65 2 2010 2 ACTA GEOGRAPHICA SINICA Vol.65, No.2 Feb., 2010 13 1 2, (1., 100045; 2., 100084) :, 13, 1 km 1 km 20%,,, 13 2, 13 ; 13 3.3 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), ( 2009-05-31; 2009-12-10 (70973065) [Foundation: National Natural Science Foundation of China, No.70973065] (1978-),,, E-mail: yizhengu@gmail.com (1977-),,,, E-mail: zhengsiqi@tsinghua.edu.cn 213-223
214 65 II, 1) 1,,, I II, ;, II, [12] (2), 1999-2006 13,,,, 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, 2.6 km 1 2 13 1999 : 1999 12 ; 2002 9 28, 2003 1 28 2.2 1999 1-2006 9 13 13, 13 4 km (4), 3 3 1 2 3, 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 Location of housing project samples, 1:2000,,, 13, (4), 1, 1254 (2) 7900 2159 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 -. TIME1 1999.1-2003.1 /01234 TIME2 2003.2-2004.12 1 5 0 TIME3 2005.1-2006.9 Y1999,,Y2006 -. /08934 1 5 0 : TYPE ;< 13=>< 0 45AB DECO 4 1 5 0 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 D1000 45 _ ] a 1000 m M34 1 5 0 GIS ZONE1,ZONE2,ZONE3 cd efg34 1 5 0 GIS
2 : 217 2 ( 1) 2, Tab. 2 Descriptive statistics for all data (Dataset 1), 1254 PRICE 7898.16 25000 2480 3386.79 (4) DECO 0.23 1 0 0.42 259 2 TYPE 0.06 1 0 0.24 1, ( GREEN 37.29 80 20 9.13 DZGC 9187.34 15899 645 3471.22 ) DSQU 10814.70 22107 3783 4589.37, 2 DSTA 2159.17 5896 105 1187.01, D1000 0.19 1 0 0.39 TIME 54.82 93 11 23.75 TIME1 0.42 1 0 0.49 TIME2 0.28 1 0 0.45 TIME3 0.29 1 0 0.46 3 Y1999 0.03 1 0 0.18 Y2000 0.11 1 0 0.32 Y2001 0.13 1 0 0.34 Y2002 0.14 1 0 0.35 (hedonic pricing model) [20] : Y2003 0.11 1 0 0.32 Y2004 0.17 1 0 0.38 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 Y2006 0.19 0.11 1 1 0 0 0.39 0.31 + a 8 * (D1000) TI ME3 + a 9 *Y2000 + a 10 *Y2001 + a 11 *Y2002 + a 12 *Y2003 + a 13 *Y2004 + a 14 *Y2005 + a 15 *Y2006 + ε (1) : a 1 a 15, c, ε (D1000) (D1000) TI ME2 (D1000) TI ME3 Y2000 Y2006 3 ( 1) Tab. 3 Descriptive statistics for each submarket (Dataset 1), 1 1 2 3 402 243 609, 5 PRICE 7920.78 3223.17 5806.31 2179.72 8717.91 3531.78 ( DECO 0.17 0.38 0.11 0.31 0.32 0.47 R 2 0.6 ) TYPE 0.05 0.21 0.21 0.41 0.01 0.10 GREEN 37.23 9.27 40.81 10.28 35.93 8.13 DECO TYPE DZGC 4976.70 1626.86 11040.23 1923.33 11227.44 2085.79 GREEN DSQU 9906.65 3575.44 17861.26 2773.32 8602.42 2573.12 DSTA 2453.44 1336.86 1997.25 1467.98 2029.53 884.70 DSTA<1000 0.20 0.40 0.25 0.43 0.16 0.36 Y1999 0.04 0.20 0.02 0.13 0.03 0.18 20%, Y2000 0.16 0.36 0.05 0.21 0.11 0.32 1400 /m 2, Y2001 0.17 0.37 0.09 0.29 0.13 0.34 Y2002 0.17 0.38 0.09 0.28 0.14 0.35, Y2003 0.11 0.32 0.11 0.31 0.11 0.32 50%, Y2004 0.15 0.35 0.17 0.38 0.19 0.39 Y2005 0.15 0.36 0.25 0.43 0.18 0.39 Y2006 1%, 0.05 0.23 0.23 0.42 0.09 0.29 4
218 4 ( 2) Tab.4 Descriptive statistics (Dataset 2) 1 2 3 259 92 46 121 PRICE 7457.02 3171.58 7308.99 2828.63 5418.98 2419.23 8344.36 3310.80 FAR 3.25 2.43 3.46 3.04 1.61 0.96 3.71 2.00 DECO 0.22 0.42 0.18 0.39 0.13 0.34 0.29 0.46 TYPE 0.07 0.26 0.07 0.25 0.24 0.43 0.02 0.13 GREEN 36.91 9.29 37.50 9.85 40.00 9.96 35.29 8.26 DZGC 8950.18 3606.78 4858.20 1731.92 11102.85 1888.30 11243.07 2107.69 DSQU 10825.05 4526.93 10062.17 3439.83 18108.98 2858.81 8636.00 2531.09 DSTA 2188.29 1189.06 2426.04 1339.68 2035.26 1457.92 2065.70 902.34 TIME 41.60 24.46 37.65 23.08 52.57 23.70 40.43 24.74 65 1%, 5 80 /m 25, 2002 C 9.06*** 2003, (178.24) DECO 0.21*** 2003 2004, (11.10) TYPE 0.52*** (14.44) 2% 6% ; 2005 GREEN 0.01*** 2006, Tab. 5 Estimation results of Equation 1 1 2 3 1254 402 243 609 (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) D1000-0.03-0.05-0.07-0.09**, (-1.55) (-1.29) (-1.08) (-2.43) D1000*TI ME2 0.02-0.02 0.19** 0.07 2002 (0.46) (-0.28) (2.27) (1.15), D1000*TI ME3 0.08-0.14* 0.094 0.10 (0.22) (-1.80) (1.18) (1.42) (5) RÁ0.64 0.64 0.72 0.54 ***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%) 2 1000 m, 3.88%; 2.47% 5 ( ) ( )
