and Economics - Issue 30 (2009)
responses in consumption. In the long-run, housing price appreciations would push up rentals. As
rising house price would be fully reflected in higher housing consumption costs (proxied by rent),
owner-occupiers would not feel richer so that increase in consumption would not take place. In the
short-run however, rental rate movements tend to be stickier than that of house prices due to the
existence of rent contracts. This being the case, it is possible for housing prices to rise faster than the
proxy for cost of housing services, leading to temporary ‘wealth gains’. While a far-sighted owner-
occupier in this case would probably not change consumption, seeing that his wealth gains over the
long-run would be nil, myopic consumers on the other hand could vary their consumption directly in
response to these temporary ‘wealth gains’.
Second, higher house prices would benefit existing homeowners if there are ways to withdraw
housing equity for consumption. This channel, known as collateral enhancement/balance sheet effect,
is operative only if the mortgage markets are sufficiently well developed. In less developed mortgage

markets where withdrawing housing equity is harder, the collateral enhancement effect would be
negligible.
Third, the impact of house price changes on consumers is also dependent on whether the
changes are temporary or permanent. Temporary changes in house prices may produce little effect on
consumption compared to permanent changes.
Fourth, there may be wealth gains among those trading houses in an environment of rising
house prices. If trading takes place within the set of existing housing stock (i.e. constant housing
stock), the number of buyers must be matched by the same number of willing sellers failing which
equilibrium would not be achieved. Since buyers and sellers are equal in number, losses suffered by
buyers of more expensive houses would be exactly offset by the gains reaped by sellers so that the net
effect on wealth among those transacting in houses is nil (Edelstein and Lum, 2004).
However, if there is substantial change in housing stock, this argument may not necessarily
hold. Particularly in developing economies where residential property markets have yet to mature,
rapid urbanisation, as seen from the growth rate of the urban population, gives rise to sharp increases in
new demand for housing. As current urban settlers are unlikely to sell their homes and move out given
ample economic opportunities in the urban areas, the number of households wanting to buy would
vastly exceed the number of households willing to sell in the secondary market, necessitating large
expansions of housing stock to meet the excess demand. Ceteris paribus, since there are more buying
households (losers) than there are households willing to sell (gainers), housing price increases would
cause a net loss among households transacting in houses, yielding a negative link between house price
and private consumption.
Finally, demographic factors can also explain the negative link from house price to private
consumption in the absence of collateral enhancement effects. Typically, households who up-grade and
buy houses for the first time consist mainly of working adults with young families. In contrast,
households trading down are constituted mostly by retirees. It is a known fact that house price
increases would make the former worse off while benefiting the latter. Thus, a country with a larger
working adult population relative to retirees would have stronger negative wealth effects from rising
house prices
2
.

The interaction of these factors makes it difficult to ascertain the net wealth effect of house
price increases on consumption. Recent works by Case et al (2005) and Ludwig and Slok (2004)
support the claim that housing wealth effect is positively related to consumption in the US and OECD
economies. However, Phang (2004) and Peng et al (2008) fail to detect such positive links for
Singapore and China, respectively.

2
Number of households trading up and buying houses for the first time need not equal number of households trading
down.
International Research Journal of Finance and Economics - Issue 30 (2009) 71
4. Empirical Models
We intend to assess the real impacts of property market developments in Malaysia. Given our
discussion on theoretical framework in the previous section, our research strategy follows a two-step
procedure:
• In step 1, we model the long-run effects of property prices on consumption and investment
spending, respectively. If both the investment and wealth effect channels are operative and
respond positively to property price fluctuations, property price would likely be a driving factor
for aggregate demand and real GDP, a statement which needs validation. This leads us to step 2.
• In step 2 we test if property price drives domestic demand and real GDP.
However, if it turns out that the two channels are not operative, or if each channel responds in a
qualitatively different manner to changes in property prices, the net impact of property prices on
demand and real GDP would be weak or non-existent. We would then expect to find that property price
does not drive real GDP in this case. Thus, the two strategies tend to reinforce one another.

4.1. Modelling consumption and investment channels
4.1.1. Investment channel
The model of investment channel captures two types of impacts of property price changes on
investment spending, namely the Tobin-q effect and the financial accelerator principle. To test the
existence of investment channel, a model of investment spending is specified and estimated, with
property price as an explanatory variable. The choice of control variables is influenced by the

investment literature particularly Acosta and Loza (2005) and Ang (2007), with the latter bearing more
influence on the model formulation here. Hence, the baseline long-run investment function is specified
as follows:
tttttt
UNCHPFCUCCGDPI
543210
β
β
β
β
β
β
+
+
+++=
(1)
where
I= Real gross fixed capital formation
GDP = Real Gross Domestic Product
UCC = Real user cost of capital
FC = Financial constraints
HP = Real property price
UNC = Macroeconomic uncertainty
The investment channel suggests that the sign on β
4
should be positive. The inclusion of GDP
and user cost of capital is consistent with the neoclassical framework of Jorgensen (1963), which
suggests that investment varies directly with output, but inversely with user cost of capital. Hence, β
1
is

hypothesised to take a positive sign whereas β
2
would take a negative value. As noted by Ang (2007),
financial constraints are important to firms in a developing country such as Malaysia. We use stock
prices as a proxy for financial constraints. Since more robust stock price tends to ease firms’ access to
financing, β
3
is hypothesised to take a positive value. Macroeconomic uncertainty is also added into the
model because higher uncertainty is reflected in terms of lower investment. To the extent that price
instability is one source of uncertainty, we use inflation to proxy uncertainty. Hence we expect β
5
to be
negative.

