A new data-driven approach to tackling rough sleeping will give fresh insights into the nature of rough sleeping in each area of England and help local leaders drive progress towards ending it. 

The approach is grounded in a shared vision of a future where, across England, rough sleeping is prevented wherever possible or is a rare, brief and non-recurring experience. It has been collaboratively created by a group of ‘Early Adopter’ areas from across England with the Centre for Homelessness Impact (CHI) and the Department for Levelling Up, Housing and Communities (DLUHC). 

The overall aim for this work, set out in the government’s recent Ending Rough Sleeping For Good Strategy, is to introduce a new national data-led approach to measure progress towards ending rough sleeping, so that every area understands what is needed, track the progress they are making and be held accountable locally.

Until now data to track rough sleeping in England has come from the annual rough sleeping ‘snapshot’, which uses an annual street count to estimate the number of people sleeping out on a single night each autumn.

While the snapshot is helpful in pointing out trends over time, we know that rough sleeping is complex - some people spend a single night out, others are experiencing harmful long-term rough sleeping and others have returned to rough sleeping after a period away.

The single night count cannot adequately capture this complexity, and means we lack the insight needed to systematically ensure that rough sleeping is prevented wherever possible or, if it does occur, ensure that is rare, brief and non-recurring.

The new set of indicators at the heart of this approach will enable us to better capture both the true level of need and the complexity of the picture in each area. Going beyond a simple snapshot, these indicators will enable local leaders, front-line teams and policy-makers to better understand the extent and nature of rough sleeping in their areas, design services to support people based on reliable data and make informed decisions about how to end rough sleeping for good.

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These are:

Prevented

P1 Number of new people sleeping rough - This captures the number of new people seen sleeping rough in the local area. Effective prevention should see this indicator decline over time.

P2 People seen rough sleeping after being discharged from institutions - This tracks the number of people sleeping rough who were discharged from an institution such as prison or the care system. Effective prevention that ensures that individuals staying in state institutions are supported into accommodation should see this indicator reduce over time.

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Rare

R1 - Number of people sleeping out - This monitors the number of people seen rough sleeping in the area. Areas should be working towards reducing this number to zero, or as close to zero as possible.

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Brief

B1 - Number of people experiencing long-term rough sleeping - This indicator reports the number of people experiencing multiple and/or sustained episodes of rough sleeping.  Areas should seek to reduce this indicator given the high levels of harm associated with long-term street homelessness.

Non-recurring

NR1 - Number of people returning to rough sleeping - This indicator reports the number of people who were seen sleeping rough previously and have returned to the streets after a period of time. This should allow areas to understand how many people are experiencing recurring episodes of rough sleeping. This number should reduce over time if prevention and off-the-streets pathways work effectively.

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The group of ‘Early Adopter’ areas who are piloting this approach are London Councils and the Greater London Authority; Greater Manchester Combined Authority; West Midlands Combined Authority; Newcastle City Council; and Bournemouth, Christchurch and Poole Council. 

The group has worked since March 2021 to co-design the framework and road-test the approach. Local authorities in these areas began collecting experimental data against this new framework from November 2022. The findings of this first phase with the Early Adopters have shown that the approach could transform our understanding of rough sleeping when it is rolled out nationally.

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How the Framework is helping Early Adopters areas in their work to end rough sleeping

Michelle Binfield, Rough Sleeping Programme Director at London Councils, said: 

“Better data will not solve rough sleeping on its own. But as it stands, we don’t know nearly enough about what went wrong before someone first slept rough, or about what chances were missed when people leave institutions like prison and hospital and go on to sleep rough. Better data means more targeted interventions are possible.”

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Jim Crawshaw, Chair of the West Midlands Combined Authority Rough Sleeping Task Group and Head of Housing & Homelessness at Coventry City Council, said:

”Our objective across the West Midlands is to ‘design out homelessness’ in all its forms, including rough sleeping. This new data-led approach will allow us to better understand how we can prevent rough sleeping by identifying what is working well and where there are gaps in provision or any specific issues that may need further attention.”

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Phil Edgar, Performance Analyst at Newcastle City Council, said:

“Newcastle City collects data on people sleeping rough in an effort to understand rough sleeping in the city, but also to get people the support they need to make rough sleeping rare, brief and non-recurring. 

"This work is helping us make our data more robust and more consistent with other parts of the country, which should build a shared understanding of what works through the use of standardised definitions and indicators. We look forward to being able to share knowledge with local areas across the country using this common language, shared data, and vision of success.”

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Fraser Nicholson, Homelessness, Partnerships Coordinator at BCP (Bournemouth, Christchurch, & Poole) Council said:

“We are ambitious and committed as both a local authority and a wider Homelessness Partnership in BCP to preventing and ultimately ending all forms of homelessness, including rough sleeping. This project has made this vision much more tangible and focused, moving us beyond a commendable but somewhat vague target.”

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Joe Donohue, Homelessness Strategy Principal at the Greater Manchester Combined Authority, said:

“The single night count is limited in what it can tell us about the people experiencing rough sleeping, or about how well we are doing as a region at ending it. It does tell us that, in Greater Manchester and indeed nationally, a significant amount of progress has been made towards ending rough sleeping in recent years.

“What we’re missing right now is the nuance: why is what we’re doing working or not and, crucially, who are our interventions not working for, and why? The Framework allows us to have these conversations in a data-led way and adapt our interventions and responses accordingly. 

"Better data is one piece of this puzzle and provides greater transparency and accountability. We can interrogate this data with insight from people with lived experience of homelessness and the expertise of our partners across GM Homelessness Action Network, to truly track whether our regional Homelessness Prevention Strategy is having an impact.“

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Dr Lígia Teixeira, CEO of the Centre for Homelessness Impact, said: 

“If we are going to end rough sleeping for everyone, it will take focus and collaboration across lots of agencies working within and between different areas. Using the same definition and indicators will help us align efforts and focus on what matters most.  The framework should help local areas to identify quickly any gaps in provision or specific issues that may need further attention.

“Importantly, better data will help us understand what works. At the moment, a lot of monitoring information is collected on rough sleeping, but this data is patchy, inconsistent and can’t be used to really understand what’s working where or why. This framework should simplify and harmonise data collection and reporting and give everyone a common language and set of data, enabling us to learn faster by making it much easier to understand what’s working, where, and why.”

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