IRES2011-033
IRES Working Paper Series
NEW SUPPLY AND PRICE DYANMICS
IN THE HOUSING MARKET
Joseph T.L. Ooi
Thao T.T. Le
April 2011
NEW SUPPLY AND PRICE DYANMICS
IN THE HOUSING MARKET
JOSEPH T.L. OOI
National University of Singapore
THAO T.T. LE 
National University of Singapore
This Draft: April 2011
Abstract
We employ VAR models to trace the price response of existing houses to the quantity of new units
launched by homebuilders in Singapore between 1996 and 2009. Contrary to the "competition"
hypothesis prediction of a negative reaction, we find that marginal supply granger-cause existing
house prices in a positive manner. The effect is robust to the inclusion of exogenous demand factors
as well as price interaction in the primary (new houses) and secondary (existing houses) market
segments. The “contagion” effect is consistent with the hypothesis that developers, due to their
ability to predict the market, are price leaders in the housing market. We also find that homebuilders
exhibit “herding” behavior in mimicking each other’s timing on when to market their new
residential projects.
Key words: housing supply, price adjustments, VAR model Acknowledgements: This research received the Singapore Ministry of Education’s Academic Research
Fund (AcRF) Tier 1 funding support: WBS No. R-297-000-096-112. An earlier version of this paper was
presented at the American Real Estate Society (ARES) meeting in Naples, Florida and won the ARES
2010 best paper award in the Housing category. We are grateful to the participants at the ARES 2010
meeting for their valuable comments and suggestions. This paper is also available on Urban Studies’ First
Online (September 5, 2011).


Corresponding author. Department of Real Estate, School of Design and Environment, National University
of Singapore, 4 Architecture Drive, Singapore 117566. E-mail: [email protected]
Department of Real Estate, School of Design and Environment, National University of Singapore, 4
Architecture Drive, Singapore 117566. E-mail: [email protected]
NEW SUPPLY AND PRICE DYNAMICS
IN THE HOUSING MARKET
1.0
Introduction
Over the past decades, house prices have escalated dramatically across different metropolitan
areas. House prices affect the general population because purchasing a house is the single largest
investment decision for most homeowners. Aspiring homeowners, in particular, worry about
being priced out of the market. Policymakers are equally concerned about potential misallocation
of resources when short-run prices diverge from their equilibrium level, while businesses fear
that a dramatic price hike would lead to higher operating costs and, consequently, erode their
level of competitiveness. Given these concerns, a popular proposition to reduce the upward price
pressure is to build more houses.
Since the price of an asset is determined by the interaction of its demand and supply, an
increase in supply would, ceteris paribus, result in a decline in existing house prices. Although
the “competition” hypothesis predicts a negative relationship between stock adjustment and price
movement, it is difficult to observe and measure empirically the direct relationship between
supply and price (DiPasquale, 1999). Tracking the number of housing permits issued against
price movements in 79 US metropolitan areas, Glaser et al. (2008) note that whilst their
movements are analogous, housing permits seemed to peak and trough earlier than prices. Thus,
in a dynamic context, Glaeser et al (2008) argue that homebuilders lead rather than follow price
movements.1 The “contagion” hypothesis predicts that the quantity and prices of new homes
supplied to the market will affect prices of existing houses positively. In summary, there are two
opposite predictions regarding the dynamic relation between new supply and house prices.
A frequently researched issue in the housing literature is the price elasticity of supply.
Meen (2002) and Hwang and Quigley (2006), amongst many other authors, have observed that
elasticity of supply varies from one region to another depending on geographical factors, such as
1 land constraints, and regulatory factors. In strictly regulated markets with limited land,
construction starts lag behind price appreciations. This leads to higher price volatility and
persistent price mutation over time.3 Finding that less responsive metropolitan areas experienced
more bubbles (due to large increases in prices and lower new starts), Glaeser, Gyourko and Saiz
(2008) recently link supply elasticity to housing bubbles and argue that models of housing price
volatility that ignore supply miss an important part of the housing market.4
The focus of this study is not on supply elasticity per se, but rather on the reverse
causality flow. Specifically, we examine the price adjustment of existing houses to a marginal
change in the housing stock at the aggregate level. We focus on the private apartment market in
Singapore because it has the following advantages. First, the new housing stocks are more
comparable to existing stocks in terms of their spatial location. In most cities, new housing
construction tends to occur at the urban fringe, thereby increasing the distance from the new
units and the city centre as an urban area grows. Consequently, the price of existing units rises
relative to the price of new housing because the location premium for existing houses increases
as the city grows in size (Mayer and Somerville, 1996). In contrast, the rent gradient in
Singapore is relatively flat because it occupies a small physical area. Moreover, the urban area
has a well-defined boundary as the city state is located on an island.5
Second, how prices adjust to an exogenous shock in demand is dependent on housing
supply elasticity. The greater the elasticity, the stronger the response of new supply to a price
change. If supply is perfectly elastic, the exogenous demand shock can be cleared quickly by
suppliers responding to price signal in the market. Thus, in a housing market with unconstrained
supply, construction starts will fluctuate in tandem with changes in housing demand. In reality,
housing supply is less than perfectly elastic. Often than not, it responds sluggishly to price
signals due to the long gestation period required to start and complete a new development
