The impact of anticipated transport improvement on property prices: A case study in Hong Kong

The impact of anticipated transport improvement on property prices: A case study in Hong Kong

Habitat International 49 (2015) 148e156 Contents lists available at ScienceDirect Habitat International journal homepage: www.elsevier.com/locate/ha...

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Habitat International 49 (2015) 148e156

Contents lists available at ScienceDirect

Habitat International journal homepage: www.elsevier.com/locate/habitatint

The impact of anticipated transport improvement on property prices: A case study in Hong Kong Wadu Mesthrige Jayantha a, *, Tsun Ip Lam (Patrick)a, Mei Lai Chong (Sarah)b a b

Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong Free lance author, Kowloon, Hong Kong

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 November 2014 Received in revised form 4 May 2015 Accepted 22 May 2015 Available online 2 June 2015

The development and improvement of transport infrastructure leads to increase in cross-boundary social and economic activities, whilst it shapes the pattern of urban development. Improvement of transport infrastructure, which is meant to enhance the urban built environment, is presumed to bring positive externalities to property values in the vicinity. Based on a sample of 6310 transactions obtained from residential developments located near a newly-proposed transport infrastructure project in Hong Kong, this study investigates whether the potential property buyers are willing to pay a premium in real transactions for the expected benefits brought about by this proposed new transport infrastructure development before its completion. The results generated from a Hedonic Price Model suggest that this infrastructure improvement project has provided net positive externalities on the neighbourhood properties. More precisely, the announcement of the transportation improvement project did originate a net positive effect on the neighbourhoods' residential property prices. Potential buyers were willing to consider the ‘expected’ improvement of transport infrastructure as a positive factor in offering their prices for a property. This study throws a positive evidence on the perception about transport facilities and property prices:- the anticipated benefits brought about by transport improvements are capitalised into property values. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Anticipated transport infrastructure Property values Externalities Hong Kong

1. Introduction As a densely populated city, Hong Kong has been experiencing residential crowding for many years. A majority of people are living in city centre and sub-urban areas in small housing units (average 45 m2) with a significantly lower usable floor area per person (14 m2) compared to many developed countries. On the other hand, a high population growth from mainland migrants, a tendency towards smaller household size and the aspiration for home ownership have resulted in a high demand for housing over the last decade. In response to this trend, the government has been compelled to open up land for residential developments in outer urban areas, in particular the New Territories, as not enough land resources are available in the city centres. Most of the recent new housing developments, built both by the government and the

* Corresponding author. E-mail addresses: [email protected] (W.M. Jayantha), [email protected] polyu.edu.hk (T.I. Lam), [email protected] (M.L. Chong). http://dx.doi.org/10.1016/j.habitatint.2015.05.023 0197-3975/© 2015 Elsevier Ltd. All rights reserved.

private sector, are located in the New Territories. However, the government needs to develop infrastructure facilities such as public transportation in order to attract people to these outer areas. It is a well established phenomenon that transport infrastructure shapes the pattern of urban development (see for example Knight & Trygg, 1977). There are many modes of local public transport in Hong Kong, including buses, mini-buses, taxis, ferries (mainly serving outlying islands), two historical tram services (on Hong Kong island only) and an efficient mass transit system. As in June 2014, the Mass Transit Railway (MTR) has a total domestic track length of 174.7 km linking 82 stations on 9 main commuter lines. Average daily patronage stands at 4.3 million passenger-journeys out of a population of over 7 million (MTRC, 2014). Hence, the MTR is a major means of transport (mostly underground) in this vibrant city. With dispersing urban development in mind, the government introduced the “Railway Development Strategy, 2000” at the turn of the century. The planning framework for the improvement and expansion of Hong Kong's railway network up to 2016 is provided in this strategy. There are several new railway links proposed under

