IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v197y2025ics1366554525001334.html
   My bibliography  Save this article

Identifying the critical features influencing warehouse rental prices and their nonlinear associations: A spatial machine learning approach

Author

Listed:
  • He, Nannan
  • Liu, Sijing
  • Cao, Jason
  • Li, Guoqi
  • Jian, Ming

Abstract

Warehouses play a crucial role in freight transportation, and their pricing strategies affect warehouse location choices and associated environmental impacts. Although most firms rent storage spaces, limited studies have examined warehouse rental prices (WRP). Furthermore, most studies assume a pre-defined relationship between WRP and its correlates. This study applies spatial machine learning models to warehouse rental data in Shanghai to examine their nonlinear associations. The results show that the primary factors influencing WRP include spatial dependence among warehouses, location and neighborhood attributes, and the floor level of warehouse spaces, whereas lease and service-related factors contribute minimally. Moreover, spatial dependence leads to segmented markets, with high-rent warehouses clustering in the central urban area and around logistics parks and transportation terminals outside the central area. Additionally, most primary correlates exhibit irregular nonlinear relationships with WRP, which shed light on warehouse pricing mechanisms and provide guidance for location choices.

Suggested Citation

  • He, Nannan & Liu, Sijing & Cao, Jason & Li, Guoqi & Jian, Ming, 2025. "Identifying the critical features influencing warehouse rental prices and their nonlinear associations: A spatial machine learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:transe:v:197:y:2025:i:c:s1366554525001334
    DOI: 10.1016/j.tre.2025.104092
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554525001334
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2025.104092?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yu, Zidong & Wang, Haotian & Liu, Xintao, 2024. "Mobility heterogeneity of urban freight areas: Geospatial evidence from shared logistics dynamics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).
    2. Muchen Zheng & Wenli Huang & Gang Xu & Xi Li & Limin Jiao, 2023. "Spatial gradients of urban land density and nighttime light intensity in 30 global megacities," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    3. Ziliak, Stephen T. & McCloskey, Deirdre N., 2004. "Size matters: the standard error of regressions in the American Economic Review," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 33(5), pages 527-546, November.
    4. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Chen, Yan & Song, Dongdong & Zhi, Danyue & Wang, Yiyun & Gao, Ziyou, 2023. "Estimating intercity heavy truck mobility flows using the deep gravity framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    5. Jin, Tanhua & Cheng, Long & Liu, Zhicheng & Cao, Jun & Huang, Haosheng & Witlox, Frank, 2022. "Nonlinear public transit accessibility effects on housing prices: Heterogeneity across price segments," Transport Policy, Elsevier, vol. 117(C), pages 48-59.
    6. Hu, Songhua & Xiong, Chenfeng & Chen, Peng & Schonfeld, Paul, 2023. "Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    7. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.
    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. Jin, Tanhua & Cheng, Long & Wang, Kailai & Cao, Jun & Huang, Haosheng & Witlox, Frank, 2022. "Examining equity in accessibility to multi-tier healthcare services across different income households using estimated travel time," Transport Policy, Elsevier, vol. 121(C), pages 1-13.
    2. Rachel G. Childers, 2011. "Being One'S Own Boss: How Does Risk Fit In?," The American Economist, Sage Publications, vol. 56(1), pages 48-58, May.
    3. Ricardo Barradas & Ines Tomas, 2023. "Household indebtedness in the European Union countries: Going beyond the mainstream interpretation," PSL Quarterly Review, Economia civile, vol. 76(304), pages 21-49.
    4. McShane, Michael K. & Cox, Larry A. & Butler, Richard J., 2010. "Regulatory competition and forbearance: Evidence from the life insurance industry," Journal of Banking & Finance, Elsevier, vol. 34(3), pages 522-532, March.
    5. Li, Qianwen & Qian, Tingyu & Wang, Hui & Sun, Chuanwang, 2025. "Exploring the value of green: The impact factors on China's second-hand green housing prices based on geographically weighted Lasso regressions," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    6. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    7. Stanley, T.D. & Doucouliagos, Chris & Jarrell, Stephen B., 2008. "Meta-regression analysis as the socio-economics of economics research," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 37(1), pages 276-292, February.
    8. Anupama Sen and Tooraj Jamasb, 2012. "Diversity in Unity: An Empirical Analysis of Electricity Deregulation in Indian States," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    9. Evangelista, Rui & Ramalho, Esmeralda A. & Andrade e Silva, João, 2020. "On the use of hedonic regression models to measure the effect of energy efficiency on residential property transaction prices: Evidence for Portugal and selected data issues," Energy Economics, Elsevier, vol. 86(C).
    10. Peter J. Veazie, 2015. "Understanding Statistical Testing," SAGE Open, , vol. 5(1), pages 21582440145, January.
    11. Sefa Awaworyi Churchill & Mehmet Ugur & Siew Ling Yew, 2017. "Does Government Size Affect Per-Capita Income Growth? A Hierarchical Meta-Regression Analysis," The Economic Record, The Economic Society of Australia, vol. 93(300), pages 142-171, March.
    12. Ron Johnston, 2005. "Editorial," Environment and Planning A, , vol. 37(1), pages 2-8, January.
    13. Thomas Mayer, 2012. "Ziliak and McCloskey's Criticisms of Significance Tests: An Assessment," Econ Journal Watch, Econ Journal Watch, vol. 9(3), pages 256-297, September.
    14. Yuhan Zhang & Youqi Wang & Yiru Bai & Ruiyuan Zhang & Xu Liu & Xian Ma, 2023. "Prediction of Spatial Distribution of Soil Organic Carbon in Helan Farmland Based on Different Prediction Models," Land, MDPI, vol. 12(11), pages 1-15, October.
    15. Jan-Peter Kucklick & Jennifer Priefer & Daniel Beverungen & Oliver Müller, 2023. "Elucidating the Predictive Power of Search and Experience Qualities for Pricing of Complex Goods – A Machine Learning-based Study on Real Estate Appraisal," Working Papers Dissertations 112, Paderborn University, Faculty of Business Administration and Economics.
    16. Thomas R. Dyckman & Stephen A. Zeff, 2019. "Important Issues in Statistical Testing and Recommended Improvements in Accounting Research," Econometrics, MDPI, vol. 7(2), pages 1-11, May.
    17. Xinyu (Jason) Cao, 2009. "Disentangling the influence of neighborhood type and self-selection on driving behavior: an application of sample selection model," Transportation, Springer, vol. 36(2), pages 207-222, March.
    18. Oyvat, Cem & Tekgüç, Hasan, 2019. "Ethnic fractionalization, conflict and educational development in Turkey," International Journal of Educational Development, Elsevier, vol. 67(C), pages 41-52.
    19. Thomas Mayer, 2006. "The Empirical Significance of Econometric Models," Working Papers 620, University of California, Davis, Department of Economics.
    20. Stephen T. Ziliak & Deirdre N. McCloskey, 2013. "We Agree That Statistical Significance Proves Essentially Nothing: A Rejoinder to Thomas Mayer," Econ Journal Watch, Econ Journal Watch, vol. 10(1), pages 97-107, January.

    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:eee:transe:v:197:y:2025:i:c:s1366554525001334. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.