The Impact of MTR Stations on Housing Price in Hong Kong Stephanie Ng

Advisor: Prof. Siodla

Abstract: The MTR subway system in Hong Kong expanded from 55 stations to 93 stations in the past two decades, serving more than 5 million passengers everyday. Due to the high reliance of residents MTR in providing public transportation to the city, it would be interesting to see to what extent do residents in Hong Kong value convenient access to an MTR station. In this paper, I analyze the impact of new MTR stations on housing prices in Yuen Long and Ma On Shan neighbourhoods. Using Rosen’s Hedonic Pricing Model and difference-in-difference method, I find that, controlling for year and structural characteristics, neighbourhoods that gain access to MTR stations encounter an increase in housing prices of at least 8 percent. These results suggest that access to public transportation is highly valued in Hong Kong. Keywords: Housing Price, Hong Kong, Hedonic Pricing Model, Infrastructure, Transportation, Subway

Acknowledgement

I would like to thank my advisor, Professor Jim Siodla for his patient supervision throughout the course of this thesis. I am also grateful to Professor Samara Gunter and Professor Randy Nelson for their guidance and feedback that c Lastly, I would like to thank my friends at Colby College and my family for endless support during this journey.

I. Introduction

Articles about housing prices in Hong Kong seem never to leave the headlines

of major newspapers. Hong Kong’s housing market has been a hot topic not only

because it catches the attention of the government and overseas investors, but also

because it fundamentally impacts the day-to-day lives of average people: our cost of

living, standard of living, and even family planning decisions are affected by the

housing market. According to data collected by Midland Realty, an average 1000

square-foot apartment in Hong Kong that cost 320,000 USD in 2003 would cost 1.4

million today.1

The 14th Annual Demographia International Housing Survey: 2018 conducted

by the Performance Urban Planning Organization assesses housing affordability by

calculating the median housing price divided by gross pre-tax annual median

household income. Result shows that among the 26 severely unaffordable major

housing markets, Hong Kong is the least affordable with a Median Multiple of 19.4.

The city has also been the least affordable market since at least 2009.

Scholarly interest in this topic has produced a number of econometric analyses

that explore the internal and external factors of housing prices in Hong Kong. Tse, Ho

and Ganesan (1999) study on the supply and demand of housing in Hong Kong. They

specifically looked into the effects of population growth, transaction volume, and the

inflation rate on housing prices in Hong Kong. As an elaboration on the pre-existing

findings, my paper focuses on one of the key factors that drive housing prices in Hong

Kong: transportation. Specifically, I look at how the expansion and development of

the Mass Transit Railway (“MTR”) has affected housing prices in Hong Kong.

1SourcefromMidlandRealty,HongKong2MTRCorporationAnnualReport2017,page2

MTR, which serves as one of the most important modes of transportation for

commuters in Hong Kong, expanded from 55 stations to 93 stations across Hong

Kong since 2000. Since the completion of three more stations in December 2016, the

MTR now spreads across all eighteen districts in Hong Kong. The MTR is also

renowned for its efficiency and cleanliness. During the morning peak hours, 8-car

trains with a capacity of 2,500 passengers each run at 2-minute intervals, carrying

around 75,000 passengers per hour per direction on the Tsuen Wan Line. Because the

trains run at frequent intervals for 19 hours per day, MTR Corporation takes a 48.4%

market share of Public Transport Market.2

With such rapid development in MTR over the past two decades, it would be

interesting to see whether or not MTR plays an important role in determining housing

prices in Hong Kong. Specifically, I look at whether or not there’s a housing price

appreciation in the neighbourhood due to an opening of a MTR station in the

neighbourhood. My hypothesis is that in an event where an MTR station is introduced

to a neighbourhood, housing price has to increase to offset the lower opportunity cost

that the neighbourhood enjoys compared to other distant locations. Second, if there is

an appreciation, I am interested to see the trend of such appreciation, whether housing

prices slowly adjust to the better access and convenience of having an MTR station,

or immediately capitalize upon the opening of an MTR station.

