Housing wealth effects in singapore public housing versus private housing

HOUSING WEALTH EFFECTS IN SINGAORE: PUBLIC HOUSING VERSUS PRIVATE HOUSING CHEN SHILU (MASTER OF SOCIAL SCIENCES), NUS A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF ECONOMICS DEPARTMENT OF ECONOMICS NATIONAL UNIVERSITY OF SINGAPORE 2010 Acknowledgement It is my pleasure to express the deepest appreciation to those who has helped me with this thesis. I owe sincere gratitude to my most respected supervisors, A/P Anthony Chin Theng Heng, and A/P Sau Kim Lum, for their patience, encouragement and illuminating guidance. Through the period of the writing of this thesis, they have spent much time on each of my drafts and offered me many valuable suggestions. I want to thank them for generously sharing me with their knowledge and time. Without their help, this thesis could not have been completed. i TABLE OF CONTENTS SUMMARY ................................................................................................. III LIST OF TABLES ...................................................................................... IV LIST OF FIGURES ......................................................................................V 1. INTRODUCTION .................................................................................... 1 2. LITERTATURE REVIEW...................................................................... 7 3. MAIN CONTRIBUTIONS .................................................................... 16 4. THEORETICAL FRAMEWORK ........................................................ 18 4.1 STANDARD LIFETIME BUDGET CONSTRAINTS ....................................... 19 4.2 PRIVATE HOUSING WEALTH EFFECTS ................................................... 22 4.3 PUBLIC HOUSING WEALTH EFFECTS .................................................... 23 4.4 CPF WEALTH EFFECTS ......................................................................... 26 5. EMPIRICAL STUDIES ......................................................................... 28 5.1 DATA...................................................................................................... 28 5.2 METHODOLOGY .................................................................................... 32 5.3 EMPIRICAL RESULTS ............................................................................. 34 5.3.1 Private Housing Wealth Impact On Consumption ......................... 34 5.3.2 Private Housing Wealth Impact On Consumption In Different Sub-Periods ............................................................................................... 37 5.3.3 Impulse Response Analysis Of Private Housing Wealth Impact On Consumption ........................................................................................ 39 5.3.4 Public Housing Wealth Impact On Consumption ........................... 44 5.3.5 Public Housing Wealth Impact On Consumption In Different Sub-Periods ............................................................................................... 45 5.3.6 Impulse Response Analysis Of Public Housing Wealth Impact On Consumption .............................................................................................. 48 5.3.7 Asymmetric Wealth Effects Of Private Housing On Consumption. 49 6. CONCLUSION ....................................................................................... 51 BIBLIOGRAPHY ....................................................................................... 53 ii Summary This study aims to explore whether appreciation in housing prices affects consumption in Singapore, and whether private housing and public housing exhibit different wealth effects. Using aggregate data, the empirical results show no significant private housing wealth effects or public housing wealth effects in the Q1:1975-Q4:2009 period and Q1:1990-Q4:2009 period respectively. Nevertheless, there is evidence of asymmetric effects in private housing market, with consumption responding significantly towards price increases while remaining stable during price downturn. It is also observed that there was a structural change in public housing and public housing wealth effects started to be significant in 2003, when a series of policies were introduced to boost the public housing market. The role of the Central Provident Fund (CPF) is extensively examined in this study. The results show that the CPF wealth effects are not statistically significant. However, the shocks in the balances of the CPF accounts tend to have a more persistent impact on consumption compared to housing wealth shocks. Additionally, the CPF is found to have a positive and significant impact on public housing prices though its influence on private housing prices is limited. iii LIST OF TABLES TABLE 1: ..................................................................................................... 31 TABLE 2: ..................................................................................................... 35 TABLE 3: ..................................................................................................... 35 TABLE 4: ..................................................................................................... 36 TABLE 5: ..................................................................................................... 38 TABLE 6: ..................................................................................................... 38 TABLE 7:. .................................................................................................... 43 TABLE 8: ..................................................................................................... 44 TABLE 9: ..................................................................................................... 45 TABLE 10: ................................................................................................... 46 TABLE 11: ................................................................................................... 46 TABLE 12: ................................................................................................... 48 TABLE 13: ................................................................................................... 50 iv LIST OF FIGURES FIGURE 1 ...................................................................................................... 4 FIGURE 2 ...................................................................................................... 6 FIGURE 3 ...................................................................................................... 6 FIGURE 4 .................................................................................................... 30 FIGURE 5 .................................................................................................... 31 FIGURE 6 .................................................................................................... 31 FIGURE 7 .................................................................................................... 40 FIGURE 8 .................................................................................................... 40 FIGURE 9 .................................................................................................... 41 FIGURE 10 .................................................................................................. 49 v 1. Introduction It has been widely observed that changes in property prices are associated with changes in national consumption in many countries. According to the Permanent Income Hypothesis (PIH) by Friedman (1957), consumption is equal to the annuity value of total wealth, including human wealth and nonhuman wealth. The lifecycle model by Modigliani and Brumberg (1955) also suggests that households accumulate and deplete their wealth to keep their consumption roughly stable. Therefore, it is expected that households will revise their consumption plan when they experience an unexpected change in their housing wealth. Various empirical studies examine the housing wealth effects and provide evidence of consumption responses to housing price changes. Bhatia (1987) and Case (1992) found significant housing wealth effects with macro data in the United States. Campbell and Cocco (2007), Case et al. (2005) and Engelhardt (1996) examined household expenditures using micro data in different countries and concluded that housing wealth effects were significant. Unlike the United States, the United Kingdom and other developed countries, the Singapore housing market consists of a dominant state-controlled public housing sector and a small private housing market that is relatively less regulated. Public housing is a unique feature of Singapore in that state-allocated units can be freely traded after a stipulated period. However, its wealth effects have not been widely examined. Nevertheless, public housing wealth effects cannot be ignored in the context of Singapore, given that more than 88% of Singapore citizens live in the subsidised public housing sector while private housing plays a limited role of supplying expensive residential units to the higher income groups. 1 New public housing is directly provided by the state on a 99-year lease through its public housing program managed by the Housing and Development Board (HDB), a sole agency in charge of the construction and sale of public housing. As the only supplier in the new public housing market, HDB offers different types of flats, including studio apartments, units with 2 rooms to 5 rooms, and Design, Build & Sell Scheme (DBSS) flats. DBSS flats were introduced in 2005 to involve the private sector in the development of public housing so as to bring about greater innovation in building and design. Demand for new public housing is also controlled as prospective buyers need to meet certain eligibility conditions such as citizenship, income, family nucleus and age. Specifically, eligible buyers must be Singapore Citizens and the family nucleus must comprise at least another Singapore Citizen or Singapore Permanent Resident. Income eligibility is adjusted by the government in accordance with the economic outlook and affordability of its citizenry. The current household income ceiling for HDB flats is $8,000. Households that are not eligible to buy new HDB flats may need to turn to the private units or resale HDB flats sold in the open market. They could also buy Executive Condominium, a hybrid form of private-public housing in Singapore if their income is below $10,000. The direct-purchase (new HDB) flats can be re-sold at market rates in the open market, known as the resale market for public housing, only after a minimum holding period (MOP) determined by HDB. Therefore, though HDB will not directly control the supply of resale HDB flats, the MOP has some impact over the number of flats that are eligible for resale. Currently, the MOP for direct-purchase HDB flats and resale flats is five years from the effective date of purchase. In terms of demand, prospective buyers of HDB 2 resale flats still need to meet certain eligibility conditions albeit less stringent than those for direct-purchase HDB flats. Private housing experienced rapid price increases in the 1980s and 1990s due to economic growth and an increase in demand for housing. Between 1986 and 1996, the Singapore Private Residential Property Price Index (RPPI) increased by 440% in nominal terms. However, prices fell by 45% between 