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Warehouse rental market segmentation using spatial profile regression

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  • Lim, Hyunwoo
  • Yoo, Eun-Hye
  • Park, Minyoung

Abstract

Warehouse rental markets can be segmented into multiple submarkets in which rental storage units share similar structural characteristics in that they are reasonably close substitutes for one another with their geographical proximity. Improved understanding of warehouse rental market segmentation enables warehouse owners to effectively formulate marketing strategies and warehouse renters to reduce search costs. Previous studies either assumed a priori submarkets or used cluster analysis to delineate submarkets based entirely on the similarity of rental prices. However, such approaches have limitations in addressing associations between warehouse rents and their determinants because of the potential spatial autocorrelation and multicollinearity in warehouse rent data sets. In the present study, we address the gap in the literature by introducing a method known as Bayesian spatial profile regression for warehouse rental submarket segmentation. This approach allows us to assess meaningful relationships between warehouse rents and their determinants as a unique profile for each submarket, while accounting for spatial autocorrelation in warehouse rents and multicollinearity among their determinants. In a case study, we demonstrated an application of spatial profile regression to a warehouse rent data set for the Seoul Metropolitan Area (SMA) of South Korea and identified two submarkets: high-rent and low-rent groups. The high-rent group was strongly associated with proximity to the urban center in Seoul and Incheon Port, higher floor area ratio, relatively older building age, higher land price, transportation, and automated warehousing services. The associations for the low-rent group were the opposite of the high-rent group and featured proximity to industrial complexes away from the urban center. The results reflected the highly polarized segmentation of the warehouse rental market in the SMA.

Suggested Citation

  • Lim, Hyunwoo & Yoo, Eun-Hye & Park, Minyoung, 2018. "Warehouse rental market segmentation using spatial profile regression," Journal of Transport Geography, Elsevier, vol. 73(C), pages 64-74.
  • Handle: RePEc:eee:jotrge:v:73:y:2018:i:c:p:64-74
    DOI: 10.1016/j.jtrangeo.2018.10.007
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    File URL: http://www.sciencedirect.com/science/article/pii/S096669231830245X
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    1. Richard J. Buttimer, Jr. & Ronald C. Rutherford & Ron Witten, 1997. "Industrial Warehouse Rent Determinants in the Dallas/Fort Worth Area," Journal of Real Estate Research, American Real Estate Society, vol. 13(1), pages 47-56.
    2. Giuliano, Genevieve & Kang, Sanggyun, 2018. "Spatial dynamics of the logistics industry: Evidence from California," Journal of Transport Geography, Elsevier, vol. 66(C), pages 248-258.
    3. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    4. Clark, David & Pennington-Cross, Anthony, 2016. "Determinants of industrial property rents in the Chicago metropolitan area," Regional Science and Urban Economics, Elsevier, vol. 56(C), pages 34-45.
    5. Sakai, Takanori & Kawamura, Kazuya & Hyodo, Tetsuro, 2015. "Locational dynamics of logistics facilities: Evidence from Tokyo," Journal of Transport Geography, Elsevier, vol. 46(C), pages 10-19.
    6. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    7. Christopher Bitter & Gordon Mulligan & Sandy Dall’erba, 2007. "Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method," Journal of Geographical Systems, Springer, vol. 9(1), pages 7-27, April.
    8. Bourassa, Steven C. & Hamelink, Foort & Hoesli, Martin & MacGregor, Bryan D., 1999. "Defining Housing Submarkets," Journal of Housing Economics, Elsevier, vol. 8(2), pages 160-183, June.
    9. Sungsoon Hwang & Jean-Claude Thill, 2009. "Delineating Urban Housing Submarkets with Fuzzy Clustering," Environment and Planning B, , vol. 36(5), pages 865-882, October.
    10. Goodman, Allen C. & Thibodeau, Thomas G., 1998. "Housing Market Segmentation," Journal of Housing Economics, Elsevier, vol. 7(2), pages 121-143, June.
    11. Cidell, Julie, 2010. "Concentration and decentralization: The new geography of freight distribution in US metropolitan areas," Journal of Transport Geography, Elsevier, vol. 18(3), pages 363-371.
    12. Bowen, John T., 2008. "Moving places: the geography of warehousing in the US," Journal of Transport Geography, Elsevier, vol. 16(6), pages 379-387.
    13. Danlin Yu & Yehua Dennis Wei & Changshan Wu, 2007. "Modeling Spatial Dimensions of Housing Prices in Milwaukee, WI," Environment and Planning B, , vol. 34(6), pages 1085-1102, December.
    14. Dablanc, Laetitia & Ross, Catherine, 2012. "Atlanta: a mega logistics center in the Piedmont Atlantic Megaregion (PAM)," Journal of Transport Geography, Elsevier, vol. 24(C), pages 432-442.
    15. R Sivitanidou, 1996. "Warehouse and Distribution Facilities and Community Attributes: An Empirical Study," Environment and Planning A, , vol. 28(7), pages 1261-1278, July.
    16. Craig A Watkins, 2001. "The Definition and Identification of Housing Submarkets," Environment and Planning A, , vol. 33(12), pages 2235-2253, December.
    17. Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
    18. Sakai, Takanori & Kawamura, Kazuya & Hyodo, Tetsuro, 2017. "Spatial reorganization of urban logistics system and its impacts: Case of Tokyo," Journal of Transport Geography, Elsevier, vol. 60(C), pages 110-118.
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    Cited by:

    1. Hyunwoo Lim & Minyoung Park, 2019. "Modeling the Spatial Dimensions of Warehouse Rent Determinants: A Case Study of Seoul Metropolitan Area, South Korea," Sustainability, MDPI, Open Access Journal, vol. 12(1), pages 1-17, December.

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