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this strategy: Shatin to Central Link (SCL), Island Line Extensions (ILE), Kowloon Southern Link (KSL), Northern Link (NL), Regional Express Line (REL) and Port Rail Line (PRL) in order to cater to increased population growth and increasing cross boundary social and economic activities. Among these, SCL is very important in linking the New Territories with the Central Business District (CBD). The SCL is not a natural extension of an existing railway system and is meant to provide connections to both the operating MTR (Mass Transit Railway) and the previous KCR (Kowloon-Canton Railway) networks. “Not only will it increase significantly the cross-harbour and Shatin-Kowloon rail capacities, it will also help to redistribute the flows and relieve the other railway lines in Hong Kong and Metro Kowloon” (Railway Development Strategy, 2000). Transport improvements normally facilitate a shift of population from city centres to sub-urban areas that results in an increase in demand for housing along these transport lines. That would affect the property values and hence price and rent gradients of these properties closer to transport routes. Previous studies have investigated the effects of transport developments and improvements on residential property prices (e.g., Chau & Ng, 1998; Henneberry, 1998; Laakso, 1992; So, Tse, & Ganesan, 1997; Yiu & Wong, 2005). Almost all of these studies focused only on the effects of transport improvements after the completion of the works, ignoring the expectation effects on property values. Only very few studies attempt to investigate the expectation effects of transport improvement works on property values (e.g. Yiu & Wong, 2005). As the construction of an infrastructure project is a lengthy process and hence lasts for years, the effects of these developments may be taken into account well in advance (before the completion of the works) by the stakeholders in making decisions. On the perspective of rational expectation, home buyers would take expected improvement into account in making investment decisions. There is no consensus about the expectation effects, however, in the literature. For example, Yiu and Wong (2005) found positive price expectation effects before the completion of the development work of a tunnel, whilst Henneberry (1998) found that housing prices fell after the announcement of construction work of tram lines. Possible expectation effects of future enhancement of transport infrastructure, however, were not taken into account by Henneberry (1998). These two studies were carried out after construction works had been completed. The present study is carried out before the completion of the project, and aims to investigate the expectation effects of the announcement of the Shatin-Central Link (SCL) project on nearby residential property values. More specifically, the study evaluates whether premiums will be paid for the expected benefits brought by the new transport infrastructure development before its completion. It is known that accessibility benefits accrue for properties near to public transit stations. These benefits, in turn, are expected to capitalise into land and property values. It is therefore expected that, as the distance to a Mass Transit Railway (MTR) station decreases, the accessibility benefits accrued by property owners will be greater, resulting in a higher property values. Thus potential property buyers would take these accessibility improvements into account in purchasing properties nearby the stations. The null hypothesis is that, as the distance to a MTR station increases, there will be no impact (expectation effect on the announcement of SCL) on sale prices of properties closer to the station. This implies that proximity to a transit station accrues insignificant accessibility benefits for nearby properties. The present study therefore would help us to better understand how investors will take expected improvements into account when

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valuing neighbourhood properties. The paper is structured as follows. The next section reviews the existing studies in the area of infrastructure and transport development related to residential properties in the literature. Section 3 then provides the theoretical framework for a Hedonic Price Model (HPM) to investigate the effect of transport infrastructure improvements on residential property prices. Section 4 discusses the empirical results of the estimated HPM, and the study concludes in Section 5 with a summary together with suggestions for further research. 2. Literature review about infrastructure/transport development related to residential properties The strong relationships between transportation improvement and the property and the land values have been well documented in the literature. Early theoretical models illustrating this link were developed by a few economists over a 50-year period (e.g. Alonso, 1965; Haig, 1926; Muth, 1969). According to these models, land and property values depend on the accessibility and transport costs. Haig (1926) illustrates the theoretical relationship between transport costs and site rents. According to Haig, the difference in rent is due to the difference in transportation costs and accessibility among sites. Alonso (1965), with the bid-rent function, postulates that rent of a piece of land is negatively related to the transport cost of moving to and from work. The bid-rent function is negatively sloped and is steeper at the CBD compared to the fringe of an urban area. Over the past 3 decades, there is a significant body of empirical studies that investigates the effect of transport development on property values. These studies can be broadly divided into two categories: studies that investigate the effects of availability of a means of transport on property values (e.g. Baum-Snow & Matthew, 2000; Perk, Catal a, & Reader, 2012; RICS, 2002; Rodriguez & Targa, 2004; Weinstein & Clower, 2003; 2005), and studies that analyse the effects of improvements in transport on property values (e.g. Chau & Ng, 1998; Laakso, 1992; Lai, 1991; Williams, 1989; Yiu & Wong, 2005). In some of the early empirical studies which have been reviewed by Miller (1982), it was noted that there exists a clear negative relationship between residential property values and transport costs, indicating the value enhancements to the property's proximity to transport nodes. There are a few studies which, however, claim that transport could generate negative externalities that would reflect through property values (Gatzlaff & Smith, 1993; Henneberry, 1998). According to them, negative externalities such as noise and air pollution as well as nuisance (during construction) may discourage people living closer to transport routes and nodes. But most of the studies found that transport improvements bring significant value enhancements to nearby properties. Besides the work mentioned earlier, there are a significant number of studies, carried out in the USA, which found that the values of properties closer to transport stations are enhanced. See Perk et al., (2012) for a detailed list of those studies. However, almost all these studies addressed only the value enhancement effects of properties (commercial as well as residential) after the transportation works/improvements were completed, and ignored the expectation effects on property values. People, as rational thinkers, may take all the information including future improvement into account when making an investment decision, in particular whilst purchasing a residential property, which may normally be the most important investment decision that most people make in their lifetime. In a study by Chau and Ng (1998), they mentioned that positive externalities of transport improvement would be reflected through property value enhancement even before the improvements work is completed.

