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Estimating the Effects of Light Rail Transit (LRT) System on the Property Values in the Klang Valley, Malaysia: A Hedonic House Price Approach

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  • GESIS Leibniz Institute for the Social Sciences (Germany) and Northumbria University (UK)

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This study investigates the increased value of land in the form of house prices as a result of improved accessibility owing to the construction of Light Rail Transit (LRT) systems. Kelana Jaya Line LRT system is chosen as the case study in this research. Hedonic house price modelling is employed to estimate the effects of the LRT system on the prices of the house that are located within the radius of two kilometres from the Kelana Jaya LRT stations. Selling prices, structural attributes, land use and socio-economic attributes were collected from the database of Department of Valuation and Services of Malaysia, selected maps and reports. Fifty-five factors that are likely to influence a house price were identified and used to estimate the overall effects of the LRT system on it. However, only significant variables were included in the final deliberation and these were identified by using correlation analysis and modified step-wise procedures. The outcome of this study shows a positive relationship between the existence of the LRT system and house prices. In other words, properties that are located within close proximity to the LRT station are valued more than properties that are located further away.
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61:1 (2013) 3547 | www.jurnalteknologi.utm.my | eISSN 21803722 | ISSN 01279696
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Teknologi
Estimating the Effects of Light Rail Transit (LRT) System on the Property
Values in the Klang Valley, Malaysia: A Hedonic House Price Approach
Mohd Faris Dziauddin
a*
, Seraphim Alvanides
b
, Neil Powe
c
a
Department of Geography and Environment, Faculty of Human Sciences, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia
b
Faculty of Humanities and Social Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom
c
School of Architecture Planning and Landscape, Claremont Tower Newcastle University Newcastle upon Tyne NE1 7RU, United Kingdom
*Corresponding author: fdziauddin@yahoo.com
Article history
Received :19 September 2012
Received in revised form :4
December 2012
Accepted :12 February 2013
Graphical abstract
Abstract
This study investigates the increased value of land in the form of house prices as a result of improved
accessibility owing to the construction of Light Rail Transit (LRT) systems. Kelana Jaya Line LRT system
is chosen as the case study in this research. Hedonic house price modelling is employed to estimate the
effects of the LRT system on the prices of the house that are located within the radius of two kilometres
from the Kelana Jaya LRT stations. Selling prices, structural attributes, land use and socio-economic
attributes were collected from the database of Department of Valuation and Services of Malaysia, selected
maps and reports. Fifty-five factors that are likely to influence a house price were identified and used to
estimate the overall effects of the LRT system on it. However, only significant variables were included in
the final deliberation and these were identified by using correlation analysis and modified step-wise
procedures. The outcome of this study shows a positive relationship between the existence of the LRT
system and house prices. In other words, properties that are located within close proximity to the LRT
station are valued more than properties that are located further away.
Keywords: Light rail transit systems (LRT); property values; hedonic price model; Klang Valley
Abstrak
Kajian ini menyiasat peningkatan nilai tanah dalam bentuk harga rumah hasil daripada peningkatan
ketersampaian menerusi pembinaan sistem Transit Aliran Ringan (TAR). Sistem TAR laluan Kelana Jaya
dipilih sebagai kajian kes dalam penyelidikan ini. Model harga rumah hedonik digunakan untuk
menganggarkan kesan sistem TAR pada harga rumah yang terletak dalam lingkungan dua kilometer dari
stesen TAR Kelana Jaya. Harga jualan, ciri-ciri fizikal rumah, lokasi dan sosio-ekonomi dikumpul
daripada pangkalan data Jabatan Penilaian dan Perkhidmatan Malaysia, peta terpilih dan laporan. Lima
puluh lima faktor yang mungkin mempengaruhi harga rumah telah dikenal pasti dan digunakan bagi
menganggarkan kesan keseluruhan sistem TAR di atasnya. Walau bagaimanapun, hanya pemboleh ubah
yang signifikan secara statistik telah dimasukkan dalam perbincangan akhir dan ini telah dikenal pasti
dengan menggunakan analisis korelasi dan prosedur ‘step-wise’ yang telah diubah suai. Hasil kajian ini
menunjukkan terdapat hubungan positif antara kewujudan sistem TAR dan harga rumah. Dalam erti kata
lain, hartanah yang terletak dalam jarak yang hampir dengan stesen TAR mempunyai nilai yang lebih
tinggi berbanding hartanah yang terletak jauh.
Kata kunci: Sistem transit aliran ringan (TAR); nilai hartanah; model harga hedonik; Lembah Klang
© 2012 Penerbit UTM Press. All rights reserved.
1.0 INTRODUCTION
Rail transit systems, namely heavy and light transit systems are
a public good and have been seen as serving a number of
purposes and producing a number of public benefits, particularly
to those areas that have been served by high service quality rail
transit systems. These public benefits can be categorised into
two; direct and indirect benefits.
The direct benefits of rail transit systems are defined in terms of
improved regional mobility, consumer savings, vehicle cost
savings, energy conservation, improved mobility for non-drivers
and disadvantaged groups, congestion reduction, roadway cost
savings, increased traffic safety, and pollution emission
reductions (see, for example, Litman, 2003, 2004a, 2004b, 2007,
2012; Banister and Banister, 1995; Knowles, 1996; Pucher,
2004).
Alongside these direct benefits, the provision of high
service quality of public transportation such as rail transit
systems has also potentially influenced local land use and
increased local property values (indirect benefits), particularly
36 Mohd Faris Dziauddin, Seraphim Alvanides
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those that are directly perceived by the person who is
purchasing or renting a property. The question is how a rail
transit system could possibly affect land use and property
values.
The concept of accessibility is the key to understanding
how transportation and land use, and transportation and land
value relate to each other. As widely recognised, public
transportation such as rail transit systems significantly promotes
spatial interaction between activities or land uses, particularly in
larger and denser metropolitan areas. This spatial interaction is
measured by accessibility, which reflects both the attractiveness
of potential destinations and the ease of reaching them (Dalvi,
1978; Giuliano, 2004; Hanson, 2004; Smith and Gihring, 2011).
According to Giuliano (2004) the discussion on
accessibility should include the element of attractiveness of a
place as an origin (what opportunities are there to reach other
destinations) and as a destination (how easy it is to get there
from all other origins). Yet, the pattern of land uses is also
important because it determines the opportunities or activities
that are within the range of a given place. The potential for
interaction between any two places increases as the cost of
movement between them either in terms of money or time
decreases. In addition, the structure and capacity of the
transportation network could also affect the level of accessibility
within a given area (Parsons, Brinckerhoff, Quade and Douglas,
Inc., 1998).
