SEGRO is a leading UK Real Estate Investment Trust (REIT) and one of Europe’s largest owners, asset managers and developers of modern warehousing and industrial property. With 1,400 customers spanning industries such as logistics, retail and distribution, the company develops and invests in industrial property across the whole of the UK and Continental Europe.

Since implementing the TravelTime API, SEGRO is able to run automated location analysis for millions of locations at a time to find the right locations for its customers’ different use cases.

The Challenge

Finding new location opportunities with analytics and data analysis

SEGRO’s assets are strategically located across Europe, particularly in and around major cities and at key transportation hubs.

SEGRO location assets across Europe

“Where SEGRO gets its value – the things that make us competitive – is that we put our buildings where people want them. And we build buildings that people want to work in,” explains Tim Hirst, Data Scientist at SEGRO.

A key part of achieving this is through location scoring. This involves identifying the most suitable locations for industrial property, such as warehouses or distribution centres, and scoring these against a set of criteria to evaluate their potential.

However, when it comes to industrial real estate, this type of location analysis is often still a largely manual or subjective process.

“The technology hasn’t quite caught up with the type of questions we’re asking,” says Tim. “Whilst there are millions of residential transactions each year, there aren’t millions of industrial transactions each year. This makes it a little bit more difficult for the data to be applied in the same way. So, we have to take a much more nuanced approach.”

For this reason, the SEGRO team wanted to bring a more automated and data-driven approach to their location analysis. This would add to their arsenal of evidence and help them spot new opportunities that may otherwise be overlooked.

“The idea of applying advanced analytics and data analysis to these location-based decisions was a strategic choice,” says Tim. “Our goal is to find opportunities that may be under-recognised and consider whether we wish to locate assets there.”

The Solution

Taking a time-based and data-driven approach to location analysis

SEGRO chose to use the TravelTime API to support its location analysis and data-driven strategy. The TravelTime API allows you to analyse location data by travel time, including creating catchment areas based on journey time and different transport modes, to assess how far you can travel in a given time. This gives a better understanding of how accessible a location truly is.

When it comes to location scoring, the data team created an internal tool with which they can run location analyses for the whole of Europe. The tool uses data provided by the TravelTime API, with which they can run very large bulk calculations automatically, using algorithms to piece the data together. On this basis, they can then create scores for different use cases.

SEGRO location analysis

The team at SEGRO use location scoring to determine the potential of future assets

“We’re taking the travel time data in bulk and producing analysis across the whole of Europe to get a much broader view,” explains Tim. “We gather and refresh travel time data on a rolling basis and incorporate that into our wider analysis of locations in Europe and where the hotspots are for any given use case.”

Understanding location accessibility through the lens of time instead of distance also offers much greater accuracy – for example, when considering how easy it is to access a warehouse or central distribution hub.

As Tim explains, “We’re finding that it’s actually time that matters, not distance. The reality is that distance doesn’t tell us how fast you can get somewhere. This is particularly true for our urban customers who might, for example, want to know how quickly they can deliver food or groceries to their customers. So, when it comes to scoring locations across Europe, we want to take a time-based approach instead of a distance-based approach.

“We’ve found that when you look at, for example, the furthest distance you can get in an hour, this correlates quite well with places that you would intuitively say are easy to access, so we now have a more objective measure for that element of what we’re looking for,” says Tim.

“If we’re looking for opportunities to build large distribution centres that are central hubs, then we’re more interested in understanding how far you can get within, let’s say, 4 or 8 hours rather than in 20 minutes. But if we’re looking to support a food or grocery delivery company, it’s more about how many people can they reach in 20 minutes.”

Layering travel time data with additional datasets

Depending on the use case, the team can use the TravelTime API to analyse different types of catchment areas and get even more insights by layering travel time data with other datasets.

“It’s been great to analyse the data we get from TravelTime alongside other datasets like census or population data, to tell us how many people we can reach from certain locations. That’s led to some very interesting insights in the locations and cities we’ve been exploring.”

SEGRO location analysis
With the TravelTime API, the SEGRO team can layer travel time data with other useful datasets

The Results

Automated location analysis for millions of locations across Europe

Since implementing the TravelTime API for their location analysis, the SEGRO team has seen impressive results.

The team is now able to automatically run analysis for vast amounts of data at a time and get more accurate results.

“With the TravelTime API, we’re able to perform location analysis for the whole of Europe,” says Tim. “We’re talking about 7 or 8 million locations that we can run analysis for automatically, to greater accuracy. This has made a huge difference.”

Travel time data has also been one of the factors considered in a number of real estate investment decisions.

Finally, the TravelTime API has been straightforward to implement. “We’re impressed with what the API does and the philosophy of time instead of distance works for us,” says Tim. “But then it’s built in a very modern way: the documentation and support from the team are great, and I’ve had no problems in understanding how to use the API.”

Create travel time polygons and matrices with the TravelTime API