Providing Local Authority Housing services with Registrations data in order to reduce the time that families on the social housing list wait for a Council property

Evolution of hypotheses during the discovery

The discovery initially explored possible use cases for Registrations across local public services in Worcestershire only. This highlighted four use cases: Adult Social Care, Children’s Services, Housing, and Revenues and Benefits.

A second round of user testing explored these use cases in more detail and sought feedback from other Councils. Housing emerged as the strongest use case, and a final round of testing therefore explored the business case for a solution. 

Problem overview – managing tenant deaths within Housing Services

All Housing services in England need prompt notification of the death of a tenant to assess whether the property can be swiftly brought back into use. Currently many LA Housing services receive reports on tenant deaths from TUO, however the discovery cited issues with incomplete data and a complex data journey (see pgs 14-16, 33-34).

• Tenancy Officers – if Officer cannot identify a deceased tenant next of kin it can result in a ‘difficult’ investigation (e.g. contacting neighbours) that can take 4 days or more
• Voids – while Tenancy Officers are tracking down a deceased tenant’s next of kin the property is void (i.e. unoccupied) and families on the social housing waiting list, including those in Temporary Accommodation (TA), cannot move in
• Rent arrears – when a social housing tenant dies the tenancy does not necessarily pass to other members of the household. If occupants do not inform the Housing service of the death they can accrue significant rent arrears

This issue is common to all authorities

We conducted user research during discovery primarily in Worcestershire. There, the take-up of TUO is high (96%), but even so Tenancy Officers report that the data frequently does not notify the service of tenant deaths.

In other places, take-up is much lower (e.g. Suffolk – 10% to 20%) or it is not offered at all. In places like this errors and subsequent negative impacts for services and residents are much more frequent (pg. 46).

Expected improvements for users

We expect that the solution to this problem is relatively straightforward. Most Housing services currently make use of TUO reports, so it is an issue of substituting into an existing process and workflow a better quality report from a different source.

• Tenancy Officer – access to direct Registrations data means Tenancy Officers will not ‘miss’ any tenants that pass away, and so will avoid difficult investigations
• Residents – social housing waiting list times will reduce

Benefits of using Registrations data within Housing services 

The discovery identified four principal economic and non-cashable benefits of addressing this problem (see pgs 44-49 for more information).

1 – Reduction in Tenancy Officer time spent conducting difficult investigations

• In the discovery we did not convert benefits in terms staff time to economic savings since any staff time saved would be used for other activities
• Total benefit: 181 hours / 26 working days saved in Redditch and Bromsgrove’s Housing service per year (across 5 Tenancy Officer roles)

2 – Residents avoid accruing rent arrears when a tenancy holder dies but other household members continue to live in the property without notifying the Council

• These savings could accrue to either residents or the local Council. This depends on whether arrears are written off (i.e. lost Council revenue), which is common, or whether residents ultimately pay back the arrears
• Total benefit: £7,085 in Ipswich Borough Council per year

3 – Total reduction in time that properties are void and reduction in spending on TA

• The number of weeks properties are void is converted to an economic saving for the Council, assuming households currently housed in TA can move into the properties earlier than they would have otherwise. We have not attempted to quantify wider benefits to residents of reduced time waiting for social housing, though it is potentially significant (e.g. health, wellbeing, improved relationship between Council and communities)
• Total benefit: £83,243 in Hackney per year

Note that the extent of the above benefits varies by authority: places where TUO take-up is low (e.g. Suffolk) will experience benefits from better data quality more frequently; and places with long housing waiting lists and expensive TA (e.g. London) will also experience greater savings from reducing void properties.

Return on Investment

For LA-run Housing services the benefit case for using Registrations data is very attractive. It is envisaged that data from Registration service will substitute straight into an existing process for almost all services, so initial set-up and ongoing cost of the solution will be minimal.