4.1.2. Wealth effect channel
To test the wealth effects of housing price changes on consumption, a long-run consumption function
is specified in the following:
ttttt
IRHMPSMPDYC
43210
α
α
α
α
α
+
+
++=
(2)
where

C = Real private consumption
DY = Aggregate real disposable income
72 International Research Journal of Finance and Economics - Issue 30 (2009)
SMP = Real stock market price
HMP = Real property price
IR = Real average lending rate of commercial banks
Notably, property and stock prices are proxies for household wealth
3
whereas disposable
income is the proxy for labour income. We have also included interest rate as another independent
variable, as what Phang (2004) has done. It is important to control for the effects of interest rates since
this may be a common factor driving both house prices and consumption. In this conventional model of
consumption behaviour, we expect α
1
to be positive because larger disposable incomes encourage more
consumption. We expect the sign of α
2
to be positive. For households investing in the stock market
over the long-term, they make profits not from capital gains but from dividends. To the extent that
higher stock prices reflect better corporate performance and dividend payouts, households would be
able to enjoy wealth gains which can be used to finance higher spending. Since interest rate represents
cost of credit, α
4
should have a negative sign
4
. However, the sign on α
3
is ambiguous for reasons
discussed in the previous section.

4.1.3. Estimating the consumption and investment channels
The investment and wealth effect channels (models (1)-(2)) can be estimated using the Autoregressive
Distributed Lag (ARDL) and bounds testing approach to cointegration introduced by Pesaran, Shin and
Smith (2001). This approach to cointegration is superior to those of Engle and Granger (1987), and
Johansen and Juselius (1990) for two reasons. Firstly, the approach particularly suitable for research
involving small samples. Second, this approach can be adopted to examine the presence of
cointegration among the underlying variables regardless of whether the underlying variables are I(0),
I(1) or mutually cointegrated. The second advantage dispenses with the need for pre-testing the order
of integration since most macroeconomic time series are either I(0) or I(1). The bounds testing
procedure can be applied even when the explanatory variables in models (1)-(2) are endogenous (Tang,
2004). Hence, the presence of endogenous regressors would not invalidate the estimation procedure.
The ARDL and bounds testing approach to cointegration starts with tests for the presence of
long-run (cointegrating) relationships in models (1)-(2). To conduct this test, a set of unrestricted error
correction models (UECM) of the following form is estimated:
∑∑∑∑∑
=
−
=
−
=
−
=
−
=
−
Δ+Δ+Δ+Δ+Δ+=Δ
N
i
ith
N

i
itf
N
i
itu
N
i
itg
N
i
itIt
HPaFCaUCCaGDPaIaaI
iiiii
11111
0

ttttttt
N
i
itn
UNCaHPaFCaUCCaGDPaIaUNCb
i
1161514131211
1
υ
+++++++Δ+
−−−−−−
=
−
∑

(3)
∑∑∑∑∑
=
−
=
−
=
−
=
−
=
−
Δ+Δ+Δ+Δ+Δ+=Δ
N
i
itr
N
i
ith
N
i
its
N
i
itI
N
i
itct
IRbHPbSMPbDYbCbbC
iiiii

11111
0

tttttt
IRbHPbSMPbDYbCb
21514131211
υ
+
+
+
+++
−−−−−
(4)
Equation (3) is set up to test whether cointegration exists between the variables in model (1).
Likewise, equations (4) set up to test whether cointegration exists between the variables in model (2).
In equation (3), the null hypothesis of no cointegration amongst the variables in model is H
0
: a
1
=a
2
=a
3
=
a
4
=a
5
=a
6

=0 against the alternative hypothesis of H
1
: a
1
≠a
2
≠a
3
≠a
4
≠a
5
≠0. In equation (4), the null
hypothesis of no cointegration amongst the variables in model (4) is H
0
: b
1
=b
2
=b
3
=b
4
=b
5
=0 against the
alternative hypothesis of H
1
: b
1

≠b
2
≠b
3
≠b
4
≠b
5
≠0. Before the bounds test can be conducted, the lag order
(i.e. value of N) of each UECM has to be determined. To accomplish this task, the approach taken by
Lee (2008) is adopted here i.e. a sufficient number of lags (N) in the first differences are added in order


3
According to Zhu (2003) and Phang (2004), changes in asset prices affect financial wealth, which in turn affects
consumption. So, asset prices can be use as proxy for wealth. In Ludwig and Slok (2004), the authors have used house
price and stock price to proxy for housing and stock market wealth respectively.
4
Higher interest rate reduces demand for consumer credit to purchase durable goods
International Research Journal of Finance and Economics - Issue 30 (2009) 73
that the disturbance terms in equations (3)-(4) do not have autocorrelation up to lag order of 2,
according to the Breusch-Godfrey Lagrange Multiplier (LM) test. The chosen value of N is the lowest
value when the test is unable to reject the null hypothesis of no autocorrelation at 5% level of
significance.
For a given level of significance, the critical values in the bounds test consist of a lower and
upper bound. The critical value bounds depend on the structure of UECM being used. In our case, we
have adopted the ‘unrestricted intercept and no trend’ structure so that the critical value bounds would
be taken from Pesaran et al’s (2001) Table CI(iii). Other studies in the literature which have chosen the
same model structure include Tang (2004), Liang and Cao (2007), Ho and Wong (2003) and Lee
(2008). There is evidence to reject the null of no cointegration if the F-statistic exceeds the upper

