Impact of Integrated Transport
Hubs on
House Prices in Singapore
LAWRENCE CHIN & NUR K HUSAINI
Dept of Real Estate
National University of Singapore
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Outline
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Introduction
Objectives of paper
Literature review
Research method
Findings of study
Conclusion
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SINGAPORE:
Size and Skyline
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New skyline of Singapore
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Constraint #1: Scarcity of land
 ultimate land area of 730 sq km
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Constraint #2: Competing uses for land
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Planning for Accessibility
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Introduction
• Residential property is a commodity of variable uses, equipped with
unique characteristics attributable to internal and external factors,
each playing a major role in determining its inherent value.
• Improvements in accessibility and enhancing the ease of
commuting would inflate property prices beyond the usual due to
positive externalities generated.
• Additionally, transportation networks attract multitudinous types
of activities such as retail and F&B to the area, further escalating
prices of properties located nearby the bustling node.
• In the case of Singapore, the concept of Integrated Transport Hubs
(ITHs) under in the 2008 Land Transport Review, which integrates a
Mass Rapid Transit (MRT) station, an air-conditioned bus
interchange and a string of lifestyle shops for added convenience
and shelter from the atmospheric conditions.
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Objectives of Paper
• To examine the effects of an Integrated
Transport Hub (ITH) presence on the prices of
public residential property.
• To apply case study of the Bukit Panjang
Transport Hub, comparing the differences in
HDB Resale prices between units located in
the influence and the control areas.
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Why this research?
• The purpose is to investigate the price increment of
residential properties caused by the presence of an
ITH; thereafter serving as an extension to existing
literature where the price effects on high rise
residential property due to the construction of a
transport hub is currently absent or lacking.
• A study in the recent past analyzed price changes on
residential property price due to improved
infrastructure (Lum, Koh, & Ong, 2004) and other
studies have explored the impact of MRT on residential
property prices(Bajic, 1983; Grass, 1992; Ong, 2001)
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Hypothesis to be tested
• That Transport Hubs would generate a housing
premium on units located in the influence
areas, using walking distance to distinguish
between units located near to or away from
the Transport Hub.
• To determine the impact of the transport on
the neighbouring residential properties using
hedonic regression model
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Literature Review
• Real estate is an immovable property; thereafter
making it susceptible to changes in its immediate
surroundings.
• Alteration of its nearby environment will
undoubtedly impact the real estate price.
• Hence, it is common to witness fluctuations in
real estate price caused not only by the economic
environment, but also by the physical
environment surrounding the real estate;
specifically in this case, the presence of a
transport hub close by.
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Location Theories
• Location theories were developed by early economists to explain
the pattern of land use by providing solutions to the problems
associated with the optimal use of the land.
• Pricing mechanisms would dictate the utility of good and services
supplied in an urban area, which ultimately leads to locations of
activity followed by the development of spatial structures in the
urban area.
• It would be difficult to assign one location theory to be applied
generally depending on the land use (industrial or commercial) of a
plot of land.
• Factors such as technology and transportation, changes in
population, government redevelopment of the built environment
and policies, as well as similar land uses, which exist within the
same area, would complicate the decision-making process about
optimal locations.
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• William Alonso (Alonso, 1975) was the first to
successfully theorise, following the works of(Isard,
1956), and Wingo (1961), the relationship between the
urban spatial structure and the location of firms and
households in the field of Urban Economics in 1964,
having expanded Johann Heinrich von Thunen’s Land
Rent Theory (Thunen, 1966).
• The latter had postulated that ‘central’ towns are able
to fetch high rents due to high demand and limited
supply of the land, supported by savings in transport
costs.
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• The willingness for a consumer to pay for housing is
dependent on how much importance they place on
individual attributes (Kain & Quigley, 1970; Wilkinson,
1973).
• Consequently, the differences in preferences stem from
varying household size, income, aspiration level and the
demographic make-up of the household (Wilkinson, 1973;
Gorter & Nijkamp, 2001).
• Thus the main influences of land rent or prices and location
decision are based upon the firm’s or household’s
preferences, restrictions on budgets, frequency and
convenience of available transportations, and transport
costs (So, Tse, & Ganesan, 1997).
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Transit-oriented Developments (TOD)
• The effect on property prices by rail transportation has
been researched by a number of researchers from
different viewpoints and perspectives, which includes
analyses from a variety of transport system types, such
as light rail, mass rapid and buses, of the residential or
commercial impacts by transport and even the causal
relationships which cause the positive or negative
effects (Brinckerhoff, 2001).
• The results from these various studies have produced
mixed results, causing researchers to continue
examining the issue.
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Impact on Property Values
• In most of these studies, a positive relationship
was discovered between housing prices and
proximity to rail stations (Grass, 1992; Nelson,
1999; Almeida & Hess, 2007; Chen, Rufolo, &
Dueker, 1997; Haider & Miller, 2000).
• In addition, there is hardly evidence that
proximity to rail transit would depress housing
prices, unless in the presence of influential
variables outside of transit station proximity
(Bowes & Ihlanfeldt, 2001).
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Proxy for Proximity Measurement
• In measuring the threshold for walking distance and
time by commuters towards LRT stations, it was proven
that in Calgary, Canada, the average walking distance
to LRT stations in suburban locations was 649m and in
Central Business District (CBD) areas, 329m (O'Sullivan
& Morall, 1996).
• Thus on average, pedestrians were willing to travel
400m or about 5 minutes to the LRT. Guidelines used in
Canada and North America for average walking
distances indicate 300 to 900 metres and 400 to 800
metres respectively.
