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Data-Driven Analytics for the UK Logistics Market: A Spatial and Predictive Approach

Author

Listed:
  • Karen Martinus
  • Jane Zheng

Abstract

Following a strong period of logistics rental growth in the UK, we are entering a period of more moderate rent growth. It is likely that certain locations and asset types will outperform but in this new environment, gauging future rental growth prospects is increasingly important to investors, developers, and managers. This research aims to provide a granular understanding of the UK logistics market by leveraging geospatial analysis, machine learning, and feature engineering techniques. By integrating high-resolution demographic projections, attributes of existing industrial properties, and rent trends, we develop top-down market analyses and predictive models to estimate rental prices and rent growth potential for individual properties and locations. In doing so, we believe our research has the potential to aid in investment decision making. Additionally, the findings will contribute to a more refined understanding of spatial dependencies and economic drivers within the logistics sector, offering a scalable framework for market forecasting and investment strategy optimization.

Suggested Citation

  • Karen Martinus & Jane Zheng, 2025. "Data-Driven Analytics for the UK Logistics Market: A Spatial and Predictive Approach," ERES eres2025_259, European Real Estate Society (ERES).
  • Handle: RePEc:arz:wpaper:eres2025_259
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    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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