HOUSING WEALTH EFFECTS IN SINGAORE: PUBLIC HOUSING … · 2018. 1. 9. · comprise at least another...

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

i

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.

ii

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

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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.

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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

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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

1

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.

2

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

3

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

4

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.

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 fully-

funded defined contribution system to cover a wide range of retirement, healthcare, home

ownership, family protection and asset enhancement needs. Working Singaporeans are

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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.

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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.

Figure 2. CPF and Private housing 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

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7

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

8

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

9

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

10

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

11

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

12

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)

13

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

14

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.

15

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

16

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,

17

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

18

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.

19

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 is financial wealth in period t, r is interest rate, is labor income in period t,

and 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. 3 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

20

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 non-

human 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.

21

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.

22

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 re-

selling 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.

23

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

24

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 long-

term 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.

25

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.

26

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

27

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.

http://www.propertyhub.com.sg/Singapore-Property-Information/Private-Housing-Market-Statistics/Private-Residential-Price-Index.html

28

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 non-

housing 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.

29

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

http://www.hdb.gov.sg/

30

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

in Figure 5 and Figure 6.

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.

24

68

1012

1975q1 1980q1 1985q1 1990q1 1995q1 2000q1 2005q1 2010q1newdate

Log consumption Log income

Log stock wealth Log public wealth

Log private wealth Log CPF

31

Figure 5. Consumption growth, income growth and stock growth

Figure 6. CPF growth, Private housing price growth and Public housing price growth

Table 1: ADF Test and summary statistics

Start date End date Obs

Dickey Fuller

Test(p-value)

Reject unit

root(95%)

Mean

Std.

Dev.

Min Max

△C Q2:1975 Q4:2009 139 0.0000 YES 0.014 0.033 -0.066 0.080

-.6

-.4

-.2

0.2

.4


△ consumption △ income

△ stock

-.2

-.1

0.1

.2.3


△ cpf △ private

△ public

32

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

33

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

34

above results. Following Calomiris et al. (2009), lags of consumption are taken as

instruments. The three Robustness Tests take 2tC , 2tC and 3tC , and 2tC ,

3tC and 4tC 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 withtCPF , 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

35

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 1tCPF is as high as 0.873 in the

regression oftCPF .

Table 2: VAR estimation results for Q1:1975 to Q4:2009, using C,Y, pri, sto, cpf

tC tY tsto tpri tCPF

1tC -0.3313***

(-4.15)

0.0874*

(1.95)

-0.2347**

(-0.17)

0.2075**

(2.16)

0.1271***

(3.59)

1tY 0.7897***

(5.08)

0.2371***

(2.72)

0.1409

(0.02)

0.1288

(0.69)

-0.02453

(0.36)

1tsto 0.04106**

(2.06)

0.0354***

(3.16)

0.05245

(0.58)

0.1281***

(5.34)

-0.008042

(-0.91)

1tpri -0.01517

(0.03)

0.01033

(0.37)

0.0588

(0.26)

0.5586***

(9.38)

-0.00736

(-0.34)

1tCPF

0.03978

(0.45)

0.2551***

(5.15)

0.5339

(1.34)

0.1283

(1.21)

0.873***

(22.29)

R sq 0.2965 0.4926 0.0313 0.6448 0.8590

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

tC (1) tC (2)

1tC -0.3313***

(-4.15)

-0.3321***

(-4.16)

1tY 0.7897*** 0.8244***

36

(5.08) (6.11)

1tsto 0.04106**

(2.06)

0.03990**

(2.06)

1tpri -0.01517

(0.03)

0.01299

(0.26)

1tCPF 0.03978

(0.45)

R sq 0.2965 0.2955



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

tC Regression(1) Regression(2) Regression(3)

tY 1.0055** 1.0584** 0.8609**

tsto 0.03838 0.02007 0.01915

tpri 0.02082 -0.0145 -0.00354

tCPF -0.118 -0.1148 -0.05556

2tC as IV Yes

2tC , 3tC as IV Yes

2tC , 3tC and Yes

37

4tC 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 sub-

sample 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

38

with Edelstein and Lum (2004). However, there is a structural change in stock wealth-

consumption 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


1tC -0.373***

(-3.87)

