Spatial and Temporal Effects of New Urban Rail Transit Lines on
Residential Property Value Uplift

doi:10.16097/j.cnki.1009-6744.2021.04.007

Abstract

Abstract: A quantitative analysis of the impact of urban rail transit on land value along the rail transit line is critical to internalize the external benefits and promote the financial sustainability of urban rail transit in China's new urbanization process. Using the open data resources, this study collected 361053 second-hand real estate transaction samples in Beijing from 2011 to 2016 and investigated the spatial and temporal effects of Beijing urban rail transit on the residential property prices along the rail transit lines through the Hedonic Price Model. The analysis results suggest that the proximity to rail transit station has a significant impact on property prices based on both Global model and Local model. Compared with Multi-variable Linear Regression(MLR), Spatial Lag Model(SLM), Spatial Error Model (SEM) and Spatial Durbin Model(SDM) which are based on global constant parameters regression, Geographically Weighted Regression(GWR) based on local variable parameters regression shows a better fitting effect and can preferably eliminate the spatial effect of residuals and depict the spatial heterogeneity of the relationship between urban rail transit and land value. The residential property prices of more than 80% residential units along the line would benefit from the provision of urban rail transit system. The uplift of the property price shows an obvious spatial heterogeneity and less increases of the prices are observed for the properties located further from the rail transitstations. The impact of rail transit on property prices also has a network effect. The new line will not only change the residential property prices in surrounding areas, but also the properties in other locations in the urban rail transit network. The spatial range of the impact of urban rail transit on residential property value is about 1 km from rail transit stations. Within this range, the land value uplift caused by the new rail transit line is relatively consistent from 2011 to 2016, which is about 3% . The land value uplifts in the area directly affected by the new line are 0.02% to 0.22% higher than the wider area.

Key words: urban traffic, urban rail transit, land value uplift, hedonic price model, geographically weighted regression; spatial and temporal effects

摘要： 定量分析城市轨道交通对沿线土地价值的影响，是通过土地价值捕获将外部效益内部化，解决中国新型城镇化过程中城市轨道交通财务可持续的关键问题。本文在开源数据环境下，获取2011—2016年北京市二手房交易数据，采用特征价格模型(HPM)分析北京城市轨道交通新建线路对沿线住宅价格增值的时空效应。研究表明：与全局常参数的多元线性回归模型(MLR)、空间滞后模型(SLM)、空间误差模型(SEM)和空间杜宾模型(SDM)相比，局部变参数的地理加权回归模型(GWR)拟合效果更优，可以更好地消除残差的空间效应，刻画轨道交通与土地价值关系的空间异质性。城市轨道交通带来的可达性提升对沿线80%以上住宅小区的房价具有显著的正效应，住宅价格增值比率随地铁站距离递远递减，且具有显著的空间异质性。轨道交通对住宅价格的影响在空间上具有网络化效应，新建线路不仅会改变周边房价，对城轨网络其他位置的住宅也具有影响。城市轨道交通对住宅价格的影响范围为1 km，在该范围内，住宅价格受到的增值效应在 2011—2016 年基本稳定，约为 3%；受新线直接影响区域的住宅价格会产生相对更大的提升 (0.02%~0.22%)。

关键词: 城市交通, 城市轨道交通, 土地价值增值, 特征价格模型, 地理加权回归, 时空效应

CLC Number:

U268.6

ZHANG Shu-jing , XU Qi , JIA Shun-ping , LIAO Jing-yi. Spatial and Temporal Effects of New Urban Rail Transit Lines on Residential Property Value Uplift[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(4): 54-62.

张书婧, 许奇, 贾顺平, 廖婧仪. 城市轨道交通新建线路对沿线住宅价格增值的时空效应[J]. 交通运输系统工程与信息, 2021, 21(4): 54-62.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2021.04.007

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References

[1] YU H, ZHANG M, PANG H. Evaluation of transit proximity effects on residential land prices: An empirical study in Austin, Texas[J]. Transportation Planning & Technology, 2017, 40(8): 1-14.

[2] DZIAUDDIN M F, POWE N, ALVANIDES S. Estimating the effects of light rail transit (LRT) system on residential property values using geographically weighted regression (GWR)[J]. Applied Spatial Analysis and Policy, 2015, 8 (1): 1-25.

