Office for National Statistics and the Ordnance Survey launch new study of Britain’s high streets.

This month saw the release of a new study designed to help people better understand Britain’s high streets, both at a national and a local level.  As we know, the high street is currently undergoing a period of rapid change, which includes a decline in retail sales and footfall, a loss of retail jobs and many other worrying tends. The Office for National Statistics (ONS), the Ordnance Survey (OS) and the Ministry of Housing, Communities and Local Government (MHCLG) are working together to make data about high streets in the UK more readily available and more accessible to local councils, planners, retailers, and other interest groups, to facilitate better decision-making.  

The new study takes a data-driven approach to defining a high street. It labels any cluster of 15 buildings containing retail outlets (including shops and places to eat and drink), along a single road, with maximum spacing between addresses of 150 metres, as a ‘high street’. This approach identifies nearly 7,000 high streets in Great Britain. Using the concentration of retail as a starting point, rather than the name or even location of the street, the method acknowledges that places can change over time. There are so-called high streets, especially in villages and other rural locations, that no longer have any shops, therefore these streets do not appear in the analysis. Nevertheless, High Street is still the most common shopping street name, with Market Place and Market Street ranking second and third, demonstrating the long legacy that retail concentrations have in our towns and cities.

Unfortunately, for people wishing to understand larger retail agglomerations (e.g. town centres) the key weaknesses of the ONS/OS approach is that it misses out retail that may link neighbouring high streets. Therefore, it is not possible to use the tool to analyse whole town centres, just the individual shopping streets within them. Nevertheless, as a method for understanding trends over time it is still very powerful, and the work is still at a very early stage.

A key component of ONS/OS collaboration is an interactive map making it possible to identify and visualize high streets at a local level, and look up relevant data about overall the mix of retail and residential property, the length of the high street, number of buildings etc. at the exact point of interest. The researchers have published some initial findings from using the tool to interrogate high streets, supplemented by other data sets from the ONS, OS, Land Registry and other sources.

Around a third of addresses on the high street in Great Britain belong to retail, whilst more than half of the addresses on British high streets are residential. In London this figure rises to 64%.  In most regions, around 10% of addresses are offices and around 2% to 3% are leisure or community facilities. The research estimates that 13% of all businesses in the country are on a high street. Figure 1 shows the mix of addresses by regions.

 1Figure One OS Story

The study confirms the general view that the number of retail businesses on the high street has been falling.  At a national level, retail has fallen as a proportion of all high street businesses from 29% to 25% between 2012 and 2017. However, as retail also includes bars, cafes and restaurants the figures suggest that food and drink establishments are not replacing all the shops that are being lost, in the way some commentators may have thought.

This is where the tools and datasets that underpin this study can really help the high street as they can help planners, place managers and place leaders monitor the impact of policies, trends, and, ultimately, ensure catchments are better served through improved local decision making. For example, the tool can be used to monitor the impact of change of use decisions. Permitted development rights (PDRs) mean that high street outlets can be converted into offices, or homes. The concern is that PDRs are reducing the available retail and commercial space, damaging a centre’s vitality and viability. This can then have an impact on the amount of business rates generated in an area, which, again, can further reduce the vitality and viability of the location, if there is less public money to spend on the public realm, cleaning and other important services.  This tool will make it easier to track the impact of PDRs.

We also see the potential to use the mapping tool to help estimate how many people live on or near high streets, and, by comparing this with the offer, identify whether communities are underserved. Our research with Springboard has demonstrated that footfall data helps us understand how a centre is used. It is a good indicator of how attractive a centre is so combining both the supply-side (where are the shops and services?) and demand (where are the people?) is very useful. We are currently working on this type of research project with Manchester City Council and Stockport Council, comparing the offer, the catchment and the attractiveness of centres (how busy are they?). These smaller centres serve hundreds of thousands of residents, but are often overlooked in retail and regeneration plans, leaving many communites underserved in terms of access to everyday retail and services.

Finally, we know that local place leaders need access to better data to improve decision making, and, sometimes, to challenge decisions made or views held by others. Mapping tools like the one developed by OS, and datasets from ONS and MHCLG, bring valuable information to communities and local councils to help them plan for their future high street. Information concerning commercial and residential aspects of the high street, as well as data on population change, house prices and transaction, and business and employment levels, whilst in the public domain, was difficult to get hold of. This made it time-consuming or expensive for local stakeholders to understand their changing high streets. The new high streets article, mapping tool, experimental data sets and subsequent studies will help adjust the balance of power between, for example, developers and communities.

Written by Professor Cathy Parker, Manchester Metropolitan University and Dr Christine Mumford, Cardiff University

For more information, go to the release: