International Research Journal of Finance and Economics ISSN 1450-2887 Issue 30 (2009) © EuroJournals Publishing, Inc. 2009 http://www.eurojournals.com/finance.htm 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: huihonchung@gmail.com 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 [...]... 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... 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. .. 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... and Zacho, L (2001), Real Estate Market Developments and Financial Sector Soundness,’ IMF Working Paper No 01/129 Ho, L and Wong, G (2003), The nexus between housing and the macroeconomy: Hong Kong as a case study’, paper presented at the Housing-Macroeconomy workshop at Chinese University of Hong Kong, August 24-25, Hong Kong International Research Journal of Finance and Economics - Issue 30 (2009)... 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... 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. .. 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... presented at the 9th Pacific Rim Real Estate Society Annual Conference, 20-22 January, Brisbane Wang, P (2001), Econometric Analysis of the Real Estate Market and Investment, Routledge: London 86 [40] International Research Journal of Finance and Economics - Issue 30 (2009) Zhu, H (2003), The Importance of Property Markets for Monetary Policy and Financial Stability,’ paper presented at the IMF-BIS Conference... residential property market and the macroeconomy’, Ministry of Trade and Industry, Economic Survey of Singapore (First Quarter) Ng, A (2006), ‘Housing and Mortgage Markets in Malaysia , in: Kusmiarso B (Ed.), Housing and Mortgage Markets in SEACEN Countries, SEACEN Publication, pp 123-188 Peng W., Tam D., Yiu M.S (2008), Property market and the macroeconomy of Mainland China: A Cross Region Study’,... consumption: do they move together? Evidence from Singapore Journal of Housing Economics, 13; pp 101-119 Robinson J The rate of interest and other essays Macmillan: London; 1952 Tang T.C (2004), ‘Demand for broad money and expenditure components in Japan: an empirical study’, Japan and the World Economy, 16, pp 407-502 Ting, K.H (2003), ‘Investment characteristics of the Malaysian residential property sector’, . 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. 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. 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