II. Literature Review

A number of scholars that have explored the relationship between housing

prices and transportation. The well-established model of Alosno (1964), Muth (1969)

and Mills (1967) gives a strong foundation for understanding the fundamental forces

2MTRCorporationAnnualReport2017,page2

that explain the overall urban structure. The model assumes a city with a fixed

population and a given income level living around the Central Business Districts

(“CBD”). It also relies on the condition that consumers must be equally well off at all

locations, achieving the same utility regardless of where they live in the city. When

commuting cost increases, the disposable income of household decreases. The only

way to keep consumers at all locations equally well of is to decrease the housing price

per floor space as distance increases. In other words, a lower housing price

compensates for the disadvantage of higher commuting costs at farther locations. The

model also incorporates the time cost component of the commuting cost, directly

drawing the tradeoff relationship between commuting costs and housing prices.

In contrast to looking at overall housing demand and city structures (where

housing is homogenous), the Hedonic Pricing Model by Rosen (1972) accounts for

the specific attributes of the apartments. The function can be estimated by using data

that are available to homebuyers, such as square footage, number of bedrooms, and

floor level. Researchers have used the Hedonic Pricing Model in estimating the value

of certain characteristics of housing. Ball (1973) and Dewees (1976) looks into the

importance of accessibility attributes while Anderson and Crocker (1971) look into

the importance of externalities and neighbourhood characteristics. Mok, Chan and

Chow (1995) use the hedonic price model to look at private properties in Hong Kong

and they found that the elasticities of housing attributes obtained from the Box-Cos

analysis indicate that the valuation of a property is sensitive to changes in housing

traits. Bajic (1983), more specifically, looked into the relationship between

transportation and housing prices, and found that the residential values near a rail line

were $2,327 higher than elsewhere.

As we have witnessed massive transportation improvements in the past two

decades in Asia, researchers have paid more attention to the effect of transportation

on housing prices in Asia. Sun, Zheng and Han (2013) study on the effect of subway

lines on housing prices in Chengdu and find that housing prices are 7% to 14% higher

within 1.5km around the subway station than outside the stations. Diao, Leonard and

Ling (2016) also did similar research on how the opening of the new Circle Line

affected housing prices in Singapore, and concluded that it increases housing prices

by 8.6% in the treated zone relative to houses in control zones.

Though many scholars have looked into the effects of transportation on

housing prices, research on the relationship between transportation and Hong Kong

housing prices is fairly limited. Ho, Tse, and Ganesan (1997) look into the influence

of transportation on housing price. Yiu and Wong’s (2005) paper is the most recent

publication that explores the relationship between transportation and housing prices in

Hong Kong. Specifically, they investigated the effect of a newly built tunnel on

housing prices in Hong Kong and found that there were positive price expectation

effects well before the completion of the tunnel, indicating a positive effect on

housing prices. However, despite the rapid development of the MTR transportation

system and the surge in housing prices experienced in the city, there is not any recent

research exploring the impact of new MTR stations on housing prices in Hong Kong.

This study attempts to estimate the impact of newly opened MTR stations on local

housing prices.

III. Methodology and Data

This paper uses Rosen (1974)’s Hedonic Pricing Model (“HPM”). The basic

premise of HPM is that price of the good, housing in this case, is determined by both

internal characteristics of the good and external factors. An improvement in the

characteristics may increase the value of the good, meaning when transportation

improves, the value of the property increases. In this study, most data will be

extracted from “Centadata”, a real estate information system that contains

comprehensive information on past property transactions in Hong Kong. I collect

transaction date, transaction price, rentable square footage, useable square footage,

apartment name, age of the building, floor level and flat information from the

Centadata online platform.

Figure 1. CBD, Treatment Zone and Control Zone location in Hong Kong

Source: Planning Data from Town Planning Board Survey Base Map from Lands Department

In this study, Yuen Long is chosen as the treatment zone and the Gold Coast

area is chosen as the control zone. Both the treatment zone and control zone are

located on the Western side of the New Territories area, and are of similar distances

from the CBD (“Commercial Building Districts”) in Kowloon and Hong Kong Island.

Central is the CBD on the Hong Kong Island and Tsim Sha Tsui is the CBD in

Kowloon. Yuen Long (treatment zone) and Gold Coast (control zone) is 36.2 km

away from Central on the Hong Kong Island and 31.6 km away from Tsim Sha Tsui

in Kowloon respectively. Gold Coast is 30.2 km away from Central on the Hong

Kong Island, and 26.6 km away from Tsim Sha Tsui in Kowloon.