This level of accessibility relies heavily upon high service
quality of urban transit systems. This service quality refers to
how transit system is directly perceived by users and it includes
the availability and coverage of a geographic area, frequency,
travel speed, reliability, integration, price structure and payment
options, comfort and security, ease of reaching transit stations
and making stops, universal design, affordability, information,
aesthetics and amenity (Litman, 2012).
Since there is an increase in accessibility from one place to
another (such as travel time saving and reduced transport costs),
land uses and property values will respond accordingly in those
places that have become more accessible. As Cevero and Kang
(2010: 102) highlighted, ‘a large body of literature confirms that
the urban real-estate responds positively to transportation
improvements, mainly in the form of higher property values
and, zoning permitting, land use intensification’.
However, in the case of the transportation-land use
relationship Parsons, Brinckerhoff, Quade and Douglas, Inc.,
(1998) argue that improving accessibility does not guarantee
that land use changes will follow. The type, amount, and timing
of land use changes will also depend upon the state of the
regional economy, the current level of accessibility, the types of
development permitted by land use regulations, the availability
of services such as sewer and water, the desirability of the area
for development, and other factors.
Land use changes can also vary because travelers have
many options about the ways they can change their behavior in
response to the change in the transportation network or the cost
of travel. They can adjust the timing, route, or mode of trips as
well as change the locations where they live, work, or shop.
Yet, as shown in the literature review (see, for example,
Renne, 2005; Lin and Gau, 2006; Loo et al., 2010; Olaru et al.,
2011; Cevero and Kang, 2011; Sung and Oh, 2011; Victoria
Transport Policy Institute, 2011) improvement in accessibility
due to the existence of public transportation such as rail transit
systems together with the provision of high service quality has
potentially played a significant role in influencing land uses and
stimulating Transit-oriented development (TOD
2
),
in particular,
for those areas that are located within close proximity to rail
transit stations.
In more developed countries such as the United Kingdom and
United States, their rail transit systems have created compact,
mixed-use and walkable urban villages around stations (Litman,
2007; Renne, 2005; Victoria Transport Policy Institute, 2011).
As a result, residents around these areas tend to own fewer cars
and drive less than if they were to live in more automobile-
independent neighbourhoods.
In the case of the effects of rail transit systems upon land
values (the main purpose of this paper), emphasis has taken into
account the research on locational externalities that are
generated by the rail transit systems, which in turn affect the
residential and commercial land. It is expected that the existence
of a rail transit system should be able to capitalise land values in
the form of property values (residential and commercial
properties). Banister and Berechman (2000) argues that the
improvements in accessibility for those areas that have been
served by the rail transit systems can potentially trigger several
major positive locational externalities, in particular for
properties located within close proximity to railway stations.
They argued further that these positive locational externalities
should be viewed as additional benefits to the primary
accessibility improvement benefits.
As mentioned above, this positive effect, however, is not
expected to be automatic. Instead this can be achieved through
high service quality of rail transit system that could bring
benefits to the local land use. The desired effect will not be
realised if the system is deployed in the wrong areas or
delivered in an unsatisfactory way.
One question that needs to be asked however is, how are
property values affected by rail transit systems? Transit systems
can be reached by accessing their transit stations. Therefore, the
ability to access transit stations conveniently and quickly should
be capitalised in property values. In other words, higher
property values are expected in places with superior access to
stations. Yet, in the case of residential property it has been
found that house prices have the potential to decrease for
properties that are located too close to a rail station due to traffic
congestion and noise pollution effects, whilst properties
radiating out from the station and within easy walking and
driving distance should increase in price (Hess and Almeida,
2007).
The purpose of this paper therefore is to report the results
of a study that estimated the value for improved accessibility,
encompassing the locational externalities that are generated by
the LRT system, which in turn affect the land values in the form
of house prices. The paper begins by reviewing the literature
with respect to the effects of rail transit systems on property
values. The empirical evidence from the previous studies forms
a base that in turn can be used in estimating the effects of the
LRT system on house prices. Section 3 of the paper discusses
some background of the LRT system in the Klang Valley.
Section 4 discusses the research methodology such as the study
area, data and data sources and the identifications of the effects
of the Kelana Jaya LRT Line on house prices. Section 5 deals
with the results of the estimation. Finally, the process of
estimating and principal findings are reviewed and discussed.
2.0 EXISTING RESEARCH
A number of existing studies have sought to estimate the effects
of heavy and light rail transit systems, mainly in terms of
locational externalities that are generated by the rail transit
systems upon land values. The evidence from empirical
researches both in the UK and North America suggest
inconsistent results and varying magnitude on the effects of
37 Mohd Faris Dziauddin, Seraphim Alvanides
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heavy and light rail transit systems on property values. This is
due to the unique research methods, unique local transport
systems and land use environments (Hess and Almeida, 2007).
Twenty-three of the thirty-three studies considering heavy
and light rail transit systems suggest a positive relationship
between property values and rail transit systems access. Early
research highlights this relationship for example Boyce et al.,
1972; Lerman et al., 1978; Bajic, 1983; Dvett et al., 1979;
Damm et al., 1980; Voith, 1991; Nelson, 1992; Al-Mosaind et
al., 1993; Gatzlaff and Smith, 1993; Benjamin and Sirmans,
1994), and in more recent studies by Chen et al. (1997),
Workman and Broad (1997), Dueker and Bianco (1999), Knapp
et al. (1999), Chesterton (2000), FTA (2000), Weinberger
(2000), Cervero (2002), Cervero and Duncan (2001, 2002),
Garrett and Castelazo (2004), Du and Mulley (2006) and Hess
and Almeida (2007).
However, some of the studies were dismissive of the effect.
Eleven of the thirty-five both heavy and light rail transit systems
studies suggest that there is no relationship between property
values and rail transit systems access (see for example, Dewess,
1976; Nelson and McClesky, 1990; VNI Rainbow Appraisal
Service, Inc., 1992; Cevero and Landis, 1993; Armstrong, 1994;
Landis et al., 1995; Landis and Loutzenheiser, 1995; Forrest et
al., 1996; Ryan, 1997; Henneberry, 1998). For example, in
Atlanta, studies discovered that rail transit systems had virtually
no effect on property values and a study of Miami’s Metrorail
system came to the same conclusion (Gatzlaff and Smith, 1993).
Over the past decade, Portland’s Metropolitan Area Express
(MAX) rail transit system has also received attention. In two
studies that were conducted, only very modest and localised
effects on land values were identified (Al-Mosaind et al., 1993).