(DiPasquale and Wheaton, 1994; Malpezzi, 1999; Harter-Dreiman, 2004; Tse and Webb, 2006;
Tse, 2008).6 In most cities, new housing supply only forms a small portion of the total housing
stock. Consequently, it is argued that the causal relationship between supply and price may be
weak (Grigson, 1986; Abelson, 1993). Singapore, on the other hand, has a fairly elastic supply
with significant level of new housing stock due to the proactive measures taken by the
2 government to facilitate property development by acquiring, amalgamating and selling prepared
vacant land to private developers.7 Between 1996 and 2009, the private housing stock in
Singapore rose by 86% from 130,737 units to 243,181 units. Furthermore, the number of new
units sold by homebuilders in the primary market accounts for 44.1% of the annual volume of
housing transactions.8
Third, the precise timing of when the new supply enters the market is known. This is
unlike studies which either assumed housing supply to be fixed in the short-run and model house
prices as a variable equating short-run housing demand with fixed short-run housing supply, or
employed land sales or building permits as imperfect proxies of housing supply. In particular, the
usefulness of these proxies is limited by inconsistencies in the time gap between land purchase,
building permit approval and physical completion.9 The influence of land supply on house prices
through the housing production chain is diminished because a portion of the new land supply
may be absorbed into the developers’ land bank for future development (Lai and Wang, 1999).
Moreover, homebuilders often start selling before the new projects are physically completed.
This practice of selling new homes prior to their physical completion, which is known as presale,
is common in other housing markets, such as China, Taiwan, Malaysia and Hong Kong. In this
study, we measure the marginal supply of new housing by the actual number of units launched
by homebuilders in each quarter over the study period. It facilitates a finer calibration of the
price adjustment process in response to new supply entering the housing market.
Fourth, there are effectively two segments existing concurrently in a housing market,
namely the primary market where new units are sold by the builders and the secondary market
where existing units are sold by their current owners. Consequently, information may flow
between the two markets with less informed investors in one market inferring information from
price changes in the other market. Tracking the path of the price discovery process between the
two markets, we investigate whether house prices in the primary market or the secondary market
respond similarly or differently to the new supply.
Using Vector Autoregressive (VAR) models which are frequently used in dynamic
modeling of time series data, the empirical results reveal interesting insights. First, the quantity
3 of new supply released in the current quarter granger-cause existing house price in the next
quarter. The direction of the price adjustment is positive, which is contradictory to the
competition story but consistent with the hypothesis that the new supply has a “contagion” effect
on existing house prices. This implies that the market views the collective action of the
homebuilders in launching new projects as good news, either because of their confidence or
ability to predict the market. This leads to an upward revision of house prices. Nevertheless, we
find an inverse (but insignificant) relationship between house prices and marginal supply in the
primary housing segment. Thus, a marginal increase in new housing supply has different impact
on prices of existing and new homes. Specifically, prices of existing houses rise, whilst prices of
new homes decrease with additional supply. We also observe that the price of existing homes
started adjusting at the point when the new supply is marketed, not when they are physically
completed. The results indicate that new housing stocks are only viewed as competitors at the
point when they are physically completed. However, during the marketing period before their
completion, they seem to have a “positive” contagion effect on the prices of existing homes.
We also observe that marginal supply is related positively to the quantity released in the
previous quarter, which suggests a “herding” behavior by homebuilders who mimic each other’s
timing on when to release units in their new development to the market. Finally, we find
evidence of a bi-directional flow of price information between the primary housing market and
the secondary housing market. Prices set by developers in the primary market cascade down to
the secondary market, but developers’ pricing decision is also influenced by prices in the
secondary market.
The rest of this paper is organized as follows. The next section introduces the Singapore
housing market to provide the necessary backdrop for the empirical study. This is followed by a
discussion of the empirical model on the causal relationship between marginal supply and house
prices and an analysis of the results. Additional robustness checks are presented. The concluding
section summarizes the main results and offers some thoughts on policy implications.
2.0
Data
4 This section aims to provide some background information on the private housing market in
Singapore. In comparison with the public housing sector, the private housing market is relatively
small (18 per cent of total housing stock).10 Tu (2004), nevertheless, contend that its impact on
the Singapore economy is significant because any fluctuation of private housing prices has
important implications for the national wealth holding. Consistent with the common practice in
many of the Asian markets, such as China, Hong Kong, Taiwan, South Korea and Malaysia,
most new condominiums in Singapore are sold before project completion. Prior to offering
residential units for sale in Singapore, the developer has to obtain a Housing Developer Sale
License, which is only granted to those with good track record (Munneke, et al. 2011). Usually,
it takes another twelve quarters for the developer to complete the new development and handover the completed units to the buyers.