On December 20th 2003, the Yuen Long MTR station was opened together

with 9 other stations on the West Rail line. The West Rail line, which has a total

travel distance of 35.7 km, connects many of the neighbourhoods in the Western part

of the New Territories and the Hung Hom station in Kowloon. Because the line does

not pass by any neighbourhood near the Gold Coast area in the Western part of the

New Territories (refer to figure 1), the Gold Coast area is chosen as the control zone.

The Gold Coast area is also chosen because of its similar distance to CBD and similar

neighbourhood characteristics to Yuen Long.

Table 1. Summary of observations in Yuen Long and Gold Coast district

To narrow the confounding factors that might influence housing prices in these

neighbourhoods, I limit the transactions that occur before and after the MTR

expansion to within two years. Table 1 shows from 20th Dec 2001 to 20th Dec 2003,

there are 169 observations in total, 97 from Yuen Long and 72 from Gold Coast. For

the post period, which is from 21st Dec 2003 to 21st Dec 2005, there are 191

observations in total, 122 from Yuen Long and 69 from Gold Coast. The data includes

Yuen Long (Treatment zone)

Gold Coast (Control zone)

Total

Prior to 20th Dec 2003 97 72 169 After 20th Dec 2003 122 69 191

Total 219 141 360

360 observations in total from Yuen Long and Gold Coast in the Western side of the

New Territories.

For the treatment zone, Yuen Long, I collect all transaction data from Sun

Yuen Long Centre. The property, which is situated next to the Yuen Long MTR

station, has a total of five buildings (“blocks”). The data contain around 30 to 50

observations per block in the Sun Yuen Long Centre. For the control zone, Gold

Coast area, I collect all transaction data from Hong Kong Gold Coast. The property

has a total of 20 blocks and I collect data from 8 of the blocks, with each block

providing around 10 to 30 observations.

Table 2 shows summary statistics for Yuen Long and Gold Coast area. The

rentable square footage area is slightly larger in Gold Coast, but actual useable square

footage area in the two neighbourhoods is very similar. The building age of both

properties is also very close, as both were built in the 1990s.

Table 2. Summary Statistics for Yuen Long & Gold Coast

The average floor levels for the observations are 13.81th in the treatment zone

and 13.59th floor in the control zone. They have similar average floor level because in

both areas, buildings have around 27 to 29 floors in total. The biggest difference

MTR expansion to Yuen Long

Yuen Long (Treatment zone)

Gold Coast (Control zone)

Total

Mean SD Mean SD Mean SD Housing Price

(USD thousands) 203 62.85 179 42.49 192 55.54

Housing Price (Log form)

5.272 0.295 5.159 0.240 5.219 0.276

Average floor level

13.81 8.46 13.59 7.587 13.70 8.05

Rentable Square Footage

683.85 151.63 744.09 109.62 712.12 136.74

Useable Square Footage

564.92 147.04 573.93 80.45 569.15 119.74

Building Age 25.00 0.000 27.21 0.978 26.03 1.30 Garden View 92% 0.270 41% 0.494 68% 0.466

Sea View 0% 0.000 57% 0.497 26% 0.443 # of bedrooms 2.43 0.497 2.34 0.474 2.38 0.488 Observations N=191 N=169 N=360

between the two properties is that Gold Coast is by the sea and hence, many of the

apartments have a sea view, whereas in Yuen Long, most of the apartments have a

garden view.

To check whether or not some neighbourhoods may encounter more or less

impact of MTR stations on housing prices, I also select a treatment zone and control

zone in the eastern part of the New Territories. The treatment zone that I look at is Ma

On Shan (“MOS”) station in Shatin district. The MOS line, which consists of 9

stations, was opened on Dec 21st 2004. Sai Kung is chosen as the control zone

because it has the most comparable geographical location as Ma On Shan, and the

MTR station does not reach that area.