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The same idea was echoed in RICS (2002). Not many empirical studies, however, attempt to investigate the expectation effects of future transport improvements on property values. In fact, there are very few. McDonald and Osuji (1995), Yiu and Wong (2005) and Henneberry (1998) attempted to investigate this aspect. In an attempt to study the effect of the announcement of a railway project in Chicago, McDonald and Osuji (1995) found out that the property prices within one-half mile of the investigated station sites were increased by 17% after the announcement of the construction of the railway projects, indicating that the property prices had begun to fluctuate well before the operation of the development. Yiu and Wong (2005) also attempted to address the expectation effects of a new tunnel (a cross-harbour tunnel) on residential property prices. They found that there were positive price expectation effects before the completion of the tunnel. However, they carried out the study after the tunnel construction work was completed. The present study is different from their study for two reasons. First, an attempt is made to investigate the expectation effects of a transport infrastructure improvement (Shatin-Central Link, SCL) on property values before the construction works are completed (to be completed in 2018). In other words, the study attempts to evaluate whether the announcement of SCL development has been capitalised into property values even before the completion of the works. Second, this SCL development connects the Hong Kong Island with the New Territories (NT), where most of the new housing developments have emerged only over the last few years (Yiu and Wong's study evaluated the price effects of existing old properties). If the transport improvement brings value enhancements to the properties in the NT, then that would encourage developers (and the government) to build more new developments and attract more people to outer urban areas, which would support the overall objective of the urban development and planning authorities in order to address the residential crowding problem and the housing shortage problem. Therefore, the findings of the present study would help the government to finance infrastructure projects with the right decisions as well as encourage further planning urban developments in outer urban fringes. More specifically, the findings of this study can help policymakers and those in the transport development industry gain a clear understanding of the overall effects of proximity to MTR stations on property values, land uses, and urban/economic development. 3. Methodology and data description 3.1. Theoretical framework and model specification By its nature, housing property is a heterogeneous commodity, and the determinants of its price can be classified mainly into three groups of attributes, namely, structural, locational and neighbourhood (Can, 1990). The overall effects of these three groups of attributes are capitalised into housing property value in order to determine the property price. Structural attributes influence the residential property price significantly and the decision to purchase a residential unit is immensely influenced by structural factors such as age, size, and floor level and so on. The residential property price is also greatly influenced by the locational attributes, and the effects of locational attributes are capitalised into the property price through accessibility (to work, schools, markets and so on). Lastly, neighbourhood attributes are also equally significant in determining the property price. These attributes are mainly capitalised into property values through amenity effect (Ding, Simons, & Baku, 2000) and positive and negative externalities brought by neighbourhood developments such as transport and infrastructure improvements. Thus, the selling price of a residential property unit

