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

Karen Martinus and Jane Zheng

ERES from European Real Estate Society (ERES)

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.

Keywords: Forecasting Title change requested on May 15 by email to help; Geospatial; Logistics; Machine Learning (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2025-01-01
New Economics Papers: this item is included in nep-big and nep-for
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