IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i22p16013-d1281646.html
   My bibliography  Save this article

Influence of Urban Railway Network Centrality on Residential Property Values in Bangkok

Author

Listed:
  • Varameth Vichiensan

    (Department of Civil Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
    Center for Logistics Engineering Technology and Management, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand)

  • Vasinee Wasuntarasook

    (Graduate School, Kasetsart University, Bangkok 10900, Thailand)

  • Titipakorn Prakayaphun

    (Department of Constructional Engineering, Graduate School of Engineering, Chubu University, Kasugai 487-8501, Japan)

  • Masanobu Kii

    (Graduate School of Engineering, Osaka University, Osaka 565-0871, Japan)

  • Yoshitsugu Hayashi

    (Center for Sustainable Development and Global Smart City, Chubu University, Kasugai 487-8501, Japan)

Abstract

In recent decades, Bangkok has experienced substantial investments in its urban railway network, resulting in a profound transformation of the city’s landscape. This study examines the relationship between railway development and property value uplift, particularly focusing on network centrality, which is closely linked to urban structure. Our findings are based on two primary analyses: network centrality and spatial hedonic models. The network centrality analysis reveals that closeness centrality underscores the city’s prevailing monocentric structure, while the betweenness centrality measure envisions the potential emergence of urban subcenters. In our hedonic analysis of condominiums near railway stations, we formulated various regression models with different specifications, incorporating spatial effects and network centrality. With Bangkok’s predominant monocentric structure in mind, we found that the spatial regression model, including a spatial error specification and closeness centrality, outperforms the others. This suggests that the impact of railways on property values extends beyond station proximity and encompasses network centrality, intricately linked with the city’s urban structure. We applied our developed model to estimate the expected increase in property values at major interchange stations with high network centralities. These numerical values indicate a considerable potential for their evolution into urban subcenters. These insights offer valuable policy recommendations for effectively harnessing transit-related premiums and shaping the future development of both the railway system and the city.