can be expressed as a function of attributes of these three categories. In other words, the value of a residential unit can be expressed by these different attributes. Given this background, the hedonic pricing model (HPM) can be effectively used to estimate the residential property price derived from these attributes, because HPM allows the total property value to be broken down into the values of small attributes (Hui, Ng, & Lo, 2011). On the other hand, the effects of these individual attributes can also be identified and analysed separately by the HPM (Hui et al., 2011). The HPM possesses a greater strength and capacity to analyse and interpret the implicit relationships between the residential property prices and its characteristics. Hence, the HPM is considered as the best model to analyse the expectation effects of transport improvement on residential property values. In fact, the HPM has been quite commonly used to estimate the effects of various attributes (derived from structural, locational and neighbourhood) on property values in the literature. For example, the effects of structural attributes such as floor area, age and size of the unit, etc. (Mok, Chan, & Cho, 1995; Tse & Love, 2000), landfills (Cartee, 1989; Nelson, Genereux, & Genereux, 1993), views (Benson, Hansen, Schwartz, & Smersh, 1998), underground storage tanks (Dotzour, 1997), and noise and air pollution (Chattopadhyay, 1999; Espey & Lopez, 2000); on residential property values have been well documented in the literature by employing HPM. As noted above, the price of housing property is determined by many attributes. In the present study, however, nine attributes (variables) that are known to have significant effects on residential property values are selected, including the variables considered as more important in representing the expectation effect of transport improvements. All these variables belong to the three main categories of housing attributes: structural, locational and neighbourhood factors. The study proposes a semi-log form of HPM. The dependent variable, the real housing property price in natural log, is regressed against a set of logged variables (original values with a non-linear relationship) and another set of unlogged variables (original values with a linear relationship). It is important to consider the real transaction price in order to control for the possible effects of time on the transaction price. Accordingly, the study proposes the following HPM model:

LnðRPÞit ¼ a0 þ a1 LnðAGEÞi þ a2 LnðFLOORÞi þ a3 LnðAREAÞi þ a4 ðCLUBÞi þ a5 ðSViewÞi þ a6 ðGViewÞi þ a7 ðGreenViewÞi þ a8 LnðDISÞi þ a9 LnðDISÞi  P1i þ εi where, Ln (RP)it is the real (logged) transaction price of property i at time t (measured in HK$), a1 … a9 are the coefficients of the variables to be estimated; a0 represents the constant term and εi is the error term of the model. Data descriptions of all the variables including data definitions are depicted in Table 1. To evaluate the expectation effects of announcement of SCL, the model introduces two variables: one to capture the effects before the announcement, and the other variable (interaction variable) to capture the effects after the announcement. An interaction variable is formed combining distance variables (continuous variables) with the respective timing (dummy) variables as mentioned in Table 1. The timing dummy captures the time period at which a property transaction takes place, whilst a continuous variable captures the distance to the property from the proposed transport development stations (i.e., Diamond Hill MTR, To Kwa Wan and Tai Wai stations). The interaction variable is used to determine the change in price gradient before and after the announcement of the SCL project. The interaction variable used in the model is DISi  P1i, which is equal to 1 if the property was transacted during the post-announcement stage of the SCL project, and 0 if otherwise.

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Table 1 The explanatory variables and their expected relationships with price. Attributes

Abbreviation

Variable

Definition

Expected sign (þ/)

LnP

Transaction price Building Age Floor level Floor area Club house Sea view Garden view Green view Displacement

Adjusted (real) transaction price of the unit (in HK$) in log form

/

Age of the property unit in years at the transaction date in log form Floor level of the property in log form Saleable floor area in square metres in log form 1 if the property has a clubhouse; 0 if otherwise 1 if the property enjoys a sea view; 0 if otherwise 1 if the property enjoys a garden view; 0 if otherwise 1 if the property enjoys a green view; 0 if otherwise The distance between property i and the centre of the proposed SCL station (measured in metres; in log form) Distance between property i and the centre of the proposed SCL station (measured in metres) and the transaction time of property i (during post-announcement stage of SCL)

e þ þ þ þ þ þ e

Structural

Ln_(AGE) Ln(FLOOR)i Ln(AREA) CLUB Location SView GView GreenView Neighbourhood Ln(DIS) Interaction variables Ln (DIS)i  P1i