[3] 裴韬, 刘亚溪, 郭思慧, 等. 地理大数据挖掘的本质[J]. 地理学报，2019, 74(3): 586- 598. [PEI T, LIU Y X, GUO S H, et al. Principle of big geodata mining[J]. Acta Geographica Sinica, 2019, 74 (3): 586-598.]

[4] 朱宇婷, 刘莹, 许奇, 等. 交通可达性与城市经济活动的空间特征分析: 以北京市为例[J]. 交通运输系统工程与信息, 2020, 20(5): 226-233. [ZHU Y T, LIU Y, XU Q, et al. Spatial characteristics analysis of traffic accessibility and city economic activity: A case study of Beijing[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(5): 226-233.]

[5] SHARMA R, NEWMAN P. Can land value capture make PPP's competitive in fares? A Mumbai case study[J]. Transport Policy, 2018, 64: 123-131.

[6] SCOTT N LIESKE, RYAN VAN DEN NOUWELANT, JUNG HOON HAN. A novel hedonic price modelling approach for estimating the impact of transportation infrastructure on property prices[J]. Urban Studies, 2019, DOI: 10.1177/0042098019879382.

[7] DEVAUX N, DUBE J, APPARICIO P. Anticipation and post- construction impact of a metro extension on residential values: The case of Laval (Canada), 1995- 2013[J]. Journal of Transport Geography, 2017, 62: 8-19.

[8] DZIAUDDIN M F. Estimating land value uplift around light rail transit stations in Greater Kuala Lumpur: An empirical study based on geographically weighted regression (GWR) [J]. Research in Transportation Economics, 2019, 74: 10-20.

[9] HE S Y. Regional impact of rail network accessibility on residential property price: Modelling spatial heterogeneous capitalisation effects in Hong Kong[J]. Transportation Research Part A Policy and Practice, 2020, 135: 244-263.

[10] 叶霞飞, 蔡蔚. 城市轨道交通开发利益的计算方法[J]. 同济大学学报(自然科学版), 2002(4): 431-436. [YE X F, CAI W. Calculating method of urban rail transit development benefits[J]. Journal of Tongji University (Natural Science Edition), 2002(4): 431-436.]

[11] 林雄斌, 刘健, 田宗星, 等. 轨道交通引导用地密度与地价的时空效应: 以深圳市为例[J]. 经济地理, 2016,36(9): 27- 34. [LIN X B, LIU J, TIAN Z X, et al. Temporal and spatial effects of land density and land price for rail transit guidance: Shenzhen as an example [J]. Economic Geography, 2016, 36(9): 27-34.]

[12] 王亚洁. 国外城市轨道交通与站域土地利用互动研究进展[J]. 国际城市规划, 2018, 33(1): 111-118. [WANG Y J. International studies on the interaction between urban rail transit and land use of station areas[J]. Urban Planning International, 2018, 33(1): 111-118.]

[13] 王迎, 张安录. 城市轨道交通土地价值增值时空效应及政府投资优先序: 基于价值捕获视角[J]. 华中农业大学学报 ( 社会科学版), 2021(2): 165- 173, 184. [WANG Y, ZHANG A L. Temporal and spatial effects of land value increment of urban rail transit and investment priority: From the perspective of government land value capture[J]. Journal of Huazhong Agriculture University (Social Sciences Edition), 2021(2): 165-173, 184.]

[14] 冯友建, 陈天一. 基于SEM模型的轨道交通对住宅价格的空间效应: 以杭州市为例[J]. 浙江大学学报(理学版), 2020, 47(1): 115-122. [FENG Y J, CHEN T Y. The spatial effect of rail transit on residential prices based on SEM model: A case study of Hangzhou[J]. Journal of Zhejiang University(Science Edition), 2020, 47(1): 115- 122.]

[15] DU H, MULLEY C. Relationship between transport accessibility and land value: Local model approach with geographically weighted regression[J]. Transportation Research Record Journal of the Transportation Research Board, 2006, 1977(1): 197-205.

[16] LIANG M, RUNING Y, HELENA T. Capitalization effects of rail transit and bus rapid transit on residential property values in a booming economy: Evidence from Beijing[J]. Transportation Research Record, 2014, 2451 (1): 139-148.

[17] JIN K, MING Z. Determining transit's impact on seoul commercial land values: An application of spatial econometrics[J]. International Real Estate Review, 2005, 8(1): 1-26.

Spatial and Temporal Effects of New Urban Rail Transit Lines on Residential Property Value Uplift

城市轨道交通新建线路对沿线住宅价格增值的时空效应

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