The Reexamination of the Impact of Mass Rapid Transportation On Residential Housing in Taipei city The Reexamination of the Impact of Mass Rapid Transportation On Residential Housing in Taipei city Ying–Hui Chiang Ying–Hui Chiang Kuo- Cheng Tai 20 TH C ONGRESS OF THE E UROPEAN R EAL E STATE S OCIETY J ULY 3 - 6, 2013, V IENNA ERES 2013 Vienna University of Technology July 3-6, Assistant Professor, Department of Land Economics, National Chengchi University, Taipei, Taiwan Master, Department of Land Economics, National Chengchi University, Taipei, Taiwan

Outline 11IntroductionIntroduction 22 Literature Reviews The Data and The Model 55 Empirical Results 2 66 Findings and Suggestions The Methodology

Background Background 3 PopulationsArea Taipei City2.6 M272 km 2 New Taipei City(Taipei County)3.8 M2,053 km 2 Political & commercial center of Taiwan

Introduction 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

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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 separated 3.Spatial autocorrelation 4.Spatial regression model Location × accessibility variables Downtown Dummy Distance to nearest MRT/MTR station Continuous/m apartment Dummy SuiteDummy floorContinuous 1 st floorDummy AgeContinuous/year Age2-- Site area-- Road width of main load Continuous/m Road width of site area Continuous/m schoolDummy parkDummy Other trafficDummy NimbyDummy yearDummy

Submarket CBD Downtown Suburb

Descriptive statistics DistrictAll samplesCBDdowntownsurburb Samples Price per ping (12.77)(13.57)(8.21)(5.35) Floor (4.13)(3.78)(3.27)(4.75) Age (10.18)(9.53)(10.14)(9.97) Site area0.08 (0.10)(0.09) (0.10) Road width of main road (15.64)(20.29)(13.89)(11.70) Road width of site area (11.26)(15.00)(9.08)(9.03) Distance to nearest MRT/MTR station (231.76)(240.35)(230.19)(223.16) apartment56.54%64.72%49.14%55.25% Suite1.81%1.84%2.51%1.33% 1 st floor9.74%8.77%11.09%9.60% In the 500m with park60.29%84.56%80.62%29.29% In the500m with school56.28%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 Nimby18.70%11.68%19.33%23.47% Conjuction 2 lines10.45%22.48%4.34%5.50% underground66.30%60.21%32.56%92.50%

Empirical results-Spatial Regression model OLSSLMSEM Coef. Constant ***9.8145*** *** CBD ***6.1386*** *** Downtown7.5815***2.7657***9.8048*** Distance to nearest MRT/MTR station *** *** *** CBD× Distance0.0078***0.0017***0.0059*** Downtown×Distance0.0025***0.0009* Age *** *** *** Age ***0.0090***0.0151*** Sitearea7.4480***6.0847***4.9524*** Road width of main road0.0319***0.0173***0.0139*** Road width of site area0.0622***0.0363***0.0370*** Floor0.1777***0.1016***0.1838*** Apartment3.4973***2.1609***1.7828*** Suite st floor7.0197***6.5667***7.0043*** In the 500m with park0.2793*0.2605** In the 500m with school0.6489***0.3953*** In the 500m with the NIMBY ** *** * In the 500m with other traffic facilities ***0.9907***2.4768*** Conjuction2.8363***0.9539***2.0006*** Underground0.6688**0.2813** Year1.8706***1.8569***1.9387*** Spatial lag coefficience ρ0.6933*** Spatial error coefficience λ0.7973*** Adj R Breusch-Pagan test *** *** *** LM test (lag) ***-- LM test (error) ***-- Robust LM test (lag)71.82***-- Robust LM test (error) ***-- AIC *** *** SC *** *** Likelihood Ratio test *** *** samples14162

Empirical results-Spatial Regression model olssem 估計係數 Constant *** *** CBD *** *** Downtown ***9.8048*** Distance to nearest MRT/MTR station *** *** CBD× Distance ***0.0059*** Downtown×Distance *** Age *** *** Age ***0.0151*** Sitearea ***4.9524*** Road width of main road ***0.0139*** Road width of site area ***0.0370*** Floor ***0.1838*** Apartment ***1.7828*** Suite st floor ***7.0043*** In the 500m with park * In the 500m with school *** In the 500m with the NIMBY ** * In the 500m with other traffic facilities ***2.4768*** Conjuction ***2.0006*** Underground ** Year ***1.9387*** Spatial error coefficience λ0.7973*** samples14162 conjuction Underground CBD compare to suburb

Empirical results-Spatial Regression model Impact on CBD

Empirical results-Spatial Regression model Impact on CBD Distance impact is more important on surburb MRT station

The End Thanks for your listening! 20