Preliminary results of a study on Toronto rail transit carried
out by Dewess (1976) have shown virtually no effect on
property values. However, a study on the same rail transit
system carried out by Bajic (1984) revealed that the city’s rail
corridors have experienced intense development and that
residential property values are significantly higher near a rail
line than elsewhere. Moreover, in the study of Pennsylvania’s
commuter rail system, Voith (1991) concluded that houses
served by a commuter rail system had a 4 per cent to 10 per cent
premium over those that were not served by a commuter rail
system. Nonetheless, he found that travel time to the CBD was
significant in estimating property values. In the UK, estimating
the effect of rail transit systems on property values commenced
in the 1990s. For instance, a study conducted by Centre for
Urban and Regional Development Studies (CURDS) (1990) on
the effect of Tyne and Wear Metro on house prices concluded
that there was no effect. However, a recent study conducted by
Du and Mulley (2006) on the same basis found that housing
units in some of the areas that are located within close proximity
to railway stations increased in value.
Several explanations are available for these inconsistencies
resulting from the effects of heavy and light rail transit systems
on property values (see for example Knight and Trygg, 1977;
Landis et al., 1995; Ryan, 1999; Giuliano, 2004). An early
explanation was given by Knight and Trygg (1977). They
concluded that the determinants of property value in an urban
area relate to land value controls and economic growth rather
than transportation investment. Ryan (1999) noted that many
had supported the conclusions developed by Knight and Trygg.
However, the inability to replicate the variables introduced by
Knight and Trygg led to weak evidence in supporting earlier
ideas of Knight and Trygg.
Alternatively, Landis et al. (1995) suggested different
arguments to support research discrepancies, for example, new
transportation facilities that could influence property values.
However, the effect of new technology on accessibility levels
will gradually decline over time. Even though new
transportation technology is introduced, which benefits adjacent
properties, they remain under-priced. Hence, the relationship
between property value and transportation is still uncertain, but
travel cost is still a strong factor to be observed (Ryan, 1999).
Yet another explanation as to why empirical evidence
(particularly from the 1980s and 1990s) differs from theoretical
expectations is provided by Ryan (1999). Ryan argued empirical
evidence is different compared to theoretical expectations. She
advocated that the distance of a property to the transportation as
a variable has proved to be more accurate compared to other
variables. The value of the properties where they are located
will be bid up if there is apparent time saving. A relationship
between access and property values is to be expected when the
measure of access captures the essence of travel time saving.
Inaccuracy in measuring changes in the travel time leads to
inaccurate changes in the property value. Thus, studies should
aim to answer whether transportation really improves the travel
time for a specific segment of travellers. Ryan argues that all of
the benefits are internalised through the transport time
dimension and that there is no reason to investigate further into
the effect on the property value.
The explanation given by Ryan seems to be realistic
because as has been noted earlier, the main objective of
introducing rail transit systems was simply to improve
accessibility to the CBD. Hence for many households, the only
way to improve accessibility to the CBD is by being located
closer to the rail transit service; households need to purchase a
house in the associated area if they wish to enjoy the advantage
of the rail transit service. Capitalising the price of houses could
be expected if the rail transit service has truly improved
accessibility to the CBD. This is due to the argument that for
those households who really appreciate the improvement of
accessibility to the CBD, they will bid-up for such service.
Giuliano (2004) offers an explanation for the inconsistency
and varying evidence of the effects of rail transit systems on
property values. She believes that the first few studies of the
effects of heavy rail transit systems on property values were too
premature since it would take decades before the land market
could respond to the availability of rail transit systems in the
area.
However, it is important to note that if the methods that
have been employed to estimate the effects are an appropriate
method, together with the quality of data, the positive
relationship between rail transit systems and property values can
be identified.
3.0 BACKGROUND INFORMATION ON RAIL
TRANSIT SYSTEMS IN THE KLANG VALLEY
The Klang Valley region consists of five administrative units
which include the Federal Territory of Kuala Lumpur (the
capital and financial as well as commercial centre of Malaysia),
and four other districts of the state of Selangor; Klang, Petaling,
Hulu Langat and Gombak. Being situated between the northern
and southern regions has made the Klang Valley the core of the
larger planning entity of the Peninsular Malaysia (see Figures 1
and 2). The Klang Valley region encompasses an area of
2,843.42 square kilometres or 1,097 square miles and, as of the
year 2010, it had a population of about 6.0 million (about 21.4
per cent of the total population of Malaysia). With 2,000
residents per square kilometre, the Klang Valley comprises the
densest urbanised area in Malaysia.
38 Mohd Faris Dziauddin, Seraphim Alvanides
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The Klang Valley region has been the most rapidly growing
region in Malaysia for the past few decades. The early growth of
this region concentrated primarily in the Federal Territory of
Kuala Lumpur. Kuala Lumpur is the major financial and
commercial centre of Malaysia and it encompasses an area of
243 square kilometres and had a population of about 1.58
million in 2010 (about 26.48 per cent and 5.67 per cent of the
total population of the Klang Valley and Malaysia,
respectively).
Figure 1 The location of the Klang Valley in Peninsular Malaysia
Source: The Federal Territory Development and the Klang Valley
Planning Division (2004)
Figure 2 The Klang Valley and its conurbation
Source: The Federal Territory Development and the Klang Valley
Planning Devision (2004)
Economic growth and rapid urbanisation have brought
about steady increases in car ownership and congestion levels in
the Klang Valley. A study conducted by Mohammad and
Kiggundu (2007) found that between 1997 and 2005, the
average motor-vehicles speed in the CBD of Kuala Lumpur
hovered around 16-28 km/h, with the worst congestion during
morning and evening peak hours. In terms of traffic fatalities, it
was recorded that there were about 4.3 accident fatality cases for
every 10, 000 registered vehicles (Marjan et al., 2007).
Realising that this problem needed to be addressed, the
construction of heavy rail transit systems in the Klang Valley
started in the 1991. The first line began operating in 1995,
connecting Kuala Lumpur and Port Klang in the Klang Valley
and Seremban (in the state of Negeri Sembilan) and Kuala
Lumpur. An additional line to Rawang and Kuala Kubu Bahru
was constructed and opened in 2000 and 2007 respectively. The
KTM commuter system, with 175 kilometres of total length of
network has fourty-five stations. It should be noted that several
prominent shopping complexes and recreational centres became
more accessible after the opening of the KTM commuter
service. In addition, the KTM commuter system has improved
accessibility for commuters from suburban areas who work in
Kuala Lumpur City Centre, as they can travel without being
caught in the traffic congestion.
In the case of the LRT system, the construction of this
system in the Klang Valley started in 1994 and it involved
several phases. The first phase of the LRT system in the Klang
Valley was under Sistem Transit Aliran Ringan (STAR),
stretching for twelve kilometres over thirteen stations between
Ampang and Jalan Sultan Ismail, linking the northern and
southern suburbs of Kuala Lumpur. This section began
operating in April 1997. However, the section between Ampang
and Plaza Rakyat began operating during the first quarter of
1996. The second section of the STAR LRT system project was
completed on 30 June 1998 extending southwards to the
Commonwealth Games complex in Bukit Jalil.