Information on the number of units launched in each quarter as well as individual sale
transactions required to construct the price index were obtained from the REALIS database,
which is maintained by the Urban Redevelopment Authority (URA), the government agency
responsible for urban planning and development in Singapore. The house price index is
constructed using the weighted repeat sales (WRS) methodology proposed by Case and Shiller
(1987). Essentially, units that are sold more than once over the sample period are identified, and
their price movements are tracked to create the WRS price index.
Table 1 reports the housing stock and the marginal supply of private non-landed residential
properties in Singapore annually between 1996 and 2009. On average, the annual flow of new
housing stock is 4.44% of existing housing stock over the whole sample period.11 Over the study
period, the private housing stock increased by 86%, from 130,737 units to 243,181 units. The
aggregate number of new houses supplied to the market is 120,173 units over the sample period,
which is sufficiently large to affect prices of existing houses. Spread evenly over the sample
period, an average 2,163 new units are launched every three months with the actual number
fluctuating considerably following the underlying property market condition.
[ Table 1 ]
5 Figure 1 charts the non-landed private residential price index as well as the supply of new
private houses in Singapore over 55 quarters from 1996:Q1 to 2009:Q3. It provides an initial
insight into how new supply and existing house prices vary over our study period. Note that the
study period covers two major economic crises, namely the Asia Financial Crisis (AFC) and the
Global Financial Crisis (GFC) in 1997 and 2008, respectively. Both crises triggered a sharp
decline in property prices. The decline is more severe during the AFC as the impact is more
localized to the region. The price index hit the bottom in 1998:Q3, after a 37.5% drop from the
previous peak in 1996:Q2. The most recent peak in 2007 surpassed the previous peak of 1996,
before the GFC struck. Not surprising, the trend shows that the two series are highly correlated.
The hottest period is 2009:Q3, which saw the most number of units launched in a quarter (5,899
units). This period coincided with a bullish sentiment in the housing market. On the other hand,
the lowest number of units launched in a quarter was in 2003:Q1 with 506 units, which coincided
with the SARS epidemic outbreak in Singapore and the East Asia region. Although the volume
of new supply fell dramatically, existing house prices only declined marginally.
[ Figure 1 ]
3.0
New Supply and House Prices
How does a marginal change in supply affect house price? Economic theories prescribe that the
new supply would upset the equilibrium relationship between supply and demand and as more
new stock is added to existing stocks, the price of existing houses fall. In short, the
“competition” hypothesis predicts the causal relationship from marginal supply to house price to
be negative. The “contagion” hypothesis, on the other hand, predicts that the marginal supply
will have a positive impact on the price of existing houses because developers, due to their
ability to predict the market, are price leaders in the housing market.
The housing literature has also commonly modeled homebuilders as profit-seeking agents
who are motivated to build more when house prices are high.12 Housing starts are therefore
expected to be related positively to house prices. To accommodate the possibility of a bi6 directional causal relationship between prices of existing houses and new supply, we employ a
Vector Autoregressive (VAR) model:
p
q
i 1
i 1
p
q
i 1
i 1
S t  1   1i S t i   1i Pt i  e1t Pt   2    2i S t i    2i Pt i  e2t (1)
(2) where S and P represent the change in housing stock and housing price, respectively. S is
defined as the total number of new units launched by all developers in Singapore in each quarter
over the study period. P is the first difference in the residential price index (in percentage).
The subscript t refers to time, which is represented on a quarterly basis. The VAR equations are
essentially a Granger-causality test on whether or not lagged values of one endogenous variable
helps to predict another endogenous variable. Equation (1) prescribes that new supply at time t
depends on its past values as well as changes in house prices. Consistent with Mayer and
Somerville (1996, 2000), we specify that new housing starts are induced by changes in house
prices rather than current level of house prices.13 Equation (2), on the other hand, prescribes that
the change in house prices at time t depends on its past values as well as changes in the housing
stock in the previous periods.
