The spatial pattern of industrial rents and the role of distance

Neil Dunse, Colin Jones, Jim Brown, William D. Fraser

    Research output: Contribution to journalArticle

    14 Citations (Scopus)

    Abstract

    Purpose - The objective of this paper is to re-appraise intra-urban rent models in the context of a multi-nodal landscape. Primarily, the study focuses on the early work of Alonso and, more recently, Di Pasquale and Wheaton. Although the latter use a more sophisticated approach, both models lead to similar outputs, notably a declining rent gradient from the central business district (CBD). However, throughout the twentieth century there has been a considerable process of urban industrial change. Di Pasquale and Wheaton recognise this and argue that this has led to an almost flat industrial rent gradient. Design/methodology/approach - To assess the current impact on industrial rents a hedonic rent regression model is applied which enables us to standardise for property characteristics. Findings - The results support the hypothesis that the rent gradient from the CBD for a large city is still downward-sloping, albeit very shallow. More interesting is the significance of proximity to motorway junctions. The analysis supports the hypothesis of a multi-nodal rent surface. Proximity to a motorway junction is the most important locational variable with a much steeper and negative gradient than that to the CBD, albeit over a shorter distance. Originality/value - These results imply that the draw of the CBD in terms of agglomeration economies and its accessibility to labour for a city the size of Glasgow still remains, but its attractions are much denuded with the development of a national motorway network. © Emerald Group Publishing Limited.

    Original languageEnglish
    Pages (from-to)329-341
    Number of pages13
    JournalJournal of Property Investment and Finance
    Volume23
    Issue number4
    DOIs
    Publication statusPublished - 2005

    Keywords

    • Industrial property
    • Property finance
    • Socio-economic regions
    • Transportation

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