AI used to help identify at-risk social housing tenants

A system being developed by Dr Ramit Debnath, Assistant Director of Cambridge Judge Business School’s Energy Policy Research Group and Executive Director of the Centre for Human-Inspired Artificial Intelligence (CHIA), and Professor Ronita Bardhan from Cambridge’s Department of Architecture combines 3 data sources to help identify at-risk social housing tenants: satellite data, conventional housing data and softer data such as rent arrears and fuel poverty indicators.

The satellite data draws off AI algorithms that use thermal imagery from low Earth orbit satellites to detect heat loss from buildings. The conventional housing data includes records of mould and damp and construction types. The softer data is already held by local councils but is not often used at scale.

“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 system creates a dashboard displaying a map of risk hotspots. For example, imagine two identical properties each 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,” Campbell says. “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.”

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.

Peter Campbell, head of Housing at South Cambridgeshire District Council

Collaboration between 2 councils and the University

The project called PRISM (Predictive Risk Intelligence for Social housing Maintenance) is a collaboration between the University of Cambridge, Cambridge City Council and South Cambridgeshire District Council, and is supported by the Local Government AI Accelerator, a new initiative from ai@cam, the University’s flagship mission on artificial intelligence. 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.

“The housing officers have a much more grounded idea of how they see vulnerability,” said Dr Ramit Debnath, 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 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.

Dr Ramit Debnath, executive director of Cambridge’s Centre for Human-Inspired AI (CHIA)

Government expectation on social housing higher following incidents

The project reflects a broader change in how social housing is regulated in England. Following a series of high-profile cases involving damp, mould and disrepair, including the death of two-year-old Awaab Ishak in 2020, the government has tightened expectations on councils and housing associations to use data more proactively in managing their social housing stock. 

“There’s been a changing approach to the way social housing is managed through the housing regulator,” said Campbell. “There’s an expectation from the government to make better use of data in order to plan our services.”

One area of particular concern is reaching tenants who, for whatever reason, have little contact with their council. This could be people with mental health problems, elderly residents who rarely seek help, or those who mask problems rather than report them.

“What we’re doing now is identifying people with whom we’ve had absolutely no contact and prioritising them for a home visit,” said Campbell. “But we don’t have the resources to do that for everybody all the time.”

Housing officers, not machines, make decisions about people’s homes and welfare

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.

“AI helps, but welfare decisions stay with trained officers,” said Bardhan. The team has also been careful about privacy: the model is built to work with anonymised data and is designed so that nothing in the outputs can be traced back to a named individual.

“Modelling a building with a machine learning model is relatively straightforward,” said Debnath. “Removing all the risks around personal data and making it tight within the context of its ethics: that’s the complex part.”

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.

“This is just a starting point,” said Bardhan. “But we hope it can be replicated across different councils across the country.”

A version of this article initially appeared on the University of Cambridge website