1996 and 1998 due to government controls and the Asian financial crisis. The private housing market recovered slightly between 1998 and 2000, and prices started rising in 2005 given the policies implemented to boost the housing market. The relaxation of foreign ownership rules on apartments, the increase of the maximum loan-to-value ratio from 80% to 90%, the reduction of cash down payments from 10% to 5% for home purchase, and permission for non-related singles to use their CPF to jointly purchase private residential properties contributed to a 23.6% (20.9% in real terms) increase in the RPPI in 2007. In spite of the downturn incurred during financial crisis in 2008, the improved economic conditions in 2009 and low interest rates enabled the housing market to recover quickly. Price movements in the public resale housing sector generally mirror those in the private housing market, as shown in Figure 1. In 1993, the housing finance policy for resale HDB flats was liberalised and flats were allowed to be financed based on market prices, resulting in a boom of the resale market. Strong economic growth in the 1990s also led to upward pressure on resale public housing prices. Resale public housing is sometimes regarded as an inferior substitute of private housing. Singapore Permanent Residents (SPR) buyers who could not afford private housing turn to resale public flats, and some Singaporean households may prefer resale public flats to new units due to their 3 convenient location or the shorter waiting time to get the unit. A high immigration rate and the impatience for new units have continued to support the demand for resale public units and to push up the resale prices even during the crisis years. Figure 1 plots both the private housing prices and resale public housing prices against aggregate non-housing private consumption. There is a general simultaneity between housing prices and consumption, though the simultaneity becomes weak during the recessions in 1997 and 2008. This suggests that appreciation in housing prices is likely to exert a positive influence on consumption. 200 25000 180 160 20000 140 120 15000 100 80 10000 60 40 5000 0 199001 199004 199103 199202 199301 199304 199403 199502 199601 199604 199703 199802 199901 199904 200003 200102 200201 200204 200303 200402 200501 200504 200603 200702 200801 200804 200903 20 Public Resale Index Private Residential Index 0 Non housing consumption Figure 1. Public housing, Private housing and Non housing consumption Given the distinct institutional character of the residential sector, this paper distinguishes between the public and private housing markets and attempts to explore the significance of respective housing wealth effects in Singapore. It also tries to examine how the Central Provident Fund (CPF) affects consumption. Singapore adopted a mandatory fullyfunded defined contribution system to cover a wide range of retirement, healthcare, home ownership, family protection and asset enhancement needs. Working Singaporeans are 4 required to contribute a certain portion of their incomes, on a monthly basis, to their individual CPF accounts that are managed by the CPF Board. The CPF members‟ contributions are channeled to three accounts: the Ordinary Account from which funds can be used to buy a home, pay for CPF insurance, investment and education; the Special Account for old age, contingency purposes and investment in retirement-related financial products; and the Medisave Account for hospitalization expenses and approved medical insurance. The Special Account is seldom touched by households due to its purpose to serve retirement needs. Savings in this account, together with the leftover in the Ordinary Account after home purchase and other expenditures, will be transferred to the CPF Minimum Sum Scheme which ensures a minimal sum of money for Singaporeans during their retirement years. The CPF members are not allowed to use the monies under the CPF Minimum Sum Scheme for any form of investment. However, at age 55, members can use the Minimum Sum to purchase an annuity, or to leave the savings in a bank or in the CPF board. At the age of 62, the money would be released monthly to create a stable annuity flow. The CPF Public Housing Scheme (PHS) and the Residential Properties Scheme (RPS), which were introduced in 1968 and 1981 respectively, allow CPF members to use their CPF savings to pay for downpayment, stamp duty and mortgage payments incurred for the housing purchase. The number of members who have utilised the two schemes to finance their homes has been increasing steadily. The number of members under the PHS grew from 2,900 in 1968 to 1.29 million at the end of 2007, while the number of those under the RPS rose from 1,000 in 1981 to 226,000 in 2007. Presently, over 70% of flat owners service housing loans solely with CPF savings. The withdrawal of the CPF savings for housing moves closely the housing prices as shown in Figure 2 and Figure 3. 5 CPF schemes ease the financing burden of housing purchases and this paper attempts to examine how the CPF may affect public and private housing wealth effects. 1800 200 1600 180 160 1400 140 120 1200 1000 100 80 800 600 60 40 400 0 198103 198203 198303 198403 198503 198603 198703 198803 198903 199003 199103 199203 199303 199403 199503 199603 199703 199803 199903 200003 200103 200203 200303 200403 200503 200603 200703 200803 200903 200 CPF withdrawal for private housing 20 0 Private Residential Price Index Figure 2. CPF and Private housing price index 3000 160 140 2500 120 2000 100 1500 80 60 1000 40 500 CPF withdrawal for Public housing 200901 200801 200701 200601 200501 200401 200301 200201 200101 200001 199901 199801 199701 199601 199501 199401 199301 199201 199101 199001 0 20 0 Resale Price Index Figure 3. CPF and Public housing price index Moreover, CPF Members may invest their Ordinary Account balances under the CPF Investment Scheme - Ordinary Account (CPFIS-OA) and their Special Account balances 6 under the CPF Investment Scheme - Special Account (CPFIS-SA), if they are confident of earning a higher return than the CPF interest rates. Under the CPFIS, the CPF savings can be invested in shares and loan stocks, unit trusts, government bonds, statutory board bonds, bank deposits, fund management accounts, endowment insurance policies, investment-linked insurance policies (ILPs), exchange traded funds (ETFs) and gold. It is expected that the CPFIS may encourage households‟ investment in shares and enhance the stock wealth effects. Apart from the possible effects on housing and stock wealth, balances in CPF accounts may directly affect consumption as a form of forced saving. According to the lifecycle model, people smoothes consumption to prevent income uncertainty in the future, known as precautionary saving. Therefore, CPF contributions reduce current consumption on the one hand; but on the other hand, they enable households to be more prudent about their future and retirement due to the accumulations in their CPF accounts. Asher (1999) examined the economic impacts of the CPF, with a focus on the CPF adequacy for retirement financing by reviewing the CPF investment schemes. This paper tries to apply econometric methods to explore how the CPF affects Singapore housing prices and consumption. The remainder of this paper is organized as follows. Section 2 reviews relevant literature followed by Section 3 which summarises key contributions of the paper. The theoretical framework is provided in Section 4. Section 5 describes the data and statistics summary, and documents the empirical results. Finally, Section 6 concludes. 2. Literature Review 7 Given the correlation between Singapore house prices and consumption shown in Section 1, it is tempting to attribute it to the housing wealth effect. However, it is crucial to understand how consumption is determined, and whether wealth is causal to consumption. Keynes (1936) marks the start of modern consumption theory by exploring the relationship between consumption and income in his General Theory. He views consumption as a function of current income, and claims that the marginal propensity to consume (MPC), as well as the average propensity to consume (APC), falls with income. Inspired by his work, other researchers extended his study on consumption theory. Friedman (1957) views consumption as a function of wealth or permanent income, known as the Permanent Income Hypothesis (PIH). The PIH maintains that households consume a fixed fraction of their permanent income, the annuity value of lifetime income and wealth, thereby introducing income expectations to consumption theory. The PIH implies that the MPC is constant and equal to the APC, which is consistent with Kuznets‟ (1946) findings that long run time series consumption data for the U.S. economy is characterized by a constant aggregate APC. Bilson (1980) provides empirical evidence for the PIH with tests on quarterly time-series data from the U.S., U.K., and Germany. Flavin (1981) applies the test to aggregate quarterly U.S. data and rejects the PIH. Weissenberger (1986) fits ARMA models to adjust for the serial correlation of changes in consumption and rejects the PIH using updated data for Germany and the U.K. Kim (1996) presents two alternatives and examines whether PIH consumption is a good approximation of postwar U.S. data. He finds that postwar U.S. consumption deviates from the PIH by less than 4 percent, which 8 indicates a reasonably good fit when viewed in a representative agent framework with so many restrictive assumptions. More recently, using the Penn World Table annual data, Dawson et al. (2001) report that the PIH holds in industrial countries but not developing countries. However, the different results observed in industrial and developing countries may be a result of systematic differences in data quality. West (1988a) and Campbell and Deaton (1989) test the relation between the variance of the revisions in consumption and the variance of the revisions in permanent income and claim that changes in consumption are much less volatile than changes in observed income. They reject the joint hypothesis implied in the PIH that per capita aggregate consumption is generated by the permanent income model and that shocks to labor income are permanent. To differentiate the consumption response arising from different asset classes, Friedman (1957) conjectures a lower MPC out of human wealth than out of financial wealth. Zeldes (1989) later proposes to “put a weight of less than one on human wealth before adding it to financial wealth, or to discount expected future income at a higher discount rate.” Hayashi (1982) tests a generalized permanent income model, using a higher discount rate for human wealth without using the theory of optimality. Wang (2006) further uses a „risk-adjusted‟ measure for human wealth by calculating expected future income at a higher discount rate based on the agent‟s optimality, and delivers a lower MPC out of human wealth than out of financial wealth. The introduction of the lifecycle theory by Modigliani and Brumberg (1955) is another milestone in consumption theory as it introduces utility maximisation to the theoretical 9 framework. Individuals choose a lifetime pattern of consumption in order to maximise their lifetime utility given their lifetime budget constraint which includes lifetime income expectations. Lifecycle theory recognises that interest rates and time preference may