3.2. Data description There are 10 MTR stations involved with the SCL development, namely Tai Wai, Hin Keng, Diamond Hill, Kai Tak, To Kwa Wan, Ma Tau Wai, Ho Man Tin, Hung Hom, Exhibition and Admiralty. The present study is focused on three geographically spread out stations, namely, the Diamond Hill station, To Kwa Wan station and Tai Wai station and so the residential properties in the vicinity of these three stations are chosen. The reason for choosing these three stations as the case study area is due to the important role of these stations after the implementation of the Shatin to Central Link (SCL). Diamond Hill MTR Station for instance will be transformed into a major hub for East Kowloon when the SCL commences service (MTR, 2014). Diamond Hill Station is now a station along the Kwun Tung Line with a transport linkage from East Kowloon to West Kowloon. After the commencement of the SCL services, Diamond Hill will be connected to the Central Business District as well as the New Territories directly. The To Kwa Wan station serves a densely populated area and the Tai Wai station provides a connection to the previous KCR line. As the government plans to open up new housing developments in the NT to address the housing shortage problem, the SCL provides a good platform for the government to pull the people towards outer urban areas from the core crowded city. The SCL is of paramount importance in this respect. This is the main reason why the SCL is selected for this case study. It is therefore worth examining the significance of the transport improvement that was brought about by the SCL in shaping urban development by analysing the effects on the neighbourhood residential property market. Ten residential developments: Galaxia, Bel Air Heights, Regent on the Hill (near Diamond Hill station); Jubilant Garden, Grandview Garden, Sky Tower (near To Kwa Wan station); Golden Lion-Phase 1, Golden Lion-Phase 2, Grandeur Garden, and Green View Garden (near Tai Wai station); all within 700 m from the centre of each station, were selected for the analysis. Figs. 1e3 show the locations of the residential developments as well as the proposed SCL stations at Diamond Hill, Tai Wai and To Kwa Wan, respectively. The reasons for choosing these residential schemes are that they possess similar property and neighbourhood attributes. Most importantly, they are all located within 700 m from the SCL; and the three groups of estates provide similar amenities to the residents such as children playgrounds, swimming pools, clubhouses and gymnasia. Sizes of housing units of these three schemes do not vary significantly. These are the type of housing that middle class people in Hong Kong can afford to buy and hence we assume that the residents of these schemes are belonging to the middle class

with the similar affordability. The details of all the residential developments are shown in Table 2. Transaction data of properties within 700 m distance on plan from the centre of the proposed SCL stations were obtained from EPRC, a subscription-based online transaction record system. The data comprises physical characteristics of the transacted properties, including saleable floor area (SFA), floor levels and views of the properties of the transactions. Only the transactions which are complete with Sales and Purchase Agreement (ASP) and flats classified as Class A (<40 m2), Class B (40e69.9 m2) and Class C (70e99.9 m2) are selected as the authenticated scope of data. 3.3. Pre and post public announcement of the SCL In order to analyse the expected transportation improvement brought by the proposed SCL, if any, on property price, the SCL project is divided into two stages: pre-announcement stage and post-announcement stage. The SCL project was first proposed in the “Railway Development Strategy, 2000” in May 2000 while it was approved by the Executive Council in March 2008. The approval of the SCL project is regarded as the announcement of the project in this study. Although the SCL project was first proposed in 2000, there were many uncertainties of the project details including the route of the SCL, the design and the location of the SCL stations, etc. Therefore, it is assumed that there is no significant effect (of the announcement of the SCL in the Railway Strategy 2000) on the property price on a specific estate, and hence such announcement would not affect this study. However, the final design of the SCL project was effectively approved by the Chief Executive in March 2008. Thus, 2008 is considered as the effective announcement date of the SCL. Therefore, the property transactions data from September 2005 to April 2012 are collected. The data is divided into two periods: property transactions during September 2005 to February 2008 are classified as Pre-Announcement stage while those property transactions during March 2008 to April 2012 are classified as Post-Announcement stage. Fig. 4. 4. Empirical findings 4.1. Statistical interpretation of the HPM model Simple standard statistical tools such as simple t-statistic, Fstatistic and the explanatory, (adjusted R square) are employed to interpret the results obtained from the HPM. The simple t-test is used to test the hypothesis about any single parameter of an explanatory variable. The null hypothesis (H0) for the t-statistic is that the independent variable has not any relation with the

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Fig. 1. Study area: Diamond Hill. Source: Centamap, 2014

Fig. 2. Study area: Tai Wai. Source: Centamap, 2014

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Fig. 3. Study area: To Kwa Wan. Source: Centamap, 2014

dependent variable. The rule of thumb is that if the absolute value of the t-statistic for a parameter (i.e. empirical t-statistic) is larger than the critical value, the null hypothesis is rejected, which suggests that a particular variable is significant. This indicates the particular independent variable has a significant influence on the dependent variable. On the other hand, the overall significance of

the model is tested using the F-statistic, in which the overall performance of the model is said to be good if the empirical F-value is greater than the critical value. The other statistical tool that is normally used to test the explanatory power of the model is the R2, value of which ranges from 0 to 1. The higher the adjusted R2 value the better the model.

