IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v14y2025i10p2004-d1765768.html
   My bibliography  Save this article

Multiscale Effects of Land Infrastructure Planning on Housing Prices in Bangkok, Thailand

Author

Listed:
  • Shichao Lu

    (AI for Digital Earth Group, School of Mathematics, Shandong University, Jinan 250100, China)

  • Zhihua Zhang

    (AI for Digital Earth Group, School of Mathematics, Shandong University, Jinan 250100, China)

  • M. James C. Crabbe

    (Wolfson College, University of Oxford, Oxford OX2 6UD, UK)

  • Prin Suntichaikul

    (AI for Digital Earth Group, School of Mathematics, Shandong University, Jinan 250100, China)

Abstract

Bangkok is the largest city in Thailand and the second largest city in Southeast Asia. Due to the rapid urbanization and upgrading of economic structures, the real estate market in Bangkok is not only constrained by domestic factors but also fluctuates with international economic cycles. Bangkok’s long history, diverse culture, developed economy, and incomplete land infrastructure make the formation of housing prices particularly complex. In this study, we collected 13,175 residence transaction data from 2076 different neighborhoods in Bangkok and explored multiscale effects of various land infrastructure factors on housing prices in Bangkok at the neighborhood level. Our analysis not only supports land planning departments of Bangkok to make more reasonable facility planning but also provides new insights into driving mechanisms of housing prices in other cities of Thailand and ASEAN countries.

Suggested Citation

  • Shichao Lu & Zhihua Zhang & M. James C. Crabbe & Prin Suntichaikul, 2025. "Multiscale Effects of Land Infrastructure Planning on Housing Prices in Bangkok, Thailand," Land, MDPI, vol. 14(10), pages 1-20, October.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:10:p:2004-:d:1765768
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/14/10/2004/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/14/10/2004/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    2. Hans R.A. Koster & Jan Rouwendal, 2012. "The Impact Of Mixed Land Use On Residential Property Values," Journal of Regional Science, Wiley Blackwell, vol. 52(5), pages 733-761, December.
    3. Kyeongsu Kim & Michael L. Lahr, 2014. "The impact of Hudson-Bergen Light Rail on residential property appreciation," Papers in Regional Science, Wiley Blackwell, vol. 93, pages 79-97, November.
    4. Wei Kang & Taylor M. Oshan, 2025. "Scale and correlation in multiscale geographically weighted regression (MGWR)," Journal of Geographical Systems, Springer, vol. 27(3), pages 399-424, July.
    5. Gatzlaff, Dean H. & Haurin, Donald R., 1998. "Sample Selection and Biases in Local House Value Indices," Journal of Urban Economics, Elsevier, vol. 43(2), pages 199-222, March.
    6. William M. Bowen & Brian A. Mikelbank & Dean M. Prestegaard, 2001. "Theoretical and Empirical Considerations Regarding Space in Hedonic Housing Price Model Applications," Growth and Change, Wiley Blackwell, vol. 32(4), pages 466-490.
    7. Basu, Sabyasachi & Thibodeau, Thomas G, 1998. "Analysis of Spatial Autocorrelation in House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 61-85, July.
    8. Steven Bourassa & Eva Cantoni & Martin Hoesli, 2007. "Spatial Dependence, Housing Submarkets, and House Price Prediction," The Journal of Real Estate Finance and Economics, Springer, vol. 35(2), pages 143-160, August.
    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. Bing Zhu & Roland Füss & Nico Rottke, 2011. "The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 42(4), pages 542-565, May.
    2. Zhijiao Qin & Yan Yu & Dianfeng Liu, 2019. "The Effect of HOPSCA on Residential Property Values: Exploratory Findings from Wuhan, China," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
    3. Monica Palma & Claudia Cappello & Sandra De Iaco & Daniela Pellegrino, 2019. "The residential real estate market in Italy: a spatio-temporal analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2451-2472, September.
    4. Bello Musa Zango & Sanni Mohammed Lekan & Mohammed Jibrin Katun, 2020. "Conventional Methods in Housing Market Analysis: A Review of Literature," Baltic Journal of Real Estate Economics and Construction Management, Sciendo, vol. 8(1), pages 227-241, January.
    5. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    6. Liang Peng, 2012. "Repeat Sales Regression on Heterogeneous Properties," The Journal of Real Estate Finance and Economics, Springer, vol. 45(3), pages 804-827, October.
    7. Füss, Roland & Koller, Jan A., 2016. "The role of spatial and temporal structure for residential rent predictions," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1352-1368.
    8. Koen Koning & Tatiana Filatova & Okmyung Bin, 2018. "Improved Methods for Predicting Property Prices in Hazard Prone Dynamic Markets," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 69(2), pages 247-263, February.
    9. Alice Barreca & Rocco Curto & Diana Rolando, 2018. "Housing Vulnerability and Property Prices: Spatial Analyses in the Turin Real Estate Market," Sustainability, MDPI, vol. 10(9), pages 1-20, August.
    10. Antonio Páez & Fei Long & Steven Farber, 2008. "Moving Window Approaches for Hedonic Price Estimation: An Empirical Comparison of Modelling Techniques," Urban Studies, Urban Studies Journal Limited, vol. 45(8), pages 1565-1581, July.
    11. Liu, Sezhu & Hite, Diane, 2013. "Measuring the Effect of Green Space on Property Value: An Application of the Hedonic Spatial Quantile Regression," 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida 143045, Southern Agricultural Economics Association.
    12. Jeffrey P. Cohen & Cletus C. Coughlin, 2008. "Spatial Hedonic Models Of Airport Noise, Proximity, And Housing Prices," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 859-878, December.
    13. Joanna Jaroszewicz & Hubert Horynek, 2024. "Aggregated Housing Price Predictions with No Information About Structural Attributes—Hedonic Models: Linear Regression and a Machine Learning Approach," Land, MDPI, vol. 13(11), pages 1-29, November.
    14. Fuad, Syed & Farmer, Michael C., 2024. "Racial bias in property taxation in Atlanta: The difficulty of reversing a legacy of discrimination," 2024 Annual Meeting, July 28-30, New Orleans, LA 343965, Agricultural and Applied Economics Association.
    15. Thom Malone & Christian L. Redfearn, 2022. "To measure globally, aggregate locally: Urban land, submarkets, and biased estimates of the housing stock," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(3), pages 656-671, September.
    16. Kobylińska Katarzyna, 2021. "The Application of Spatial Autoregressive Models for Analyzing the Influence of Spatial Factors on Real Estate Prices and Values," Real Estate Management and Valuation, Sciendo, vol. 29(4), pages 23-35, December.
    17. Xiaolong Liu, 2013. "Spatial and Temporal Dependence in House Price Prediction," The Journal of Real Estate Finance and Economics, Springer, vol. 47(2), pages 341-369, August.
    18. Alice Barreca & Elena Fregonara & Diana Rolando, 2021. "EPC Labels and Building Features: Spatial Implications over Housing Prices," Sustainability, MDPI, vol. 13(5), pages 1-21, March.
    19. Todd H. Kuethe, 2012. "Spatial Fragmentation and the Value of Residential Housing," Land Economics, University of Wisconsin Press, vol. 88(1), pages 16-27.
    20. Devaux, Nicolas & Dubé, Jean & Apparicio, Philippe, 2017. "Anticipation and post-construction impact of a metro extension on residential values: The case of Laval (Canada), 1995–2013," Journal of Transport Geography, Elsevier, vol. 62(C), pages 8-19.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jlands:v:14:y:2025:i:10:p:2004-:d:1765768. 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.