II, 1) 1,,, I II, ;, II, [12] (2), ,,,, R R I II RC RC RS RS CBD CBD X XC = XS CBD XS CBD X (a) I (b) II 1 I (a) II (b) Fig. 1 Effe

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1 ACTA GEOGRAPHICA SINICA Vol.65, No.2 Feb., , (1., ; 2., ) :, 13, 1 km 1 km 20%,,, 13 2, 13 ; km, 1.0 (TOD), : ; ; ; ; ; 1, [1],,, (TOD),,,,, (Value Capture, ),, ( Benjamin Sirmans [2], Al-Mosaind [3], Voith [4] ), (Gatzlaff Smith [5], Cervero Landis [6] [7-8] ) Kim Zhang [9],,, Kim Zhang [10], ( [11] I, 1), ( ; ( ) [Foundation: National Natural Science Foundation of China, No ] (1978-),,, [email protected] (1977-),,,, [email protected]

2 II, 1) 1,,, I II, ;, II, [12] (2), ,,,, R R I II RC RC RS RS CBD CBD X XC = XS CBD XS CBD X (a) I (b) II 1 I (a) II (b) Fig. 1 Effect I (a) and Effect II (b) R R RC RC RS RS CBD XCÁÁÂ XS CBD X CBD XC XS CBD X (a) 2 (a) (b) Fig. 2 Different impacts of rail transit on commercial (a) and residential (b) properties 1 I X RC RS II ( XS) ( XC) RS RC (b)

2 : 215,,,,, Intriligator Sheshinski [13] Knaap [14], Huang [15],, 13, 2 13 2.1 13 2008 12 31, 8, 200 km (3) : 1 2 5 10 13 13 "U",,,,, 5 13 40.95 km, 16, 2.

3 2 : 215,,,,, Intriligator Sheshinski [13] Knaap [14], Huang [15],, 13, , 8, 200 km (3) : "U",,,,, km, 16, 2.6 km : ; , , 13 4 km (4), , 1 3 3, 2, Fig. 3 Existing rail transit network in the central city of Beijing 2, 13 (1.6 km 1 ) 3,,, Goodman Thibodeau [16] Schnare Struyk [17], Goodman [18, 19], Goodman Thibodeau [16]

216 65 4, 3, 1 3, 2,, 3 : 1999 1-2003 1 ( ) 2003 2-2004 12 ( ) 2005 1-2006 9 ( ) 1 4 Fig.

4 , 3, 1 3, 2,, 3 : ( ) ( ) ( ) 1 4 Fig. 4 Location of housing project samples, 1:2000,,, 13, (4), 1, 1254 (2) m,, 1000 m 19%, 3 42% 28%29% (3) 1 3, 2,, b 167 &?@ CDEF $?@ 1 Tab. 1 Variable definition and data source PRICE /má!" FAR #$ %& (!)* (! +" TIME1,TIME2,TIME3 -. TIME /01234 TIME TIME Y1999,,Y / : TYPE ;< 13=>< 0 45AB DECO G " GRATIO % HI&J* KLMG (!)HI&J * (!N+ )OPQ RS DZGC m )OPQTUVW RS GIS )XYZ RS DSQU m GIS ) ^ ] RS DSTA m )[\ ] RS GIS D _ ] a 1000 m M GIS ZONE1,ZONE2,ZONE3 cd efg GIS

5 2 : ( 1) 2, Tab. 2 Descriptive statistics for all data (Dataset 1), 1254 PRICE (4) DECO TYPE , ( GREEN DZGC ) DSQU , 2 DSTA , D TIME TIME TIME TIME Y Y Y Y (hedonic pricing model) [20] : Y Y Ln PRICE = c + a 1 *DECO + a 2 *TYPE + a 3 *GREEN + a 4 *DZGC + a 5 *DSQU + a 6 * (D1000) + a 7 * (D1000) TI ME2 Y2005 Y a 8 * (D1000) TI ME3 + a 9 *Y a 10 *Y a 11 *Y a 12 *Y a 13 *Y a 14 *Y a 15 *Y ε (1) : a 1 a 15, c, ε (D1000) (D1000) TI ME2 (D1000) TI ME3 Y2000 Y ( 1) Tab. 3 Descriptive statistics for each submarket (Dataset 1), , 5 PRICE ( DECO R ) TYPE GREEN DECO TYPE DZGC GREEN DSQU DSTA DSTA< Y %, Y /m 2, Y Y , Y %, Y Y Y2006 1%,