bound critical value. On the other hand, the null of no cointegration is not rejected if the F-statistic is
smaller than the lower bound critical value. Ambiguity arises if the F-statistic lies between the upper
and lower bound, in which case one cannot conclude whether cointegration exists until the order of
integration for the variables are established using unit root tests
5
.
After the presence of cointegration is found to exist in all relationships, (1)-(2) are then
estimated as ARDL models. The ARDL (p, q
1
, q
2
,…,q
k
) model has the following general structure
6
:
tt
k
i
tiixty
wXqLYpL
i
μδ
++Φ=Φ
∑
=
'),(),(
1
,
(5)

where
p
yyyy
LLLpL
p
Φ−−Φ−Φ−=Φ 1),(
2
21
(6a)
i
ii
q
iqiitiix
LLXqL Φ++Φ+Φ=Φ ),(
10,
, i=1,2,…,k (6b)
L is a lag operator such that Ly
t
= y
t-1
while wt is an sx1 vector of deterministic variables
including dummies, trends and other exogenous variables. The estimated ARDL models can be re-
parameterised to obtain the long-run coefficients in the respective cointegrating relationships as well as
their error correction representations (Pesaran and Pesaran, 1997). The magnitude and sign on the
estimated coefficients can subsequently be interpreted, as what had been done in most studies
involving the application of the ARDL and bounds testing procedure (see for instance, Narayan and
Smyth, 2006, Liang and Cao, 2007 and Ho and Wong, 2003).

4.1.4. Testing the impact of property prices on domestic demand and GDP
After estimating the consumption and investment channels, step 2 of our research involves testing

whether total expenditure and real GDP are driven by fluctuations in property prices.

4.1.4.1. The property priceÆdomestic demand link
Following the convention in Ho and Wong (2003, 2008), we define domestic demand or expenditure as
the sum of gross investment and private consumption
7
. Given the determinants of consumption and
gross investment spending in equations (1) and (2), a model on determinants of domestic demand
(DEM) can be obtained:
tttttttt
IRUCCUNCHPFCTaxGDPDEM
76543210
γ
γ
γ
γ
γ
γ
γ
γ
+
+
+
+
+++= (7)
Equation (7) is estimated using the ARDL and bounds testing procedure similar to equations (1)
and (2). Particularly, we specify (7) in the following UECM:


5

In this paper, we can dispense with the need to do pre-testing for unit roots, given the advantages of the bounds testing
approach which can be used irrespective of whether the underlying variables are I(1) or I(0). This convention follows Ho
and Wong (2003) and Lee (2008)
6
For more details on ARDL models, interested readers can refer to Pesaran et al (2001)
7
In other words, components of demand which are directly affected by property price
74 International Research Journal of Finance and Economics - Issue 30 (2009)
∑∑∑∑∑
=
−
=
−
=
−
=
−
=
−
+Δ+Δ+Δ+Δ+Δ+=Δ
N
i
ith
N
i
itf
N
i
itT
N

i
itg
N
i
itdet
HPcFCcTaxcGDPcDEMccDEM
iiiii
11111
0
131211
111
−−−
=
−
=
−
=
−
+++Δ+Δ+Δ
∑∑∑
ttt
N
i
itu
N
i
itu
N
i
itun

TaxcGDPcDEMcIRcUCCcUNCc
iii

tttttt
IRcUCCcUNCcHPcFCc
11817161514
υ
+
+
+
+
++
−−−−−
(8)
The null hypothesis of no cointegration amongst the variables in model is H
0
:
c
1
=c
2
=c
3
=c
4
=c
5
=c
6
=c

7
=c
8
=0 against the alternative hypothesis of H
1
: c
1
≠c
2
≠c
3
≠c
4
≠c
5
≠ c
6
≠c
7
≠c
8
≠0. The
bounds test procedure is the same as that used in testing cointegration in equations (3)-(4). If
cointegration is detected in (8), we estimate (7) as an ARDL model and re-parameterise the coefficients
to obtain the long-run coefficients and short-run dynamics.
We are interested in the statistical significance of the HP coefficient. If property price booms
strongly and significantly increase both consumption and investment, the net impact on total domestic
spending (DEM) would also be significantly positive. However, if both channels are not operative,
property prices would have no significant effect on DEM.

4.1.4.2. Testing property priceÆreal GDP link
To assess whether property price is a driving factor for real GDP fluctuations, we first test if property
price and GDP are cointegrated when GDP is the dependent variable. To test for cointegration, we
employ the ARDL and bounds testing procedure again. Particularly, we set a similar UECM just like
what we have done in equation (3), (4) and (8):
1211
11
0 −−
=
−
=
−
++Δ+Δ+=Δ
∑∑
tt
N
i
ith
N
i
itgt
HPdGDPdHPdGDPddGDP
ii
(9)
For equation (9), the null hypothesis of no cointegration amongst the variables in model is H
0
:
d
1
=d

2
=0 against the alternative hypothesis of H
1
: d
1
≠d
2
≠0. If cointegration is detected, we proceed to
estimate the GDP and property price link as an ARDL model.
If property prices booms drive up consumption and investment strongly, this would also lead to
an unambiguous and significant increase in real GDP via increases in domestic demand. The results of
the test can be used as a basis for conducting test on whether there is long-run and short-run causality
in a Granger sense running from property prices to GDP
8
.


