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  • Writer's pictureTom O'Connell

How Social Housing Income Teams Can Maximise Productivity and Free Up Time

Housing Associations: It’s Time to Let Your Data Do the Hard Work

Any person with even fleeting experience in a housing association will know one thing: time is the most precious resource for Income teams.

Finishing your caseload can often feel like an endless and futile task, especially when the caseload contains technical arrears that require no action, such as cases where the debt is a product of erratic payments or seasonality. Not only do such technical arrears slow down Income Officers and give them unnecessary work, they can also feel demotivated. This is especially true when the tenants are known to the Income Officers, as repeatedly actioning these cases can lead to pushback from tenants who feel their situation is already under control.

The cause of these issues is a systematic problem with the way that Income Officers’ caseloads are organised. Far too often, housing associations use systems that only offer the most rudimentary priorisation (for example, ranking the cases with the highest debt first, ignoring those with debts that appear more manageable). These systems are plagued by issues such as the technical arrears mentioned above, and the Income Officers working in these systems consequently lose time and productivity.

Solving this should be easy!

The solution: use all your data, not just the data regarding each tenant’s current arrears.

Occupi, the new housing data analysis product from Pivigo, uses AI technology to review caseload data and create a behavioural profile for each tenant. By considering factors such as timing of payments, length of tenancy, contact history and more, Occupi can determine the stability of each tenancy.

The AI technology then predicts where each tenancy will be in 16 weeks’ time, so that tenancies with escalating and unstable debts are pushed to the top of an Income Officer’s caseload, while more stable tenancies are lower down (even if they have a higher debt balance).

This stability-focused behavioural analysis has myriad advantages in terms of identifying problematic cases, predicting future arrears, and avoiding erratic payment debt cases.

The problem of technical arrears is eliminated overnight. Occupi understands where late UC payments are the contributing factor to self-resolving arrears cases. With organisations we’ve worked with so far, this removes 30-40% of a caseload, immediately.

But more importantly than that, it creates an environment in which Income teams can maximise their productivity and use their time efficiently. The most pressing cases are presented to Income Officers, which leads to a more compassionate and effective caseload.

Tenants with large but managed debts are not constantly flagged and then chased, whereas those with small but escalating debts are given more attention, allowing you to get ahead of the problem. Income Officers using this method of organising their caseload will only tackle cases where significant debt reduction can be achieved, either by eliminating existing debts or preventing future debts from arising.

By using data correctly, Occupi allows your Income Officers to work effectively and make significant contributions to managing your tenants’ debts, which in turn reduces their future caseloads and allows for a more productive working environment. Working caseloads will always be tough at times, but by taking a data-focused approach to caseload management, we can keep Income Officers engaged and effective.

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