6 218 4 ( 2) Tab.4 Descriptive statistics (Dataset 2) PRICE FAR DECO TYPE GREEN DZGC DSQU DSTA TIME %, 5 80 /m 25, 2002 C 9.06*** 2003, (178.24) DECO 0.21*** , (11.10) TYPE 0.52*** (14.44) 2% 6% ; 2005 GREEN 0.01*** 2006, Tab. 5 Estimation results of Equation (11.00) 1 ( : in PRICE) 9.33*** (111.47) 0.24*** (8.04) 0.47*** (8.70) 0.01*** (6.48) (Dependent variable: in PRICE) 8.75*** (58.72) 0.25*** (5.72) 0.50*** (14.91) 0.01*** (6.35) 8.94*** (101.58) 0.19*** (8.07) 0.56*** (5.33) 0.01*** (7.41), 15% DZGC -6.70E-06*** -6.29E-05*** -3.88E-05*** 8.65E-06* (-3.03) (-8.48) (-5.40) (1.70) DSQU -6.15E-05*** -6.59E-05*** -2.47E-05*** -6.76E-05***, (-38.44) (-18.45) (-4.49) (-15.80) ( 1) D **, (-1.55) (-1.29) (-1.08) (-2.43) D1000*TI ME ** (0.46) (-0.28) (2.27) (1.15), D1000*TI ME * (0.22) (-1.80) (1.18) (1.42) (5) RÁ ***p = 0.01 **p = 0.05 *p = 0.10 t (!") DSQU # $%& ( ) *+,-./ DZGC, 1000 m, 6.15%; 1000 m, 0.67% 1 (4),, (6.29%6.59%) m, 3.88%; 2.47% 5 ( ) ( )

7 2 : 219 3,,,, :? ( ) ( )????,, ( 2) ( ) ( 1 3),,,,, 13, 2003, 1000 m 1000 m ( 1 ) 6, 3, 2, 13, 1000 m 1000 m 20%, 1000 /m 2, 95% 1 3, 7, 13 8, I, II,,,,, 13 2, m, m /m 2, ,, 6 D1000*TIME1 D1000*TIME [21] 8 9

8 220 65, :, ;,, ( : DSQU) Tab. 6 Estimation results of 1000 m,, 1000 /m 2 Equation 2 ( (Dependent Variable: DSQU) );, 輥輯訛 ;, C *** (14.73), 2 Y , (-0.54) Y , (0.85), 13, Y ** [22] (1.85), Y ** (1.75) Y ** (2.45) 4 Y *** (2.69) : Y **,, (1.69) 296 RÁ 0.04,,,,,, 輥輰訛,,, DSQU = c + a 1 *Y a 2 *Y a 3 *Y a 4 *Y a 5 *Y a 6 *Y a 7 *Y ε (2) 6 2, 2002,, km 13, ( km ),,, 4 輥輱訛,,,,, , ( 2) 75% 輥輯訛輥輰訛輥輱訛 2

9 2 : 221, ( : FAR) 50%,, Tab. 7 Estimation results of Equation 3 (Dependent Variable: FAR) , km 48%44% t t t t C 5.74*** 6.11*** 4.98*** 4.03***, (8.72) (3.29) (5.28) (3.74) DSTA -7.74E E E-04** 6.02E-05 : (-0.45) (-0.48) (-2.23) (0.19) 13 DSQU -2.02E-04*** -2.11E E-04*** -3.74E (-4.59) (-1.45) (-2.60) (-0.33) RÁ ??,,? : FAR = c + a 1 *DSTA + a 2 *DSQU + ε (3), , 5000 m, , 13, 3300 m, ,, 2, 40%,,,,,?? , 1 km 20%,,, 13, ,, (, ),,,