5. Data Sources and Definitions of Variables
Data for all variables are taken from various issues of Bank Negara Malaysia’s Monthly Statistical
Bulletin. Property price is measured by the Malaysian House Price Index (MHPI), published by the
Property and Valuation Services Department under the Ministry of Finance. Data are quarterly, and
span 1991Q1-2006Q2. Table 1 summarises the definitions of variables. All variables are expressed in
natural logs, except UNC, IR and SPREAD which can take negative values.


8
Studies on Granger causality which carried out similar procedures include Liang and Cao (2007) and Lee (2008).
International Research Journal of Finance and Economics - Issue 30 (2009) 75
Table 1: Variables and definitions

Variables Definition
1. I
a/
Gross fixed capital formation at constant 1987 prices
2. GDP Gross domestic product at constant 1987 prices
3. UCC
b/

(average lending rate+ rate of depreciation)
X 1-corporate income tax
deflated by producer price index (1989=100)
4. HP
Malaysian house price index (hereafter, MHPI), deflated by producer price index
(1989=100). Quarterly data only became available beginning 1999. Hence, data prior to
1999 were interpolated using cubic-spline method
c/

5. FC
Kuala Lumpur Composite Index (hereafter, KLCI), deflated by producer price index
(1989=100)
6. UNC Producer Price Index inflation
7. C
d/
Private consumption at constant 1987 prices
8. DY Disposable income at constant 1987 prices
9. SMP
Kuala Lumpur Composite Index (hereafter, KLCI), deflated by producer price index
(1989=100)
10. IR Average lending rate of commercial banks, adjusted for producer price index inflation
11. DEM

Sum of gross fixed capital formation at constant 1987 prices and private consumption at
constant 1987 prices
12. Tax Sum of corporate and individual income taxes deflated by producer price index (1989=100)
a/
This aggregate captures both public and private sector investment in fixed assets.
b/
The definition of nominal user cost of capital was taken from Ang (2007). In particular, Ang (2007) assumes rate of
depreciation to be 5% whereas price of capital is the gross capital formation deflator. In the sample, rate of corporate
income tax was 35% from 1991 to 1992, 32% from 1993 to 1995, 30% from 1996 to 1997 and 28% from 1998-2006.
c/
The MHPI is the only property price index available for Malaysia. It is important to note that the interpolated MHPI data
does not obviously contain more information than the original annual data. Hence, interpolation merely offers suggestions
as to how the missing quarterly time series may look like. One objection to using interpolated data is that the constructed
time series seems smooth and devoid of short-term volatility. Despite this shortcoming, the interpolated series gives a
reasonably good depiction of the actual behaviour of MHPI, because MHPI is in reality a relatively smooth index as well.
This smoothness is attributed to the characteristics of the housing market such as infrequent trading (Hilbers et al, 2001),
lack of short-selling/short-term speculation (Davis and Zhu, 2004), the long-term nature of the market (Wang, 2001) and
more importantly, valuation smoothing (Davis and Zhu, 2004), especially because the MHPI was constructed using
valuations data (Ting, 2003). Other studies which have interpolated annual real estate data to obtain quarterly data include
Chen and Patel (1998), Iacoviello (2002), Chirinko, De Haan and Sterken (2004) and Ludwig and Slok (2004).
d/
Aggregate consumption includes both durables and non-durable consumption. We did not use data for consumption of
durables and non-durables because no such data exists


6. Main Findings and Discussions
We first report findings in step 1 of our research. The results of the bounds test for the baseline models,
summarised in Table 2, rejects the null hypothesis of no cointegration at both 5% and 10% significance
levels. Thus, it can therefore be concluded that the consumption and investment functions are
cointegrating equations. The consumption and investment functions are thus estimated as ARDL

models. The underlying ARDL model can be re-parameterised to obtain the long-run cointegrating
coefficients and a short-run error correction representation. Since quarterly data is used, the maximum
order of ARDL is set equal to four. The most appropriate lag structure is selected using the Schwarz
Bayesian Criterion (SBC) (Pesaran and Shin, 1999). Detailed results of the underlying ARDL model
for the baseline investment and consumption functions are not reported, but can be produced upon
request
9
.


9
Seasonal dummy variables were included in the initial round of the estimation procedure. In the process of estimating the
consumption function, the F-test on the joint significance of the seasonal dummies was statistically significant at 5%
level, indicating that seasonality effects are present. Hence, the seasonal dummies were retained in the final model.
However, while estimating the investment function, seasonal dummies were not statistically significant. The inclusion of
these dummies in the investment regression also caused model misspecification as detected by the RESET test. As such,
the seasonal dummies have been dropped from the final investment function.
Price of
cap
it
a
l
76 International Research Journal of Finance and Economics - Issue 30 (2009)
The final estimation results pass a battery of diagnostics tests at 5% significance level.
Particularly, the null of ‘no serial correlation’ and ‘homoskedasticity’ cannot be rejected. Ramsey’s
RESET test does not indicate evidence of model misspecification while the Jarque-Bera test on
normality of residuals suggests that the residuals are normally distributed. The test for the existence of
Autoregressive Conditional Heteroskedasticity (ARCH) effects developed by Engle (1982), fail to
reject the null of no ARCH effects. Finally, the CUSUM and CUSUM of squares (CUSUMSQ) test
developed by Brown, Durbin and Evans (1975) indicate that the estimated equations are structurally

stable
1011
.

