The Reexamination of the Impact of Mass Rapid Transportation On Residential Housing in Taipei

city

Ying–Hui Chiang Kuo- Cheng Tai

20TH CONGRESS OF THE EUROPEAN REAL ESTATE SOCIETY JULY 3 - 6, 2013, VIENNA

ERES 2013 Vienna University of Technology July 3-6, 2013 1

Assistant Professor , Department of Land Economics,National Chengchi University,Taipei, Taiwan

Master, Department of Land Economics,National Chengchi University,Taipei, Taiwan

Outline1 Introduction

2 Literature Reviews

3

4 The Data and The Model

5 Empirical Results

2

6 Findings and Suggestions

The Methodology

Background

3

Populations AreaTaipei City 2.6 M 272 km2

New Taipei City(Taipei County)

3.8 M 2,053 km2

Political & commercial center of Taiwan

IntroductionThe ratio of public facilities for intended buyers

Resources: Housing Demand Survey on 2nd Quarter 2011 Ministry of Interior

MRT/railway station

Bus station

Park

School

Market

其他

Survey area： Taipei city

70％

Introduction Most of us know how important living close to an

MRT station is accessibility. Often times, you’ll read the discussion about how housing that is near to an MRT station is good because prices are likely to rise in the long term. But do prices of properties near MRT stations really increase because of accessibility?

5

6

Literature Reviews　—MRT impact on housing price

★ MRT has positive impact on housing price:→ Bajic（ 1983）， Voith（ 1991）， Coffman & Gregson（ 1998）， Craig etc.(1998）， Bowes & Ihlanfeldt（ 2001）， McMillen & McDonald（ 2004）， Feng etc.（ 1994）， Hong & Lin1999）， Peng & Yang（ 2009）

★ MRT has no positive impact on housing price:→ Nelson & McCleskey（ 2007）， Gatzlaff & Smith（ 1993）， Dornbusch（ 1975）， Burkhardt（ 1976）

Estimation results : positive impact The impact will decrease when the distances between the housing and the MRT station increase.

Literature Reviews　—MRT impact on housing price with different track types and station locations

★ Feng and Yang （ 1994）→the different station modes(urban, marginal

and suburb mode): urban > marginal ,

marginal > suburb→the different track types impact : underground

> Suspension Bridge , Suspension Bridge >

ground rail

★Peng and Yang（ 2009）→the impact range of a MRT station is

different， Suburb >urban

Housing Price=Location+ MRT accessibility + building characteristics

★ DO they have the same impact with location differences?

CBD Suburbs ？

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Research questions:★ 1：

★Location differences Location ? Accessibility?

★ 2：★track types differences

• Transfer station 2 lines• Underground, suspension bridge, bridge

★OLS model in case of spatial autocorrelation may be biased estimates

★Spatial regression model

The Data★ Subjects

→ red、 blue、 brown route→ Apartment、 mansion

★ periods→ 2007、 2008

★ Areas→ In the 1 km along the MRT route

The methodology

1. Hedonic model

Price per ping

2. Submarket separated3. Spatial autocorrelation4. Spatial regression model

Location × accessibility

variablesDowntown Dummy

Distance to nearest

MRT/MTR station

Continuous/m

apartment DummySuite Dummyfloor Continuous

1st floor DummyAge Continuous/year

Age2 --Site area --

Road width of main load

Continuous/m

Road width of site area

Continuous/m

school Dummy park Dummy

Other traffic DummyNimby Dummyyear Dummy

Submarket

CBD

Downtown

Suburb

Descriptive statisticsDistrict All samples CBD downtown surburb

Samples 14162 4391 3824 5947Price per ping 28.42 40.60 27.49 20.02

(12.77) (13.57) (8.21) (5.35)Floor 5.37 5.28 4.68 5.89

(4.13) (3.78) (3.27) (4.75)Age 21.55 24.46 22.41 18.84

(10.18) (9.53) (10.14) (9.97)Site area 0.08 0.08 0.08 0.08

(0.10) (0.09) (0.09) (0.10)Road width of main road 18.64 22.44 18.46 15.96

(15.64) (20.29) (13.89) (11.70)Road width of site area 9.40 10.52 8.75 9.00

(11.26) (15.00) (9.08) (9.03)Distance to nearest MRT/MTR station 533.88 499.64 537.46 556.87

(231.76) (240.35) (230.19) (223.16) apartment 56.54% 64.72% 49.14% 55.25%

Suite 1.81% 1.84% 2.51% 1.33%1st floor 9.74% 8.77% 11.09% 9.60%

In the 500m with park 60.29% 84.56% 80.62% 29.29%In the500m with school 56.28% 60.60% 61.92% 49.45%