The theory prescribes that if a housing market is efficient and in equilibrium, all general
macroeconomic conditions affecting demand would be fully incorporated into prices, which
would then be the sole factor determining new supply (DiPasque and Wheaton, 1994). However,
in an inefficient housing market, other factors such as interest rate, economic growth rate and
employment rate may still be significant determinants of housing supply. We thus incorporate d
as a set of exogenous demand variables to the VAR model:
p
q
k
i 1
i 1
i 1
p
q
k
i 1
i 1
i 1
S t   3    3i S t 1    3i Pt i    3i d t i  e3t Pt   4    4i S t i    4i Pt i    4i d t i  e4t 7 (3)
(4) Following prior studies which have shown that credit market conditions and financial
affordability are major determinants of housing demand (Bover, Muellauer and Murphy, 1989;
Mankiw and Weil, 1989; Runnels, 1989; Mayer and Somerville, 1996), we include two
exogenous variables in the VAR models to control for demand factors. They are average income
growth (INC) and change in real interest rate (INT). Mayer and Somerville (1996) observe
that credit constraints are correlated with prolonged periods of lower than predicted
constructions. Similarly, economic theories suggest that interest rates and house prices are
inversely related. Generally, cheap loans tend to increase housing demand, and therefore push up
house prices although Ho and Cuervo (1999) contend that in a dynamic setting, this is softened
by an increase in the supply of housing in response to higher prices and lower construction
financing costs. In addition to the two financial variables, we also incorporate population growth
as the third demand variable in the models. The results, not reported, are robust and the
coefficient for population growth is not significant in the price equation.15
Data on these two factors are obtained from DATASTREAM. The income series is
adjusted for the seasonal effect since bonuses are usually paid in the last quarter of the year.
Descriptive statistics of the variables included in the VAR models are presented in Table 2. The
mean quarterly change in existing house prices over the study period is 0.47%, ranging from a
high of 18% (2007:Q3) to a low of -12.9% (1998:Q1). The average number of units launched in
a quarter is 2,163, ranging from a low of 506 units (2003:Q1) to a high of 5,899 units (2009:Q3).
Except for a major increase during the AFC, real interest rates have stayed relatively stable,
whilst the average income of Singapore residents has increased over the study period.
[ Table 2 ]
The Augmented Dickey-Fuller test results for unit roots confirm that all the variables are
stationary. Consequently, both equations in the VAR model can be estimated using ordinary least
squares (OLS). In addition, results of the Akaike information criteria (AIC) test indicate that the
most appropriate structure contains one lag value of the endogenous variables. The estimation
results are presented in Table 3. The similarity of the estimation results for equations (3) and (4)
8 with equations (1) and (2) suggests that adding exogenous variables to the original VAR system
only improve the forecasting power marginally. Interest rate not surprisingly has an inverse
impact on house prices. Income, however, are not correlated with house prices.
P and S are autocorrelated to their values in the preceding quarter. An implication of
the positive serial correlation of P is that prices in the housing market are somewhat
predictable and inefficient.17 In other words, a price increase in the current quarter tends to be
followed by an increase in the next quarter, and vice versa. This phenomenal could be attributed
partially to speculative or psychological behavior. Specifically, momentum is created when
consumers facing rising house prices act swiftly and speculate on further price increases
(Reichert, 1990; Levin and Wright, 1997). In a recent paper, Glaser et al. (2008) also endogenize
asset bubbles by assuming that home buyers believe that future price growth will resemble past
price growth. Meanwhile, the implication of a positive serial correlation in S is that new
housing supply tends to cluster together. Consistent with Lai and Wang (1999) who posit that the
timing decisions of major homebuilders are followed by other small homebuilders, our results
illustrates the “herding” instinct of homebuilders. They tend to mimic each other’s decisions on
when to start marketing their new projects.
[ Table 3 ]
With respect to our research question, the estimation results of equations 2 and 4
interestingly indicate that an increase in new supply this quarter is followed by an upward price
movement in the next quarter. Figure 2 charts the impulse response function, which is an
alternative but more visual way, to show how existing house prices react to a supply shock.18
Also plotted in the figure are + 1 standard error bands (in red lines), which yield an approximate
66% confidence interval for the impulse response. The response function illustrates that an
unexpected one standard deviation addition in new supply (945 units) will result in the price of
existing houses to increase by 1.4% in the next quarter. The response fades away gradually over
ten quarters. [ Figure 2 ]
9 The positive granger-causal relationship appears to contradict conventional economic
theory which prescribes that an increase in supply should, ceteris paribus, leads to a price
reduction. Thus, the “competitive” hypothesis is not substantiated by our empirical results.
However, if the increased supply is simply reflecting homebuilders’ ability to anticipate higher
future demand, then the positive relationship is consistent with the “contagion” hypothesis where
new property launches by homebuilders is viewed by the market as good news; thus, lifting the
valuation of existing houses as well.
4.0
Price Dynamics in the Presale and Resale Market Segments
The housing market can essentially be separated into two segments, namely the primary market
and the secondary market.20 In the secondary (resale) market, buyers and sellers transact or
“churn” on existing housing stock. Suppliers in this market segment are individual homeowners
and investors who do not transact regularly in the market. Meanwhile, transactions in the primary
(presale) market involve newly constructed units that will add to the existing housing stock. The
suppliers are professional homebuilders and traders who are generally more well-informed.