Table 3. Summary of observations in MOS and Sai Kung district

Table 3 shows that there are in total 248 observations for studying the effect of

MTR expansion to Ma On Shan (MOS) location, where 144 observations are

collected from the treatment zone, MOS, and 104 are collected from the control zone,

Sai Kung. Of the 144 observations collected from MOS, 66 transactions were made

prior to 21st Dec 2004 and 78 were after 21st Dec 2004. Of the 104 observations

collected from Sai Kung, 45 transactions were made prior to 21st Dec 2004 and 59

were after 21st Dec 2004.

Table 4 shows that the structural features slightly differ between MOS and Sai

Kung. Sai Kung apartments have a relatively smaller square footage, are older, and

consequently less expensive. Though both are located near the coastline, Ma On Shan

also has more apartments that enjoy a sea view than in Sai Kung. That is because

MOS (treatment zone)

Sai Kung (control zone)

Total

Prior to 21st Dec 2004 66 45 111 After 21st Dec 2004 78 59 137

Total 144 104 248

Table 4. Summary Statistics for MOS and Sai Kung

apartments are relatively taller in Ma On Shan, and developers took advantage of the

view and have designed more flats to face the sea (see Appendix 3). Though Sai Kung

is near the coast, apartments are centered near the town center rather than surrounding

the sea. However, by using difference-in-difference method, I can account for the

differences in these features.

IV. Estimating equation and Identification Strategy

The standard hedonic pricing model predicts that housing prices are

determined by internal and external characteristics of the housing. Scholars have

conducted studies on different characteristics that contribute to the housing price of a

specific apartment or house. In the equation below, 𝛽!(𝑘 = 1,… , 𝑘) shows the

marginal change in the unit price of the kth characteristic 𝑥!of the apartment, where

𝑃! is housing price.

𝑃! = 𝛼! + 𝛽!𝑋!"!!!! + 𝜇! (1)

MTR expansion to MOS

MOS (Treatment zone)

Sai Kung (Control zone)

Total

Mean SD Mean SD Mean SD Housing Price

(USD thousands)

249 68.15 135 48.63 201 82.79

Housing Price (Log form)

5.481 0.269 4.844 0.350 5.214 0.438

Average floor level

15.19 7.64 6.90 2.43 11.7 7.28

Rentable Square Footage

613.19 125.96 552.66 161.22 587.80 144.63

Useable Square Footage

458.79 94.20 404.09 120.24 435.85 109.07

Building Age 23.10 0.288 28.12 4.06 25.20 3.62 Garden View 34% 0.478 30% 0.460 33% 0.470

Sea View 38% 0.488 1% 0.098 23% 0.419 # of bedrooms 2.40 0.492 2.37 0.483 2.39 0.488 Observations N=148 N=104 N=248

Common characteristics in a hedonic model include accessibility features, structural

features and neighbourhood features. Accessibility variables may include the number

of shopping malls in the district, distance to key shopping malls and distance to the

CBD. Neighbourhood variables can include quality of the schools in the district and

population density. Structural variables include actual floor area, number of rooms,

floor level and age of the apartment. Sirmans et al. (2005) suggest that age of the

building and square footage show up the most frequently in hedonic models, and in

my paper, I will take these features into account.

In this study, I test for changes in housing prices that correspond to the timing

of the MTR expansion by estimating the following difference-in-difference equation.

The equation also incorporates elements from the hedonic price model. The first

specification is:

log (𝑃!) = 𝛼 + 𝛽!𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡! + 𝛽!𝑃𝑜𝑠𝑡𝑝𝑒𝑟𝑖𝑜𝑑! + 𝛽!𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡! ∗ 𝑃𝑜𝑠𝑡𝑝𝑒𝑟𝑖𝑜𝑑! + 𝛾! log 𝑆!! + 𝛾!!

!!! 𝑆!" .+𝜀! (2)

Where 𝑃! refers to the transaction price, 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 is a dummy variable

that takes a value of one if the apartment is located in the treatment zone, where it is

less than 1 km away from the MTR station. Because residents living within 1 km of

the MTR station will have a walking distance of less than 10 minutes to the station,

they will most likely capitalize the amenity into housing values. For residents living

beyond 1 km from the station, they may be partially benefited from it because

walking distance to the station may be too high that it may be better off for residents

to wait for alternative transportation such as buses or mini buses. In this study, we

will be focusing on the apartments that likely have direct benefits from the opening of

a MTR station. 𝑃𝑜𝑠𝑡𝑝𝑒𝑟𝑖𝑜𝑑 is a dummy variable that takes a value of one if the

transaction is made after 20th December, 2003 (after the opening of the Yuen long

MTR station). I am primarily interested in the coefficient estimate of 𝛽!, which

describes the change in housing price in Yuen Long due to the opening of the Yuen

Long MTR station.