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, 14 1000 m, 821.5 m 2 1000 /m 2, 82.15 13 65.7 9,, 6 D1000*TIME1 D1000*TIME3 7 1 13 1000 [21] 8 9 http://www.bii.com.cn/zcgl.asp
220 65, :, 6 2 2 10 ;,, ( : DSQU) Tab. 6 Estimation results of 1000 m,, 1000 /m 2 Equation 2 ( (Dependent Variable: DSQU) );, 輥輯訛 ;, C 9674.13*** (14.73), 2 Y2000-486.51, (-0.54) Y2001 781.89, (0.85), 13, Y2002 1885.71** [22] (1.85), Y2003 1675.64** (1.75) Y2004 2474.41** (2.45) 4 Y2005 2657.47*** (2.69) : Y2006 2424.45**,, (1.69) 296 RÁ 0.04,,,,,, 輥輰訛,,, DSQU = c + a 1 *Y2000 + a 2 *Y2001 + a 3 *Y2002 + a 4 *Y2003 + a 5 *Y2004 + a 6 *Y2005 + a 7 *Y2006 + ε (2) 6 2, 2002,, 1999 2 km 13, (1999 2.5 km ),,, 4 輥輱訛,,,,, 13 13 2002 2006, ( 2) 75% 2 10 1 3 輥輯訛 輥輰訛 輥輱訛 2
2 : 221, 2002 7 3 ( : FAR) 50%,, Tab. 7 Estimation results of Equation 3 (Dependent Variable: FAR) 13 1 3 1 2 3 139 45 34 60, 2002 2 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-05-1.81E-04-2.98E-04** 6.02E-05 : (-0.45) (-0.48) (-2.23) (0.19) 13 DSQU -2.02E-04*** -2.11E-04-1.50E-04*** -3.74E-05 1999 (-4.59) (-1.45) (-2.60) (-0.33) RÁ0.12 0.01 0.39-0.03 13??,,? : FAR = c + a 1 *DSTA + a 2 *DSQU + ε (3), 2002 7 3, 5000 m, 1.0 13 1 2, 13, 3300 m, 1.0 1 3,, 2, 40%,,,,,?? 5 13 13, 1 km 20%,,, 13, 13 82.15,, (, ),,,
222 65,,, 13 2002,,, 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): 268-275. [.., 2001, 20 (3): 268-275.] [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: 90-94. [4] Voith R. Changing capitalization of CBD-oriented transportation systems: Evidence from Philadelphia, 1970-1988. Journal of Urban Economics, 1993, 33(3): 361-376. [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): 54-66. [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): 13-22. [7] Hess D B, Almeida T M. 2007. Impact of proximity to light rail rapid transit on station-area property values in Buffalo. New York. Urban Studies, 44(5): 1041-1068. [8] Gu Yizhen, Xu Zhiyi. Review on the impact of rail transit on real estate value. Urban Problems, 2007, (12): 45-50. [,.., 2007, (12): 45-50.] [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): 1-26. [10] Alonso W. Location and Land Use: Toward a General Theory of Land Rent. Cambridge: Harvard University Press, 1964. [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): 39-42. [,,. : 13., 2004, (6): 39-42.] [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): 411-414, 453. [,. :., 2008, 28(3): 411-414, 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, 1973. [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): 25-34. [15] Huang H. The land-use impacts of urban rail transit systems. Journal of Planning Literature, 1996, 11(1): 54-66.
2 : 223 [16] Goodman A C, Thibodeau T G. Housing market segmentation. Journal of Housing Economics, 1998, 7: 121-143. [17] Schnare A, Struyk R. Segmentation in urban housing markets. Journal of Urban Economics, 1976, 3: 146-166. [18] Goodman A C. Hedonic prices, price indices, and housing markets. Journal of Urban Economics, 1978, 5: 471-484. [19] Goodman A C. Housing submarkets within urban areas: Definitions and evidence. Journal of Regional Science, 1981, 21: 171-185. [20] Sheppard S C. Hedonic analysis of housing markets//cheshire P C, Mills E S//Handbook of Regional and Urban Economics. Elsevier, 1999: 1595-1635. [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): 36-38. [,.?13., 2004, (11): 36-38.] [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): 32-39. 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 100045, China; 2. Institute of Real Estate Studies, Tsinghua University, Beijing 100084, 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