In the 500m with other traffic facilities 3.30% 8.15% 2.80% 0.03%

In the 500m with the Nimby 18.70% 11.68% 19.33% 23.47%Conjuction 2 lines 10.45% 22.48% 4.34% 5.50%

underground 66.30% 60.21% 32.56% 92.50%

Empirical results-Spatial Regression model OLS SLM SEM

Coef. Coef. Coef.Constant 27.4939 *** 9.8145 *** 29.9148 ***

CBD 17.0611 *** 6.1386 *** 16.6132 ***Downtown 7.5815 *** 2.7657 *** 9.8048 ***

Distance to nearest MRT/MTR station -0.0046 *** -0.0017 *** -0.0038 ***CBD× Distance 0.0078 *** 0.0017 *** 0.0059 ***

Downtown×Distance 0.0025 *** 0.0009 * -0.0006

Age -1.0429 *** -0.6177 *** -1.0204 ***

Age2 0.0170 *** 0.0090 *** 0.0151 ***Sitearea 7.4480 *** 6.0847 *** 4.9524 ***

Road width of main road 0.0319 *** 0.0173 *** 0.0139 ***Road width of site area 0.0622 *** 0.0363 *** 0.0370 ***

Floor 0.1777 *** 0.1016 *** 0.1838 ***Apartment 3.4973 *** 2.1609 *** 1.7828 ***

Suite -0.5128 0.5177 0.2962 1st floor 7.0197 *** 6.5667 *** 7.0043 ***

In the 500m with park 0.2793 * 0.2605 ** -0.0001 In the 500m with school 0.6489 *** 0.3953 *** 0.2434

In the 500m with the NIMBY -1.6838 ** -0.6203 *** -0.5099 *In the 500m with other traffic

facilities1.8581 *** 0.9907 *** 2.4768 ***

Conjuction 2.8363 *** 0.9539 *** 2.0006 ***

Underground 0.6688 ** 0.2813 ** 0.5575 Year 1.8706 *** 1.8569 *** 1.9387 ***

Spatial lag coefficience ρ 0.6933 ***Spatial error coefficience λ 0.7973 ***

Adj R2 0.6573 0.8284 0.8527Breusch-Pagan test 11802.42 *** 9162.08 *** 16079.2 ***

LM test (lag) 11551.08 *** -- --LM test (error) 14554.34 *** -- --

Robust LM test (lag) 71.82 *** -- --Robust LM test (error) 3075.83 *** -- --

AIC -- 88935.30 *** 87451.70 ***SC 89109.20 *** 87618.00 ***

Likelihood Ratio test -44444.70 *** -43703.87 ***samples 14162 14162 14162

Empirical results-Spatial Regression model

ols sem估計係數 估計係數

Constant 27.4939 *** 29.9148 ***CBD 17.0611 *** 16.6132 ***

Downtown 7.5815 *** 9.8048 ***Distance to nearest MRT/MTR station -0.0046 *** -0.0038 ***

CBD× Distance 0.0078 *** 0.0059 ***Downtown×Distance 0.0025 *** -0.0006

Age -1.0429 *** -1.0204 ***Age2 0.0170 *** 0.0151 ***

Sitearea 7.4480 *** 4.9524 ***Road width of main road 0.0319 *** 0.0139 ***Road width of site area 0.0622 *** 0.0370 ***

Floor 0.1777 *** 0.1838 ***Apartment 3.4973 *** 1.7828 ***

Suite -0.5128 0.2962 1st floor 7.0197 *** 7.0043 ***

In the 500m with park 0.2793 * -0.0001 In the 500m with school 0.6489 *** 0.2434

In the 500m with the NIMBY -1.6838 ** -0.5099 *In the 500m with other traffic facilities 1.8581 *** 2.4768 ***

Conjuction 2.8363 *** 2.0006 ***

Underground 0.6688 ** 0.5575 Year 1.8706 *** 1.9387 ***

Spatial error coefficience λ 0.7973***samples 14162 14162

conjuction

Underground

CBD compare to suburb

Empirical results-Spatial Regression model

020

040

060

080

010

0010

20

30

40

50

47.06 48.09

30.44 28.26

20.64 18.73

市中心市區市郊

Distance

price

Impact on CBD

Empirical results-Spatial Regression model

100

200

300

400

500

600

700

800

90010

00-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

2.19%-7.19%

-9.23%

市中心市區市郊

捷運車站距離

ratio

Impact on CBD

Distance impact is more important on surburb MRT station

The EndThanks for your listening!

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