Table 2: Results of bounds test for baseline consumption and investment function
a/


Critical bounds
b/
Breusch-Godfrey LM statistic
c/

Equation Lag length (N) F-statistic
at 5%
significance
at 10%
significance
At lag=1 At lag=2
0.0194 2.4788
(3) 2 8.4951** (2.62, 3.79) (2.26, 3.35)
[0.890] [0.097]
3.5540 2.2215
(4) 1 4.8530** (2.86, 4.01) (2.45, 3.52)
[0.065] [0.120]
Note:
a/
*, ** and *** indicate 10%, 5% and 1% level of significance, respectively
b/
Critical bounds taken from Table CI (III) in Pesaran, Shin and Smith (2001).

c/
Values in parenthesis are p-values

6.1. Is there an investment channel?
6.1.1. Long-run relationship
Table 3 summarises the results of the equilibrium relationship between investment in fixed assets and
property (house) prices. In the baseline model, the magnitude of the coefficient on property price
indicates that a 1% increase in prices yields approximately 0.5% increase in gross investment spending
12
. This effect supports the Tobin q-ratio and financial accelerator principle mentioned in Section 3.
13

Notably, the estimated property price coefficient is qualitatively and quantitatively consistent with
those in Peng et al (2001) and Peng et al (2008).


10
Detailed results are not reported here to conserve space but interested readers can request from the author.
11
Additionally, we have experimented with dummy variables to test for structural change caused by the crisis and capital
controls. The coefficient on the dummy variables turned out to be statistically insignificant, so the dummy variables
have been dropped in the final specification
12
Importantly, aggregate investment in this case includes both public and private investment. We did not separate the two
because disaggregated quarterly data on investment are not available.
13
The private sector and government hold a combined share of about 12% (government and statutory bodies: 5.5%,
private sector: 6.4%; see Ng, 2006 for details) of total housing units available in the country. Ownership of these
property assets would surely induce some liquidity/balance sheet effects on private sector firms and government
statutory bodies leading them to increase capital expenditure

International Research Journal of Finance and Economics - Issue 30 (2009) 77
Table 3: Long-run coefficients and error-correction mechanism of investment function

Long-run coefficients
Variables Coefficients
0.3585**
GDP
(0.1596)
0.5226***
HP
(0.2208)
0.5848***
FC
(0.0697)
-0.0831
UCC
(0.1052)
0.0035
UNC
(0.0039)
5.0723**
Constant
(1.9904)
Short-run dynamics (error correction mechanism)
Variables Coefficients
1.7523***
ΔGDP
t

(0.3190)

-0.5565**
ΔGDP
t-1

(0.2607)
0.8382***
ΔGDP
t-2

(0.2988)
-0.0586
ΔUCC
t

(0.0698)
-1.0836**
ΔHPt
(0.5293)
1.5984***
ΔHP
t-1

(0.4729)
0.1134*
ΔFC
t

(0.0669)
-0.3026***
ΔFC

t-1

(0.0838)
-0.2285***
ΔFC
t-2

(0.0691)
-0.0039
ΔUNC
t

(0.0039)
3.5733**
Constant
(1.3329)
-0.7045***
ECM
(0.1169)
Diagnostics test for the presence of:
Serial correlation F (4, 38) = 2.2327 [0.084]
Model misspecification F (1, 41) = 2.5418 [0.119]
Non-normality of residuals Χ
2
(2) = 2.0571[0.358]
Heteroskedasticity F (1, 56) = 2.1991 [0.144]
ARCH (1) F (1, 41) = 0.0236 [0.879]
ARCH (2) F (1, 41) = 0.0618 [0.940]
*, ** and *** indicate 10%, 5% and 1% level of significance, respectively; all values in (.) represent standard errors; values
in [.] represent p-values

It can be noted that the stock price coefficient has the expected sign and is statistically
significant at 5% level. This suggests that financial constraints are an important consideration in firms’
expenditure plans (Herrera and Perry, 2001). In addition, while both the coefficients on GDP and UCC
have expected signs only the coefficient on GDP is statistically significant at 5% level, implying that
Jorgensen’s (1963) view is only partially supported. The statistical insignificance of the UCC
78 International Research Journal of Finance and Economics - Issue 30 (2009)
coefficient implies that cost of capital has little influence on investment decisions. The inclusion of
public investments (which tend to be influenced less by cost of capital and profitability of investment
than by ‘national interests’) in our gross investment aggregate can perhaps explain this result.
The reported long-run coefficients also show that inflation did not have the expected sign and is
also statistically insignificant. One explanation for the positive but insignificant inflation coefficient is
that two separate effects were at work, but pulling in opposite directions. On the one hand, a limited
amount of inflation is required to keep spending levels growing but on the other hand, inflation also
causes uncertainty, which makes planning more difficult and discouraging spending as a result. Thus,
no obvious effects on investment can arise when these two opposing forces net out.