The second phase of the LRT system in the Klang Valley
was under Projek Usahasama Transit Ringan Automatik
(PUTRA). The PUTRA LRT line covers the eastern and western
suburbs of Kuala Lumpur. The line services some of Kuala
Lumpur’s most affluent and heavily populated areas. The total
alignment of the line, which starts from the depot in Subang and
ends at Terminal Putra in Gombak is twenty-nine kilometres in
length. In 2004, the operation of this service was taken over by
Rangkaian Pengangkutan Integrasi Deras Sdn Bhd (RapidKL).
Since then, the name of PUTRA LRT has been changed to
Kelana Jaya Line LRT system.
According to Md Nor et al. (2011), presently Kelana Jaya
Line and Ampang Line LRT system carries some 350,000
passengers daily (180,000 passengers on Kelana Jaya Line and
170,000 on Ampang Line) and a monorail system with a daily
patronage of 100,000 passengers.
In order to raise public transport modal share from the
current 13 per cent to 25 per cent by 2015 for the morning peak
period, the Malaysian government in 2010 announced to
reinvest with large amount of investment (nearly US$12 billion)
in public transportation that is by constructing Mass Rapid
Transit (MRT) in the Klang Valley. The MRT is seen as a
system that will operate at higher speeds and carries more
passengers than the existing LRT system.
Although the main objectives of implementing rail transit
systems in the Klang Valley were to improve accessibility,
increase regional mobility, conserve energy, reduce congestion,
save roadway cost, increase traffic safety, and reduce pollution
emission, the Klang Valley rail transit system planners also
hoped a modern era rail transit systems would also bring
indirect benefits such as to guide future population and
employment growth in the region, influence local land use and
39 Mohd Faris Dziauddin, Seraphim Alvanides
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increase property values for those areas served by the system.
Furthermore, by providing one of the largest incremental
additions to regional accessibility, rail transit systems was
expected to strengthen the Klang Valley’s urban centres while
guiding suburban growth along radial corridors.
4.0 RESEARCH METHODOLOGY AND DATA
SOURCES
To estimate the effects of the LRT system upon the house prices
in the Klang Valley, the cross-sectional method was identified
to be an appropriate method for this study. The selling price for
each of the individual houses located within two kilometres of
the LRT station was collected after the construction of the LRT
system had been completed.
4.1 Data Sources
The literature has shown that hedonic data can be obtained from
two sources; primary data and secondary data sources. In the
context of this study most of the data were collected from
secondary data sources. The secondary data were collected
during fieldwork in the Klang Valley. The fieldwork consisted
of several phases, spreading over a period of five months from
July to September 2006 and July to August 2007. In the first
fieldwork, data on several categories were collected from
various agencies in Malaysia. However, after completing the
first fieldwork, the researcher identified several other relevant
data that are still needed for this study. Thus, the second
fieldwork needed to be carried out in order to fill the gaps. As
noted earlier, the second fieldwork was completed in August
2007. Several categories of data have been identified with
regards to this study.
These data can be grouped into five categories; the selling
price of individual houses and their structural attributes,
locational attributes, socio-economic attributes, property market
and transportation access variables. House price transactions for
2004/05 were chosen to be the sample for this study. This marks
a period after several years of rail transit systems operated in the
Klang Valley. In total, 2338 units of housing selling prices were
collected. However, after going through several steps to clean
the sample dataset by eliminating the unsuitable data and
updating the unavailable data, the study was left with 1,580
observations. This cross-sectional data refers to the residential
property located within two kilometres (straight-line-distance)
of LRT stations. Figure 3 shows the twenty-nine-kilometres of
the Kelana Jaya Line LRT system route with twenty-three
stations whilst Figure 4 shows a two kilometre radius buffer
surrounding the Kelana Jaya Line stations. However, due to the
Kelana Jaya Line LRT system stations being located close to
each other, this means that the two kilometre buffer areas
around the stations overlap as shown in Figure 3. The selling
price of an individual house and its structural attributes were
collected from the Department of Valuation and Property
Services, Malaysia.
The data on the base map, land parcel, locational attributes
(type of land use) and socio-economic were obtained from the
Centre of Spatial Analysis, Science University of Malaysia,
Kuala Lumpur City Hall, Department of Agriculture of
Malaysia and Department of Statistics of Malaysia. Land use or
locational attributes data were collected for two different
periods of time; 2004 and 2005. The purpose of dividing these
data was based on different time periods because we needed to
see the land use change during these two periods of time. Thus,
we would be able to measure how these attributes could affect
the house prices in the study area. The data was believed to be
of high quality and reliability as these come from the centre
involved in the GIS application of the Klang Valley project for
the Prime Minister’s Department of Malaysia.
Figure 3 Case study: the Kelana Jaya Line LRT system
Figure 4 Two kilometre radius buffer areas surrounding stations
In order to measure the distance to an LRT station and
other amenities from a given house, the geographical
information systems (GIS), and in particular, network analysis
was used in this study. GIS was used to organise and manage
large spatial datasets (that is, units of houses) and of course their
structural and locational attributes too, and most importantly
GIS was used to position each observation and locational
attribute accurately on a local map by using the geographical
coordinates. Moreover, the combination between GIS and
spatial analysis has been particularly useful in this study in
which the distance and proximity were measured accurately by
40 Mohd Faris Dziauddin, Seraphim Alvanides
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measuring the distance from one point to another using network
distance such as the distances from the observations to the
nearest station and other locational attributes.
4.2 Property Value Estimation
In order to measure the locational externalities generated by rail
transit systems upon residential property values, this paper uses
a standard hedonic pricing model where the house price is a
function of structure, locational and neighbourhood variables.
The general form of a hedonic pricing model can be presented
as:
P (Z) = ƒ (R, S, L, N) + ε (1)
where,
P = a vector of observed house prices;
R = a vector of focus variables
S, L and N = the vectors of structural variables,
locational variables and
neighbourhood variables respectively;
ε = a vector of random error terms.
Typically, the specification of this function has been
represented as:
P
i
= α X
i
+ ∑ β
k
S
ki
+ ∑γ
q
L
qi
+ ε
i
X
i
(2)
where,
i = 1, …, N is the subscript denoting each property;
P
i
= the price of property i;
k = 1, …, K is the number of structural attributes;
q = 1, …, Q is the number of locational attributes;
α, β, γ and ε are the corresponding parameters;
X
i
= a column vector that consists entirely of ones.
This has been termed the traditional hedonic specification
and has been the basic model in the most of the studies (Can,
1992). Table 2 provides a list of the eighteen independent
variables that was used to estimate the effects of the LRT
system on house prices in this study, along with the definition,
unit of measurement and data source for each variable.
