Social housing tenants face rent increases in April 2022 of around £200 per household per year, alongside rising energy and food costs.
According to the Joseph Rowntree Foundation, 46 per cent of social housing tenants are living in poverty, equating to just under five million people across the UK. These increases in rent and costs of living will hit the most vulnerable the hardest, leading to further financial difficulties and debts. And with many tenants receiving housing benefit or universal credit, many of them are already living on the breadline before the expected price hikes.
Propensity for arrears
More tenants will find themselves in rent arrears during 2022 as the financial impacts of the pandemic catch up with the economy. But how can housing providers predict which tenants will go into arrears?
Predicting rent arrears isn’t just beneficial for tenants, it also allows housing providers to prioritise tenant support, keep money coming into the organisation and learn more about tenants’ behaviours and their particular circumstances. Patterns in behaviour can be monitored to prioritise support for those continuously in financial trouble, rather than needlessly chasing tenants who only go into arrears on one-off occasions.
What’s the answer?
With numerous integration opportunities, Microsoft Dynamics 365 can create a rich, joined-up view of tenants’ data which can be used to predict their behaviours. Dynamics 365 provides the core data housing providers need, including rent and arrears management. This data can then be leveraged to predict future trends and spot previously-unnoticed patterns in tenants’ behaviour. The CRM system also has intuitive segmentation and filtering capabilities, helping housing providers to analyse top-level arrears and rental data.
Intelligent caseloads
Pivigo, an AI service provider in the housing sector, uses AI-powered solutions for rent and arrears management. Using predictive analytics, Pivigo generates smart and intelligent caseloads for housing providers’ income teams, with predictive algorithms helping to prioritise activity and achieve the best possible impact on arrears. Pivigo can also predict future arrears up to six months before they happen, thereby also enabling housing providers to create proactive, preventative strategies to support more sustainable tenancies.
Using machine learning, Pivigo can accurately predict whether a tenancy is in long-term arrears or not, allowing the assignment of an arrears risk score to each tenancy. This enables a data-driven approach to arrears, resulting in a considerable reduction in the risk of long-term arrears cases.
The rich tenant data in Pivigo is derived from Dynamics 365 – discover more about the importance of predicting rent arrears and how Dynamics 365 and Pivigo can create a data-driven housing provider at our next ‘housing breakfast’ webinar (see crimson.co.uk).
Jordan Wheat is a new business consultant at Crimson.