affect consumption, and that consumption may vary at different stages of life. The credit market, specifically borrowing and lending, is also introduced to the theoretical framework. Modigliani and Brumberg (1955) incorporate microeconomic choice theory into macroeconomic consumption theory, and draw microeconomic implications with cross-section data. Modigliani and Brumberg (1980) further look at the time-series and macroeconomic implications and conclude that households tend to average income over the life span, with increases in life-time resources leading to proportionate increases in consumption in all periods of life. Subsequent literature has developed methods for dealing with uncertainty which is not addressed in the Modigliani and Brumberg lifecycle model. Hall (1978) claims that consumption is a random walk using time-series analysis and the theory of rational expectations. Flavin (1981), Hall and Mishkin (1982), Hayashi (1982), Muellbauer (1983) and Bernanke (1985) build their research on the random walk consumption. Flavin (1981) argues that detrended per capita consumption exhibits excess sensitivity to predictable changes in detrended per capita income. Mankiw and Shapiro (1985) further claim that income can be well approximated by a random walk with drift, and the excess sensitivity may be the spurious result of the presence of unit roots in the detrended per capita consumption and income data. Hall and Mishkin (1981) and Bernanke (1985) intend to analyse the correlations between the change in consumption and the econometric 10 estimates of contemporaneous or led innovations in other variables, but their work is constrained by the fact that the true innovations are unobservable to agents. Various work attempts to explain the motives of savings and consumption smoothing. Carroll (1997) believes that people will never borrow even with uncertain future earnings and the possibility of not being able to repay their debts, if they are sufficiently prudent. Deaton (1991) also claims that people can save to smooth out their consumption, but they cannot have consumption greater than their income, except when they already have some assets in the bank. Consumption may also be smoothed over a few years when liquidity is constrained, rather than over the whole life-cycle. Clarida (1991) maintains that the MPC out of any permanent increment (in expectation) to labor income during the working years will be less than one, as workers tend to save more to finance higher consumption during retirement. Furthermore, the MPC out of permanent shifts (in expectation) in labor income declines monotonically with age. Banks, Blundell and Tanner (1998) observe that saving for retirement seems to start only in middle-age, and is therefore insufficient to prevent a sharp fall in consumption at retirement. The elderly do not dispose of their assets and indeed that many of the elderly appear to save part of their incomes. Duesenberry (1948) documents „relative‟ income hypothesis, which maintains that an individual‟s desire to consume increases with the ratio of his expenditure to some weighted average of the expenditures of his contacts. Merton (1971) shows that consumers maximise their expected utility and set consumption to be proportional to their total assets when risk is confined to financial assets. Gourinchas and Parker (2002) show the relative roles of precautionary and retirement 11 motives for accumulating liquid assets, and construct a measure of precautionary saving and wealth. They indicate that wealth is accumulated early in life for precautionary reasons. Households would instead borrow against future labor income if they are sufficiently prudent. Following Merton (1971), later studies examine the wealth effect of different asset classes. Housing, as part of total assets, is also extensively examined. According to the PIH and the lifecycle theory, an increase in housing value is likely to lead to an increase in consumption. This housing wealth effects can be realised through several channels. First, an appreciation in housing value increases the households‟ wealth and thus increases consumption, which is regarded as direct wealth effects. Second, households that face binding credit restrictions can borrow more against housing which can be used as collateral in a loan. An increase in housing price relaxes credit constraints and allows homeowners to borrow more to smooth consumption over the life cycle. This is regarded as collateral effects or indirect wealth effects. Various papers have provided empirical evidence for significant housing wealth effects in different countries. Muellbauer and Murphy (1990) argue that the consumption boom in the United Kingdom in the late 1980s could be attributed to the increase in housing prices as well as financial liberalisation. Case (1992) finds substantial consumption increase during the real estate price boom in the late 1980s using aggregate data for New England. Skinner (1989) examines the link between housing wealth and consumer expenditure using data on individual households from the Panel Study of Income Dynamics (PSID) and found statistically significant housing wealth effects. However, the effects become insignificant after correcting for heterogeneity among homeowners. Engelhardt (1996) 12 tries to examine the homeowners‟ marginal propensity to consume (MPC) out of real capital gains, and obtained a MPC of 0.03 for Canadian households. Benjamin, Chinloy, and Jud (2010) estimate the United States‟ consumption function using the value of the real estate and financial wealth for the period Q1:1952-Q4: 2001 and find the real estate would impose larger wealth effects on consumption compared to financial assets. Campbell and Cocco (2007) use United Kingdom micro data to show that housing wealth effects tend to be large for older homeowners and small for young homeowners. Case et al. (2005) have done a comparison of housing wealth effects and stock wealth effects. New measures of wealth are constructed for the cross-sectional analysis. Large housing wealth effects are observed both across states in the US using quarterly data for the period 1982-1999, and a panel of 14 countries using annual data during the period 1975-1996. Some researchers claim that high housing prices increase consumption through a lower incentive to save. Yoshikawa and Ohtake (1989) apply micro data in Japan and find that the net effect of higher housing prices is to increase consumption via a lower incidence to purchase houses by households. Engelhardt (1994) claims that high housing prices lead to a fall in the Canadian households‟ incentive to save for a down payment and an increase in consumption. Despite the evidence of housing wealth effects shown in numerous empirical studies, there are some theoretical works posing doubt on such effects. Elliott (1980) argues that housing would not exert as significant wealth effects as financial wealth. Sheiner (1995) claims that households may actually increase savings due to higher down payment requirements to purchase houses when housing prices increase, in contrast to the conclusion by Engelhardt (1994). There are also quite a lot of empirical works that show 13 no evidence of housing wealth effects. Hoynes and McFadden (1997) find that households would hardly change their savings in non-housing assets in response to expectations about capital gains in owner-occupied housing. Levin (1998) claims that homeowners do not consume their housing wealth which therefore would not affect their consumption. Some skeptics of housing wealth effects believe that housing price fluctuations would not affect the economy at an aggregate level as housing is a consumption durable which is necessary for everyone. Sinai and Souleles (2005) provide evidence that fluctuations in house prices would not have real wealth effects, because the gain from higher housing prices is simply compensation for a higher implicit cost of living in the house. The consumption choices may be affected if there are substitution effects, and it may lead to the change in the distribution of consumption, but not the change in the aggregate amount. Recently, Buiter (2008) finds that housing wealth gain would be offset by higher housing costs during boom periods at the aggregate level, and thus might not necessarily increase consumption. Psychological factors are also considered as the cause of insignificance of housing wealth effects. Shefrin and Thaler (1988) provide an explanation from the psychological perspective and claim that people tend to take certain assets as more appropriate for current expenditures while take others, such as housing wealth, as for long-term savings. Thaler (1990) further argues that housing wealth would be classified into the mental account which is not for current consumption, and thus would not lead to significant wealth effects. A related branch of the literature suggests that house prices and consumption are correlated as both are affected by a same third factor, rather than through housing wealth effects. Calomiris et al. (2009) find no significant housing wealth effects after adjusting for common macroeconomic factors. 14 The asymmetric effects of housing price increases and decreases on consumption have also been explored. There is no consensus so far. Case et al. (2005) show evidence of positive wealth effects when housing prices increase, but no significant effects when prices decrease. This is in contrast to the conclusion by Skinner (1993) and Engelhardt (1996) who find significantly negative effects on consumption when housing prices decrease but no effects when prices increase. Given contradicting findings in different literature, the significance of housing wealth effects on consumption is likely to be an empirical issue, subject to econometric methods and data analysis. Regarding the Singapore housing market, the empirical studies on housing wealth effects have been all based on macro data due to the lack of micro data. There is no consensus on whether the housing wealth effects are significant among the limited research. Ng (2002) argues that private housing effects are significantly negative. Phang (2004), using the same set of data, shows no significant direct wealth effects or collateral effects based on the empirical results of both regressions when the CPF is included or excluded in the measure of disposable income. Phang (2004) also finds an asymmetric consumption response to increases and decreases of private housing prices. Abeysinghe and Choy (2004) claim that housing wealth effects are insignificant in Singapore. Co-integration of income and consumption did not exist, probably because the sample period was not long enough for the two to exhibit long-run equilibrium. Edelstein and Lum (2004) conclude that changes in public house prices would significantly and persistently affect aggregate consumption, while there are no significant private housing wealth effects in Singapore. These studies, however, have been constrained by the limitations of data as public resale market was first introduced only in 1990. The high 15 multicollinearity of private housing and public housing also makes it difficult to disaggregate the wealth effects. In terms of econometric methods, Durlauf and Hall (1988, 1989a, 1989b) are the first to develop a general framework for computing estimates of specification error or deviation, which is treated as an unobserved component in a signal extraction problem in the model. Campbell (1987) finds that saving, a linear combination of income and consumption is stationary in its level under the PIH, though neither income nor consumption is stationary. Therefore, he sets up a vector autoregression (VAR) and uses the theory of cointegration in time series to test the model. Edelstein and Lum (2004) apply the vector-autoregressive model with exogenous variables (VARX), followed by testing the impulse response of consumption to different variables, to estimate wealth effects of both private and public housing in Singapore. 