Furthermore, the independent variables that influence
residential property values were categorised into four distinct
groups; focus variables, structural variables, locational variables
and neighbourhood variables. The details of these data and how
they were prepared are described below.
(1) Focus Variables (R); Two types of measures have
been identified in order to measure the distance (in
metre) from each observation to the nearest LRT
station; straight-line-distance and network-distance.
By using network-distance, the accessibility between
each observation and a rail station is the shortest route
on the road network connecting them. Straight-line-
distance (STRDIST) was calculated within ArcView
3.2, and drawn using lines to connect each observation
to the nearest LRT station. In the network-distance
(NETDIST), the distance was measured along the
street network by using a user-developed GIS
programmed named Multiple Origins to Multiple
Destinations, obtained from the Environmental
Systems Research Institute (ESRI) support centre. The
programme was written based on Avenue
programming language of ArcView by Dan Paterson
from the US and it was made accessible to the public.
The network-distance measurement using this
programme requires three layers of spatial data; points
of origin (observations), points of destinations (LRT
station) and the road network data. The distances
between the origins and destinations measured were
automatically saved in a shapefile. Figure 5a and 5b
illustrates straight-line-distance and network-distance
methods in measuring the distance for each
observation to the nearest LRT station for three station
areas.
(2) Structural Variables (S); Structural attributes are in
short, those physical structures of a property and the
land parcel within which it is located. Orford (1999)
explained that structural attributes represent the
shelter afforded by housing and the physical
investment by the owner. Orford argued further that
structural attributes are conceptually more tangible
compared to locational attributes. Since the nature of
structural attributes is more tangible, it is a much
easier and straight forward process to measure the
effects of structural attributes on house prices.
Structural attributes of the house that were included in
the analysis are the floor area of the property in square
foot (FLRAREA), the number of bedrooms of the
property (BEDS) and a set of dummy variables that
illustrate the type of house which are further described
as follows:
TYPTRRD is 1 if the property is terraced, 0 otherwise;
TYPSEMID is 1 if the property is semi-detached, 0
otherwise;
TYPDETCH is 1 if the property is detached, 0 otherwise;
TYPCONDO is 1 if the property is condominium, 0
otherwise.
Figure 5a Straight-line-distance
41 Mohd Faris Dziauddin, Seraphim Alvanides
&
Neil Powe / Jurnal Teknologi (Sciences & Engineering) 61:1 (2013) 3547
Figure 5b Network-distance
(1) Locational Variables (L); Locational attributes are in
short, those attributes whose benefits are realised
mainly in the form of externalities, and hence they are
collectively shared by a large number of people and
houses. Locational attributes can be categorised into
two groups; fixed and relative locational attributes.
Fixed locational attributes are those attributes that
capture the location of a property with respect to the
whole urban area, and pertain to some form of
accessibility measure, typically accessible to the CBD.
Relative locational attributes are those attributes that
reflect the externalities of the local neighbourhood and
are unique to an individual property such as
environmental quality (Orford, 1999). In considering
the decentralised mechanisms for the efficient
provision of such attributes, their spatial attributes are
found to be crucial. Locational attributes tend to be
spatially concentrated in their impact on the quality of
people’s lives and the value of their property. In this
study, CBD, COMMERCIAL, SECONDARYSCH,
PRIMARYSCH, PARK, RECREATION,
HOSPITAL, and INDUSTRY are the distances
measured from the property to Kuala Lumpur city
centre, commercial areas, secondary schools, primary
schools, parks, recreational areas, hospitals and
industrial areas respectively.
(2) Neighbourhood Variables (E); Neighbourhood
attributes for socio-economic and racial composition
were constructed from the census data. The proportion
of the Malay ethnic group (MALAY) was calculated
by dividing the number of population with the
smallest administrative area that is Mukim.
The presence of multicollinearity among parameter
estimates was detected first by using Pearson’s correlation
analysis in SPSS. As mentioned previously, the correlation
coefficients above 8.0 indicated serious multicollinearity
those independent variables that produce a correlation
coefficient of 0.8 or higher were removed from the
particular regression model. Table 3 provides descriptive
statistics for the dependent and independent variables used
in the analysis; mean, maximum, minimum and standard
deviation
4.3 Analysis
The discussion in the preceding section presents the traditional
hedonic specification that has been identified as the basic model
in the many studies related to the house-price analysis. Based on
the specification 2, a log-log specification is found to be the best
functional form for hedonic specification in estimating the effect
of the LRT system on house prices. A log-log specification is
chosen for this study because it produces robust and reliable
results in estimating the effect of the LRT system on house
prices compared with two other specifications (linear and semi-
log specification). In order to identify the difference between
perceived-distance to a station and actual-distance to a station,
the following hedonic house price model is constructed. The
model for straight-line-distance can be stated as:
LnP
i
= α
0
+ α
1
LnSTRDISTi + α
2
LnFLRAREA
i
+ α
3
LnBEDS
i
+
α
4
TYPTRRDi +
α
5
TYPSEMID
i
+ α
6
TYPDETCH
i
+ α
7
TYPCONDO
i
+
α
8
LnCBD
i
+ α
9
LnCOMMERCIAL
i
+
α
10
LnSECONDARYSCH
i
+ α
11
LnPRIMARYSCH
i
+
α
12
LnPARK
i
+ α
13
LnRECREATION
i
+
α
14
LnHOSPITAL
i
+ α
15
LnINDUSTRY
i
+
α
16
LnMALAY
i
+ ε
i
(3)
where i is the subscript denoting each property; P
i
is the
price of property i in Malaysia Ringgit (MYR); Ln is natural
logarithm; STRDIST is the straight-line-distance from the
property to an LRT station measured in metres; FLRAREA is
the floor area of the property in square foot; BEDS is the
number of bedrooms of the property; TYPxxx is a set of dummy
variables that illustrate the type of house which are further
described as follows:
TYPTRRD is 1 if the property is terraced, 0
otherwise;
TYPSEMID is 1 if the property is semi-detached, 0
otherwise;
TYPDETACH is 1 if the property is detached, 0
otherwise;
TYPCONDO is 1 if the property is condominium, 0
otherwise.
CBD, COMMERCIAL, SECONDARYSCH,
PRIMARYSCH, PARK, RECREATION, HOSPITAL, and
INDUSTRY are the network-distances from the property to
Kuala Lumpur city centre, commercial areas, secondary schools,
primary schools, parks, recreational areas, hospitals and
industrial areas respectively. These variables are all measured in
metres. Finally, MALAY is the percentage of the Malay ethnic
at the Mukim level; α denotes a parameter to be estimated; and ε
denotes the standard error of the estimation.
