A data driven approach to housing and homelessness: Reigate & Banstead Borough Council

Reigate & Banstead Borough Council faced a growing challenge: how to house those most in need, while managing increasing demands. The council’s housing department had a wealth of data but lacked the precision and transparency that robust data analysis could provide. With the cost-of-living crisis intensifying, more residents were seeking support and the council found itself under greater scrutiny to justify its decisions. At the same time, there was a clear corporate priority to improve outcomes for vulnerable residents and reduce the use of expensive temporary accommodation.

Prior to this project, there had been no comprehensive analysis of the council’s housing data. Officers had a general sense of local trends but lacked the tools to see the full picture, such as where social housing was most needed, or which properties had specific features like accessibility adaptations.

The council’s Data and Insight Team set out to transform this situation by delivering a package of data-driven solutions. Their approach was threefold:

First, they visualised years of housing and homelessness data using Power BI and Geographic Information System (GIS) tools. This enabled officers to interact with the data for the first time, exploring geographical trends, identifying clusters of need and understanding the characteristics of both applicants and available properties. The dashboards made it easy for staff and councillors to grasp complex patterns at a glance, supporting more informed and transparent decisions founded on reliable data evidence.

Second, the team improved data sharing with Registered Providers. Unique Property Reference Numbers (UPRNs) enabled them to join up the data and plot all social housing properties on a map, including details such as number of bedrooms, accessibility features, floor level and age restrictions, resulting in a comprehensive, up-to-date view of the local housing stock. This not only streamlined internal processes but also fostered stronger partnerships with housing providers.

Third, they experimented with a semi-automated methodology for property allocations. By compiling key metrics and organising the data, the team built a process that could suggest suitable applicants to promote each property to, taking into account policy factors and the current balance of need. This decision-support tool demonstrated that data could increase the pace and confidence of allocations, reduce the risk of appeals, and make departmental processes more resilient.

The impact of this work was immediate and far-reaching. The new dashboards provided clear evidence of areas in the borough with high housing need but limited social housing, supporting funding bids and strategic planning. For the first time, the council could proactively identify opportunities to develop new housing – a key step towards reducing unnecessary spending on expensive emergency accommodation.

The housing team recognised that the allocations decision-support tool had immediate potential to support officers responding to corporate complaints. Early trials demonstrated that the tool could provide robust validation for officer decisions by leveraging its comprehensive evidence base. This capability was seen as a way to strengthen confidence in decision-making and mitigate the risk of future challenges or repercussions.

The project also improved working relationships with social housing providers, as data sharing became more routine and collaborative. Officers had more information at their fingertips, consolidated and presented in intuitive ways to help them process applications with confidence and efficiency. Perhaps most importantly, the approach of exploring and experimenting with data made advocates of key stakeholders, those who had once been wary of data became champions for the new way of working, using the insights to influence policy and secure additional resources.

Of course, the journey was not without its challenges. One of the biggest hurdles was cultural, winning over hearts and minds, especially among staff who were unfamiliar with data and the work it might involve to achieve the desired output. The team found that demonstrating the practical benefits, such as validating decisions, saving time and improving outcomes, was key to building trust and enthusiasm.

Another challenge was the need for service knowledge. As a central data team, they had to quickly get up to speed with specifics of housing allocation, working closely with service experts to ensure the models were relevant, accurate and effective. This highlighted the importance of collaboration between data specialists and frontline teams.

Finally, ensuring the quality and reliability of the underlying data required ongoing effort. The team recognised that for data-driven approaches to be sustainable, data management needed to be addressed across the organisation, with clear roles and responsibilities for keeping information accurate and up to date.