6.1.2. Short-run dynamics
Taking into account the disequilibrium, short-run relationship between investment and its
determinants, the second part of Table 3 suggests that the coefficient on the error correction term
(ECM) which is close to 1 in absolute value for the baseline model, statistically significant and has the
correct sign, indicates a very rapid process of adjustment towards steady state. This implies that
whenever the system is perturbed, 70% of the disequilibrium gap can be closed up from one quarter to
the next. Hence, the system could almost reach equilibrium within a year.
Nonetheless, the signs on the coefficients of lagged first-differences in GDP, property and stock
prices are not consistently positive
14
. We reason that in the very short-term firms could over- or under-
invest relative to their long-run investment plans, due to temporary cash flow problems, mistaken
decisions or irrational exuberance

15
. To correct their ‘errors’ to be more in line with long-run
investment trends, it becomes necessary to cut (raise) short-term investments even when economic
activities are strengthening (dampening). Importantly, it can be observed that while there are positive
and negative housing price coefficients in the error correction representations, the positive coefficients
tend to be more dominant, either in terms of having a larger magnitude or being more statistically
significant. Generally, this implies that higher housing prices in the short-run could strengthen
investment spending.

6.2. Is there a wealth effect channel?
6.2.1. Long-run relationship
The equilibrium relationship between household consumption spending and house price, reported in
Table 4, suggests a statistically significant negative link between the two variables. Particularly, the
long-run consumption function suggests that a 1% increase in house prices leads to a 0.65% decline in
private consumption
16
. This finding contradicts those in Ludwig and Slok (2004) and Case et al
(2005). The claim that housing price is positively associated with consumption cannot be generalised
to Malaysia. Consequently, our findings also vindicate those in Ng (2002) who observed negative links
between housing price and consumption in a cointegrating consumption function for Singapore.
The negative house price coefficient reflects the absence of positive housing wealth effect on
consumption and the fact that housing price inflation benefits only few households but leads to the
majority of households becoming worse off. There are numerous reasons to rationalise this outcome.


14
Similar observations were made by Ng (2002) in his study of the investment function in Singapore
15
This fact ought not be surprising textbook macroeconomics informs us that investment is generally a very volatile
component in GDP

16
This negative sign is robust to different specifications of the model. Of note, Malaysia has never experienced any
bubble phenomenon in housing prices throughout the sample period (see Hui, 2008). Thus, any house price increase
will be viewed as permanent so that consumers respond fully to any price increases. In contrast, if bubble phenomenon
were widespread, any increase in prices would be followed by a collapse upon the bursting of the bubble. In such a
circumstance, consumers would not take the house price increase seriously and hence refrain from changing their
consumption.
International Research Journal of Finance and Economics - Issue 30 (2009) 79
Firstly, opportunities for housing equity withdrawals are very limited in Malaysia
17
. Secondly, while
higher house prices may induce a feeling of optimism and hence make an owner-occupier feel
wealthier, they would find that higher house prices also translates to higher cost of consuming houses
given that the house is both asset and consumption good. Hence, there is limited positive housing
wealth effect among these consumers.

Table 4: Long-run coefficients of cointegrating consumption equation (2)

Long-run coefficients
Variables Coefficients
1.2950***
DY
(0.1057)
-0.6554***
HMP
(0.1944)
0.2434***
SP
(0.0740)
-0.0057**

IR
(0.0023)
-4.2810***
Constant
(1.2655)
Short-run dynamics
-0.498***
ΔC
t-1

(0.1072)
-0.3248**
ΔC
t-2

(0.1271)
-0.517***
ΔC
t-3

(0.1015)
0.4234***
ΔDY
(0.0691)
0.1220
ΔHMP
(0.1469)
0.3797**
ΔHMP
t-1

(0.1440)
0.3058**
ΔHMP
t-2

(0.1353)
0.0315
ΔSP
(0.0205)
-0.0019**
ΔIR
(0.0008)
-1.400***
Constant
(0.2768)
-0.327***
ECM
(0.0703)
Diagnostics test for the presence of:
Serial correlation F (4, 38) = 1.4756 [0.229]
Model misspecification F (1, 41) = 0.2012 [0.656]
Non-normality of residuals Χ
2
(2) = 0.6178 [0.734]
Heteroskedasticity F (1, 56) = 0.5344 [0.468]
ARCH (1) F (1, 41) = 0.0347 [0.853]
ARCH (2) F (2, 40) = 0.0347 [0.966]
*, ** and *** indicate 10%, 5% and 1% level of significance, respectively; all values in (.) represent standard errors; values
in [.] represent p-values; seasonal dummies included but not reported

17
Existing housing loan products in Malaysia are mainly for financing housing purchases rather than withdrawing housing
equity per se. Arguably, one other option to withdraw housing equity is through mortgage refinancing. But this option is
not available to all consumers and is only undertaken by those whose debt profiles are such that the benefits of
refinancing outweigh the costs. Furthermore, the decision to refinance a mortgage could be motivated not so much by
the desire to increase but to maintain usual consumption habits in view of higher living expenses. Also of note, re-
financing a housing loan does not depend on house prices alone but also on interest rates.
80 International Research Journal of Finance and Economics - Issue 30 (2009)
Fourthly, Malaysia is a developing country which underwent rapid urbanisation as a result of
structural change in the economy. Urbanisation rate was merely 38.8% in 1980 before increasing
nearly two-fold to 62% in 2000 and 66.9% in 2005 (see Jaafar, 2004 for details). As argued in Section
3, such developments tend to create ‘excess demand’ for houses, the magnitude of which can be
observed from the growth rate in new housing stock
18
. Since there tends to be more buying households
(losers) than there are households willing to sell (gainers) in the secondary market during periods of
rapid urbanisation, housing price increases would cause a net loss among households transacting in
houses, yielding a negative link between house price and private consumption.
Finally, demographic statistics from Ng (2006) suggests that population in Malaysia consists of
more working adult population relative to retirees. Over 60% of the population was in the age group of
15-64, while less than 5% of the population are over 65, in contrast to US and Singapore who have
larger over-65 population. This implies that a bigger pool of first-time buyers and up-graders exists
relative to the pool of households trading down
19
. As house price increases benefits the latter but
affects the former, rising house prices can induce a net negative effect on consumption.
Meanwhile, the signs on other coefficients appear to conform to their respective a priori
expectations. The estimated coefficients on real disposable income and stock price are significant at
5% level and have the correct signs. Particularly, private consumption is highly responsive to changes