Table 2 Details of the residential projects. Diamond hill station

Occupation permit date Number of blocks Number of flats Number of stories Saleable area

Galaxia

Bel air heights

Regent on the hill

3rd March 1998 5 1684 50 527e1027 ft2

2nd February 1992 4 798 48 and 4 442e657 ft2

4th April 2000 1 186 31 497e910 ft2

To Kwa Wan station

Occupation permit date Number of blocks Number of flats Number of stories Saleable area

Jubilant Garden

Grandview garden

Sky tower

January 1998 7 900 e 566e853

April 1999 4 512 e 355e647

June 2004 6 2209 e 463e698

Tai Wai station

Occupation permit date Number of blocks Number of flats Number of stories Saleable area

Golden Lion-I

Golden Lion-2

Grandeur Garden

Greenview Garden

December 1986 6 1200 e 273e382

October 1987 7 1568 e 273e382

June 1985 6 756 e 295e453

June 1987 3 450 e 571e730

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W.M. Jayantha et al. / Habitat International 49 (2015) 148e156 Table 4 Estimated results for the hedonic price model.

Fig. 4. Timeline of the SCL project. Source: Authors

4.2. Results The estimated empirical results of the Hedonic Price Model (HPM) are discussed in this section. The descriptive statistics of the data used in the model are summarised in Table 3. The results of the HPM (shown in Table 4), including the explanatory power and other measures that reflect the goodness-of-fit of the model, suggest that the model performs very well. The adjusted R2 of the model is found to be 0.903, suggesting that more than 90 percent of the total variation of residential property price is explained by the selected independent variables. The F-value of 5879 (exceeding the critical value) means that the selected independent variables are jointly statistically significant and at least one variable can explain the variation of the residential property prices. All the estimated coefficients of variables (except one variable) are statistically significant at 1% and 5% levels and also carry the expected signs. Even though the main focus of this study is on the transport improvement variables, it is worth analysing the behaviour of the standard property variables in the model first. The empirical results of the HPM indicate that all the standard variables (except Mountain View) are highly statistically significant at 1% level, and also all the variables carry the anticipated theoretical signs. Results reveal that real residential property prices (LnP) are negatively correlated with the building age (LnAGE), whilst positively linked to the floor level (LnFLR), saleable floor area (LnAREA), the presence of clubhouse in the building (CLUB) and the building views (SView, GView). Findings of these standard property attributes in the model are quite consistent with common expectations as well as with the findings of previous studies in the literature. In particular, Some previous studies found out that these attributes are very significant in determining property prices in Hong Kong's high-rise residential buildings (e.g., Tse & Love, 2000; Wong, Chau, Yau, & Cheung, 2011). Introduction of these standard key property attributes (variables) into the model therefore has avoided any misspecification error in the model. Put differently, the significance of these standard property variables along with transport improvement variables indicate that the model performs very well.

Independent Variable

Coefficient

t-statistic

p-value

Constant Ln(AGE) Ln(FLOOR) Ln(AREA) CLUB SView GView GreenView Ln (DIS) Ln (DIS)  P1i Adjusted R-squared F-statistic Prob (F-statistic) N

1.0323 0.4773*** 0.0312*** 2.6267*** 0.6913*** 0.1406*** 0.1164** 0.0303 0.1647*** 0.0006*** 0.903 5879.57 0.0000 6310

4.4086 31.3957 3.6103 90.2621 25.6858 3.1121 2.1170 0.5580 8.9559 20.6543

0.0000 0.0000 0.0003 0.0000 0.0000 0.0018 0.0343 0.5768 0.0000 0.0000

Note: (***), (**) and (*) denote that the estimated coefficients of the variables are significant at the 1%, 5% and 10% level respectively.

