AI is already cutting costs and improving services in housing. But how might this technology be used to support tenants in the future?
AI has brought big changes to the housing sector. For example, it can already help to predict which homes are more likely to develop damp and mould. The technology simply combines data on known problems with ventilation, a history of late rent payments (which could be a sign of heat poverty) at the property and the number of tenants in a property and comes up with a score to identify, with incredible accuracy, the likelihood of mould developing.
We’re also using it to review information on gas and electricity certificates to automate the planning of required maintenance. And its use to predict which tenants may struggle to pay their rent so that support can be provided is now well established. But what developments can we expect next?
A renewed focus on tenants
There is no doubt that using AI to improve tenant care will be a key focus for the sector. The Grenfell Tower disaster and the tragic death of Awaab Ishak were watershed moments. Often in those large-scale investigations, an ongoing theme is that tenants reported that they didn’t feel their concerns were listened to.
Local authorities and housing providers are acutely aware that this needs to change and AI is providing the catalyst to allow this to happen more efficiently, starting with the very first point of contact.
Actions tailored to individuals
One of the problems with the traditional set-up in housing systems is that data is stored in multiple applications and even kept in Excel spreadsheets in many cases. AI can help bring all that information together into a coherent story about a property, an event or the tenants themselves.
When someone calls into report a problem with mould, AI brings all the data together. The call handler can see instantly whether this is a problem that has been reported before and what, if any action, has been taken.
They can also see if there are any additional contributing factors that change the way they might want to prioritise a call.
Some housing providers are already using AI to highlight additional vulnerabilities of tenants. Perhaps a housing officer visiting the property reported in their notes that there was someone who is wheelchair-bound or who has a long-term health condition living there. This information might not be in any official records but surfaced by AI searching through any free-text comments made during previous contacts with the tenant. This type of information ensures the report is dealt with appropriately.
Improving communication
Once a call is completed, AI can then be used to generate a summary of the conversation and send it to the caller.
Tenants often complain that call outcomes are often not communicated clearly. Providing the summary shows the tenant that the problem is properly understood and any agreed actions from the discussion are clearly documented.
Any follow-up calls from tenants become easier to manage because there is consistent documentation for every contact with tenants, who in turn don’t need to re-explain their problem from the start.
This approach would also help call-centre agents. Currently, agents type up notes from their calls from memory and then send an email to the tenant. Not only does this method risk errors creeping in but it can also waste valuable time. With AI handling the summaries, the agents could simply check the summary and move on to the next call.
Agentic magic
The real difference is felt when AI agents are introduced to automate regular workflows, linking one system to another. Currently, housing staff must manually raise a report to log a repair or a complaint so that it can be assigned to a specific person or team. With AI, these actions can be automatically triggered and assigned to the right individual or team.
Of course, there would still need to be humans monitoring this chain of events but AI would do most of the heavy lifting.
Greater performance & productivity
The same systems could ensure that tenants are given progress updates on their reported problems while also alerting housing staff if priority actions haven’t been completed within certain time thresholds.
In the not-too-distant future, AI will be used to analyse which actions are more likely to produce positive outcomes. For example, are problems relating to ASB resolved faster if a housing officer calls a resident or makes a visit to the property?
The financial cost of inaction or delayed action on a specific problem could be added to these predictions, helping housing providers assign the true priority to a problem rather than the short-term cost vs. return view. Essentially, the AI would be constantly learning and figuring out how to deliver better and more cost-effective services.
A brighter future
There’s no doubt that housing has been looking at numerous difficulties, such as ageing stock, tighter budgets, tenants with greater needs and increasingly complex regulations.
AI is the one technological development that allows us to believe that much of this change is achievable. Rather unexpectedly, it also allows us to plan for a more human approach to housing – enabling the sector to personalise services to the needs of their tenants more efficiently than they could have hoped for.
Trevor Hampton is the director of housing solutions at NEC Housing.
For further details, please see: www.necsws.com/housing.
