A report by Shelter and YouGov last week revealed that over half of teachers working in state schools have worked with children who are homeless, or who became homeless in the last three years.
The findings also showed that teachers attribute homelessness as a key factor in pupil absenteeism, hunger and tiredness.
Alarmingly, the research was conducted before the first lockdown with the pandemic only exacerbating housing inequalities. The latest estimates are that 2.5 million households are worried about paying their rent over the winter period, with 700,000 already in arrears and 350,000 at risk of eviction.
Although there is a temporary pause on evictions from 11 December to 11 January 2021, it’s never been more critical for housing providers to have the right tools and information so they can provide the best support possible.
So, what can providers do to prevent a potential avalanche of evictions, whilst staying true to their social purpose and keeping the rents coming in?
Adopt a holistic approach
Having a 360-degree view of tenants has never been more needed to help keep people in their homes and never more accessible to providers. In the wake of unprecedented economic uncertainty and the worsening of profound social issues like domestic abuse, mental health and income inequality, it’s vital for providers to have access to the complete picture. Only then can they provide the best targeted support.
These social issues combined with the rising unemployment levels suggest the number of missed rental payments is likely to soar, meaning income managers will find themselves chasing more debt than ever.
But what if it’s possible to see exactly why a payment has been missed? Then there’s more chance preventative measures can be put in place to sustain a tenancy, helping to avert court action or the upheaval of putting a family in temporary accommodation.
The brain behind the brawn
Data has been powering social housing for some time and much of the heavy lifting has been done by providers to become data fit. But it’s not enough on its own to make truly informed decisions. Predictive analytics takes decision making to another level.
Let’s consider the arrears list. An automated check can be run against the arrears data so tenants can be assessed and ranked into those most likely to pay and those less likely to do so. But what if the list could be further segmented and had the ability to dig deeper into the information to get a clearer idea of each tenant’s situation?
For example, a single parent household with a long history of paying their rent on time has begun to miss payments. Now without the cross-referencing of information, we have no idea if the late payments are the result of illness, unemployment, or that they have changed jobs and have a new payday.
The point is, the fact that someone has moved from being a good payer to a late payer often gets lost. If we can drop this information into the hands of a housing officer, we can eradicate a blanket one-size-fits-all approach to late payment. It allows the provider to make an informed decision as to whether more help and support is needed to get them back on track and stay in their home.
Social housing has always been more than just a numbers game and being able to see the information in the round ensures people remain at the heart of it.
Using data analytics tools to overlay key information on households helps providers put the right support plan in place earlier. It gives them the time and space to see what help will work best for the tenant on an individual basis and is a game changer for the sector.
One legacy of lockdown has been to underline the importance of having a roof over your head as, up and down the country, homes morphed into classrooms, offices and gyms. To avoid homelessness being another, it’s time to harness data analytics to start the dialogue and prevent a surge of evictions. Proactive, targeted and timely intervention can flatten the curve and make all the difference.
Trevor Hampton is director of housing product solutions at Northgate Public Services.