Case Study: South Staffordshire HA
South Staffordshire Housing Association (SSHA) was established in 1997 as a stock transfer from the local authority, South Staffordshire Council. Today, SSHA own and manage 6,000 properties, most of which are based throughout rural South Staffordshire.
Caseload Reduction: 83% 4,390 down to 672
Arrears: from 1.63% to 1.34%
In 2012 the sector, as a whole, began to undergo huge changes as a wide range of welfare reform was introduced by the coalition government, for many landlords, including SSHA this signalled a shift in behaviour, according to Neighbourhoods Director, Jan Goode:
“These changes made landlords put a more commercial focus on revenue, and income became king, before, for many, it was taken for granted.”
This lead to a review of the income function and arrears management at SSHA, by Jan (Goode) and Samantha Allcott, Income Manager.
“Before welfare reform – we were performing well, but arguably we could have performed better. At that time as long as arrears did not go over 2% then everyone was happy.” Commented Samantha.
The review emphasised issues with a high and inaccurate caseload generated by the Housing Management System (HMS). SSHA were already aware of this, and as a result IT tried to build their own in-house automated arrears process, however the income team ended up disregarding 90% of the recommended actions. Moreover, it was becoming increasingly difficult to manage and morale was impacted by the never ending and inaccurate caseload. This was compounded by a lack of management visibility of team and individual performance.
This highlighted that SSHA either needed to increase resources or find a way to increase capacity within the income team, after reviewing the market SSHA looked at RentSense.
“Rather than invest extra resource and money in working through an inaccurate caseload we decided to invest in RentSense,” explains Jan.
Welfare reform had shifted SSHA’s way of thinking, and they began to assess and look at risk, and take a risk-based approach to arrears. They wanted to identify those tenants at risk of being affected by welfare reform and then respond with an appropriate approach to rent collection.
“RentSense reduced our caseload by over 80%, our HMS was recommending around 4,500 each week whereas RentSense recommends around 750. In the early days we would double-check the caseload, as it seemed so low, but it (RentSense) is picking up the cases we need to action, and not missing any,” commented Samantha.
The investment in RentSense created capacity, which was critical for the income team to be able to manage a more complex caseload created by on-going welfare reform.
“If we did not have RentSense we have would have to increase the team by four FTE just to get through all the cases flagged by the HMS, many of which are not genuine arrears cases,” highlights Samantha.
It is also helping the income function be more efficient and effective with their workload as Jan explains:
“RentSense is much more effective in enabling officers to prepare for their calls. It cleverly filters cases into groups, such as UC, under occupiers, our intervention pilots and so on so they are just looking at cases they need to and they can get in a rhythm.”
Alongside RentSense, SSHA have tested various pilots in terms of looking to deliver further efficiencies to mitigate on-going welfare reform, as their own internal research shows that Universal Credit cases take three times as long to manage, as other cases. However, as an organisation they are aware they have limited resource so it has to be a proportionate response.
“The question we wanted to ask was, if we pared back our approach would evictions and arrears rise, and were there issues with vulnerabilities?” explains Jan.
So SSHA created groups of customers to test the theory. One group got business as usual, another group of customers had support stripped right back to the regulatory minimum. In the first three months of that pilot arrears did initially increase for the group where SSHA were offering limited support, but after that period their arrears started to reduce, and the group SSHA were offering normal levels of support to their arrears rose and crossed over.
“By reducing the hand holding this created capacity for an additional 95 arrears cases. This pilot cost no money and now we can more appropriately target our resources,” explains Samantha.
“This piece of work helped identify where we can create capacity with a proportionate response, we are looking at what will be our optimum performance with the least amount of resource and that is why RentSense is key.” Commented Jan.
The change in regulation and welfare raise many challenges for social landlords and ones that will not go away according to Jan:
“The social housing environment will never be the same again, even with 1% rent cut housing associations have made it work and still developed.”
Once Universal Credit is fully deployed SSHA expect 30%of their tenants to be claiming UC. Whereas many landlords may look to invest in additional headcount to counter this transition, SSHA’s income function has remained at four FTE since investing in RentSense, and officers maintain a patch of 1,500.
“We invested in RentSense instead of additional resource and our arrears have reduced year on year whilst the risks and challenges have increased throughout. The risk has been higher, the resource has been static and yet arrears have come down, that is because of how we have targeted our resource, and that comes down to RentSense,” explains Jan.
“Operationally it helps at every level. We can plan for leave and allocate other people’s workload to other officers when people away, we plan this up to three months in advance now as we can map everyone’s leave out,” highlights Samantha. “The team trust the system and don’t feel swamped and it has improved morale and performance.”
“We could develop, invest and make different choices but the world changed and won’t change back. You have decisions where you can spend your resource and this is one case where a software solution saves you capacity and de-risks against the threat,” concludes Jan.