Similarly, the model for network-distance can be stated as:
LnP
i
= α
0
+ α
1
LnNETDIST
i
+ α
2
LnFLRAREA
i
+ α
3
LnBEDS
i
+
α
4
TYPTRRDi +
α
5
TYPSEMID
i
+ α
6
TYPDETCH
i
+ α
7
TYPCONDO
i
+
α
8
LnCBD
i
+ α
9
LnCOMMERCIAL
i
+
42 Mohd Faris Dziauddin, Seraphim Alvanides
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Neil Powe / Jurnal Teknologi (Sciences & Engineering) 61:1 (2013) 3547
α
10
LnSECONDARYSCH
i
+ α
11
LnPRIMARYSCH
i
+
α
12
LnPARK
i
+ α
13
LnRECREATION
i
+
α
14
LnHOSPITAL
i
+ α
15
LnINDUSTRY
i
+
α
16
LnMALAY
i
+ ε
i
(4)
where the actual-distance to a station is given by
NETDIST: the network-distance from the property to an LRT
station measured in metres. The results from the hedonic house
price models are presented in a standard format. This standard
format is shown in Table 4. Note that the results from straight-
line-distance and network-distance were separately displayed.
As can be seen in Table 4, the results from the hedonic house
price models include five attributes which represent the
predictor, coefficient, standard error, t-value and VIF. The
predictor is the variable that has been used to measure the house
prices and it has included focused, structural, locational and
socio-economic variables. The second column provides
coefficients which represent the prices of each predictor used in
the model. The standard errors of the coefficients are presented
in the third column. The fourth column gives us the results of a
t-value of the predictor. The greater t-value of the predictors
implies that the greater its function in determining a house price.
The multicollinearity level of the predictors used in the final
model is shown in the final column, namely the variance
inflation factor (VIF). Meanwhile, the adjusted R
2
for the
number of variables in the model is shown below each model.
Table 2 Definition of variables and data sources
Table 3 Characteristics of dependent and independent variables
Vector
Predictor
Mean
Standard deviation
Minimum
Maximum
Dependent variable
House price transactions (P)
Independent variables
Focus variables (R)
Structural variables (S)
Locational variables (L)
Socio-economic and ethnic
variables (N)
LOGSELLING
LOGSTRDIST
LOGNETDIST
LOGTIMESAVINGS
LOGFLRAREA
LOGBEDS
TYPTRRD
TYPSEMID
TYPDETCH
TYPCONDO
LOGCBD
LOGPRIMARYSCH
LOGSECONDARYSCH
LOGCOMMERCIAL
LOGPARK
LOGHOSPITAL
LOGRECREATION
LOGINDUSTRY
LOGMALAY
12.49
6.73
7.15
2.15
6.17
1.07
0.46
0.01
0.12
0.19
9.08
6.65
6.65
6.62
6.94
7.87
8.67
7.19
3.73
0.59
0.64
0.65
1.14
0.68
0.26
0.49
0.12
0.32
0.39
0.29
0.82
0.64
0.99
2.27
0.89
0.49
0.55
0.24
10.82
4.33
4.33
-4.07
4.79
0.00
0.00
0.00
0.00
0.00
7.91
1.45
2.73
2.27
0.52
4.25
7.09
4.29
3.46
14.43
7.64
8.08
3.38
8.54
1.79
1.00
1.00
1.00
1.00
9.43
7.85
7.93
7.94
9.04
9.04
9.44
7.94
4.49
Vector
Predictor
Operational definition
Units
Data source
Dependent variable
House price transactions
(P)
Independent variables
Focus variables (R)
Structural variables (S)
Locational variables (L)
Socio-economic variables
(N)
SELLING
STRDIST
NETDIST
TIMESAVINGS
FLRAREA
BEDS
TYPTRRD
TYPSEMID
TYPDETCH
TYPCONDO
CBD
PRIMARYSCH
SECONDARYSCH
COMMERCIAL
PARK
HOSPITAL
RECREATION
INDUSTRY
MALAY
House price transactions
Straight-line-distance
Network-distance
Travel time savings to CBD
Floor area
Number of bedrooms
Terraced house
Semi-detached house
Detached house
Condominium
Proximity to CBD
Proximity to primary schools
Proximity to secondary schools
Proximity to commercial areas
Proximity to parks
Proximity to hospitals
Proximity to recreational areas
Proximity to industrial areas
Proportion of Malays
Malaysia Ringgits (MYR)
Metre
Metre
Minutes
Square foot
Number
Dummy (0 or 1)
Dummy (0 or 1)
Dummy (0 or 1)
Dummy (0 or 1)
Metre
Metre
Metre
Metre
Metre
Metre
Metre
Metre
Proportion of Malays (for
each Mukim)
Department of Valuation
and Property Services of
Malaysia (DVPA)
Calculated using GIS
Calculated using GIS
Calculated using GIS
DVPA
DVPA
DVPA
DVPA
DVPA
DVPA
Calculated using GIS
Calculated using GIS
Calculated using GIS
Calculated using GIS
Calculated using GIS
Calculated using GIS
Calculated using GIS
Calculated using GIS
Malaysia Census 2000
43 Mohd Faris Dziauddin, Seraphim Alvanides
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Neil Powe / Jurnal Teknologi (Sciences & Engineering) 61:1 (2013) 3547
Table 4 The traditional hedonic specification of Model 3 and Model 4
Vector
Predictor
Straight-line distance (Model 3)
Network distance (Model 4)
Coefficient
Standard
error
t-value
VIF
Coefficient
Standard
error
t-value
VIF
Focus variables
Structural
variables
Locational
variables
Socio-economic
and ethnic
variables
Constant
STRDIST
NETDIST
TIMESAVINGS
FLRAREA
BEDS
TYPTRRD
TYPSEMID
TYPDETCH
TYPCONDO
CBD
PRIMARYSCH
SECONDARYSCH
COMMERCIAL
PARK
HOSPITAL
RECREATION
INDUSTRY
MALAY
35667.67
-10.56
2019.25
310.63
31485.34
71160.14
139696.87
173145.42
184622.86
-17.96
26.25
-11.11
-10.13
-1.13
3.40
-4.81
28.22
3673.45
1.74
4.95
296.16
13.09
3922.05
9509.88
22626.95
17052.20
10165.74
1.52
3.94
4.66
3.28
0.31
1.29
1.70
3.72
613.52
18.85
-2.16
6.82
24.07
8.01
7.44
6.19
10.16
18.23
-11.75
6.75
-2.40
-3.17
-3.33
2.64
-2.84
7.74
5.99
1.85
3.27
6.23
1.87
4.33
1.40
6.02
3.02
3.18
1.93
1.41
2.41
1.24
3.27
5.87
1.70
7.61
43739.2
-6.61
2046.17
312.65
31594.29
72143.93
138713.09
170849.93
184950.79
-18.61
26.57
-11.11
-9.54
-1.13
3.58
-4.65
26.91
3520.07
1.76
3.03
296.15
13.09
3922.04
9509.88
22626.95
17052.20
10165.73
1.55
3.93
4.66
3.28
0.31
1.28
1.70
3.71
605.85
18.89
-2.31
6.87
23.99
8.06
7.61
6.15
10.02
18.25
-11.92
6.79
-2.36
-2.94
-3.36
2.73
-2.76
7.23
5.83
1.70
3.30
6.35
1.87
4.29
1.40
6.03
3.03
3.33
1.93
1.41
2.50
1.24
3.32
5.80
1.80
7.37
R
2
(adj.) = 78.2 per cent
R
2
(adj.) = 78.2 per cent
5.0 RESULTS: HEDONIC PRICE MODELS
Table 4 presents the summary of the parameter estimates of
Models 3 and 4 the basic model for straight-line-distance and
network-distance in estimating the effect of the LRT system on
house prices. To reduce the complication of the interpretation
process, continuous independent variables are deviated around
their means. In other words, the models are estimated with
respect to the average property size of 651.35 square feet. The
results of both models show that most of predictor variables that
have been used to estimate the LRT-house prices relationship
produce correct signs (positive or negative) as theoretically
expected, except for primary school and significance level of
0.01 and 0.05. In terms of the R
2
statistic, the model explains the
variation of the house price within a two-kilometre radius from
an LRT station in the Klang Valley reasonably well for both
straight-line and network-distance model with 78.2 per cent.