3. Main Contributions The contributions of this paper are as follows. First of all, the role of the CPF is extensively examined in this study. To my knowledge, this is the first paper which examines the wealth effects of balances in CPF accounts. It considers the direct effects of the CPF on consumption as a standalone financial asset, as well as the indirect effects through its influences on the housing and stock wealth. The results show that CPF imposes a more persistent impact on consumption than housing wealth. My interpretation is that the positive shocks to the CPF will lead to higher future income, especially after retirement, and expected life-long resources are enhanced. This shows that CPF is considered as nonhuman wealth or permanent income by households, 16 and the empirical study gives that the MPC of CPF is highest among all forms of nonhuman wealth. Second, it compares the wealth effects of private housing and public housing. Most of the previous literature examined only the private housing market without taking the public resale market into account. Although Edelstein and Lum (2004) examine the differences between public housing wealth and private housing wealth, take both public and private housing indices as measures of housing wealth and include them in one regression. Additionally, the relatively short sample period may fail to result in a robust conclusion. Different from Edelstein and Lum (2004), I regress aggregate consumption on private and public housing wealth separately instead of putting them in one regression. This strategy has several merits. It allows a much longer sample period when investigating the private housing wealth effect. The RPPI is available since 1975 but the RPI is only available since 1990. The separation of these two variables can enable a longer sample period for the private housing regression. Moreover, it helps to overcome the multicollinearity problem given that the correlation between the public and private housing is 0.9037 from 1990 to 2009. In the unreported robustness tests, I also follow Edelstein and Lum (2004)‟s strategies and find that the results remain the same. Third, this paper extends Phang (2004) and explores the asymmetric wealth effects in private housing. More specifically, this paper includes the stock wealth and balances in the CPF accounts in the model and further explores the asymmetric wealth effect in housing. Phang (2004) applies the OLS method to study the contemporary effect of housing wealth. However, Phang (2004) assumes no leads or lag effects and fails to capture the dynamic features of housing wealth effect. In my study, a VAR method is 17 applied and the impulse response analysis helps to capture the dynamic features and identify the cause-effect relationship. Moreover, in contrast to Phang (2004), the results show that house price increases have a positive effect on aggregate consumption. One of the explanations is that when private housing prices increase, homeowners tend to view their private units as investment assets and are eager to reap the return from housing price appreciation. The direct wealth effects are significant and consumption is increased. However, during housing price depreciation, homeowners may tend to treat the private housing for self-occupation rather than for investment, and stay in the current units so that they will not lose in the depreciation. This result is consistent with Case et al. (2005)‟s findings in the US housing market. Last but not least, this study takes stock wealth as a control variable while previous literature on Singapore housing wealth effects tend to ignore it in the regressions.1 While our study provides an important implication of housing price, it should be admitted that we do not have access to individual consumption data. As a result, this paper is unable to evaluate the heterogeneity of housing wealth effect across income groups. However, it can be concluded that housing wealth effect seems to be weak in general at least during 1975 to 2009 periods. More comprehensive studies will require micro-level data on household consumption and we expect that the further researches might help to fill in this important gap. 4. Theoretical framework 1 However, various studies have explored how the volatility in the stock market affects consumption in different markets other than Singapore. For example, Elliott (1980) examined risky financial assets and concluded that stocks influenced consumer expenditure in US. Starr-McCluer (1998) found differentiated stock wealth effects given different responses from groups with different holdings. A survey exhibiting some time-series evidence of stock market wealth effects was also shown by Poterba (2000). However, Case et al. (2005) found weak evidence of a stock market wealth effect using both a panel of annual observations for 14 developed countries from 1975 to 1996, and another panel of quarterly data for each of the US states for the period from 1982 to 1999. 18 4.1 Standard lifetime budget constraints According to the Permanent Income Hypothesis and Lifecycle Model, identical consumers choose in each period t in order to maximize the utility conditional on the information set in time t2: (4.1) 3 subject to (4.2) (4.3) (4.4) where and is financial wealth in period t, r is interest rate, is labor income in period t, is the consumption in period t, T is the end of lifetime. This paper assumes that the utility function is concave. The following is the solution for this maximization problem: (4.5)4 Therefore, (4.5) can be written as (4.6), assuming that the best prediction for is the 2 is the conditional expectation on time t. As indicated by Zeldes (1989), I assume that the discount rate equals to interest rate. 4 The model setup mainly follows Zeldes (1989). In this stylized model, the interest rate is assumed to be constant while the prediction remains similar even given a stochastic interest rate. Edelstein and Lum (2004) take into account the variance in interest rate and obtain similar results. Thus, the stochastic feature of interest will not influence our findings 3 19 linear projection of . (4.6)5 The Permanent Income Hypothesis and Lifecycle Model suggest that consumption is a function of different asset classes. More specifically, following Case et al. (2005), consumption is a function of human wealth which takes real income as a proxy, and nonhuman wealth including financial asset, fixed asset (housing wealth), and other assets. (4.7) As housing is a necessity, homeowners are forced to buy higher-priced flats when they sell their current units. Owners of public housing may realise the gain in housing wealth during price appreciation, by selling off the appreciated unit in the resale market at the market rate and buying a new HDB flat at the subsidised rate from HDB, or by selling off a higher-value unit and trading down to a lower-value unit in the HDB resale market. Owners of private housing may also realise gain in housing wealth, by selling their private properties during price hike if they have more than one residential unit, or by selling off higher-value unit and trading down to a lower-value unit in the private housing resale market. Nevertheless, on an aggregate basis, public and private housing wealth effects are unclear since the homeowners may make a gain by selling the flats in the open market while the buyers may suffer from the high housing prices. Therefore, private and public housing 5 H (.) is a linear function of Y and F (.) is a linear function of W and Y. 20 wealth effects at an aggregate level are worth examining as an important empirical question. Additionally, Thaler (1990) argues that different wealth would be segregated into separate mental accounts, and certain assets are more appropriate to use for current expenditures while others are earmarked for long-term savings. The price appreciation of the assets for long-term savings may not result in significant consumption responses. Following this mental account theory, housing price appreciation may not significantly affect consumption as households generally tend to regard housing as suitable for long-term investment. Apart from the direct housing wealth effects, we also attempt to examine indirect wealth effects, for example, the collateral or balance sheet effects which enable homeowners to pledge their flats as collateral and unlock housing equity for consumption. NTUC Income introduced the first RM scheme for private housing in 1998, and to public housing in March 2006. The other provider, OCBC Bank offered RM for private properties only with two different loan options – term-based and annuity linked. The types of mortgages on offer to unlock housing equity have been quite limited and have frustrated much of the demand to release net housing equity. Currently, both NTUC-Income and OCBC have ceased issuing RM loans. According to Chia and Tsui (2009), the Reverse Mortgages (RM) Market in Singapore remains thin until the Lease Buyback Scheme (LBS), a monetisation option for low-income elderly Singaporean households living in HDB flats, was implemented in March 2009. Under the LBS, the HDB purchases the tail-end of the flat lease, while the elderly are able to unlock their housing equity and receive a lifelong income stream to supplement their retirement income, and age-in-place in their own flats. 21 Another explanation for higher consumption during house price appreciation is the increase in perceived wealth of homeowners due to an upward trend in housing prices. A rational consumer will smooth his consumption throughout the time horizon according to his current and future income and assets, therefore an increase in housing price is likely to result in a long run increase in total perceived assets and therefore impose positive effects on consumption. As discussed in Section 1, housing wealth consists of public housing wealth and private housing wealth in the context of Singapore. Given the different institutional features, we expect that public housing and private housing mat have different wealth effects. 4.2 Private housing wealth effects Hypothesis 1: Private housing wealth effects are significant (insignificant) Private housing may impose significant wealth effects on consumption. As the buying and selling of private housing is subject to less rigorous restrictions, households are able to reap the asset return easily. For example, there is no MOP requirement before reselling private units in the open market. Recently, there is a trend that more private housing owners tend to purchase resale HDB units. According to data in The Strait Times (Feb 15, 2010), the proportion of resale HDB flat buyers with private home addresses ranged between 8% and 19% of transactions. If these private housing owners trade down to reside in the public units, they will be able to channel the return from private housing appreciation to consumption. The direct wealth effects of private housing are likely to be large as it is likely to be classified into the mental account for current consumption. 