Furthermore, sellers in the presale market have a large quantity of units for sale in contrast to
individual sellers in the resale market. Since sellers in the presale market are more wellinformed, prices are expected to move and adjust faster to new information in the presale market
as compared to the resale market. In other words, prices in the primary market become a leading
indicator of price movements in the secondary market.21
To examine price interactions in the primary and secondary markets as new stocks are
added, we expand the VAR system to include an additional variable and equation.
p
q
r
k
i 1
i 0
i 0
i 1
p
q
r
k
i 1
i 1
i 0
i 1
St  5    5i St i   5i Pt i    5i Ft i    5i dt i  e5t Pt  6    6i St  i    6i Pt i    6i Ft  i    6i dt  i  e6t 10 (5)
(6) p
q
r
k
i 1
i 0
i 1
i 1
Ft  7    7 i St  i    7 i Pt  i    7 i Ft  i    7 i dt  i  e7 t (7)
where S and P and d are as defined previously. F is the quarterly change in prices of
presale homes, which is derived from a price index we constructed to track price movements in
the primary market. Note that the current terms for P and F are included in equations (5),
(6) and (7) to accommodate the possibility of contemporary relationships between house prices
in the two market segments. The estimation results of the recursive VAR model are presented in
Table 4. Once again, based on AIC test, the most appropriate structure for the model contains
one lagged values of all endogenous variables.
[ Table 4 ]
On the supply elasticity of housing (Equation 5), the coefficients for St-1 and Pt-1
have the same sign and statistical significance as reported earlier in equations (1) and (3). The
strong positive serial correlation in S indicates that new housing supply tend to cluster
together. This is consistent with the “herding” instinct of homebuilders. It is interesting to note
that new supply in the current quarter is not dependent on past price movements in the primary
and secondary markets.
The strong contemporaneous positive relationship between quantity of new supply and
price movement of existing house is also consistent with the “contagion” hypothesis that new
property launches by homebuilders are good news to existing homeowners. This effect is further
substantiated by the positive coefficient for St-1 in Equation 6, which indicates that new supply
leads to price increases of existing homes. In contrast, the relationship between supply and price
for new homes in the primary market (although statistically weak) is negative, which is
consistent with the “competitor” hypothesis based on normative economic theory. Thus, a
marginal increase in new housing supply has different effect on house prices in the primary and
secondary market. Prices of existing houses rise, whilst prices of new homes decrease with
additional supply. This indicates the “contagion” effect of new supply is dominant in the resale
market for existing homes, whilst the “competitive” effect is marginally stronger in the presale
market for new homes.
11 On the price dynamics of existing homes (Equation 6), the impact of Ft and Ft+1 is
positive and statistically significant. This is consistent with the view of information flowing from
the primary market to the secondary market. On the price dynamics of new houses (Equation 7),
Pt and Pt+1 also have positive and significant coefficients. The two results combined
indicate a bilateral flow of information between the presale market and the resale market. Whilst
this is consistent with the hypothesis that homebuilders are price leader, that is prices set by them
in the primary market cascades to the secondary market, we also find a reverse flow of
information. Specifically, homebuilders’ decisions on the pricing of their new projects are
influenced by prices in the secondary market.22
5.0
Robustness Test
In the housing literature, new supply is often defined by the number of new completed units
adjusted for demolition (C). So far, we have defined change in housing stock as the number of
new units launched by homebuilders in each quarter (S). Although the new units may not have
been physically completed, we take the view that they affect existing house prices the moment
they are launched in the marketplace. This definition is appropriate in the context of our study on
how marginal supply affects the prices of existing houses. Intuitively, S and C are expected
to be highly correlated, albeit with some lag due to construction time. As a robustness test, we reestimate equations (5), (6), and (7) using C in place of S. The results are reported in Table 5.
[ Table 5 ]
Generally, the estimation results are weaker than those reported in Table 4. The
bidirectional price discovery between the primary and secondary markets still holds. C is not
dependent on current and previous quarter prices in the primary and secondary markets.
Nevertheless, consistent with the time lag between construction start and completion,
experiments with longer lag values (up to seven and eight quarters) reveal that C is influenced
by presale and resale prices. This indicates that the adjustment speed is faster in housing markets
which allow presale. The timing of housing supply can be adjusted at a lower cost since sales can
12 be made as soon as construction permits are obtained. If house prices rise, supply can be
increased quickly by releasing more units and or initiating more new developments. However, if
price falls, builders can reduce supply by simply postponing sale or construction (see Hua,
Chang and Hsieh, 2001).
With respect to our research question, the change in available stock does not seem to
have any significant effect on house prices in both the primary and the secondary market. The
sign of the coefficients is negative, which is consistent with the “competitive” hypothesis.
Interpreted together with the earlier set of results in Table 4, the empirical evidence suggests that
new housing stocks are only viewed as competitors at the point when they are physically
completed. However, during the marketing period before their completion, they have a positive
“contagion” effect on existing homes. Furthermore, prices of existing house start adjusting to
new supply at the point when they are launched, rather than when they are physically completed.