The argument now deals with the most important aspect of this model: the transport improvement variables. Two variables, Ln(DIS) and Ln (DIS)  P1i, were introduced to capture the effect of expected transport improvement facilities on residential property prices. The results reveal that the coefficients of these two variables are statistically highly significant (at 1 percent), suggesting a strong relationship between transport improvement variables and the neighbourhood residential property prices. The coefficient of Ln(DIS) was found to be negative and statistically highly significant at 1 percent level. That means, before the announcement of SCL project, as the distance between the property and the MTR station increases by 1 percent, residential property price decreases by 0.165 percent. On the other hand, the interaction variable, Ln (DIS)  P1i, which was employed to determine the price gradient after the announcement of SCL project, was also highly significant at 1% level. Put differently, this variable captures the expected effects of the announcement of the SCL on property prices. The significance of this variable indicates that the SCL project did change the spatial-price gradients of the residential properties in the vicinity. In other words, the results indicate that the effect of announcement of SCL on property price is significant. The negative sign of the coefficient of the variable suggests that 1% decrease in distance between the property and MTR stations led to a rise in property prices by 0.0006%. This suggests that potential buyers recognized the positive externalities brought about by the improvement of the transportation and are willing to pay more for properties in the vicinity of these stations. Even though the degree (0.0006%) of effect is low, this is in line with the presumption that proximity to a transport (node) improvement should result in an increase in property price. In other words, potential property buyers seem to have taken the announcement of the SCL, to a certain extent, into account (positively) and hence properties'

Table 3 Descriptive statistics of the property transaction data. Variable Ln(P) Real transaction price (million HKD) Ln(AGE) Ln(FLOOR) Ln(AREA) CLUB SView GreenView GView Ln (DIS) Ln (DIS)  P1i

Mean

s

13.8876

1.6158

4.9845 2.6753 6.0237 0.3700 0.0200 0.2300 0.4400 5.9823 192.44

0.7674 0.8671 0.3068 0.4830 0.1450 0.4190 0.4960 0.4871 84.992

Min. 10.996 2.4850 0.0000 3.6376 0.0000 0.0000 0.0000 0.0000 5.1240 0.0000

Max. 16.5850 5.7777 4.0600 6.9340 1.0000 1.0000 1.0000 1.0000 6.5510 693.00

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proximity to these MTR stations results in an increase in the property price. This finding, however, somewhat contradicts with the findings of previous researchers (e.g, Gatzlaff & Smith, 1993; Henneberry, 1998). A possible explanation for the low degree of effect (0.0006%) is that the expected improvement in transport may have been undermined to a certain extent by the foreseen air and noise pollution caused by the construction of the SCL. The anticipated completion date and the commencement of actual services of the SCL are in 2018, and this quite clearly implies that there will be a long construction period over 6 years. The effect of the expected long construction work seems to overshadow the benefits (to a certain extent) of the expected transport improvement. It is not uncommon in Hong Kong that a potential property buyer regards the neighbourhood construction and other disturbance activities as negative when making a property purchase decision. Thus, the “anticipated” nuisance (air and noise pollution) caused by the construction works brought by the construction of the Shatin to Central Link may have caused some concern in the minds of potential buyers. This implies that potential buyers may be waiting towards the end of construction period to purchase properties. Thus, this is an indication that potential buyers may begin to appreciate and demand more properties near these transport nodes towards the end of construction period. 5. Conclusions and recommendations Development and improvement of transport infrastructure leads to increases in cross-boundary social and economic activities, whilst it shapes the pattern of urban development. As large sums of money are needed to finance these projects, and with both the public and private sectors competing for limited funding resources, it merits a comprehensive analysis and understanding of the benefits that these projects would bring to society. Improvement of transport infrastructure, which is meant to enhance the built urban environment, is presumed to bring positive externalities to the property values in the vicinity. To test this hypothesis, a hedonic housing price analysis was carried out with a set of residential property transaction data in the vicinity of three MTR stations yet to be completed in Hong Kong. The study is aimed at finding whether premiums will be paid (by the potential property buyers) for the expected benefits brought about by this new transport infrastructure development before its completion. The findings of the HPM suggest that this infrastructure improvement project has generated net positive externalities on the neighbourhood properties. More precisely, the announcement of the transportation improvement project (SCL) did originate a net positive effect on the neighbourhood residential property prices. Put differently, potential buyers are willing to consider the ‘expected’ improvement of transport infrastructure as a positive factor in pricing a property. More specifically, properties' proximity to the project results in an increase in the property price. It is not surprising that potential buyers understand the positive externalities that this transport project is expected to generate. It is expected that residents in the vicinity of these areas (stations) would be willing to purchase more and more properties, especially towards the end of the construction stage. Even though the findings of the study is not consistent with some previous findings due to different circumstances (e.g., Henneberry, 1998), given the size and the nature of this infrastructure in the densely built environment of Hong Kong, both the opportunities and potential externalities that can be brought by this transport project have been recognized by the potential buyers. The findings of this study have some important implications. The results of this study can help policymakers and those in the