22 In addition, the financing of private housing has been less difficult since the CPF liberalisation in 1981. Prior to February 2010, the downpayment was 10% of the total housing value with only a 5% cash outlay and 5% from the CPF Accounts. Even though the down payment requirement was recently lifted to 20%, the cash outlay remains at 5% of total value and the rest can be drawn from the CPF accounts. This enables the households to invest in private housing and reap the return for current consumption. However, at an aggregate level, private housing wealth effect can also be insignificant. Although homeowners may make a gain by selling the flats in the open market, the families pursuing a flat may suffer from the high housing price as mentioned previously. Moreover, if the jump in housing price is transitory, we should not expect any significant impact on current consumption as rational consumer will smooth the consumption through the time horizon. 4.3 Public housing wealth effects Hypothesis 2: Public housing wealth effects are insignificant (significant) Public housing shares some commons with private housing in that homeowner can gain by selling the flats in the open market and lose by pursuing a high priced flat. Nevertheless, public housing market is highly regulated, resulting in a relatively stable price and fewer speculation opportunities compared to private housing market. For example, homeowners can only sell their flats in the open market to eligible buyers after a MOP of up to five years. Besides, prior to 2003, homeowners were not allowed to sublet 23 their flats unless they left Singapore for work or study. Therefore, public housing is more likely to be viewed as a safe asset for long-term saving according to Thaler (1990). Second, homeowners of public units may tend to view their public housing as a hedge against risk for their whole life, as their expected future income is generally lower than those living in private properties. Singaporeans have been considered as „asset-rich and cash-poor‟ according to Chia and Tsui (2009), and the majority of the households residing in public units are constrained by limited budgets. As housing is a necessity, households need to purchase another unit for self-residence when they sell the current one, unless they own a second flat. According to HDB policy, a household is entitled to purchase subsidized new HDB flats twice in a lifetime given that the purpose of public housing is not for investment but to provide a permanent home for Singaporeans. An existing owner or ex-owner of a new HDB flat can apply for a second flat only after five years excluding any period of subletting of the whole current flat, and the second-time purchaser is required to pay a resale levy which can be as high as 25% for a 5-room or Executive flat. Therefore, the homeowners tend to reside in their heavily-subsided public units for longterm occupancy. Consequently, the appreciation in public housing prices may not significantly alter consumption. In addition, public housing is built for long-term owner occupation and the HDB has been trying to emphasise this purpose by continuously upgrading HDB flats. Recently, aggressive plans, which are tailored to meet the changing needs of the communities, have been drawn up to further improve the physical environment of HDB estates. Maintenance and upgrading have been taking place for middle-aged HDB flats so that the residents can enjoy new facilities and amenities similar to those of new flats or even private housing. 24 This deters homeowners‟ incentive to move away from the existing units and enhances the perspectives that public housing should be retained for long-term occupation. However, it is noticed that regulations on public housing have been loosened since 2003. For example, the HDB has progressively relaxed the subletting rule. Subletting allows eligible homeowners to rent out a room or the entire flat to generate rental income. In 2003, homeowners could rent out their flats after 15 years for lessees with an outstanding HDB loan, and 10 years if the loan has been paid-up. In 2005, this was cut to 10 and 5 years respectively. Since 2007, all HDB flats can be rented out after meeting the MOP. When the price of resale flat goes up in the open market, the rental income from subletting will generally increase. This proportion of income is likely to be viewed to be current, and thus will be channeled to consumption rather than long-term savings. The Lease Buyback Scheme (LBS), implemented in March 2009, enabled the elderly to unlock their housing equity and age-in-place in their own flats as mentioned previously. In addition, sellers also receive $10,000 as cash transfer of which they can keep $5,000 as an up-front lump sum subsidy. The value unlocked depends on both the property value and the length of remaining lease of the HDB flat. The collateral effects, or the indirect wealth effects, are enhanced during public housing price appreciation. Such a change in public housing market may also impose structure shocks to wealth effects. In sum, the unique institutional features in public housing market may result in distinction between public and private housing wealth effects. Therefore, it is essential to examine public and private housing wealth effects separately. 25 4.4 CPF wealth effects As a mandatory social pension scheme, the CPF requires working Singaporeans below 55 years old to make a monthly contribution to their interest bearing CPF accounts. This contribution is a fraction of the monthly income for the working residents and differs by income levels. The monies can be used for retirement financing, housing purchase, medical treatment or services whenever they are in need. Furthermore, employers are also required to contribute to the CPF accounts of their employees, which mean that the future budgets (wealth) of households are improved by the employers‟ portion. These balances in the CPF can be taken as financial assets with certain rates of return. If kept with the CPF Board, the Ordinary Account now pays 2.5% and Special Account pays 4%. Compared to bank deposit rates, the returns of monies in the CPF accounts are much higher. The CPF members also have a choice to transfer money from the Ordinary Account to the Special Account to enjoy a higher interest rate. The planning horizon for the accumulation in the Special Account is relatively long. Therefore, it is reasonable to view CPF as an important asset class and impose effects on consumption. The equation (4.7) can be rewritten as (4.8) And we have a following hypothesis: - Hypothesis 3: Central Provident Fund (CPF) net wealth effects are significant 26 Except for the direct effect on consumption, the CPF may indirectly affect consumption by its impact on housing prices. Though the CPF savings are essentially for old age, Singaporeans have increasingly used their CPF savings to purchase homes especially during the boom of housing market. As shown in Figure 2 previously, the liberalisation of the CPF policies in 1981, which allowed households to use the CPF savings to finance residential properties under Residential Properties Scheme (RPS), was likely to have supported the rise in the RPPI since then. Lum (2002) found that the CPF liberalisation in 1981 affected private housing prices significantly. Similarly, Figure 3 shows the RPI and the CPF withdrawal for the Public Housing Scheme. The rise in resale prices since 1994 may have been supported by the 172% increase in the net CPF withdrawals for housing from 1994 to 1999. However, we do observe that resale prices continued to go up high despite the sharp decrease in the CPF withdrawals after 2003. Another CPF indirect wealth effects on consumption may be realised via CPF Investment Scheme (CPFIS), which has helped to increase stock ownership among the CPF members. Since the inception of the CPFIS, the CPF members have progressively turned to professional fund managers to help manage their money, particularly in unit trusts. Households indirectly participate in the stock market through the unit trusts, which are investment vehicles which pool money from numerous investors to invest in a portfolio of securities such as shares, bonds, and deposits. Out of the 349 Collective Investment Schemes managed by unit trusts in Singapore, 162 were 'CPF-included' under the CPFIS. According to Singapore Asset Management Industry Survey in 2007, CPF-included funds represented 67% (or $26 billion) of the unit trust industry's assets under management, from 16 percent in 1997. The booming asset management industry, propelled by the CPFIS, adds to the turnover of the stock market with frequent trading. 27 Additionally, there were some circumstances where the CPF members were allowed to purchase discounted shares with their accounts. For example, Singtel offered Group A shares and ST-2 shares at preferential fixed prices of S$1.90 and S$2.50 per share in October 1993 and August 1996 respectively. The CPF members who bought the discounted Singtel Share can sell them at the market price, and the sale proceeds will be refunded to their CPF Ordinary Accounts. Such special schemes initiated by the CPF Board also help to increase stock ownership. 5. Empirical Studies 5.1 Data The data comprise quarterly observations from Q1:1975 to Q4:2009 for aggregate nonhousing consumption expenditure, disposable income, housing wealth in the private residential market, financial wealth in the stock market and the aggregate CPF outstanding balances in Singapore. As the public resale market was introduced in 1990, the data for public housing wealth indicators are from Q1:1990 to Q4:2009. The definitions of variables for the empirical study mainly follow those in Edelstein and Lum (2004). More specifically, the definitions and the sources of the main variables are as follows:  Real aggregate non-housing consumption (C): it is defined as logarithm of nominal aggregate private consumption expenditure, subtracting expenditure on housing and utilities and deflated by the Consumer Price Index (CPI). The series are provided by the Singapore Department of Statistics and it starts from Q1:1975 to Q4:2009. 28  Real disposable income (Y): it is defined as logarithm of nominal Gross Domestic Product (GDP), subtracting taxes from and deflated using the CPI. The CPF Contributions are not excluded from the disposable income. The series are provided by the Singapore Department of Statistics and it starts from Q1:1975 to Q4:2009.  Real private housing wealth (pri): it is defined as logarithm of Private Residential Property Price Index (RPPI), deflated using the CPI. It is used as a measure of real private housing wealth, following Case et al. (2005) that take housing index as an indicator of housing wealth. The RPPI is a capital value-weighted, transaction based index compiled by the Urban Redevelopment Authority (URA) and it can be downed load from DataStream and it starts from Q1:1975 to Q4:2009..  Real public housing wealth (pub): it is defined as logarithm of Resale Price Index (RPI), deflated using the CPI. It is taken in this paper to measure public housing wealth. The price of public resale units are determined by the open market, and therefore, can better reflect the value of public housing compared to the price of new public units. The RPI series are provided by the Housing and Development Board and it can be downloaded from HDB website (www.hdb.gov.sg). This data starts from Q1:1990 to Q4:2009.  