Suggested Citation

  • Varameth Vichiensan & Vasinee Wasuntarasook & Titipakorn Prakayaphun & Masanobu Kii & Yoshitsugu Hayashi, 2023. "Influence of Urban Railway Network Centrality on Residential Property Values in Bangkok," Sustainability, MDPI, vol. 15(22), pages 1-25, November.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16013-:d:1281646
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/22/16013/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/22/16013/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Duy Q. Nguyen-Phuoc & William Young & Graham Currie & Chris Gruyter, 2020. "Traffic congestion relief associated with public transport: state-of-the-art," Public Transport, Springer, vol. 12(2), pages 455-481, June.
    2. Morikawa, So & Aoyama, Miwa & Kato, Hironori, 2023. "Development of railway station plazas: Impact on land prices of surrounding areas," Transport Policy, Elsevier, vol. 142(C), pages 1-14.
    3. Daquan Huang & Xiaoqing Yang & Zhen Liu & Xingshuo Zhao & Fanhao Kong, 2018. "The Dynamic Impacts of Employment Subcenters on Residential Land Price in Transitional China: An Examination of the Beijing Metropolitan Area," Sustainability, MDPI, vol. 10(4), pages 1-22, March.
    4. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).
    5. Chakrabarti, Sandip & Kushari, Triparnee & Mazumder, Taraknath, 2022. "Does transportation network centrality determine housing price?," Journal of Transport Geography, Elsevier, vol. 103(C).
    6. Sebastian Brandt & Wolfgang Maennig, 2012. "The impact of rail access on condominium prices in Hamburg," Transportation, Springer, vol. 39(5), pages 997-1017, September.
    7. Cats, Oded, 2017. "Topological evolution of a metropolitan rail transport network: The case of Stockholm," Journal of Transport Geography, Elsevier, vol. 62(C), pages 172-183.
    8. Zhang, Min, 2023. "Value uplift from transit investment-Property value or land value? A case study of the Gold Coast light rail system in Australia," Transport Policy, Elsevier, vol. 132(C), pages 88-98.
    9. Yuchen Zhou & Yuhong Tian & Chi Yung Jim & Xu Liu & Jingya Luan & Mengxuan Yan, 2022. "Effects of Public Transport Accessibility and Property Attributes on Housing Prices in Polycentric Beijing," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    10. Sheng Wei & Shuqing N Teng & Hui-Jia Li & Jiangang Xu & Haitao Ma & Xia-li Luan & Xuejiao Yang & Da Shen & Maosong Liu & Zheng Y X Huang & Chi Xu, 2019. "Hierarchical structure in the world’s largest high-speed rail network," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-11, February.
    11. Pagliara, Francesca & Papa, Enrica, 2011. "Urban rail systems investments: an analysis of the impacts on property values and residents’ location," Journal of Transport Geography, Elsevier, vol. 19(2), pages 200-211.
    12. Li, Zheng, 2018. "The impact of metro accessibility on residential property values: An empirical analysis," Research in Transportation Economics, Elsevier, vol. 70(C), pages 52-56.
    13. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liang, Fachao & Zhu, Runmiao & Lin, Sheng-Hau, 2023. "Exploring spatial relationship between landscape configuration and ecosystem services: A case study of Xiamen–Zhangzhou–Quanzhou in China," Ecological Modelling, Elsevier, vol. 486(C).
    2. Doan, Quang Cuong, 2023. "Determining the optimal land valuation model: A case study of Hanoi, Vietnam," Land Use Policy, Elsevier, vol. 127(C).
    3. Tongning Li & Daozheng Li & Diling Liang & Simin Huang, 2022. "Coupling Coordination Degree of Ecological-Economic and Its Influencing Factors in the Counties of Yangtze River Economic Belt," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    4. Yuchen Zhou & Yuhong Tian & Chi Yung Jim & Xu Liu & Jingya Luan & Mengxuan Yan, 2022. "Effects of Public Transport Accessibility and Property Attributes on Housing Prices in Polycentric Beijing," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    5. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    6. Piotr Rosik & Julia Wójcik, 2022. "Transport Infrastructure and Regional Development: A Survey of Literature on Wider Economic and Spatial Impacts," Sustainability, MDPI, vol. 15(1), pages 1-19, December.
    7. Raul-Tomas Mora-Garcia & Maria-Francisca Cespedes-Lopez & V. Raul Perez-Sanchez & Pablo Marti & Juan-Carlos Perez-Sanchez, 2019. "Determinants of the Price of Housing in the Province of Alicante (Spain): Analysis Using Quantile Regression," Sustainability, MDPI, vol. 11(2), pages 1-33, January.
    8. Vitor Pestana Ostrensky & Alexandre Alves Porsse & Leonardo Matsuno da Frota, 2022. "Public transport and gentrification. Evidence from São Paulo metro new stations," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(6), pages 254-269, December.
    9. Lan, Hao & Moreira, Fernando & Zhao, Sheng, 2023. "Can a house resale restriction policy curb speculation? Evidence from a quasi-natural experiment in China," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 841-859.
    10. Yigong Hu & Binbin Lu & Yong Ge & Guanpeng Dong, 2022. "Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression," Environment and Planning B, , vol. 49(6), pages 1715-1740, July.
    11. Tao Wang & Kai Zhang & Keliang Liu & Keke Ding & Wenwen Qin, 2023. "Spatial Heterogeneity and Scale Effects of Transportation Carbon Emission-Influencing Factors—An Empirical Analysis Based on 286 Cities in China," IJERPH, MDPI, vol. 20(3), pages 1-17, January.
    12. Jean-Philippe Meloche & Vincent Trotignon & François Vaillancourt, 2021. "Densification ou prolongement des réseaux de transport structurants ? Une recension des écrits sur les coûts et les bénéfices attendus," CIRANO Project Reports 2020rp-28, CIRANO.
    13. Junfeng Wang & Shaoyao Zhang & Wei Deng & Qianli Zhou, 2024. "Metropolitan Expansion and Migrant Population: Correlation Patterns and Influencing Factors in Chengdu, China," Land, MDPI, vol. 13(1), pages 1-20, January.
    14. Shaopei Chen & Dachang Zhuang, 2020. "Evolution and Evaluation of the Guangzhou Metro Network Topology Based on an Integration of Complex Network Analysis and GIS," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    15. Xin Lao & Hengyu Gu, 2020. "Unveiling various spatial patterns of determinants of hukou transfer intentions in China: A multi‐scale geographically weighted regression approach," Growth and Change, Wiley Blackwell, vol. 51(4), pages 1860-1876, December.
    16. Zhenbao Wang & Jiarui Song & Yuchen Zhang & Shihao Li & Jianlin Jia & Chengcheng Song, 2022. "Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    17. Pearson, Jonathan & Muldoon-Smith, Kevin & Liu, Henry & Robson, Simon, 2022. "How does the extension of existing transport infrastructure affect land value? A case study of the Tyne and Wear Light Transit Metro system," Land Use Policy, Elsevier, vol. 112(C).
    18. Jiansheng Qu & Lina Liu & Jingjing Zeng & Tek Narayan Maraseni & Zhiqiang Zhang, 2022. "City-Level Determinants of Household CO 2 Emissions per Person: An Empirical Study Based on a Large Survey in China," Land, MDPI, vol. 11(6), pages 1-14, June.
    19. Li Yue & Hongbo Zhao & Xiaoman Xu & Tianshun Gu & Zeting Jia, 2022. "Quantifying the Spatial Fragmentation Pattern and Its Influencing Factors of Urban Land Use: A Case Study of Pingdingshan City, China," Land, MDPI, vol. 11(5), pages 1-15, May.
    20. Cohen, Jeffrey P. & Schaffner, Sandra, 2019. "A new highway in Germany and the impacts on real estate prices," Ruhr Economic Papers 821, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16013-:d:1281646. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.