6.0
Conclusion
As compared to the extensive amount of research on housing demand, there is a dearth of
literature on housing supply. As noted by a number of authors (see Rosenthal, 1999; DiPasquale,
1999; Mayer and Somerville, 1999), this is puzzling given that housing supply comprises half of
the market. To control escalating house prices, a popular proposition is to increase housing
supply. The argument goes that the new supply would, all else being equal, increase competition
and hence, lead to a price decline in existing homes. We call this the “competitive” hypothesis.
In reality, the empirical evidence shows that the dynamic relationship between marginal supply
and house prices is not that straightforward. For example, an increase in housing supply could
signal the homebuilders’ confidence or ability to predict the market. We call this the “contagion”
effect, which predicts a positive price response.
Focusing on the private apartment market in Singapore, we seek to answer the following
research question: How a marginal change in supply affects prices of existing houses? Using the
actual number of new dwelling units launched by property developers to represent marginal
supply, VAR models are employed to trace the response of existing house prices to the new
13 supply being added to existing stock. The empirical results show the price reaction to new supply
differs in the primary and secondary housing markets. In the primary (presale) market, house
prices react negatively, albeit weakly, to the quantity of new units supplied in the previous
quarter. This is consistent with the competitive hypothesis, which prescribes that all things being
equal an increased supply would lead to a price decline. In the secondary (resale) market, house
prices react positively and significantly to new supply. This is consistent with the contagion
hypothesis that property developers are price leaders in the housing market. Their decision on
when and how many units to launch affect price movements in the secondary markets. Other
interesting results we glean from the current study are developers exhibit a herding behavior and
the flow of information between the presale and the resale housing markets is bi-directional.
In summary, housing supply and price adjustments are inextricably interrelated. Contrary
to the competitive hypothesis, new supply has a positive price effect on existing houses. The
positive impact of marginal supply on existing house prices continues when we control for
demand factors and price discovery process between the primary (new houses) and secondary
(existing houses) markets. At the macro level, the market views the collective action of the
homebuilders in launching new projects as good news, either because of their confidence or
ability to forecast market trend by adjusting supply ahead of the price movements.
This
“contagion” effect of new supply on existing house prices could also be attributed at the micro
level to new buildings pushing up the prices of existing houses in the vicinity because they add
attraction of a neighborhood, which we are currently investigating in a follow-up study using
transaction data at the neighborhood level.
Since the study focuses on private apartments, our results will be more relevant to an
urban city context where more dwelling units are in high-rise buildings. Moreover, our study on
price dynamics of the pre-sale mechanism will be applicable in East Asian markets, such as
Malaysia, Hong Kong, Taiwan and Taiwan. Nevertheless, the results on the impact of marginal
supply on asset pricing can still be generalized to include other assets or types of residential
units, such as single family homes, so long as the housing supply is fairly elastic and the new
units are of similar quality with the existing units.
14 15 References
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16 Malpenzzi, S. (1999) “A simple error correction model of house prices”, Journal of Housing Economics 8, 27-62.
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18 Units
Figure 1. Residential Price Index and New Launches
7000
140
6000
120
5000
100
4000
80
3000
60
2000
40
1000
20
0
0
New launch
19 Price Index
Figure 2. Impulse Response Function of Price to New Supply Shock
The chart traces the impulse responses of existing house price to an unexpected one standard deviation supply
shock. The blue lines represent the impulse response functions, whilst the red lines show the + 1 standard error
bands of the response. The horizontal axis is the time period in quarters.
20 Table 1. Marginal Supply versus Existing Housing Stock
This table tracks the stock and marginal supply of private housing in Singapore between 1996 and 2009. Note that housing stock
includes dwelling units that are physically completed at the beginning of each year, whilst marginal supply covers the aggregate
number of new dwelling units marketed by all developers in the particular year. The reported figures include all private housing
(landed and non-landed).
Year
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009*
1996 – 2009
Housing Stock#
(beginning of the year)
130,737
151,790
163,873
176,331
185,503
193,980
199,584
206,669
214,275
223,686
230,196
233,195
236,903
243,181
-
Marginal Supply
(Dwelling Units)
11,520
9,869
5,324
5,087
8,143
8,357
9,507
5,216
5,881
8,201
11,069
14,016
6,107
11,876
120,173
* Figures as at end of 2009Q3.
21 Marginal Supply
(% of Stock)
8.81%
6.50%
3.25%
2.88%
4.39%
4.31%
4.76%
2.52%
2.74%
3.67%
4.81%
6.01%
2.58%
4.88%
4.44%
Table 2. Descriptive Statistics
S and P represent the change in housing stock and housing price, respectively. S is defined as the total
number of new units launched by all developers in Singapore in each quarter over the study period (1996:Q3 to
2009:Q3), whilst P is the first difference (in percentage) in the residential price index. INT refers to quarterly
change in real interest rate, which is measured by the real prime lending rate adjusted for inflation. Income growth
(INC) is the average monthly income which has been adjusted for seasonal effects.