Real stock wealth (sto): it is defined as logarithm of Singapore MSCI, deflated using the CPI and is taken as the measure of stock wealth in Singapore. The series are source from DataStream and starts from Q1:1975 to Q4:2009.  Real Central Provident Fund wealth or balances (CPF): it is defined as logarithm of aggregated CPF Amount due to members, deflated by CPI. It is taken as the measure of wealth accumulated in the CPF. The series are provided by Singapore Central Provident Fund Board. I obtain the data from Singapore Department of Statistics and starts from Q1:1975 to Q4:2009. In the model, CPF is viewed as an asset class which 29 might affect the consumption growth. The logarithmic forms of above variables are plotted in Figure 4. As indicated by Phang (2004), consumption and income exhibit unit roots in levels. Table 1 shows the results of standard Augmented Dickey-Fuller (ADF) unit root test for stationarity6 for log of real consumption, income, stock price, public housing price and private housing price, as well as the first difference of the log of real consumption, income, stock price, public housing price and private housing price. Consistent with Phang (2004) and Edelstein and Lum (2004), the results show that the consumption and income exhibit unit roots in levels but first difference of these variables are stationary. I further plot the first difference of the log of real consumption, income, stock price, public house price and private house price 2 4 6 8 10 12 in Figure 5 and Figure 6. 1975q1 1980q1 1985q1 1990q1 1995q1 newdate Log consumption Log stock wealth Log private wealth 2000q1 2005q1 2010q1 Log income Log public wealth Log CPF Figure 4. Variables in logarithm 6 The number of lags is set to be 0. The results remain the same even if it is set to be 3. 30 .4 .2 0 -.2 -.4 -.6 1975q1 1980q1 1985q1 1990q1 1995q1 newdate △ consumption △ stock 2000q1 2005q1 2010q1 △ income -.2 -.1 0 .1 .2 .3 Figure 5. Consumption growth, income growth and stock growth 1975q1 1980q1 1985q1 1990q1 1995q1 newdate △ cpf △ public 2000q1 2005q1 2010q1 △ private Figure 6. CPF growth, Private housing price growth and Public housing price growth Table 1: ADF Test and summary statistics Dickey Fuller Start date End date Q2:1975 Q4:2009 139 Std. Mean Test(p-value) △C Reject unit Obs 0.0000 root(95%) YES Min Max -0.066 0.080 Dev. 0.014 0.033 31 C Q1:1975 Q4:2009 140 0.7931 NO 9.128 0.593 8.092 10.035 △Y Q2:1975 Q4:2009 139 0.0000 YES 0.016 0.018 -0.041 0.057 Y Q1:1975 Q4:2009 140 0.3579 NO 9.898 0.694 8.662 10.969 △Sto Q2:1975 Q4:2009 139 0.0000 YES 0.014 0.140 -0.539 0.370 Sto Q1:1975 Q4:2009 140 0.4154 NO 6.566 0.600 5.166 7.660 △Pub Q2:1990 Q4:2009 79 0.0019 YES 0.019 0.050 -0.074 0.271 pub Q1:1990 Q4:2009 80 0.1898 NO 4.476 0.436 3.515 5.016 △Pri Q2:1975 Q4:2009 139 0.0000 YES 0.019 0.058 -0.152 0.240 Pri Q1:1975 Q4:2009 140 0.2335 NO 4.177 0.821 2.518 5.201 △CPF Q2:1975 Q4:2009 139 0.0001 YES 0.030 0.021 -0.061 0.074 CPF Q1:1975 Q4:2009 140 0.0000 YES 10.513 1.128 7.880 12.025 5.2 Methodology Different econometric strategies have been applied in various papers on wealth effects. For example, Phang (2004) uses OLS and finds no significant impact of housing prices on aggregate consumption. Using a co-integration specification, Ng (2002) finds positive 32 short run and negative long run housing price effects on aggregate consumption. However, this paper follows Edelstein and Lum (2004) and uses the vector-autoregressive model for several reasons. First, Vector autoregression models (VARs) can statistically estimate the dynamic interactions between a set of variables without imposing strong theoretical assumptions. Therefore, VARs have the advantage of capturing average past experience in a less restricted way. Second, the Error Correction Model cannot be applied as the data show no evidence of long run cointegration between income and consumption. This is consistent with the finding by Abeysinghe and Choy (2004) and may be the result of the insufficient sample size for the series of data to exhibit equilibrium. Third, there is no precise time series specification or structural form for the relationship between and among many of our variables, though the Permanent Income Hypothesis provides some insights on the relationship between consumption and wealth. I estimate the unrestricted parameters of the VAR by ordinary least squares (OLS). Taking the first differences of C,Y, pri, pub, sto, cpf, a lag order of 1 is selected according to the Akaike Information Criterion. However, as the private housing index and the public resale price index are highly correlated, with a correlation of 0.9037, VAR using △C, △ Y, △ pri, △sto, △cpf, and △C, △Y, △pub, △sto, △cpf, are applied separately. Then, I use the estimated VAR in Eq. (4.8) to examine the response of the aggregate consumption to random shocks in the different wealth variables. These impulse response functions (IRFs) map out the dynamic response path of a variable due to a one-period standard deviation shock to another variable. Two-step Least Square (2SLS) Regressions are also run as Robustness Tests for the 33 above results. Following Calomiris et al. (2009), lags of consumption are taken as instruments. The three Robustness Tests take Ct 2 , Ct 2 and Ct 3 , and Ct 2 , Ct 3 and Ct 4 as instruments respectively. 5.3 Empirical results 5.3.1 Private housing wealth impact on consumption Table 2 gives the results when using C, Y, pri, sto, cpf as variables in the regression. Stock wealth effects are positively significant, and a one dollar gain from stock will lead to an increase of 4 cents in consumption. However, there is no evidence of private housing wealth effects, suggesting that long-run investment story is more suitable for Singapore market. The net wealth effect of the CPF is not significant. Moreover, it has little impact on private housing price. This result can be explained by the fact that private housing owners do not rely on CPF when they making investment decisions. In public housing wealth effect part, I will further discuss why CPF have insignificant effect on private housing price but significant effect on public housing. The results in Table 2 also show that stock market has significant effect on aggregate consumption. This result is consistent with Benjamin et al. (2004). However, most of the existing studies on Singapore consumption fail to take into account the stock wealth effects. The results show that in the regression with CPFt , R square is as high as 0.85. One concern of such a high R square is that first difference of the log of CPF may be a unit 34 root process, which is however rejected by the results of ADF Test shown in Table 1. Although it may not fully rule out the possibility that first difference of the log of CPF is non-stationary, it should at least alleviate our concerns about spurious regression. Therefore, the high R square could be due to the high correlation between leads and lags, demonstrated by the fact that the coefficient of CPFt 1 is as high as 0.873 in the regression of CPFt . Table 2: VAR estimation results for Q1:1975 to Q4:2009, using C,Y, pri, sto, cpf Ct Yt stot prit CPFt Ct 1 -0.3313*** 0.0874* -0.2347** 0.2075** 0.1271*** (-4.15) (1.95) (-0.17) (2.16) (3.59) Yt 1 0.7897*** 0.2371*** 0.1409 0.1288 -0.02453 (5.08) (2.72) (0.02) (0.69) (0.36) stot 1 0.04106** 0.0354*** 0.05245 0.1281*** -0.008042 (2.06) (3.16) (0.58) (5.34) (-0.91) prit 1 -0.01517 0.01033 0.0588 0.5586*** -0.00736 (0.03) (0.37) (0.26) (9.38) (-0.34) CPFt 1 0.03978 0.2551*** 0.5339 0.1283 0.873*** (0.45) (5.15) (1.34) (1.21) (22.29) 0.2965 0.4926 0.0313 0.6448 0.8590 R  sq Note: t statistics are in ( ). *** denotes 1% significance, ** denotes 5% significance, and * denotes 10% significance. Table 3: Comparison of private housing effects on consumption when the CPF is included or excluded Ct Ct 1 Yt 1 (1) Ct (2) -0.3313*** -0.3321*** (-4.15) (-4.16) 0.7897*** 0.8244*** 35 (5.08) (6.11) stot 1 0.04106** 0.03990** (2.06) (2.06) prit 1 -0.01517 0.01299 (0.03) (0.26) CPFt 1 0.03978 (0.45) R  sq 0.2965 0.2955 Note: t statistics are in ( ). *** denotes 1% significance, ** denotes 5% significance, and * denotes 10% significance. Table 3 further compares the private housing effects on consumption when the CPF is included or excluded from the regression, and there are no significant wealth effects in both cases. Table 4 shows the result of 2SLS Regressions using different lags of consumption as instruments. The private housing wealth effects remain insignificant. The CPF does not have significant wealth effects either. Table 4: 2SLS Regression results using IV and C,Y, pri, sto, cpf Ct Regression(1) Regression(2) Regression(3) Yt 1.0055** 1.0584** 0.8609** stot 0.03838 0.02007 0.01915 prit 0.02082 -0.0145 -0.00354 CPFt -0.118 -0.1148 -0.05556 Ct 2 as IV Yes Ct 2 , Ct 3 Ct 2 , as IV Ct 3 and Yes Yes 36 Ct 4 as IV 5.3.2 Private housing wealth impact on consumption in different sub-periods One concern of the previous test is that there might be structural change in the relationship between wealth and consumption. To address this potential problem, following Edelstein and Lum (2004), this paper divides the whole sample into two subsample periods using the Asian financial crisis in 1997 as break point. It is reasonable to pick the Asian financial crisis as break point. First of all, the Asian financial crisis resulted in a sharp decrease of GDP growth. Such negative impact might change people‟s expectation of future income and thus affect their consumption choice. Moreover, regime shifts also occurred in the private housing sector. The government had been undertaking cooling measures to curb speculation prior to financial crisis, but started to introduce expansionary private housing policies in November 1997. For example, quantum for private residential units was to be increased by 1000 to 7000 units in early 1997. However, this was subsequently reduced to 5000 units in November 1997. Project completion period for projects where units had not been launched for sale was extended to 8 years subject to the payment of a premium of 5% of the land price per year of extension. Moreover, vendor of a private housing unit no longer needs to pay stamp duty surcharge. Therefore, it is likely to capture the effects of regime shifts using Asian financial crisis as break point. I estimate Eq. (4.8) over two sub-periods, from Q1:1975 to Q2:1997 and from Q1:1975 to Q4:2009, and report the results in Tables 5 and 6 respectively. The results show that housing market impact on consumption is not statistically important, which are consistent 37 with Edelstein and Lum (2004). However, there is a structural change in stock wealthconsumption relationship. During the first sub-period, shock to stock wealth does not have significant effects on consumption. However, it is observed that stock wealth effects become more pronounced in the second sub-period.7 Table 5: VAR estimation results for Q1:1975 to Q2:1997 using C,Y, pri, sto, cpf Ct Yt stot prit CPFt Ct 1 -0.373*** 0.087** 0.048 0.217* 0.172*** (-3.87) (2.00) (0.12) (1.90) (3.77) Yt 1 0.948*** 0.319*** -0.471 0.106 0.109 (4.14) (3.08) (-0.51) (0.39) (1.00) 0.003 0.012 -0.0517 0.085*** -0.009 (0.12) (0.93) (-0.47) (2.61) (-0.72) -0.009 0.055* 0.298 0.627*** -0.002 (-0.14) (1.85) (1.12) (8.00) (-0.05) 0.192*** 0.578 0.117 0.831*** (-0.11) (3.86) (1.31) (0.90) (15.93) 0.2955 0.6122 0.0511 0.6393 stot 1 prit 1 CPFt 1-0.012 R  sq 0.8590 Note: t statistics are in ( ). *** denotes 1% significance, ** denotes 5% significance, and * denotes 10% significance. Table 6: VAR estimation results for Q3:1997 to Q4:2009 using C,Y, pri, sto, cpf Ct Yt stot prit CPFt Ct 1 -0.219 0.118 -0.384 0.216 -0.0179 (-1.57) (1.07) (-0.46) (1.23) (-0.41) Yt 1 0.498*** 0.111 0.780 0.150 0.0188 (2.59) (0.74) (0.68) (0.62) (0.31) 7 These results remain the same even if I exclude the samples for Q3:2008 to Q4: 2009. 