What Results Can You Expect From Our AI (Artificial Intelligence) Rent Arrears Social Housing Solution?

April 6, 2022

Let’s take a typical housing client with 10,000 properties and £2million of arrears debt which was made worse by the pandemic.

A third of their tenants are in arrears at any one time.

They rely on their housing management system to flag up arrears cases each week. This list is often unprioritised and lumps everyone in, regardless of their situation (long and short-term arrears and temporary/benign cases).

They plough through the long list each week in the hopes of getting through it, but it takes time and they don’t seem to be able to reduce it. In fact, it’s increasing.

It’s a frustrating process.

If they continue to tackle arrears with this approach, we predict that their arrears debt is likely to increase by over 40% in the next 12 months to almost £3m.


The cost-of-living crunch is putting immense pressure on tenants like never before with inflation, April rent increases, energy costs and national insurance increases taking effect, the old approach to managing rent arrears is no longer working.

It’s time to reimagine a better way.

Social Housing Rent Arrears Prediction with AI

Using our AI technology, we use their tenancy data to analyse current and historical trends and performance to provide a report on how they could improve team performance, reduce caseloads and bring arrears down with a couple of simple changes.

Pivigo's AI technology creates an intelligent and prioritised caseload, focused on existing arrears cases with a higher propensity to be settled. In other words, we identify who is most likely to pay and can recommend what interventions might work best.

Additionally, our AI model is the only one available that also identifies at-risk tenants before they fall into arrears (up to 4 months before), enabling a preventative approach.

So if we go back to our typical housing client, we know that of the 30% of tenants in arrears, we can segment them into three categories:

  • 20% are temporary and don’t require any intervention. 
  • 50% are complex long-term cases, where interventions have a much lower chance of immediate success, and a longer-term approach is needed.
  • The final group is the one we recommend focusing on. This is what we call the ‘smart caseload’. To maximise rent collection, they would have a targeted list of tenants with a high probability of making a payment given a little nudge. This may be sending a text reminder that their rent is due or explaining how to set up a direct debit to give them the support they need to avoid spiralling further into debt.

With this approach our housing client would see a smart caseload reduction of 75% and arrears reduction of almost £1million within two years.

Plus, our ‘future arrears predictor’ will tell them which tenants not currently in arrears are highly likely to fall into arrears within the next few months giving them the intelligence to support those tenants and prevent that from happening.

If you’d like a tailored data review report to see the difference AI could make to your organisation, get in contact or sign up for your free Data Preview.

Tariq Khan CDIO

"Camden has worked with Pivigo over the last 6 months to implement their innovative AI solution. As a technology and transformation leader in local government, it's clear that technology like this will re-shape how arrears are managed and tenants supported, and we’re proud to be early adopters."

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