The Key Points

  1. Industrial stock built before the digital era is not ideally fitted or located to cater for new e-commerce-driven demand.
  2. The top 30 locations with the most online shoppers (one of the major variables in our Industrial Warehousing Model), are all located in London. This is escalating demand for space in what is already an undersupplied market.
  3. Floorspace in popular urban locations competes for land with other sectors, including home builders. Lack of stock will continue to have a material impact on location choices and pricing.
  4. Demand for Greater London and South East locations is further fuelled by faster internet connections, effective power supply and a skilled labour force necessary to accommodate trends in automation.
  5. The level of unemployment plays an important role. High scores achieved by Leicester and Outer London centres such as Croydon, Brent and Walthamstow are hugely driven by relatively high unemployment and a large and specialised labour force generally.
  6. Locations in the West and East Midlands score highly due to proximity to large distribution hotspots. However, re-locating or opening of a new distribution centre closer to London and the South East may alter current dynamics.
  7. Besides London, other strong locations are the areas around Northampton, Leicester, Ashfield, Staffordshire and Coventry, driven by a combination of a large and specialised working population, proximity to distribution centres and existing supply.
  8. There is significant variation within regions and cities. Stakeholders should not just focus on general regional statistics, but rather look at specific local circumstances.
  9. What makes a favourable location will always depend on the user/stakeholder. Levels of technological sophistication may demand less labour but more power/faster internet. Our Model can be specified to factor in these different needs.
  10. Changing elements in terms of available floorspace, infrastructure, automation and delivery methods can hugely impact the outcomes of our Model. Different scenarios can be included to predict how this may change optimum location choices.