P
S
INT
INC
Mean
0.0047
2,162.8
-0.0165
0.0159
Median
-0.0076
1,846.5
0.0010
0.0150
Maximum
0.1802
5,899.0
1.2693
0.0900
Minimum
-0.1297
506.0
-1.1990
-0.0400
Std. Dev.
0.0618
1,081.4
0.2660
0.0245
54
54
54
54
No. of observations
22 Table 3. VAR Estimation Results
Estimation is based on a VAR model. Standard errors are in parentheses. S and P represent the change in
housing stock and housing price, respectively. S is defined as the total number of new units launched by all
developers in Singapore in each quarter over the study period (1996:Q3 to 2009:Q3), whilst P is the first
difference (in percentage) in the residential price index. The subscript t refers to time, which is represented on a
quarterly basis. The two exogenous variables are average income growth (INC) and change in real interest rate
(INT).
Dependent Variable
Constant
Pt-1
St-1
INCt-1
Equation (1)
St
913.7292***
(344.45)
217.5922
(2447.65)
0.593748***
(0.15)
Equation (2)
Pt
-0.029926*
(0.02)
0.346213***
(0.14)
1.52E-05**
(8.7E-06)
0.249121
9.626066
0.221808
8.410796
INTt-1
Adj. R-squared
F-statistic
Equation (3)
St
1003.659***
(370.15)
238.228
(2505.13)
0.588309***
(0.16)
-48.3276
(57.50)
11.12893
(531.28)
0.23046
4.893276
*.** and *** denote that coefficient is significant at 10%, 5% and 1% level respectively.
23 Equation (4)
Pt
-0.0434**
(0.02)
0.2995**
(0.13)
1.75E-05 **
(0.00)
0.0048
(0.00)
-0.0666**
(0.03)
0.280
6.063097
Table 4. VAR Model with Price Interactions
Estimation is based on a recursive VAR model. Standard errors are in parentheses. S , P and F represent the
change in housing stock, existing housing price, and new housing price, respectively. S is defined as the total
number of new units launched by all developers in Singapore in each quarter over the study period (1996:Q3 to
2009:Q3). P is the first difference (in percentage) in the existing house price index, whilst F is the first
difference in the new house price index. The subscript t refers to time, which is represented on a quarterly basis. The
two exogenous variables are average income growth (INC) and change in real interest rate (INT).
Dependent Variable
Constant
Pt-1
Pt
St-1
Ft-1
Ft
INCt-1
INTt-1
Adjusted R-squared
F-statistic
Equation (5)
St
1474.68***
(376.68)
237.3041
(3828.851)
11576.86***
(3801.283)
0.410945***
(0.15455)
-2861.83
(3653.009)
-2842.52
(3283.508)
-102.848*
(56.58266)
644.0633
(532.8545)
Equation (6)
Pt
-0.03175**
(0.013841)
-0.28396*
(0.142488)
0.369343
5.350524
0.692099
20.48095
1.07E-05*
(5.78E-06)
0.429103***
(0.126781)
0.617968***
(0.088986)
0.003899*
(0.002118)
-0.03189
(0.020126)
*.** and *** denote that coefficient is significant at 10%, 5% and 1% level respectively.
24 Equation (7)
Ft
0.006609
(0.016887)
0.324688*
(0.165131)
0.828228***
(0.119263)
-3.93E-07
(6.94E-06)
-0.27282*
(0.159026)
-0.00093
(0.002537)
-0.01136
(0.023868)
0.63156
15.85596
Table 5. Robustness Test
Estimation is based on a VAR model. Standard errors are in parentheses. C and P represent the change in
housing stock and housing price, respectively. C is defined as the quarter-on-quarter change in available housing
stock in Singapore, whilst P is the first difference (in percentage) in the residential price index over the study
period (1996:Q3 to 2009:Q3). The subscript t refers to time, which is represented on a quarterly basis. The two
exogenous variables are average income growth (INC) and change in real interest rate (INT).
Dependent Variable
Constant
Pt-1
Pt
Ct-1
Ft-1
Ft
INCt-1
INTt-1
Adjusted R-squared
F-statistic
Equation (8)
Ct
1298.397***
(299.5527)
-1031.63
(3664.489)
-2604.75
(3767.284)
0.334715**
(0.150377)
-763.191
(3601.65)
1426.317
(3371.073)
-45.3624
(57.1826)
186.0152
(539.6975)
Equation (9)
Pt
-0.00525
(0.011698)
-0.19788
(0.14042)
0.057038
1.449344
0.670171
18.60956
-1.98E-06
(5.88E-06)
0.386435***
(0.128931)
0.658031***
(0.089408)
0.00339
(0.002181)
-0.02556
(0.020783)
25 Equation (10)
Ft
0.008994
(0.013034)
0.309903**
(0.153624)
0.821801***
(0.11166)
-1.93E-06
(6.57E-06)
-0.26744*
(0.152512)
-0.00081
(0.002498)
-0.01309
(0.023526)
0.632226
15.89853
End Notes
1
Topel and Rosen (1988) also observe that price movements and construction activity are positively
correlated, though construction activity appears to turn down prior to the downturn in prices. 3
De Vries and Boelhouwer (2005) accuse that local spatial planning policy often seriously disrupts the
balance between supply and demand on the housing market with the result that the supply is not at the right place at
the right time. Consequently, although causality between price and production can be formulated in theory, they
argue that it is in practice difficult to prove with statistical models.