0.087**

(2.00)

0.048

(0.12)

0.217*

(1.90)

0.172***

(3.77)

1tY

0.948***

(4.14)

0.319***

(3.08)

-0.471

(-0.51)

0.106

(0.39)

0.109

(1.00)

1tsto

0.003

(0.12)

0.012

(0.93)

-0.0517

(-0.47)

0.085***

(2.61)

-0.009

(-0.72)

1tpri

-0.009

(-0.14)

0.055*

(1.85)

0.298

(1.12)

0.627***

(8.00)

-0.002

(-0.05)

1tCPF

-0.012

(-0.11)

0.192***

(3.86)

0.578

(1.31)

0.117

(0.90)

0.831***

(15.93)

R sq

0.2955 0.6122 0.0511 0.6393 0.8590



Table 6: VAR estimation results for Q3:1997 to Q4:2009 using C,Y, pri, sto, cpf


1tC -0.219

(-1.57)

0.118

(1.07)

-0.384

(-0.46)

0.216

(1.23)

-0.0179

(-0.41)

1tY

0.498***

(2.59)

0.111

(0.74)

0.780

(0.68)

0.150

(0.62)

0.0188

(0.31)

7 These results remain the same even if I exclude the samples for Q3:2008 to Q4: 2009.

39

1tsto 0.097***

(3.82)

0.071***

(3.56)

0.197

(1.30)

0.193***

(6.10)

-0.003

(-0.41)

1tpri -0.0143

(-0.20)

-0.063

(-1.10)

-0.270

(-0.62)

0.416***

(4.58)

-0.007

(-0.30)

1tCPF 0.221

(1.00)

0.384**

(2.23)

0.511

(0.39)

-0.114

(-0.41)

0.907***

(13.18)

R sq 0.4209 0.4075 0.0637 0.7107 0.8170



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

40

Private housing ->Consumption CPF->Consumption

Figure 7. Impulse response analyses for Q1:1975 to Q4:2009





41





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.

42

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.

43

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 Q3:1997-Q4:2009

Time

Private housing

price

Stock Index CPF

Private housing

price

Stock Index CPF

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

44

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

tC tY tsto tpub tCPF

1tC -0.2051*

(-1.82)

0.08708

(1.05)

-0.01963

(-0.03)

0.0870

(0.54)

0.0559

(0.79)

1tY 0.5553***

(3.38)

0.1357

(1.12)

0.0641

(0.07)

-0.0918

(-0.39)

0.1318

(1.28)

1tsto 0.0653***

(2.95)

0.0570***

(3.49)

0.1282

(1.07)

0.0622**

(1.97)

-0.0048

(-0.35)

1tpub 0.076

(1.29)

0.0427

(0.98)

0.206

(0.65)

0.6138***

(7.28)

-0.0393

(-1.06)

1tCPF 0.0808 0.3905*** 0.626 0.4954** 0.6751***

45

(0.57) (3.17) (0.81) (2.43) (7.53)

R sq 0.3572 0.4552 0.0454 0.5611 0.5616



Table 9: Comparison of coefficient of tC , with or without CPF

tC (1) tC (2)

1tC -0.2051*

(-1.82)

-0.2054*

(-1.82)

1tY 0.5553***

(3.38)

0.5910***

(3.88)

1tsto 0.06533***

(2.95)

0.0629***

(2.89)

1tpub 0.0760

(1.29)

0.0833

(1.44)

1tCPF 0.0808

(0.57)

R sq 0.3572 0.3545



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

46

previously.