in income. Stock prices appear to have a reasonable influence on private consumption. The coefficient
on real interest rate is also statistically significant at 5% level and possesses the expected sign. Given
that durables are also included in the consumption aggregate, it would not be surprising to observe a
statistically significant interest rate coefficient.

6.2.2. Short-run dynamics
Our estimated error correction model (second part of Table 4) turned up error correction coefficients
(ECM) which are statistically significant with the correct sign, implying that error correction
mechanism exists. From the magnitude of the coefficient of error correction, it can be concluded that
approximately 20-33% of the disequilibrium gap in consumption is closed between the present and
next quarter. However, the short-run housing price coefficient turns out to be positive and statistically
significant.
We do not think that this result is unreasonable. Importantly, it should be noted that in the
absence of collateral enhancement effects, losses suffered by buyers of more expensive housing tend to
be negligible in comparison to the gains reaped by sellers in the short-run. This is because mortgage
payments serviced by buyers are spread over many years (long-run) whereas proceeds from the sales of
houses tend to be received by the seller in full payment almost instantly. Additionally, owner-occupiers
could be myopic in the short-run as what we have argued in Section 3. The observation that property
price coefficient is positive in short-run but negative in long-run is corroborated by Ng (2002) in the
case of Singapore.

6.3. Impact of property prices on demand and GDP
Our findings on the consumption and investment channels suggest that in the long-run, property booms
have a significantly negative effect on private consumption and a significantly positive effect on gross
investment. Thus, the net effect on domestic demand would likely cancel out. This hypothesis is tested
by estimating the model on determinants of domestic demand (Equation 7). Importantly, since UCC
and UNC were statistically insignificant in determining investments, both variables were dropped from
equation (7) to yield a more parsimonious model which we estimated.

18
Housing stock expanded at an average of 4.1% from 4.1 million units 1991 to 5.5 million units in 2000. The total
increase in housing stock in 2000-2004 alone exceeds 1 million units (Ng, 2006). In aggregate, the increase in housing
stock was not excessive but was in line with population growth because the ratio of household per occupied housing
unit remained steady at 1 during 1991-2000
19
Up-graders and down-graders do not always balance out. See Phang (2004) for instance.
International Research Journal of Finance and Economics - Issue 30 (2009) 81
Results of the bounds test as reported in Table 5 rejects the hypothesis that there is no stable
long-run relationship between domestic demand and its determinants. We thus conclude that equation
(7) is a cointegrating relationship. Next, we estimated the ARDL regression corresponding to equation
(7). The standard diagnostic tests on the existence of serial correlation, heteroskedasticity, ARCH
effects, model misspecification, non-normality and structural stability (CUSUM and CUSUMSQ tests)
were replicated and passed. In Table 6 below, the long-run coefficients and short-run dynamics of
equation (7) are reported. We can see that the coefficient for property is statistically insignificant which
confirms our claim. Other coefficients appear to be intuitively compelling. Particularly, real stock price
and real GDP are important driving factors for domestic demand. Increases in either variable would
positively influence total demand through increases in consumption and investment.

Table 5: Results of bounds test for the existence of long-run, cointegrating relationship in the domestic
demand equation (7)
a/


Critical bounds
b/
Breusch-Godfrey LM statistic
c/

F-statistic Lag length (N)

at 5% significance at 10% significance At lag=1 At lag=2
0.1226 0.129
4.2683* 5 (2.62, 3.79) (2.26, 3.35)
[0.730] [0.880]
Note: a/ *, ** and *** indicate 10%, 5% and 1% level of significance, respectively.
b/ Critical bounds taken from Table CI (III) in Pesaran, Shin and Smith (2001).
c/ Values in parenthesis are p-values
82 International Research Journal of Finance and Economics - Issue 30 (2009)
Table 6: Long-run coefficients of cointegrating domestic demand equation (7) and corresponding error
correction model

Variables Coefficients
0.8351***
GDP
(0.1435)
0.2008
HP
(0.2657)
0.2917***
FC
(0.0934)
-0.0099**
IR
(0.0043)
-0.0543
Tax
(0.1010)
1.2764
Constant
(1.4969)

Short-run dynamics (error correction mechanism)
Variables Coefficients
1.2662***
ΔGDP
(0.3269)
-1.0011***
ΔGDPt-1
(0.3378)
1.0291***
ΔGDPt-2
(0.3022)
-0.7413**
ΔGDPt-3
(0.2959)
-0.0125
ΔHP
(0.2811)
0.7729***
ΔHPt-1
(0.2718)
0.0997***
ΔSP
(0.0338)
-0.0034**
ΔIR
(0.0015)
-0.0186
ΔTax
(0.0344)
0.4360