4
Recognizing the difficulties in explaining the rapid rise and fall of housing prices with a purely rational
model based on market fundamentals, Glaeser et al. (2008) also propose that irrational exuberance plays an
important role in dictating house price movements.
5
The development site furthest from the Singapore CBD is only 23 km (Ooi, Sirmans and Turnbull, 2006). 6
The magnitude of the elasticity of housing supply varies widely. Depending on the data and how change in
housing stocks is modeled as a function of house prices, the magnitude can range from perfectly elastic (infinite) to
perfectly inelastic (zero). Pioneering studies, such as Muth (1960), Follain (1979) and Stover (1986), which relied
on national or regional-level data and reduced-form models of house price, conclude that housing supply is infinitely
elastic. Blackley (1999) and DiPasquale and Wheaton (1994), on the other hand, provide evidence that housing
supply has finite elasticity. More recently, Harter-Dreiman (2004), employs co-integration and vector-error
correction models (VECM) to distinguish long-run from short-run dynamics in the housing market. If price and
income are cointegrated, this is interpreted as evidence that a long-run steady state relationship exists between the
two series, and thus implies that supply is less than perfectly elastic. The logic behind this is that a long-run positive
relationship between income and price is indicative that movements in demand as a result of income shocks result in
some ultimate increase in price, even after supply has fully adjusted. If supply were perfectly elastic, one would
expect to find no relationship between house price and income in the long run. 7
The two major sources of sites for residential developments in Singapore are the government land sales
program and private land market. To facilitate redevelopment of older buildings, new legislation was also introduced
to reduce the threshold for condominium owners to sell their houses collectively to developers. See Ooi, Sirmans
and Turnbull (2010) for a good write-up on the dual land markets in Singapore.
8
The balance 55.9% of the annual transactions constitutes resale of existing houses in the secondary market.
9
Tse (1998) and Ooi and Lee (2006) find that house prices in Hong Kong and Singapore are not responsive
to land supply. Hwang and Quigley (2006), on the other hand, observe that increases in supplier activity, as
represented by building permits, affect US house prices. 10
Ong and Sing (2002) find a bidirectional price discovery and interaction between public and private
housing markets in Singapore between 1990 and 1999. In other words, past public and private housing returns both
affect the current public and private housing returns.
11
In comparison, Mayer and Somerville (1996) document that annual starts as a percentage of the total stock
range from a low of 0.3 in the Northeast, USA in 1982:Q1 to a high of 4.2 in the West, USA in 1977:Q2. 12
Studies on the determinants of housing supply have often modeled housing starts as a function of asset
price and replacement cost of housing (Poterba, 1984; Topel and Rosen, 1988; DiPasquale and Wheaton, 1994;
Somerville, 1999). Other determinants include the credit market (Chan, 1999) and abnormal weather (Fergus, 1999). 26 13
Mayer and Somerville (1996) argue that new housing starts are best explained by the change in prices
rather than the level of house prices. Since housing starts are a flow variable, they should therefore be a function of
other flow variables. 15
Note that the population growth variable is reported on an annual basis, whilst the frequency of the VAR
models are on a quarterly basis. Thus, we had to make a simplifying assumption that the population growth rate are
evenly distributed over the four quarters within the year. 17
In their seminal paper, Case and Shiller (1989) similarly observe that year-on-year changes of single-family
homes prices tend to be followed by changes in the same direction in the subsequent year. They thus conclude that
the market for single-family homes does not appear to be efficient. 18
A shock to an exogenous variable not only directly affects the variable itself, but the shock is also
transmitted to all other endogenous variables through the dynamic lag structure of the VAR system.
20
Hua et al. (2001) provides a good commentary on the differences between the two markets. 21
Along the same line of argument, Wong et al (2006) categorize traders in the Hong Kong residential market
into three types: (i) developers, who are the first sellers, (ii) liquidity provider (including speculators), who actively
buy and sell properties for immediate profits through price appreciation, and (iii) end purchasers, who purchase
homes for consumption or long-term investment. They prescribe that liquidity providers are more inclined to
arbitrage in the presale market than the spot market because the former involves lower transaction costs. In contrast,
end purchasers, being less experienced in property trading, tend to be more involved in the spot market. Note that once we control for prices in the presale market, P is now negatively correlated with its value
in the preceding quarter. This means a price increase in the current quarter tends to be followed by a decrease in the
next quarter, and vice versa. Thus, instead of a momentum effect, we now observe a reversionary effect of price
adjustments in the secondary market. A similar effect is also observed in the primary market. 22
27