Similar to the standard hedonic pricing model, there are structural effects and

building effects that contribute to the housing price. 𝑆!(𝑘 = 1,… , 𝑘) specifically

refers to the kth structural characteristic 𝑆! of the apartment. In this paper, I take into

account five structural characteristics: building age, useable square footage, floor

level, number of bedrooms, and views of the apartment in estimating the housing

prices. I also take the log for usable square footage 𝑆!, so that for one percent increase

in usable square footage, it results in 𝛾! percent change in housing price, holding all

other factors fixed. Year is the year of sale. On a block level, Sun Yuen Long Centre

(block 1) in Yuen Long share very similar amenities with Sun Yuen Long Centre

(block 2). They share the same security system, as well as the same access to the

swimming pools and recreational facilities. Therefore, it is not necessary to control on

a block level. The only differences may be the age of the building, which is already

captured as one of the structural characteristic variables. The second specification is:

log (𝑃!) = 𝛼 + 𝛽!𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡! + 𝛽!𝑌𝑒𝑎𝑟! + 𝛽!𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡! ∗ 𝑌𝑒𝑎𝑟! + 𝛾! log 𝑆!! + 𝛾!!

!!! 𝑆!" .+𝜃𝑓. 𝑒.+𝜀! (3)

In equation (2) I break down the percentage change in housing price by year.

𝑌𝑒𝑎𝑟 dummy variables takes the value of 1 if the transaction is made within that

specific year. 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 variable still takes the value of 1 if the apartment is located

in the treatment zone. By separating transactions by years, we could observe whether

or not there are differences in the percentage change in housing price.

There are a few identifying assumptions in this model. First, it assumes that

over the 2-year pre-opening period housing prices in the treatment zone and control

zone follow similar trends. It also assumes that there are no significant changes in the

availability and quality of public goods in the two areas that would impact

households’ decisions in buying or selling their apartments. The model omits the

impact of being furnished or damaged on housing prices. Because our main treatment

and control areas are both in the Western part of the New Territories, and are similar

neighbourhoods, it is not likely that the impact of furnished home or damaged home

would cause a positive or negative bias.

V. Results V.I Impact in Western Part of the New Territories

Figure 4 shows the trend in housing price per square foot in USD from 21st

December 2001 to 21st December 2005 for Yuen Long and Gold Coast. The long-

dash line represents the date of the opening of the Yuen Long MTR station. Though

the figures simply display the changes in housing prices per square foot without

controlling for other factors, we can see that generally Yuen Long has a higher

housing price per square foot after 2004.

Figure 2. Housing prices per Square Foot in Yuen Long and Gold Coast from 2002 to 2005

Source: Centadata, transactions made between 21st Dec 2001 and 21st Dec 2005. Notes: Transactions in Yuen Long location are drawn from Sun Yuen Long Centre. Transactions in Gold Coast are drawn from Hong Kong Gold Coast. Vertical long-dashed line: Opening date of the Yuen Long MTR Station; Vertical short-dashed line: Announcement date

100

200

300

400

500

Hou

sing

pric

e pe

r s.f

(USD

)

1/1/2002 1/1/2003 1/1/2004 1/1/2005 1/1/2006Year

Yuen Long (Treatment) Gold Coast (Control)

The short-dashed line represents the date, 19th May 2003, when news media

announced the expected opening date of the West Rail Line. There is not a large

difference between the two neighbourhoods during the window from 19th May 2003

to 20th Dec 2003, suggesting that residents might not have perceived the potential

benefit of the Western Rail Line upon the announcement date. However, the housing

prices per square footage between Yuen Long and Gold Coast widened after Dec 20th

2003. This gap seems to suggest that the benefit of the access to the new MTR station

was capitalized into housing prices.