38 stot 1 0.097*** 0.071*** 0.197 0.193*** -0.003 (3.82) (3.56) (1.30) (6.10) (-0.41) prit 1 -0.0143 -0.063 -0.270 0.416*** -0.007 (-0.20) (-1.10) (-0.62) (4.58) (-0.30) CPFt 1 0.221 0.384** 0.511 -0.114 0.907*** (1.00) (2.23) (0.39) (-0.41) (13.18) 0.4209 0.4075 0.0637 0.7107 0.8170 R  sq Note: t statistics are in ( ). *** denotes 1% significance, ** denotes 5% significance, and * denotes 10% significance. 5.3.3 Impulse response analysis of private housing wealth impact on consumption The next step in the VAR models is to introduce shocks to the error terms in Eq. (4.8). In effect, a shock imposes a change in consumption. By tracking the consequent changes in aggregate consumption growth, the impulse response analysis help to identify the causal effect of housing wealth on consumption while holding all other variables constant. Figures 7, 8 and 9 present the impulse response functions (IRF) for whole sample period, and the two sub-periods, respectively. Response to Cholesky one S.D innovation Income ->Consumption Stock market->Consumption 39 Private housing ->Consumption CPF->Consumption Figure 7. Impulse response analyses for Q1:1975 to Q4:2009 Response to Cholesky one S.D innovation Income ->Consumption Stock market->Consumption Private housing ->Consumption CPF->Consumption Figure 8. Impulse response analyses for Q1:1975 to Q2:1997 40 Response to Cholesky one S.D innovation Income ->Consumption Stock market->Consumption Private housing ->Consumption CPF->Consumption Figure 9. Impulse response analyses for Q3:1997 to Q4:2009 The results show that consumption significantly responds to unanticipated changes in income. However, all results show that the impact of private house wealth is negative but insignificant. The impact of stock market changes on consumption is not significant in first sub-sample period but becomes positive and significant in second sub-sample period. Further, I compute the short run response of consumption to the private housing wealth, stock market and CPF. The results are reported in Table 7. 41 According to Table 7, it takes 5 to 6 quarters for a housing wealth shock to die out (less than 0.001) and 4 to 6 quarters for a stock market shock to die out. However, it takes 13 to 18 quarters for a CPF shock to die out. If I redefine die out time as the time when the value of response equals to 0.00001, it takes 46 quarters for a housing wealth shock to die out in first sub-period and 64 quarters in the second sub-period. In terms of shock on CPF, it takes 59 quarters to die out in first sub-period and 81 quarters in the second sub-period. These results indicate that the effects from CPF shocks are much more persistent. One possible explanation is that ownership of CPF is much more widespread compared to housing wealth or stock wealth. In the event of housing wealth or stock price shock, only the owners are likely to be affected. However, the positive shock of CPF will lead to higher future income, especially after retirement, and expected life-long resources are enhanced. In an unreported table, I also find that the effects of CPF shocks are more persistent than those of income shocks. 8 This exactly proves that CPF is considered nonhuman wealth or permanent income by households, and the empirical study gives that MPC of CPF is highest among all nonhuman wealth. This result sheds light on policy making. However, most of the existing studies on Singapore housing market tend to ignore this important role, while this study helps to fill in this important gap. The impulse response analysis provides insights on the housing wealth-consumption relationship in the Singapore and these results appear to be consistent in different sample periods. These results are also consistent with Phang (2004) and Edelstein and Lum (2004) who find that private housing wealth does not significantly affect consumption. Moreover, these results further demonstrate that stock market significantly affect consumption after 8 Figure 7, 8 and 9 also indicate this conclusion. 42 Asian financial crisis. This will shed light on future study stock market-consumption relationship and how such link change after Asian financial crisis. Last but not least, my findings show that the unexpected shock on CPF will result in a more persistent impact on consumption than the housing wealth shock does. Table 7: Impulse response functions of aggregate consumption to shocks in private house wealth, stock market and CPF. Q1:1975-Q2:1997 Private Time Q3:1997-Q4:2009 housing Private Stock Index CPF price housing Stock Index CPF price 1 -0.00034 0.003351 -0.00019 -0.00076 0.01312 0.00164 2 0.00210 0.002308 0.00264 -0.00233 0.00315 0.00292 3 0.00128 0.001215 0.00217 -0.00172 -0.00056 0.00298 4 0.00136 0.000921 0.00248 -0.00123 -0.00161 0.00283 5 0.00102 0.000746 0.00226 -0.00080 -0.00130 0.00251 6 0.00092 0.000656 0.00220 -0.00059 -0.00087 0.00223 7 0.00077 0.000596 0.00205 -0.00049 -0.00054 0.00198 8 0.00068 0.000549 0.00193 -0.00044 -0.00038 0.00177 9 0.00060 0.000509 0.00180 -0.00040 -0.00031 0.00160 10 0.00055 0.000473 0.00169 -0.00037 -0.00027 0.00144 11 0.00049 0.000441 0.00157 -0.00034 -0.00025 0.00130 12 0.00045 0.00041 0.00146 -0.00031 -0.00023 0.00117 13 0.00041 0.000382 0.00136 -0.00028 -0.00021 0.00106 14 0.00038 0.000356 0.00127 -0.00025 -0.00019 0.00096 15 0.00035 0.000332 0.00118 -0.00023 -0.00018 0.00086 16 0.00033 0.000309 0.00110 -0.00020 -0.00016 0.00078 17 0.00030 0.000288 0.00102 -0.00018 -0.00014 0.00070 18 0.00028 0.000268 0.00095 -0.00017 -0.00013 0.00063 19 0.00026 0.00025 0.00088 -0.00015 -0.00012 0.00057 20 0.00024 0.000233 0.00082 -0.00014 -0.00011 0.00052 43 5.3.4 Public housing wealth impact on consumption In the previous section, I comprehensively study the effect of private housing wealth on consumption. Next, we further study the impact of public housing wealth. Table 8 shows the results when using C, Y, pub, sto, cpf as variables in the regression. Similar to the previous regression, the stock wealth effects are highly significant. Public housing and the CPF do not have significant wealth effects. Though the CPF plays a role in pushing up public resale prices, it does not alter the insignificance of public housing wealth effects according to Table 9. CPF has positive and significant impact on public housing, though its impact on private housing is found to be limited. This result can be explained by the fact that private housing owners do not rely on the monies in their CPF accounts when they are purchasing houses. However, public housing owners have comparatively lower incomes, thus are more likely to rely on CPF. Table 8: VAR estimation results for Q1:1990 to Q4:2009, using C,Y, pub, sto, cpf Ct Yt stot pubt CPFt Ct 1 -0.2051* 0.08708 -0.01963 0.0870 0.0559 (-1.82) (1.05) (-0.03) (0.54) (0.79) Yt 1 0.5553*** 0.1357 0.0641 -0.0918 0.1318 (3.38) (1.12) (0.07) (-0.39) (1.28) stot 1 0.0653*** 0.0570*** 0.1282 0.0622** -0.0048 (2.95) (3.49) (1.07) (1.97) (-0.35) pubt 1 0.076 0.0427 0.206 0.6138*** -0.0393 (1.29) (0.98) (0.65) (7.28) (-1.06) CPFt 1 0.0808 0.3905*** 0.626 0.4954** 0.6751*** 44 R  sq (0.57) (3.17) (0.81) (2.43) (7.53) 0.3572 0.4552 0.0454 0.5611 0.5616 Note: t statistics are in ( ). *** denotes 1% significance, ** denotes 5% significance, and * denotes 10% significance. Table 9: Comparison of coefficient of Ct , with or without CPF Ct (1) Ct (2) Ct 1 -0.2051* -0.2054* (-1.82) (-1.82) Yt 1 0.5553*** 0.5910*** (3.38) (3.88) stot 1 0.06533*** 0.0629*** (2.95) (2.89) pubt 1 0.0760 0.0833 (1.29) (1.44) CPFt 1 0.0808 R  sq (0.57) 0.3572 0.3545 Note: t statistics are in ( ). *** denotes 1% significance, ** denotes 5% significance, and * denotes 10% significance. 5.3.5 Public housing wealth impact on consumption in different sub-periods Next, I further divide the whole sample into two sub-samples using Asian financial crisis as breakpoint and estimate Eq. (4.8) over two sub-periods. The results are reported in Tables 10 and 11 respectively. In Tables 10 and 11, the results show that public housing impact on consumption is not statistically significant, which are consistent with the findings of Edelstein and Lum (2004). We also find that there is a structural change in stock market-consumption relationship, consistent with the results in Table 4 and 5 shown 45 previously. Table 10: VAR estimation results for Q1:1975 to Q2:1997, using C,Y, pub, sto, cpf Ct Yt stot pubt CPFt Ct 1 -0.0755 0.126 1.181 0.370 0.194 (-0.42) (1.04) (1.52) (0.90) (1.21) Yt 1 0.757** 0.257 -1.856 -0.178 0.697** (2.43) (1.22) (-1.37) (-0.25) (2.50) stot 1 -0.0482 0.0246 -0.110 0.0786 -0.0154 (-1.11) (0.84) (-0.58) (0.79) (-0.40) pubt 1 0.0836 0.0683 0.596* 0.524*** -0.114* (1.14) (1.37) (1.88) (3.10) (-1.75) -0.188 0.264** 0.500 0.771* 0.354** (-0.98) (2.03) (0.60) (1.75) (2.07) 0.6175 0.1928 0.5727 0.5850 CPFt 1 R  sq 0.3942 Note: t statistics are in ( ). *** denotes 1% significance, ** denotes 5% significance, and * denotes 10% significance. Table 11: VAR estimation results for Q3:1997 to Q4:2009, using C,Y, pub, sto, cpf Ct Yt stot pubt CPFt Ct 1 -0.246 0.098 -0.501 -0.085 -0.0251 (-1.81) (0.90) (-0.61) (-0.85) (-0.58) Yt 1 0.485*** 0.078 0.627 0.0455 0.0140 (2.61) (0.52) (0.56) (0.33) (0.24) stot 1 0.094*** 0.067*** 0.177 0.061*** -0.00416 (3.83) (3.40) (1.19) (3.38) (-0.54) pubt 1 0.131 -0.045 0.005 0.733*** 0.0260 (1.14) (-0.48) (0.01) (8.59) (0.71) 0.226 0.438*** 0.734 0.122 0.912*** (1.08) (2.62) (0.58) (0.79) (13.81) CPFt 1 46 R  sq 0.3572 0.4552 0.0454 0.5611 0.5616 Note: t statistics are in ( ). *** denotes 1% significance, ** denotes 5% significance, and * denotes 10% significance. Another finding of the empirical study is that public housing wealth effects started to be significant since 2003 although there are no significant housing wealth effects for the whole sample period. Taking the sub-sample of Q1:2003-Q4:2009, wealth effects of public housing become marginally significant, and the coefficient is twice as much as that of stocks, shown in Table 12. The reason for such a change may be that the restrictions on public housing were loosened so that households are able to reap the return from the public resale market easily since 2003. For example, the required cash outlay for HDB purchases was reduced to 5% of the total housing value. The MOP for resale flat buyers who take an HDB concessionary loan and a bank loan was shortened to 2.5 years and 1 year respectively. Households may find the public resale units suitable for investment and the owners of the HDB flats may not aim to hold the units for long-term occupancy. Therefore, direct wealth effects become significant. Most importantly, due to the relaxation of subletting rules, homeowners do not need to sell out houses to gain from housing appreciation, but they can enhance budgets by renting out rooms. The extra stream of income can be used to increase consumption, especially when rental rates increase. Recently the rental market has become quite active as more foreign workers, international students or expatriates move to Singapore. Some of them turn to the rental market and push up the rentals. The higher rentals further reduce homeowners‟ incentive to sell their flats. The demand for resale flats, from the immigrants who intend to buy, remains strong. Given the tightened supply of and expanded demand for public resale housing, the prices go up tremendously. 