In 2024, social housing providers in the UK spent more on repairs and maintenance than ever before – almost £9bn, which was 13 per cent more than the previous year and 55 per cent more than five years ago. Of course, a significant amount of this has been spent on upgrades around fire remediation, building safety and energy efficiency. It’s also a time when housing providers need to develop better financial resilience so value for money is being scrutinised more closely than ever.
Research from LocalDigital, part of the Ministry of Housing, Communities and Local Government, estimates that as much as £400m is wasted every year on repairs and allocations that are not delivered correctly. Perhaps there were challenges accessing a property if the residents were unavailable, the wrong parts or equipment were brought to the job, insufficient time was allocated for the work or the wrong skillsets assigned. Overall, these factors can result in 40-50 per cent of responsive repairs not being fixed first time.
Finding ways to reduce these avoidable failures and the costs they carry is essential, so what stands in the way?
Artificial intelligence is often touted as being akin to a silver bullet but does it have a role to play here, and if so, how? A good place to start is to look at the private sector to see how similar challenges are solved and where AI fits in.
Learning from the private sector
In the private sector, if up to half of all maintenance jobs weren’t completed first time, alarm bells would be ringing, both metaphorically and probably literally as well. In fact, the alarms would probably be triggered at a much lower threshold and long before the problems had a chance to materialise.
Take manufacturing, for example. This sector is reliant on operations continuing without disruption or delay. Keeping assets running – machinery, equipment and vehicles – can mean the difference between success and failure.
Industry 4.0 technologies such as the internet of things (IoT) ensure that a manufacturer’s assets are all connected digitally so that real-time data on their performance can be collected. This data is fed into digital twins that closely monitor operations and use AI to anticipate when maintenance should be completed. It’s a way to optimise the asset and extend its lifetime.
The gains from predictive maintenance like this are huge: 15 per cent reductions in downtime; 20 per cent increases in productivity; 30 per cent savings from carrying lower inventory; and five per cent reductions in the cost of new equipment (source: Deloitte).
Social housing isn’t going to start adopting these advanced maintenance processes – not yet, at least – but what can we learn from them?
Irrespective of the level of available technology, taking a more predictive approach to maintenance, rather than a reactive or periodically-planned approach, is driven by having reliable asset data. It needs effective data standards so that the data is consistent and usable. This doesn’t require state-of-the-art technologies; it can be managed via much simpler software, but this is exactly where the social housing sector is struggling.
Housing’s data challenges
LocalDigital’s research suggests that fewer than half (46 per cent) of housing providers trust the reliability of their data.
One of the most common data problems is having lots of disparate, unconnected records that live in silos. There might be different standards for recording information from one department to the next or between one person’s spreadsheet on their desktop and the colleague who sits next to them. This makes the data incompatible for sharing, cross-referencing and gathering insights, and almost impossible for AI.
Much of the time, housing providers simply don’t have the tools for collecting and inputting data. Instead, it’s collected manually or using cumbersome legacy technologies, leaving it prone to errors and ultimately not fit for its purpose. There is also a lack of data skills within the sector and sometimes no training available to remedy that.
This all leads to poor data quality and prevents housing providers from unlocking the potential of their data.
Data as an asset
Data should instead be considered as an invaluable asset. We’ve already highlighted the role of predictive maintenance, but its role in improving operational efficiency extends from there. Housing providers can use data to optimise staff allocation and resources, leading to greater cost efficiencies. It can be used to ensure compliance in areas such as health and safety checks or energy efficiency standards by tracking and scheduling essential inspections.
It’s also useful in strategic decision-making, such as using demographics, location and tenant data to make smarter decisions about where and how to invest in new housing developments or refurbishments.
This is all only possible with reliable, standardised data collection and analysis.
Aside from operational improvements, the other main objective of data management is improving tenants’ experience.
By analysing tenant data, housing providers can identify individual needs such as disability requirements or rent payment patterns to provide tailored support. They can predict potential arrears or risks of eviction and develop early intervention strategies.
It’s about being able to offer much more personalised services by having a consistent and accurate view of tenants’ interactions with their housing provider and a reliable history of the property.
A single view of the tenant
To borrow insights from another part of the private sector, retail has been a trailblazer in building a ‘single view of the customer’. From this, retailers can deliver consistent and efficient multi-channel customer experiences.
You can order an item on your mobile, collect it in-store and then receive a tailored discount for your next purchase a few days later via email. If you have a problem with your purchase, AI-powered customer service is available 24/7 through online instant messaging services to resolve it. This level of data integration, where the retailer knows it’s you at every touchpoint, has completely transformed the sector to the point where we all now take it for granted.
Of course, there is a natural desire that these kinds of seamless experiences should be found in all aspects of life, including in tenants’ interactions with their housing providers.
How about creating a single view of the tenant or a single view of the property? While a joined-up, multi-channel approach to delivering personalised services in social housing is a perfectly realistic goal, there are first some fundamental data management practices that need to be built.
This means removing siloes and creating a single version of the truth. It means building data literacy and data skills to improve self-reliance. It means developing capabilities for internal data governance, data sharing and opening up to integrations. Completing a data maturity assessment should be the first step, and from there creating a data strategy and roadmap.
There is no silver bullet, but part of the later roadmap will probably include AI integrations to support service delivery. Since AI is only as good as the data that feeds it, cleaning up your data is the essential first step.
Andy Baker is a customer solutions consultant for data and AI at Civica.