The result of hedonic model also shows that the parameter
estimates of several independent variables are found to be
slightly different between these two models.
(1) Value of the LRT system; An examination of Table 4 shows
that the parameter estimates of focus variables are found to
be statistically significant with the correct sign. Evidently,
it can be seen that the house price decreases as we move
further away from the LRT station for every metre away
from an LRT station, the value of a residential property
decreases by MYR10.56 (straight-line-distance model) and
by MYR6.61 (network-distance model). This implies that a
residential property located anywhere within 1,000 metres
of an LRT station would generally be valued at an average
rate between MYR10,560 (straight-line-distance model)
and MYR6,610 (network-distance model) more than a
residential property located outside of this distance. As
for the magnitude of effect, the LRT system has
significantly contributed at -2.16 (t-value of straight-line-
distance) and -2.31 (t-value of network-distance) in
determining the house price in the study area. It is notable
that the t-value of the parameter estimate of straight-line-
distance measures is slightly higher than that of network-
distance measures.
(2) Value of structural attributes; The result of the estimation
indicates that the most influential factor in determining the
house price is floor area. For every square-foot increase in
the floor area, the house price increases by an average of
around MYR310.63 for straight-line-distance model.
Similarly, an increase around MYR312.65 is deduced for
network-distance model. The greater magnitude of the
effect of floor area was expected since floor areas are
always associated with the size of the property this is
consistent with the most of the hedonic house price
literature. In the case of number of bedrooms, a potential
buyer has to pay on average MYR31,000 for one additional
bedroom of a property for both models. As for property-
type attribute, its role is only to indicate the price for
different types of housing in the study area. As to be
expected, the price for a detached or semi-detached house
would be higher than a terraced house. After examining the
results for both models, the conclusion that can be made is;
there are significant differences in price between different
types of housing. In other words, implicit prices of
detached (MYR170,000), semi-detached (MYR139,000),
condominium (MYR184,000) and terraced houses
(MYR71,00) for both models should all have reflected
some value-added by the attributes that they possess. In the
case a condominium property, the explanation that could be
given is; since a condominium property has various
facilities such as swimming pool, sauna, club house, sport
facilities, landscape and security, it can be expected that a
condominium property has a positive effect on house price.
The same thing can be expected from a detached property,
since it stands exclusively on its own and normally it
comes with a bigger plot of land with lower density these
attributes will definitely contribute to the positive effect on
house price.
44 Mohd Faris Dziauddin, Seraphim Alvanides
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Neil Powe / Jurnal Teknologi (Sciences & Engineering) 61:1 (2013) 3547
(3) Value of locational attributes; The results of the estimation
have also confirmed the importance of locational attributes
in determining house prices. Distance from the CBD is
significant with the correct sign of the estimated parameter
for both models. The model suggests that a rent gradient
for the Klang Valley of around MYR17.96 per metre for
the straight-line-distance model and MYR18.61 per metre
for the network-distance model. This can be interpreted as
a distance decay relationship between land rent and the
distance from the CBD. For every metre away from the
CBD (Kuala Lumpur city centre), the property value
decreases around MYR17.96 and MYR18.61 for straight-
line and network-distance respectively. The other
locational attributes that also show significant contribution
to the house price is the proximity to commercial areas.
The interpretation that can be made is that for every metre
away from the commercial areas, house price decreases at
the rate of MYR10.15 (straight-line-distance model) and
MYR9.54 (network-distance model). The parameter
estimates for proximity to a secondary school also shows
statistical significance with the anticipated sign. The
implicit price for proximity to secondary schools suggests
that for every metre away from secondary schools, house
price would generally decrease by about MYR11 for both
models. With regards to the proximity to primary schools,
the result is statistically significant, however, with
unexpected signs. The model suggests that for every metre
away from primary schools, there is a degree of increment
in housing value. It clearly shows that on average, for
every metre away from primary schools, the house price
experiences an increase by about MYR26 for both straight-
line and network-distance models. This supports the study
on the impact of school on house prices carried out by
Cheshire and Sheppard (2004). The logical reason for the
decreasing house value by being located too close to the
primary schools is due to negative externalities such as
noise and traffic congestion that can be associated with the
existence of primary schools. The other possible reason is
that house price would normally respond positively
(increase) when located in the neighbourhood where the
school is regarded to perform well in major examinations
(in practice most of the major examinations take place in
secondary schools). In other words, the quality of schools
assessed by their performance in major examinations is
found to be more important than just being located near to
an ordinary school. Thus, it is reasonable to expect house
prices to increase for every metre away from primary
schools after considering the explanation that has been
given above. Proximity to parks is also found to be
statistically significant in determining house prices with the
expected signs. The parameter estimates for proximity to
parks is MYR1.13 per metre (rent gradient) for both
models. Similarly, proximity to recreational areas is also
found to be significant in determining house prices. For the
proximity to recreational areas, the parameter estimates
indicate that for every metre away from recreational areas,
the house price reduces at the rate of MYR4 for both
models. In terms of proximity to health centres that is
hospitals, the parameter estimates suggest that house price
increases by about MYR3 for straight-line and network-
distance models of every metre away from this amenity.