Table 5: The Impact of the Yuen Long MTR Station on Housing Prices (1) (2) (3) (4) VARIABLES Model 1 Model 1 Model 2 Model 2 YL 0.0560* -0.1106** -0.00667 -0.133*** (0.0427) (0.0388) (0.0537) (0.0369) postperiod 0.1590*** 0.1644*** (0.0394) (0.0234) YL*postperiod 0.0723* 0.1078*** (0.0549) (0.0326) Year2003 -0.224*** -0.203*** (0.0571) (0.0291) Year2004 -0.0140 -0.0159 (0.0506) (0.0254) Year2005 0.127** 0.176*** (0.0504) (0.0254) YL*Year2003 0.129 0.0476 (0.0810) (0.0410) YL*Year2004 0.121* 0.118*** (0.0724) (0.0364) YL*Year2005 0.123* 0.0841** (0.0682) (0.0344) Floor 0.0033** 0.00343*** (0.00110) (0.000902) Building Age -0.0920*** -0.0941*** (0.012) (0.00977) Log (square footage) 0.8741*** 0.918*** (0.0661) (0.0538) No. of bedroom 0.0415* 0.0313 (0.0263) (0.0214) Garden View 0.00271 0.0136 (0.0373) (0.0303) Sea View 0.0643* 0.0644* (0.0433) (0.0354) Constant 2.855*** -0.439 2.866*** -0.572 (0.0379) (0.473) (0.0370) (0.416) Observations 360 360 360 360 R-squared 0.166 0.713 0.246 0.814

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 5 shows the results for Yuen Long MTR station. The specification in Column

(1) does not control for the structural characteristics of the apartment, and shows that

Yuen Long experienced a 7.2 percent increase in housing prices compared to Gold

Coast after the Yuen Long MTR station was introduced. Column (2) shows that after

controlling for structural characteristics of the apartment, which include usable square

footage, floor level, building age, number of bedrooms, and views of the apartment,

Yuen Long had an additional 10.8 percent increase in their housing prices compared

to Gold Coast. This effect is statistically significant at the 1% level and is more

precisely measured after controlling for a variety of characteristics.

Columns (3) and (4) break down the variable, 𝑌𝐿 ∗ 𝑝𝑜𝑠𝑡𝑝𝑒𝑟𝑖𝑜𝑑 into

𝑌𝐿 ∗ 𝑌𝑒𝑎𝑟2004 and 𝑌𝐿 ∗ 𝑌𝑒𝑎𝑟2005, allowing us to observe the impact of the Yuen

Long MTR station in those two years. Without controlling for the structural

characteristics, housing prices in Yuen Long encountered a 12.1 percent increase in

2004 and 12.3 percent increase in 2005 relative to those in Gold Coast. After

controlling for the structural characteristics, the opening of the Yuen Long MTR

station is associated with an 11.8 percent increase in housing prices in 2004 and an

8.41 percent increase in 2005 compared to Gold Coast. The coefficients also become

statistically significant. In all four models, the coefficient for 𝑌𝐿 ∗ 𝑌𝑒𝑎𝑟2003 is not

statistically significant, meaning the difference between Yuen Long and Gold Coast

housing prices had kept the same until 2004, soon after the Yuen Long MTR station

was opened. This supports the identifying assumption of the model that over the 2-

year pre-opening period housing prices in the treatment zone and control zone follow

similar trends.

V.II Impact in the Eastern Part of the New Territories To understand if the percentage change in housing price due to the opening of MTR

station is exclusive to the location in Yuen Long, I also include another treatment

zone and control zone in the Eastern Part of the New Territories in this study. Figure 3

shows a gap in housing prices in the two neighbourhood, with Ma On Shan (treatment

zone) having a higher housing price per square footage than in Sai Kung (control

zone). The long-dashed line on Dec 21st 2004 represents the opening of the Ma On

Shan MTR station while the short-dash line on Jan 6th 2004 represents the news

release of the expected opening of the station. Compared to the opening of the Yuen

Long station, homebuyers in Ma On Shan appear to capitalize the expected benefit of

the MTR station before the opening at the end of December 2004. Within the window

from the announcement date and the opening of the station, housing price per square

footage in Ma On Shan location surged up while the housing price per square footage

increased steadily over the period in Sai Kung. Nevertheless, prior to 2004, the

neighbourhoods showed similar trends in housing prices.

