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Challenges
Strategic decision makers lack data to understand the routes into and out of rough sleeping, making it hard to develop preventative measures. Key gaps include the conditions people face before becoming homeless and the outcomes for those receiving services like hostels or temporary housing.
Approach
By analysing movement within the rough sleeping ecosystem, LOTI can identify those likely to return to rough sleeping after short-term accommodation or probation, helping design targeted services to prevent it. The project aimed to:
- Measure success in making homelessness rare, brief, and non-recurrent.
- Identify effective strategies to inform policy and service delivery.
- Understand the complexity of needs within the rough sleeping population.
- Ensure proper use and linking of solutions and resources.
- Enhance the operation of services and performance management.
Outcomes and benefits
The project created a tool to provide strategic decision-makers with insights into rough sleeping in London. It aims to help design new services to prevent rough sleeping by analysing data from sources like CHAIN, In-Form, and H-CLIC returns.
Initially, an MVP solution was piloted with Camden, Hillingdon, Lambeth, Westminster, and their hostel providers. In February 2024, ‘phase 2’ rolled out the tool to all London boroughs and additional homelessness service providers.
Currently, the project is enhancing the dataset, adding functionality, and experimenting with advanced data science capabilities.