Continuing the observation of the study, the price-distance
function for proximity to industrial areas shows positive
signs. The estimated parameter indicates that there is an
increase in house price at the rate of MYR28.22 (straight-
line-distance model) and MYR26.91 (network-distance
model) for every metre away from industrial areas. The
rationale behind this observation is that being located
adjacent to industrial areas suggests that residential
property is prone to suffer from traffic congestion and air
and noise pollution. Additionally, the norm of industry
workers to populate surrounding areas close to factories for
example, result in social problems and hence, brings the
perceived value of properties in the area down. Therefore,
it is very understandable at least in the context of this study
(where house price increases for every metre away from
industrial areas) since people tend to avoid negative
externalities caused by the industry, the associated
community and its activities.
(4) Neighbourhood variables; The only significant factor of
socio-economic and ethnic attributes in determining house
prices in this study is the percentage of Malay ethnic
(Malay proportion). The model suggests that if a property
is located in the areas with a higher percentage of Malay
ethnics, it would result in an increase in housing price with
an average of MYR3,600 (straight-line-distance model)
and MYR3,500 (network-distance model). The
contribution of the Malay ethnic towards higher house
prices in these areas can be reasoned out by examining the
history of these affected areas. Unlike today’s well spread-
out city of Kuala Lumpur or CBD of the Klang Valley,
over 40 years ago these areas were considered to be
suburban areas for the Klang Valley. Those days, these
areas were considered to be affordable among the ethnic
Malays while the actual downtown of the city centre was
dominated by high-income dwellers. However, due to the
rapid urbanisation in the Klang Valley these areas that were
once considered as suburban areas have inevitably become
an integral part of the city of Kuala Lumpur that are well
connected by the roads and highways with no distinction of
differences. These newly integrated demographically
Malay dominated areas have quickly emerged as attractive
housing estates among middle and high income Malays.
The house price difference can easily be explained through
the gain of premium value for the mentioned areas.
Consequently, observation can be made today that the more
affluent second and third generation of the Malays would
have the preference to purchase a house in these Malay
dominated areas since they are willing to pay a higher price
in order to be located in these areas with perceived
advantages of becoming an integral part of the city and
belonging to a community of the same race that they are
familiar with.
6.0 CONCLUSIONS AND POLICY IMPLICATIONS
The study in the Klang Valley was carried out in order to
provide empirical evidence for the estimation of the negative
rent gradient with respect to the trade-off between the
improvement of accessibility to the CBD and house prices.
Hedonic house price models was employed the role of hedonic
house price models is to provide empirical evidence of a
negative land rent gradient for a unit of house from an LRT
station. In other words, it has been assumed that house prices
would decrease for every metre further away from an LRT
station.
The estimating of the effects of the LRT system on house
prices using hedonic house price models in this study reveals a
number of key findings. Firstly, the hedonic house price models
estimate that houses located within two kilometres of an LRT
station in the Klang Valley decrease in price as the distance
45 Mohd Faris Dziauddin, Seraphim Alvanides
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Neil Powe / Jurnal Teknologi (Sciences & Engineering) 61:1 (2013) 3547
from an LRT station increases for both straight-line-distance
and network-distance models reasonably well with 78.2 per cent
of adjusted R-square. In other words, there is strong evidence to
suggest that a distance decay relationship between house prices
and the LRT system strongly exists. Secondly, the results of
both straight-line and network distance suggest that, throughout
the system, a typical home located within two kilometres of a
rail station can earn a premium of MYR7,000-11,000, or 2-5 per
cent of the city’s average home value in which can be
considered as weak effect. Finally, the results also reveal that
structural attributes of the house played a greater role in
determining house prices in the study. As discussed above, the
size of the floor area and the number of bedrooms have shown
to contribute more greatly to the house price. This is of course in
line with most of the hedonic house price analysis. This study
thus finds support for the hypothesis that proximity to stations
increases property values.
The following conclusions can be drawn from this finding.
Firstly, the results of this study support the micro-economic
theory of the bid-rent function and the trade-off between
accessibility to the CBD, transportation and house prices. As
extensively examined, the improvement of accessibility to the
CBD through the introduction of the LRT system has increased
house prices for those houses that have superior access to its
station, however as mentioned above, the effect in the Klang
Valley is weak. The weak effect found in this study may relate
to the method that was used in this study; hedonic pricing
method. Although this method is useful and widely employed to
study the relationship between house prices and its determinant
factors, it has been argued in the literature hedonic house price
model is a global model which naturally has a tendency to
assume that the relationship between house prices and housing
attributes are stationary over space, and therefore may hide
some very interesting and important local differences such as
different types of houses and areas may respond differently in
terms of prices towards structural and locational attributes.
Therefore, several other local models such as geographically
weighted regression (GWR), multilevel modeling and spatial
expansion method need to be considered in estimating the
effects of rail transit systems upon house prices. By employing
these techniques, it allows local rather than global parameters to
be estimated, and thus provides a way of accommodating the
local geography of house prices-housing attributes relationships.
Secondly, the increase of house prices due to the presence
of the LRT system found in this study has implications for the
government’s decision to introduce the LRT system or different
types of rail transit systems in other places. In other words, the
introduction of new transport infrastructures such as an LRT
system to improve the accessibility of city centres for those
living in residential areas could also bring indirect benefits to
the local area because it can uplift land value for those areas that
have been served by the system. Hence, it could increase
government revenues through land value taxation. In addition,
the research findings provided justification for the potential
implementation of a Land Value Capture (LVC) policy. The
strategies in a LVC policy that may be implemented involved at
least in six respects such as property and sales taxes, real estate
lease and sales revenues, fees on everything from parking to
business licenses, join development, tax increment financing,
special assessment districts and public-private partnership. But,
a Land Value Capture (LVC) policy need to be carefully
implemented where the premium associated with the rail transit
systems on land values/house prices should be well estimated
beforehand.
Thirdly, most locational attributes including the LRT
variable that have been used in determining house prices in this
study are local in their impact, with a distance decay effect in
their extent and intensity. Fourthly, structural attributes of the
houses still play a major role in determining house prices.
Finally, as previously mentioned, the evidence from empirical
research both in the UK and North America shows inconsistent
results and varying magnitude of the effects of rail transit
systems on property values. However, if the methods that have
been employed to estimate the effects are an appropriate
method, together with the quality of data, the positive
relationship between rail transit systems and property values can
be identified at least in the context of this research. These have
proven to be true from the outcomes of this study the increase
in house prices results from an improvement in the transport
system. In general, this study has achieved its aims to critically
investigate the effects of the Kelana Jaya Line on house prices
in the Klang Valley by employing the hedonic house price
models.
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