Figure 3. Housing prices per Square Footage in Ma On Shan and Sai Kung from 2003 to 2006

Source: Centadata, transactions made between 21st Dec 2002 and 21st Dec 2006. Notes: Transactions in MOS location are drawn from SunShine City and Bayshore Towers. Transactions in Sai Kung are drawn from Sai Kung Garden, Sai Kung Tower and Kam Po Court. Long dashed line: Opening Date of the Ma On Shan MTR station; Short dashed line: Announcement Date

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700

Hous

ing

Price

per

s.f

(USD

)

1/1/2003 1/1/2004 1/1/2005 1/1/2006 1/1/2007Year

Ma On Shan (Treatment) Sai Kung (Control)

The housing price trend due to the opening of the Ma On Shan MTR station is

similar to the result in the Yuen Long MTR station, where we see a bigger effect

within a year after the opening of the MTR station.

Table 6: The Impact of the Ma On Shan MTR Station on Housing Prices (1) (2) (3) (4) VARIABLES Model 3 Model 3 Model 4 Model 4 MOS 0.600*** 0.329*** 0.521*** 0.226*** (0.0566) (0.0339) (0.0885) (0.0362) postperiod 0.135** 0.167*** (0.0580) (0.0253) MOS*postperiod 0.074 0.061** (0.075) (0.0329) Year2004 0.0626 0.106*** (0.0892) (0.0322) Year2005 0.174** 0.221*** (0.0867) (0.0316) Year2006 0.160* 0.252*** (0.0873) (0.0315) MOS*Year2004 0.125 0.164*** (0.115) (0.0414) MOS*Year2005 0.145 0.180*** (0.113) (0.0410) MOS*Year2006 0.171 0.128*** (0.114) (0.0412) Floor 0.00446*** 0.00394*** (0.0014) (0.00114) Building Age -0.00840** -0.00897*** (0.0032) (0.00268) Log (square footage) 1.117*** 1.115*** (0.0734) (0.0608) No. of bedrooms -0.0546* -0.0476 (0.0354) (0.0295) Garden view 0.0300** 0.0418*** (0.0193) (0.0160) Sea view 0.0948*** 0.129*** (0.0251) (0.0210) Constant 4.768*** -1.585*** 4.736*** -1.640*** (0.0437) (0.3989) (0.0686) (0.331) Observations 248 248 248 248 R-squared 0.559 0.921 0.570 0.947

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 6 shows the result for the effect of the Ma On Shan MTR station on housing

prices. After controlling for structural characteristics, the variable 𝑀𝑂𝑆 ∗ 𝑃𝑜𝑠𝑡𝑝𝑒𝑟𝑖𝑜𝑑

in column (2) shows that Ma On Shan had an additional 6.1 percent increase in

housing price compared to that of Sai Kung after the opening of the MTR station. In

variable 𝑀𝑂𝑆 ∗ 𝑌𝑒𝑎𝑟2005 and 𝑀𝑂𝑆 ∗ 𝑌𝑒𝑎𝑟2006, we also see that the first year of

post-opening of the MTR station shows a higher appreciation in housing prices. In

2005, there was an additional 18 percent increase if the apartment is located in Ma On

Shan instead of Sai Kung, and an additional 12.8 percent increase in 2006.

While apartments face similar trend in housing prices after the opening of the

MTR Station, the perceived benefit of Ma On Shan MTR station seems to be larger in

Ma On Shan than in Yuen Long. In 2004, before the opening of the station,

apartments in Ma On Shan experienced an additional 16.4 percent increase in housing

prices relative to Sai Kung as seen in Variable 𝑀𝑂𝑆 ∗ 𝑌𝑒𝑎𝑟2004 in Column (4). The

positive and statistically significant coefficients of the 𝑌𝑒𝑎𝑟 variables also suggest

that housing prices were on a rise from 2003 to 2006. The earlier announcement date

for the opening of the station may explain the price appreciation in 2004.