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Sources of Data
• Residential housing prices between influence and control areas are
compared to obtain the housing price premium attributable to the
presence of a transport hub
• Resale transactions from 15th July 2008 were collected from
Streetsine, a database for real estate agents and investors, and
brought forward to 2012 prices using the HDB Resale Price Index.
• A total of 524 public housing resale transactions were collected
from data sources for the study areas from 15th July 2008 till
present (February 2012).
• This large time frame was essential to provide capture sufficient
housing transactions since the announcement of the transport hub
creation in order to arrive at a sample size which would allow for a
normal distribution.
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Methodology
• A controlled experiment was done to compare the
differences in housing prices to show the impact the
transport hub has on the housing units.
• Hence, two separate data sets were collected namely
from the influence group and the 3 control groups,
which can be categorized as follows:
• Influence Group: HDB resale units which fall within 500
metres walking distance from Bukit Panjang ITH
• Control Groups: HDB resale units which are more than
500 metres walking distance from Bukit Panjang ITH
(Senja LRT, Petir LRT)
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Model Specification
• Used extensively in the housing economic
analysis, hedonic analysis statistically unbundles
these different attributes and estimates their
separate value (Cortright, 2009).
• More specifically, the analysis determines several
regression coefficients that measure, at the
margin, each respective characteristic’s
independent effect on property value (Fuguitt &
Wilcox, 1999).
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Figure 1. Overview of Bukit Panjang
ITH, Influence and Control Areas
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Equation
• Thus, the hedonic model is represented using the log-log form in
the equation below:
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(LnPricei,y) = β0,y +β1,y (LnAgei,y) +β2,y (Leveli,y) +β3,y (LnAreai,y)
+β4,y(Proximity to Transport Hub,y) +εi,y
Where (LnPricei,y) = Transacted price for property i in year y,
(LnAgei,y) = Age of property i in year y
(Leveli,y) = Level (in strata of 5) of property i in year y
(LnAreai,y) = Size of property i in year y
(Walking Distancei,y) = Walking distance to Transport Hub
εi,y
= Error term
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Independent Variables
• The independent variables in the study include age, floor level, floor area
or size of unit and walking distance. Further explanations are provided for
below.
• Age
• The age of a housing unit is the determinant of housing price. Both have a
negative relationship which arises due to deterioration of building quality
and structures. Usually, this is called the depreciation factor of buildings.
• Floor Level
• It is well established that higher floors would command greater premium
due to factors like better view and air circulation. However, as HDB as well
as the database used only provided for categories of data level i.e. stratum
of 5, the strata are grouped according to height where 1,2,4,5
corresponded to Level 1 to 5, Level 6 to 10, Level 11 to 15, Level 16 to 20
and Level 20 to 25 respectively.
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Independent Variables
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Floor Area
Area forms the basis of calculating housing prices. In most cases, ceteris paribus, a
bigger floor area would generate a higher housing price. Area per square feet (psf)
housing has an inverse relationship with housing price as a larger unit would
generally tend to have a lower price psf as compared to a smaller unit, even
though overall, the larger unit has a higher price.
Proximity to Transport Hub
The proximity to the transport hub is categorized as a dummy variable. Units in the
control area are categorized as being more than 500 metres from the transport
hub hence are represented by “0” to indicate insignificant influence by the
transport hub. Units in the influence area, contrastingly, are categorized as “1” to
indicate the influence of a transport hub.
HDB Resale Price Index (RPI)
The RPI tracks the overall price movement of the public residential market. Using
the 4th quarter of 1998 as the base period, resale transactions regardless of
location, flat types and models, can be calculated. In the study, the RPI was utilized
in order to compare past transactions to today’s prices.
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Results
• The basic functional form of multiple linear regression adopted in
this study is the log-log function due to statistical significance of the
explanatory variables, employing the Ordinary Least Squares (OLS)
method which estimates the "best fit" of a set of independent (X)
variables against the dependent variable (Y) variable which is to be
predicted.
• Most importantly, all the explanatory variables displayed
coefficients which were statistically significant with p-values of
zero.
• Additionally, all the equations proved that floor area is the
explanatory variable that has the strongest impact on housing price
with a t-statistic of 24.532 (log-log).
• Subsequently, floor level followed by proximity to the transport hub
are the second and third most important factors influencing
housing prices.
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Summary of Key Findings
• After generating the regression results from the OLS method,
the results showed that a 12.6% premium was commanded
for housing units located within the influence area which is
located at most 500 metres from the Bukit Panjang ITH.
• Other independent variables that were accounted for other
than proximity to the transport hub were area of the units,
age of the units and the level of the units, which were
classified in strata of 5.
• Results showed that area of the units was the most
important factor determining house price, followed by level
and then proximity to the transport hub.
• It was also discovered that all the explanatory variables
utilized in the study were highly significant in predicting
house price.
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• Furthermore, the results of the study are
consistent with the previous studies which
indicated that proximity to a transport hub would
generate a higher premium for housing units
located in influence areas.
• With this knowledge, locations near a transport
hub would bode well with high-density housing
as compared to areas located further away.
• On top of that, it shows that walkability to a
transport hub is priced.
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Significance of Study
• The findings will contribute to the field by
understanding the ultimate value (positive or
negative) the construction of the transport hub
will bring to the residential properties in the area.
• In land tenders, developers can use this insight in
submitting bids.
• This would supply valuable insights into the
determinants of varying property prices across
the different regional hubs.
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THANK YOU!
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