We can help end homelessness with big data

The government’s response to homelessness and its “Everyone In” policy, in March 2020 during the first national lockdown, showed us what was possible in terms of responding to street homelessness quickly, and at scale. This also helped the UK to avoid an immediate US-style homelessness crisis.

The big question is – what happens next? Homelessness is often the end product of many different factors prompted by debt, family breakdown, mental illness or leaving a big institution – from prison to the armed forces.

Policy has to work on three dimensions at once. It has to work upstream stemming the flows into homelessness. It has to address immediate needs for food and shelter. And it has to work downstream to ensure sustainable pathways back to a stable home and life.

Each of these rests on our ability to observe and understand – mobilising the right data and intelligence. For example, we need to understand how housing affordability and landlord behaviour might drive homelessness patterns, or how different patterns of prison discharge either fuel homelessness or reduce it.

Getting this right can be expensive, but it can also save money. For example, work by the Centre for Homelessness Impact (CHI) shows local authorities in England are spending an average of £12,500 per year/person or £240 per week/person on temporary accommodation. CHI estimates that moving 25 per cent of temporarily accommodated households, in the 15 local authorities with the highest rates of temporary accommodation, could produce savings of up to £500m over a five-year period.

The best way to find out if this is right is through experiments with rigorous measurement of both the costs and the results – including the better life experiences of the people involved.

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In these respects, the housing field can learn from others. Almost every area of life is now being influenced by data, and by new ways of combining data. Much of the power of Amazon comes from its extraordinary marshalling of data, combined with constant experimentation to find out what’s most effective in getting consumers to spend.

Governments around the world are using similar methods, and have accelerated these during the crisis. South Korea and Taiwan combined data from credit card transactions and mobile phone companies to help track infections and respond in a very targeted way. Taiwan even had managed digital fences that alerted police and local officials if quarantined people left their homes.

Even here in the UK local authorities have just started using CCTV footage and data from banks and mobile phone companies, combined with artificial intelligence, to track people’s behaviour on high streets.  Some of these methods may sound scary and Orwellian. But used well, they can help us to better understand the nature of problems and to respond more quickly and effectively and in more fine-grained ways that recognise the great diversity of the homeless population.

We are now at a decisive point. Homelessness levels and the demand for services are rising in many places. The end of leniency on rent arrears combined with the mental health and economic effects of the crisis could easily drive homeless numbers up sharply. Many local authorities are close to bankruptcy.

It’s therefore all the more important that we use all the tools available to get on top of the problems.

The End It With Evidence campaign is calling on people to back this approach by signing a pledge. We believe we can end homelessness in three ways.

First, building the evidence of the policies, practices and programmes that achieve the most effective results to improve the lives of people who are homeless or at risk. Next by building the capacity needed to act promptly on the best knowledge available to improve decisions and help limited resources go further. And finally using evidence-led communications to change the conversation around homelessness, challenging stereotypes, and making sure that homelessness is not a defining factor in anyone’s life.

Sir Geoff Mulgan is professor of Collective Intelligence, Public Policy and Social Innovation at University College London