1tC -0.0755

(-0.42)

0.126

(1.04)

1.181

(1.52)

0.370

(0.90)

0.194

(1.21)

1tY 0.757**

(2.43)

0.257

(1.22)

-1.856

(-1.37)

-0.178

(-0.25)

0.697**

(2.50)

1tsto -0.0482

(-1.11)

0.0246

(0.84)

-0.110

(-0.58)

0.0786

(0.79)

-0.0154

(-0.40)

1tpub 0.0836

(1.14)

0.0683

(1.37)

0.596*

(1.88)

0.524***

(3.10)

-0.114*

(-1.75)

1tCPF

-0.188

(-0.98)

0.264**

(2.03)

0.500

(0.60)

0.771*

(1.75)

0.354**

(2.07)

R sq 0.3942 0.6175 0.1928 0.5727 0.5850





1tC -0.246

(-1.81)

0.098

(0.90)

-0.501

(-0.61)

-0.085

(-0.85)

-0.0251

(-0.58)

1tY 0.485***

(2.61)

0.078

(0.52)

0.627

(0.56)

0.0455

(0.33)

0.0140

(0.24)

1tsto 0.094***

(3.83)

0.067***

(3.40)

0.177

(1.19)

0.061***

(3.38)

-0.00416

(-0.54)

1tpub 0.131

(1.14)

-0.045

(-0.48)

0.005

(0.01)

0.733***

(8.59)

0.0260

(0.71)

1tCPF

0.226

(1.08)

0.438***

(2.62)

0.734

(0.58)

0.122

(0.79)

0.912***

(13.81)

47

R sq 0.3572 0.4552 0.0454 0.5611 0.5616



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.

48

Table 12: VAR estimation results for Q1:2003 to Q4:2009, using C,Y, pub, sto

tC tY tsto tpub

1tC -0.5648***

(--3.40)

-0.03441

(-0.2)

-1.1288

(-1.50)

-0.2059*

(-1.68)

1tY 0.4333**

(2.12)

0.00694

(0.03)

1.342

(1.45)

0.1922

(1.28)

1tsto 0.14***

(3.39)

0.1279**

(3.03)

0.4415**

(2.36)

0.06133**

(2.01)

1tpub 0.2869*

(1.78)

0.08088

(0.49)

-1.115

(-1.53)

0.8049***

(6.77)

R sq 0.5128 0.3278 0.3713 0.6727



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.

49


(Public housing wealth->Consumption)

Q1:1975 to Q4:2009 Q1:1975 to Q2:1997

Q3:1997 to Q4:2009 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

50

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

tC tY tsto tpri (+) tpri (-) tCPF

1tC -0.3335***

(-3.80)

0.0939*

(1.82)

0.0034

(0.01)

1.115

(2.16)

-0.3733

(-0.38)

0.1234***

(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. Lower-

income people are more likely to treat housing as for self-occupation rather than for investment.

51

1tY 0.5657***

(3.34)

0.0529

(0.53)

0.1953

(0.25)

3.626*

(1.70)

-2.854

(-1.49)

-0.03661

(0.43)

1tsto 0.05261**

(2.41)

0.0435***

(3.38)

0.0444

(0.43)

0.5441**

(1.97)

-0.4626*

(-1.88)

-0.0004

(-0.04)

1tpri (+) 0.0125**

(2.30)

0.0107***

(3.35)

-0.001

(-0.04)

0.6975***

(10.19)

0.1719**

(2.18)

0.0039

(1.41)

1tpri (-) 0.0068

(1.27)

0.0054*

(1.72)

-0.0279

(-1.11)

0.0698

(1.03)

0.829***

(13.76)

0.0056**

(2.05)

1tCPF -0.1193

(1.06)

0.1311***

(1.98)

0.726

(1.38)

1.486

(1.05)

0.2929

(0.23)

0.791***

(13.85)

R sq 0.4891 0.5839 0.3471 0.9193 0.8296 0.9548



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 sub-

periods 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

52

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.

This paper also extensively examines the roles of the balances in CPF accounts. In all

regressions, there are no statistically significant CPF wealth effects. However, in the

impulse response analysis, the results show that the impacts of CPF shocks on consumption

are more persistent than those of private or public housing wealth. Moreover, there is no

strong evidence observed that the CPF directly pushes up private housing price though it

does result in the increase in public resale housing prices.

53

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