There may also be a positive upward bias in the effect of the MTR station in

Ma On Shan due to the renovations of the shopping malls that took place in Ma On

Shan. This may explain why the Ma On Shan Station faced a higher housing

appreciation than in Yuen Long Station. Nevertheless, the results from Ma On Shan

station are consistent with the results we found in Yuen Long, where apartments see

an additional 10 to 13 percent increase in housing prices when an MTR station is

established nearby.

V.III Additional Robustness Check Another concern for the model may be that quarterly effects may impact the changes

in housing prices. Because weather changes are not extreme in Hong Kong, it is not

likely to have a strong impact on transaction prices and volume. Therefore, I also

check on whether or not controlling on a quarterly basis would impact my results for

Yuen Long and Gold Coast. Housing prices in Yuen Long increased by an additional

11.8 percent in 2004 and 8.3 percent in 2005 after the opening of the new Yuen Long

station when I control on a quarterly basis (Appendix 1). Housing prices in Ma On

Shan increased by an additional 18.6 percent in 2005 and 13.0 percent in 2006 after

the opening of the new Ma On Shan Station when I control on a quarterly basis

(Appendix 2).

VI. Conclusion

The analyses in this paper deal with two specific issues. The first is whether or

not access to an MTR station is capitalized into housing values. Second is whether

housing prices slowly adjust to the added convenience of having an MTR station, or

immediately capitalize upon the opening of an MTR station, causing a significant

jump in housing price in the affected neighbourhood. In both treatment zones,

housing prices increase by 10 to 13 percent after an opening of the MTR station. Both

eastern and western part locations in the New Territories of Hong Kong experienced a

jump in housing prices in the first year after the MTR was opened, indicating that

households immediately realized the benefits and convenience of better access to

other areas of the city, including the CBD. Compared to the opening of the Yuen

Long MTR station, the opening of the Ma on Shan station caused an even faster

reactionary increase in housing prices. The data also demonstrates that residents in

Ma On Shan faced a higher overall increase in housing prices than the Yuen Long

station. More studies are required to examine the possible explanations for the

differences in housing price increases. One possible explanation may be the relative

pricing of other modes of transportation in these neighbourhoods. It may play a role in

determining how much homebuyers value the apartments in these locations.

Nevertheless, the results suggest that access to public transportation is highly valued

by homebuyers.

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Appendix 1. Results for Yuen Long and Gold Coast area (with quarterly fixed effect)

VARIABLES Log(HpriceUSD) YL -0.132*** (0.0368)Year2003 -0.217*** (0.0297)Year2004 -0.0240 (0.0255)Year2005 0.177*** (0.0255)YL*Year2003 0.0533 (0.0409)YL*Year2004 0.119*** (0.0362)YL*Year2005 0.0831** (0.0343)Floor 0.00343*** (0.000898)BuildingAge -0.0924*** (0.00977)Log(squarefootage) 0.907*** (0.0539)room 0.0314 (0.0213)GardenView 0.0125 (0.0303)SeaView 0.0621* (0.0353)Q2 0.0214 (0.0175)Q3 0.0161 (0.0194)Q4 0.0480** (0.0195)Constant 1.736*** (0.416) Observations 360R-squared 0.818

Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1

Appendix 2. Results for Ma On Shan and Sai Kung (with quarterly fixed effect)

VARIABLES Log(HpriceUSD) MOS 0.230*** (0.0363)Year2004 0.107*** (0.0323)Year2005 0.218*** (0.0317)Year2006 0.252*** (0.0314)MOS*Year2004 0.158*** (0.0418)MOS*Year2005 0.186*** (0.0412)MOS*Year2006 0.130*** (0.0412)Floor 0.00402*** (0.00115)BuildingAge -0.00858*** (0.00270)Log(squarefootage) 1.103*** (0.0623)#ofbedroom -0.0422 (0.0301)Gardenview 0.0386** (0.0162)Seaview 0.127*** (0.0210)Q2 0.0145 (0.0196)Q3 0.0159 (0.0196)Q4 0.0370* (0.0201)Constant -1.608*** (0.339) Observations 247R-squared 0.948

Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1

Appendix 3. i. Floor plan of Sun Yuen Long Centre

ii. Floor plan of Gold Coast Area

iii. Apartments at Ma On Shan location

iv. Apartments at Sai Kung location