Two Gates Cambridge Scholars are involved in a local project using AI to better anticipate social housing problems for the most vulnerable.
The interesting bit [about this project], which is unique to this project, is that we’re predicting not just on observation data, but also on data from lived experience.
Ramit Debnath
Cambridge researchers, including Gates Cambridge Scholars Adhib Hussain Syed [2025] and Ramit Debnath [2018], are developing an artificial intelligence tool that could tell UK councils which social housing tenants are most at risk before a potential crisis hits.
The project, called PRISM (Predictive Risk Intelligence for Social housing Maintenance), is a collaboration between the University of Cambridge and two local councils: Cambridge City Council and South Cambridgeshire District Council. It is supported by the Local Government AI Accelerator, a new initiative from ai@cam, the University’s flagship mission on artificial intelligence.
Instead of waiting for a tenant to report a leaking roof or a damp bedroom wall, a computer model would scan data from thousands of properties and flag the ones most likely to deteriorate, and the residents most likely to be harmed if they do.
“At the moment we’re very much waiting for things to break before we act,” said Peter Campbell, Head of Housing at South Cambridgeshire District Council, which manages around 5,500 social housing properties. “Quite often when things break, it’s not only the item itself that gets damaged, but also the damage caused by the break. For example, it’s not just the roof that needs replacing; it’s where the water has gotten in and damaged the rest of the property.”
The two councils together manage thousands of tenancies across an unusually wide geography – the urban density of Cambridge city, and the more suburban and rural sprawl of South Cambridgeshire, where it can take over an hour to travel between two addresses.
Campbell says that better data could make his teams far more efficient. In a previous role, he introduced route-planning software for repair staff and watched their daily visit count jump from six to eight.
A single risk score
The system being developed by Professor Ronita Bardhan and Debnath from Cambridge’s Department of Architecture and the Centre for Human-Inspired AI (CHIA) combines three sources of data into a single risk score for each property.
The first source is satellite data. Bardhan’s team has spent years developing AI algorithms to detect heat loss from buildings using thermal imagery captured by low Earth orbit satellites. That research produced a building-level dataset covering all of England and Wales, mapping energy efficiency property by property.
The second source is conventional housing data: construction type, Energy Performance Certificate ratings, records of damp and mould and repair histories.
The third source is what the researchers call ‘soft’ data: fuel poverty indicators, rent arrears, and accumulated logs of tenant contacts that councils already hold but rarely use at scale.
“The housing officers have a much more grounded idea of how they see vulnerability,” said Debnath, who is Associate Professor in the Department of Architecture and also Executive Director of Cambridge’s Centre for Human-Inspired AI (CHIA). “They have information about things like fuel poverty, repair logs, tenancy history and health calls. The interesting bit, which is unique to this project, is that we’re predicting not just on observation data, but also on data from lived experience.”
The result, the researchers say, will be a dashboard displaying a map of risk hotspots – not just flagging buildings in poor condition, but highlighting where a vulnerable person lives in one.
Campbell gives a concrete example of what the dashboard might do. Imagine two identical properties, both with a crack in an outside wall. “One is occupied by a family who are out at work all day, so the heat loss caused by the crack has a minimal effect on them,” he said. “The identical property next door is occupied by a single person who is housebound with disabilities, and the heat loss could have a much bigger impact. The tool would allow us to target the person most in need – it’s not just about the properties, it’s about the people who live in them.”
A shifting approach to social housing
The project reflects a broader change in how social housing is regulated in England, with an expectation from government to make better use of data in order to plan services.
One area of particular concern is reaching tenants who, for whatever reason, have little contact with their council — people with mental health problems, elderly residents who rarely seek help, or those who mask problems rather than report them.
Keeping humans in the loop
The researchers emphasise that PRISM is not designed to make automated decisions about people’s homes or welfare. All alerts generated by the model would be reviewed by housing officers, not acted on directly by machines.
The project is designed as a proof of concept over 12 months. If it works, both councils say they hope it could serve as a template for social housing authorities elsewhere in the UK. The researchers have already built a roadmap to help other councils replicate it.
*Photo by BEN ELLIOTT on Unsplash
