Rent collection and supporting tenants in financial difficulty has barely changed during the past two decades. Despite advances in technology and data analysis, teams are still being held back by manual, inefficient methods of working. And as the number of homes managed by housing providers increases, the problems are becoming even more evident.
With the economic hardships that the UK has experienced over the past few years, culminating in the current cost-of-living crisis, the percentage of people in social housing who are struggling with debt is rising. According to Resident Voice Index’s ‘Cost of Living’ report, 80 per cent of the 5,700 social housing residents surveyed said they were in some form of debt, including credit cards and rent arrears. Around the same number also said they were worried most or all of the time about meeting their monthly expenses. That report was published in November 2022, before the expensive winter months; the overall picture is likely to be worse now.
With few financial resources to fall back on, housing providers increasing their rents by the maximum 7 per cent, and the expectation that food, water and energy costs will continue to rise, many tenants are concerned about how they will manage to afford basic necessities in 2023.
While income teams do their best to help tenants, their efforts are being crippled by business intelligence and data analytics practices that haven’t changed in a generation.
The human approach
I refer to the current, most common method of data analysis in income teams as the ‘human approach’. In short, the human approach is where the management of debt (including assessing tenants’ data regarding financial inclusion support, future arrears risk and current arrears management) is performed by a human.
On the face of it, this doesn’t sound awful. Income and financial inclusion teams are specialists when it comes to helping people manage their financial situations, so let them take the reins, right?
However, as most readers will know, it’s not so simple. If you consider that 4.4 million homes are managed by housing providers (October 2022), and that most housing providers’ income teams comprise just a handful of people, it’s obvious that demand outstrips supply when it comes to housing professionals. Yet despite their limited capacity, housing providers are still tasking their income teams with sifting through thousands of cases by hand to find those that actually require attention.
Organisation by arrears
To create some semblance of order in their caseloads, most housing providers using the human approach organise their cases by the size of each tenant’s debt. The idea is to contact those with the greatest debt first and do what can be done to manage their arrears, and then move onto the next case.
However, limiting their analysis to just one data point leads to significant problems. For example, imagine Tenant A has a large but slowly decreasing arrears total, and Tenant B has a small but quickly escalating arrears total. Tenant A would be contacted first and Tenant B wouldn’t be prioritised unless their debt grows, making it harder to manage. Tenant A would also be contacted regularly by income teams, despite their debt being under control.
Prioritising cases by arrears also results in false-positive cases (also known as ‘technical arrears’) clogging up an income team’s caseload. Technical arrears are where a tenant regularly goes in and out of debt because they get paid days or weeks after their rent is due, meaning they’re always paying late. Caseloads based only on arrears include 30-40 per cent technical arrears, which could just be removed.
Instead, a more sophisticated system would analyse the stability of each arrears case and then prioritise the most unstable cases first. Going back to our previous example, Tenant B’s arrears would be considered unstable because the debt is rising quickly, whereas Tenant A’s arrears would be considered stable and managed so would be deprioritised. Finally, as technical arrears cases are self-resolving by nature, they would be removed from the main caseload.
Reactive rather than proactive
The second problem with the human approach is that it leads to a reactive form of income management and financial inclusion. This is because income teams can only provide people with help once they are already in large amounts of debt. Due to the limitations of the human approach, only cases with large arrears totals are prioritised, meaning that many tenants accrue preventable debts before they are actually given support.
Of course, income teams would love to be proactive. When we asked income teams in January 2023 what they liked most about their jobs, the top two answers were: helping tenants to manage their finances; and helping tenants to plan for their futures. Yet the current approach limits the positive impact they can have and also adds to their housing provider’s total arrears by leaving many unstable arrears cases unchecked.
If housing providers could instead create robust stability profiles for each tenant, they could intervene earlier and prevent larger arrears from arising. In some cases, they could even identify and help with arrears before the debt even happens.
Using AI analytics
Thankfully, there is another approach that uses all of the technological advancements available to us in 2023. What I’m referring to is the AI approach, where artificial intelligence is placed at the heart of your data analytics.
AI systems are no longer a technology of the future. They are here now and not taking advantage of them is a mistake that’s affecting millions of people in social housing in the UK. As Oren Etzioni of the Allen Institute for Artificial Intelligence said, “AI is a tool; the choice about how it gets deployed is ours.”
By using AI, your team can have access to a full analysis of all the data points you have on a tenant (including debt total, payment schedules, benefits received, changes to rent, changes to income, seasonality and much more) which then creates a stability profile for each tenant.
Using these profiles as the main method by which cases are prioritised (another job the AI can do for you) will lead to a huge reduction in the size of caseloads and better managed arrears across the board. The information contained in each profile will also tell your teams which tenants should receive financial inclusion support, additional funds from benefits, tenancy health checks and so on.
The AI approach increases tenant wellbeing by prioritising support based on a detailed understanding of a tenant’s financial stability, cutting out the guesswork. With enough data and a little bit of machine learning, AI can also predict which tenancies are likely to fall into arrears in the next few months so that income teams can intervene to prevent that from happening.
Finally, the AI approach removes the strain from income teams by reducing their caseloads, allowing them to spend their time doing what they do best: helping people manage their finances and plan their futures, not endlessly assessing cases.
As Agent Smith put it so aptly in The Matrix, “Never send a human to do a machine’s job.”
At Occupi, we know such an AI system exists because we’ve built it, and we know it works because we’ve seen it in action. The only question remaining is whether this technology will be adopted fast enough to help those who are already struggling to pay their rent.
Neil Forrest is the chief commercial officer at Occupi.