10 222 65,,, ,,, 75% 2 km,,, 3300 m, 1.0,,,,, : ( ), : (References) [1] Zhang Wenzhong. An analysis of the factors that influence the urban residential location selection. Progress in Geography, 2001, 20(3): [.., 2001, 20 (3): ] [2] Benjamin J D, Sirmans G S. Mass transportation, apartment rent and property values. Journal of Real Estate Research, 1996, 12(1): 1-8. [3] Al-Mosaind M A, Duecker K J, Strathman J G. Light-rail transit stations and property values: A hedonic price approach. Transportation Research Record, 1993, 1400: [4] Voith R. Changing capitalization of CBD-oriented transportation systems: Evidence from Philadelphia, Journal of Urban Economics, 1993, 33(3): [5] Gatzlaff D H, Smith M T. The impact of the Miami metrorail on the value of residences near station locations. Land Economics, 1993, 69(1): [6] Cervero R, Landis J. Assessing the impacts of urban rail transit on local real estate markets using quasi-experimental comparisons. Transportation Research A, 1993, 27(1): [7] Hess D B, Almeida T M Impact of proximity to light rail rapid transit on station-area property values in Buffalo. New York. Urban Studies, 44(5): [8] Gu Yizhen, Xu Zhiyi. Review on the impact of rail transit on real estate value. Urban Problems, 2007, (12): [,.., 2007, (12): ] [9] Kim J, Zhang M. Determining transit's impact on Seoul commercial land values: An application of spatial econometrics. International Real Estate Review, 2005, 8(1): [10] Alonso W. Location and Land Use: Toward a General Theory of Land Rent. Cambridge: Harvard University Press, [11] Wang Xia, Zhu Daolin, Zhang Mingming. Analysis of the influence of rail transit on the distribution layout of real estate prices: A case study of Beijing Light Rail Line No.13. Urban Problems, 2004, (6): [,,. : 13., 2004, (6): ] [12] Gu Yizhen, Guo Rui. The impacts of the rail transit on property values: Empirical study in Batong Line of Beijing. Economic Geography, 2008, 28(3): , 453. [,. :., 2008, 28(3): , 453.] [13] Intriligator M, Sheshinski E. Toward a theory of planning//heller W, Starr R, Starrett D//Social Choice and Public Decision Making. UK: Cambridge University Press, [14] Knaap G J, Hopkins L, Donaghy K. Does planning matter? A framework for examining the logic and effects of land use planning. Journal of Planning Education and Research, 1997, 18(1): [15] Huang H. The land-use impacts of urban rail transit systems. Journal of Planning Literature, 1996, 11(1):

11 2 : 223 [16] Goodman A C, Thibodeau T G. Housing market segmentation. Journal of Housing Economics, 1998, 7: [17] Schnare A, Struyk R. Segmentation in urban housing markets. Journal of Urban Economics, 1976, 3: [18] Goodman A C. Hedonic prices, price indices, and housing markets. Journal of Urban Economics, 1978, 5: [19] Goodman A C. Housing submarkets within urban areas: Definitions and evidence. Journal of Regional Science, 1981, 21: [20] Sheppard S C. Hedonic analysis of housing markets//cheshire P C, Mills E S//Handbook of Regional and Urban Economics. Elsevier, 1999: [21] He Jianhua, Zheng Siqi. Can new rail line increase housing prices? A case study of Rail Line No.13 of Beijing. Urban Development, 2004, (11): [,.?13., 2004, (11): ] [22] Knaap G J, Ding C, Hopkins L D. Do plans matter? The effects of light rail plans on land values in station area. Journal of Planning Education and Research, 2001, 21(1): The Impacts of Rail Transit on Property Values and Land Development Intensity: The Case of No.13 Line in Beijing GU Yizhen, ZHENG Siqi (1. Detailed Planning Department, Beijing Municipal Institute of City Planning & Design, Beijing , China; 2. Institute of Real Estate Studies, Tsinghua University, Beijing , China) Abstract: Theoretical analysis shows that the effect of rail transit on housing price is greater in suburbs than that in the central area. Empirical results validate this point by using the hedonic pricing method and housing transaction data nearby No.13 rail line in Beijing. In suburban areas, the housing prices within 1 km of rail stations are nearly 20% higher than those beyond that distance. However, with the development of surrounding urban transportation networks, the impact of rail transit is declining. Also, rail transit has significant impacts on urban land development intensity. The operation of No.13 Line encourages the land development in the north suburbs of Beijing, and raises the land development intensity around the stations. The FAR of new housing projects decreases by 1.0 every 3.3 km further away from the stations in suburban areas. These empirical results have important policy implications for the TOD strategy, the cost-benefit analysis and value capture strategy of rail transit construction in Chinese cities. Key words: rail transit; housing price; land development intensity; hedonic pricing method; submarket; Beijing

UrbanSim UrbanSim GIS / UrbanSim UrbanSim F A office building % 32. 8% 25. 9% 14. 4% Location locat

UrbanSim UrbanSim GIS / UrbanSim UrbanSim F A office building % 32. 8% 25. 9% 14. 4% Location locat 1006-3862 2012 02-0116 - 09 UrbanSim 100084 UrbanSim GIS / UrbanSim UrbanSim F291. 1 A office building 2010 31. 2% 32. 8% 25. 9% 14. 4% Location location and location / UrbanSim 70973065 4D-GIS 116 UrbanSim