47 Table 12: VAR estimation results for Q1:2003 to Q4:2009, using C,Y, pub, sto Ct Yt stot pubt Ct 1 -0.5648*** -0.03441 -1.1288 -0.2059* (--3.40) (-0.2) (-1.50) (-1.68) Yt 1 0.4333** 0.00694 1.342 0.1922 (2.12) (0.03) (1.45) (1.28) stot 1 0.14*** 0.1279** 0.4415** 0.06133** (3.39) (3.03) (2.36) (2.01) pubt 1 0.2869* 0.08088 -1.115 0.8049*** (1.78) (0.49) (-1.53) (6.77) 0.5128 0.3278 0.3713 0.6727 R  sq Note: t statistics are in ( ). *** denotes 1% significance, ** denotes 5% significance, and * denotes 10% significance. 5.3.6 Impulse response analysis of public housing wealth impact on consumption I further conduct impulse response analysis based on above VAR results. Figure 10 presents the IRFs for whole sample period, the first and second sub-periods. The results show that consumption positively responds to unanticipated changes in public housing. Compared with private house wealth, public housing wealth affects consumption positively and more profoundly, especially during the 2003 and 2009 sample period. 48 Response to Cholesky one S.D innovation (Public housing wealth->Consumption) Q1:1975 to Q4:2009 Q3:1997 to Q4:2009 Q1:1975 to Q2:1997 Q1:2003 to Q4:2009 Figure 10. Impulse response analyses for four sub-sample periods 5.3.7 Asymmetric wealth effects of private housing on consumption As explained in the previous section, the RPPI and RPI are used as proxies of housing wealth in two regressions separately, due to the fact that the correlation between private 49 housing and public resale housing price changes or levels is as high as 0.9037. The results in both regressions show that overall housing wealth effects are insignificant. There is no evidence of private or public housing wealth effects for the whole sample. One possible explanation is that this econometric study uses aggregate data, and therefore, the effects of housing price appreciation on buyers and sellers are offset at the aggregate level. However, there is evidence of asymmetric wealth effects of private housing. The increase in private housing wealth significantly increase aggregate consumption but a decrease has insignificant effects on consumption, shown in Table 13. There are several explanations for the asymmetry. When private housing prices increase, the homeowners tend to view their private units as investment assets and are eager to reap the return from housing price appreciation. The direct wealth effects are significant and they will increase the consumption. However, during housing price depreciation, the homeowners may tend to treat the private housing as for self-occupation rather than for investment, and stay as in the current units so that they will not lose in the depreciation. Meanwhile, the buyers will tend to watch the market rather than become the owner of the housing wealth, as they expect the prices to further dip in the future. Their consumption would not be sacrificed for home purchase, and the perceived wealth would not change. 9 Table 13: VAR estimation results for Q1:1975 to Q4:2009, desegregating positive and negative private housing change Ct 1 Ct Yt stot prit (+) prit (-) CPFt -0.3335*** 0.0939* 0.0034 1.115 -0.3733 0.1234*** (-3.80) (1.82) (0.01) (2.16) (-0.38) (2.77) 9 I also conduct the same test for public housing. The results show no asymmetric effect of public housing wealth. These results further demonstrate that the asymmetric effects are due to the different perspectives on housing. Lowerincome people are more likely to treat housing as for self-occupation rather than for investment. 50 Yt 1 0.5657*** 0.0529 0.1953 3.626* -2.854 -0.03661 (3.34) (0.53) (0.25) (1.70) (-1.49) (0.43) stot 1 0.05261** 0.0435*** 0.0444 0.5441** -0.4626* -0.0004 (2.41) (3.38) (0.43) (1.97) (-1.88) (-0.04) prit 1 (+) 0.0125** 0.0107*** -0.001 0.6975*** 0.1719** 0.0039 (2.30) (3.35) (-0.04) (10.19) (2.18) (1.41) prit 1 (-) 0.0068 0.0054* -0.0279 0.0698 0.829*** 0.0056** (1.27) (1.72) (-1.11) (1.03) (13.76) (2.05) -0.1193 0.1311*** 0.726 1.486 0.2929 0.791*** (1.06) (1.98) (1.38) (1.05) (0.23) (13.85) 0.4891 0.5839 0.3471 0.9193 0.8296 0.9548 CPFt 1 R  sq Note: t statistics are in ( ). *** denotes 1% significance, ** denotes 5% significance, and * denotes 10% significance. 6. Conclusion This paper aims to explore whether wealth effects are significant for private housing and public housing in Singapore. The net CPF wealth effect is also examined and discussed extensively in the study. Using the RPPI from Q1:1975 to Q4:2009 and RPI from Q1:1990 to Q4:2009, there are no significant public or private housing wealth effects found for the respective whole sample. Using Asian Financial Crisis in 1997 as a break point, the main results for the two subperiods are the same as those in full sample regression for both public and private housing. These findings support Thaler (1990)‟s mental account theory, suggesting that most Singaporeans regard properties as for long term saving. However, it is observed that there was a structural change in public housing wealth effects in 2003, when a series of policies 51 were introduced to boost the public housing market. One of the possible explanations is that such policies, such as the reduction in MOP and the ease of financing of the HDB flats, may alter the households‟ perception of the public housing. They may tend to view the public units suitable for speculation, and the direct wealth effects start to be significant. The permission to sublet is considered as another explanation for the significant public housing wealth effects after 2003. The rental income is likely to be regarded current, and channeled into consumption. Additionally, this paper suggests that private housing exhibits asymmetric wealth effects. Consumption responds positively to price increase in private housing but remains unchanged during price decrease. Stock wealth effects, though ignored by previous literature in Singapore, are found to be significant in Q3:1997 to Q4:2009, which may be attributed to the government‟s encouragement to invest in shares via CPFIS so that the Singaporean households‟ participation in stock market is deepened. With the recent volatility in stock market, households are likely to reap return which is then channeled to consumption. 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Section 1, housing wealth consists of public housing wealth and private housing wealth in the context of Singapore Given the different institutional features, we expect that public housing and private housing mat have different wealth effects 4.2 Private housing wealth effects Hypothesis 1: Private housing wealth effects are significant (insignificant) Private housing may impose significant wealth effects. .. nonhuman wealth Second, it compares the wealth effects of private housing and public housing Most of the previous literature examined only the private housing market without taking the public resale market into account Although Edelstein and Lum (2004) examine the differences between public housing wealth and private housing wealth, take both public and private housing indices as measures of housing wealth. .. in public housing market may also impose structure shocks to wealth effects In sum, the unique institutional features in public housing market may result in distinction between public and private housing wealth effects Therefore, it is essential to examine public and private housing wealth effects separately 25 4.4 CPF wealth effects As a mandatory social pension scheme, the CPF requires working Singaporeans... consumption through the time horizon 4.3 Public housing wealth effects Hypothesis 2: Public housing wealth effects are insignificant (significant) Public housing shares some commons with private housing in that homeowner can gain by selling the flats in the open market and lose by pursuing a high priced flat Nevertheless, public housing market is highly regulated, resulting in a relatively stable price and... unit in the HDB resale market Owners of private housing may also realise gain in housing wealth, by selling their private properties during price hike if they have more than one residential unit, or by selling off higher-value unit and trading down to a lower-value unit in the private housing resale market Nevertheless, on an aggregate basis, public and private housing wealth effects are unclear since... evidence of housing wealth effects Hoynes and McFadden (1997) find that households would hardly change their savings in non -housing assets in response to expectations about capital gains in owner-occupied housing Levin (1998) claims that homeowners do not consume their housing wealth which therefore would not affect their consumption Some skeptics of housing wealth effects believe that housing price... extensively examined According to the PIH and the lifecycle theory, an increase in housing value is likely to lead to an increase in consumption This housing wealth effects can be realised through several channels First, an appreciation in housing value increases the households‟ wealth and thus increases consumption, which is regarded as direct wealth effects Second, households that face binding credit... Edelstein and Lum (2004) conclude that changes in public house prices would significantly and persistently affect aggregate consumption, while there are no significant private housing wealth effects in Singapore These studies, however, have been constrained by the limitations of data as public resale market was first introduced only in 1990 The high 15 multicollinearity of private housing and public housing. .. suitable for long-term investment Apart from the direct housing wealth effects, we also attempt to examine indirect wealth effects, for example, the collateral or balance sheet effects which enable homeowners to pledge their flats as collateral and unlock housing equity for consumption NTUC Income introduced the first RM scheme for private housing in 1998, and to public housing in March 2006 The other... the consumption boom in the United Kingdom in the late 1980s could be attributed to the increase in housing prices as well as financial liberalisation Case (1992) finds substantial consumption increase during the real estate price boom in the late 1980s using aggregate data for New England Skinner (1989) examines the link between housing wealth and consumer expenditure using data on individual households ... public housing wealth and private housing wealth in the context of Singapore Given the different institutional features, we expect that public housing and private housing mat have different wealth. .. 4.3 Public housing wealth effects Hypothesis 2: Public housing wealth effects are insignificant (significant) Public housing shares some commons with private housing in that homeowner can gain... different wealth effects 4.2 Private housing wealth effects Hypothesis 1: Private housing wealth effects are significant (insignificant) Private housing may impose significant wealth effects on consumption