Constant
(0.5645)
-0.3416***
ECM
(0.0876)
Diagnostics test for the presence of:
Serial correlation F (4, 38) = 2.4620[0.062]
Model misspecification F (1, 41) = .44014[0.511]
Non-normality of residuals Χ
2
(2) = 6.6498[0.036]
Heteroskedasticity F (1, 56) =.019183[0.890]
ARCH (1) F (1, 41) = .0045233[0.947]
ARCH (2) F (2, 40) = .95988[0.392]
*, ** and *** indicate 10%, 5% and 1% level of significance, respectively; all values in (.) represent standard errors; values
in [.] represent p-values; seasonal dummies included but not reported

Meanwhile, the short-run dynamics of the model’s adjustment towards long-run equilibrium
suggests that an error-correction mechanism exists. The error correction term has the correct sign and
is highly statistically significant at even 1%. Its magnitude indicates a moderate speed of adjustment.
The short-run dynamics reflects those exhibited in the consumption and investment models.
Particularly, as we have argued in Section 6.1.2 and 6.2.2, stronger property prices could strengthen
investment and consumption spending in the short-run, giving rise to a positive impact on domestic
demand. This claim is supported here in the second part of Table 6. While there is a positive and
International Research Journal of Finance and Economics - Issue 30 (2009) 83
negative property price coefficient, the positive coefficient is larger and strongly statistically significant
while the negative coefficient is not.
So far, our evidence above shows that property price has no significant effect on long-run
domestic demand. Consequently, property prices should not exert any impact on long-run GDP either.
To confirm this argument, we carried out bounds testing based on the UECM in (9) with house price as

proxy for HP and real GDP as proxy for GDP. Results of the bounds test reported in Table 7 show that
there is indeed no cointegration between property price and real GDP when real GDP is the dependent
variable. Thus, property price is not an important long-run, driving variable for the explanation of
GDP, again confirming our argument. There is no long-run Granger causality from property to real
GDP.
However, the scenario is slightly different in the short-run. Because property prices may have
some positive short-run impact on domestic demand as shown in the error correction model for
domestic demand above, prices would thus exert some effects on short-run fluctuations in real GDP.
To test this hypothesis, we conduct a simple Granger non-causality test by modifying equation (9) and
estimating the following VAR equation using OLS:
t
N
i
ith
N
i
itgt
HPdGDPddGDP
ii
ε
+Δ+Δ+=Δ
∑∑
=
−
=
−
11
0
(10)
Testing Granger non-causality involves testing the hypothesis that all the coefficients on lagged

house price are jointly equal to zero, i.e. d
h1
=d
h2
=…=d
hN
=0 against the alternative that at least some of
the coefficients are non-zero. We determine the lag length by increasing N until the Breusch-Godfrey
LM test could no longer detect serial correlation up to lag order of 2 (Lee, 2008). Our chosen optimal
lag is five
20
. Results of the test, reported in Table 8 indicate that property prices do Granger-cause real
GDP in the short-run at 5% level of significance.

Table 7: Results of bounds test – does property price drive GDP?
a/


Critical bounds
b/
Breusch-Godfrey LM statistic
c/

F-statistic Lag length (N)
at 5% significance at 10% significance At lag=1 At lag=2
1.7512 0.8850
1.7636 5 (4.94, 5.73) (4.04, 4.78)
[0.193] [0.420]
Note:
a/

*, ** and *** indicate 10%, 5% and 1% level of significance, respectively.
b/
Critical bounds taken from Table CI (III) in Pesaran, Shin and Smith (2001).
c/
Values in parenthesis are p-values

Table 8: Does property price Granger-cause GDP?
a/


Sample: 1991Q1-2006Q2
Lags: 5
Observations: 56
Null hypothesis F-statistic p-value
HP does not Granger-cause GDP 2.4790** 0.047
*, ** and *** indicate 10%, 5% and 1% level of significance, respectively.


7. Policy Implications and Concluding Remarks
This paper investigates the impact of property market developments on the real economy for the case
of Malaysia. We address this topic not only because there is no literature examining Malaysia’s
experience, but also because the debates on the property-economy linkage is yet to be conclusive as
can be seen from numerous country case studies cited in the earlier part of the paper. Moreover, there
seems to be crucial policy lessons to be drawn from whatever findings obtained from our analysis.


20
Seasonal dummies were included in the estimation of (10)
84 International Research Journal of Finance and Economics - Issue 30 (2009)
Our findings show that firstly, in the long-run, domestic demand and GDP are neutral to

fluctuations in property prices. The reason is that while property booms drive higher gross investments,
there is an offsetting decline in private consumption. In the short-run however, the neutrality of
demand and GDP to property price fluctuations is less certain. It is conceivable that property booms
can reinforce real economic booms since property prices do seem to exert temporary pro-cyclical
effects on both consumption and investment. These findings imply that stimulating property market
activities is not an effective way to drive sustained growth in the real economy. Nonetheless, there may
be room to consider the property market as a policy tool for short-term macroeconomic management.
Additionally, while the analysis of long-run relationships between property markets and the real
economy seems robust, there are further research areas to be explored, particularly with respect to
whether property prices and the real economy are fractionally or seasonally cointegrated. Meanwhile,
the findings on short-run relationships between property and the economy could be further confirmed
in a business cycles framework. Addressing these concerns is an on-going endeavour.


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