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transport development industry gain a clearer understanding of the overall effects of proximity to MTR stations on property values, land uses, and urban development. Thus, this study may encourage authorities/stakeholders to plan and strategise the way that any proposed transport infrastructure development or improvement project be carried out so as to achieve the main objective underlying the widespread implementation of a transport development/ improvement process: namely, the ability for the market to capitalise positive externalities (improved environment) of the neighbourhood in investment decisions. Typically, transport improvement programmes should improve the urban built environment and enhance the quality of city life, public safety and the image of the city. This may enhance the land and building values in the vicinity, which in turn enhances the tax base for authorities. This goal can be achieved only if these transport infrastructure development projects are planned and carried out properly, with the participation of a wider group of stakeholders to be engaged in making such vital decisions. On the other hand, since the SCL connects the NT with the CBD directly, it is expected that this transport improvement would bring the value enhancements to the properties in the NT. This would then encourage developers (and the government) to build more new developments and attract more people to outer urban areas, which in turn would be the overall objective of the urban development and planning authorities in order to address the residential crowding problem and the housing shortage problem. Therefore, the findings of the present study would support the good intentions of government when making decisions for financing infrastructure projects, as well as planning urban developments in outer urban fringes. Overall, the findings of this research enrich public administrators and urban planners with valuable insights into how transport infrastructure improvement projects should proceed with a view to achieving more justifiable economic, social and environmental sustainability. The findings may thus help to further refine transport development policy, whilst stimulating overall market demand for infrastructure development in the territory. References Alonso, W. (1965). Location and land use. Cambridge: Harvard University Press. Baum-Snow, N., & Matthew, K. H. (2000). The effects of new public projects to expand urban rail transit. Journal of Public Economics, 77(2), 241e263. Benson, E. D., Hansen, J. L., Schwartz, A. L., Jr., & Smersh, G. T. (1998). Pricing residential amenities: the value of a view. Journal of Real Estate Finance and Economics, 16(1), 55e73. Can, A. (1990). The measurement of neighborhood dynamics in urban house prices. Economic Geography, 66(3), 254e272. Cartee, C. P. (1989). A review of sanitary landfill impacts on property values. The Real Estate Appraiser and Analyst, 55(1), 43e46. Centamap. (2014). Retrieved from Centamap website http://hk.centamap.com/gc/ home.aspx. Chattopadhyay, S. (1999). Estimating the demand for air quality: new evidence based on the Chicago housing market. Land Economics, 75(1), 22e38. Chau, K. W., & Ng, F. F. (1998). The effects of improvement in public transportation capacity on residential price gradient in Hong Kong. Journal of Property Valuation and Investment, 16(4), 397e410. Ding, C., Simons, R., & Baku, E. (2000). The effect of residential investment on nearby property values: evidence from Cleveland, Ohio. Journal of Real Estate Research, 19(1), 23e48. Dotzour, M. (1997). Ground water contamination and residential property values. Appraisal Journal, 65(3), 261e266. Espey, M., & Lopez, H. (2000). The impact of airport noise and proximity on residential property values. Growth and Change, 31(3), 408e419. Gatzlaff, D. H., & Smith, M. T. (1993). The impact of the Miami Metrorail on the value of residences near station locations. Land Economics, 69(1), 54e66. Haig, R. M. (1926). Major economic factors in metropolitan growth and arrangement. Regional Survey of New York and its Environments, 1, 38e39. Henneberry, J. (1998). Transport investment and house prices. Journal of Property Valuation and Investment, 16(2), 144e158. Hui, E. C. M., Ng, I. M., & Lo, K. K. (2011). Analysis of the viability of an urban renewal project under a risk-based option pricing framework. Journal of Urban Planning

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