National cost: benefit case

Following MHCLG feedback the business case has been updated to assess the potential benefits across England.* Since the problem is common to all Housing services and all Registrations services are covered by the Digital Economy Act, we believe a solution is highly replicable.

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* Note. National business case draws on national data (e.g. Housing Dwelling Stock Estimates: 2017, England; Local authority average weekly rents, by district, Jan 2019). For assumptions where we were unable to find national data (e.g. tenant deaths per 1,000 properties) we in every case used the most conservative assumption from the four LAs that provided us with data during the discovery. Assumptions have also been adjusted for optimism and other bias as per Government CBA guidelines.

Tools to ensure a collaborative approach

This alpha involves a large number of organisations (see 2.6 for partnership details). The discovery project worked with a similar group (though with only one LA Housing service) and successfully achieved a collaborative, co-working approach. We will employ similar methods and in the alpha build on them based on feedback from two retrospectives conducted at the middle and end of the discovery:

• Full team kick-off workshop – discuss and agree objectives, roles and responsibilities, and high-level project roadmap; also co-design user research materials (key assumptions, interview scripts, template outputs etc.)
• Public Trello board – all partners are sighted on key workstreams, deadlines, and dependencies; managed by WODA
• Slack – this was useful in the discovery for partners in different locations to communicate. We will run daily Slack ‘stand-ups’ in the alpha (e.g. at a set time each morning) to share key information and coordindate workstreams

Iterative process

• Full team workshops every 2-3 weeks – given that partners are in different locations these will serve as both show-and-tells and sprint planning sessions. For instance, in the morning team members share more detailed findings of their work from the preceding weeks and take joint decisions (e.g. prioritise user needs) and in the afternoon plan the next phase of work
• Design approaches – we will employ methods like Storyboarding, Co-Creation Sessions and Lean Canvases to quickly generate, develop and test solutions. We will do iterate this process (both at full project team workshops and in smaller groups) prior to developing real-data prototypes to test with services
• Retrospectives – we will hold retrospectives at the project mid-point and end point. The 3 Ls and Starfish approaches worked well during discovery. They improved team working (e.g. we initiated Slack stand-ups and decided to set clearer objectives and outputs for full-team workshops) and partners not familiar with the ceremony found them fun, empowering and valuable for improving team working

Governance structures

• The alpha project will form part of Worcestershire County Council’s ‘Digital Transformation’ Board and therefore receive full transparency, guidance and approval as part of existing processes
• The WODA Lead Officer is a member of this board so will represent the project at a governance level

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As well as grant funding we would benefit from the support of the Local Digital Collaboration Unit (LDCU) in upskilling the project team in user research, agile ways of working and digital transformation.

The external provider is also expected to play this role, however LDCU’s input was valuable during the discovery and we would benefit from similar support as a critical friend throughout the alpha.

• LDCU direct involvement – an LDCU representative attended several workshops and project calls during the discovery, feeding into the design of user research materials and highlighting outstanding hypotheses we needed to test. This would support would be helpful again in the alpha
• Review of outputs – an LDCU representative also provided feedback on outputs at the end of the project. We will seek this input earlier on and throughout (e.g. user research materials, initial user research outputs, draft solution templates) rather than just at the end of the project to ensure we follow a best practice approach
• Training – Worcestershire County Council has committed to upskilling staff in agile, user research and other new ways of working that are more consultative and put user and resident voices at the heart of services. For instance, the Director of Commercial and Commissioning have recently attended GDS training sessions and the CEO is booked on a future course. There is now a push to ensure that frontline and other service personnel get to grips with this approach. The project team will therefore encourage staff in both the Housing and Registration services to attend training sessions

In addition, if the alpha is successful then MHCLG may be able to support with disseminating the learnings. As described in question 2.9 below, we anticipate that the main way of replicating a solution is by clearly documenting the new process in Worcestershire / Suffolk and then encouraging other Housing services to take up the opportunity. MHCLG could support this by advertising the findings through its networks or through direct communications.