Can data improve your tenants’ lives?

Transforming social housing

By Andy Baker, customer solutions consultant – Data and AI, Civica 

In 2024 alone, spending on repairs and maintenance in the UK social housing sector reached an unprecedented £8.8 billion, a figure that’s up 13% year-on-year and a staggering 55% higher than five years ago.

While much of those costs are channelled into vital safety and sustainability work, from fire remediation and building-safety upgrades to energy-efficiency measures, research from LocalDigital, part of the Ministry of Housing, Communities and Local Government, says that up to £400 million annually may be wasted on repairs and allocations that are not delivered correctly.

The causes are a familiar story: the wrong parts were ordered; the tenants were not at home; an engineer was sent without the correct skills or tools for a particular job. These instances add up to nearly half of all responsive repairs failing at their first visit.

For housing associations and local authorities already navigating huge cost pressures, this kind of inefficiency is simply unsustainable. Beyond cost-cutting, how can social housing providers boost service, improve asset performance, and build financial resilience?

The answer may lie in better use of data.

From responsive to predictive maintenance

In the private sector, downtime is a red flag. If machinery, vehicles or equipment aren’t performing at their best, the cost is immediate and measurable. Recording the same level of maintenance failure in the private sector would push many organisations out of business.

To counter this, one of the major changes in sectors such as manufacturing and utilities was to shift from reactive to predictive maintenance.

By linking equipment via the Internet of Things (IoT), collecting real-time performance data and running that information through AI-powered models like digital twins, these organisations are anticipating faults and avoiding downtime, minimising any impact on the bottom line. And the savings are tangible. One analysis by Deloitte cited reductions of around 15% in unplanned downtime, productivity increases of 20%, 5% reductions in the costs of new equipment and spare-parts cost savings of roughly 30%.

OK, social housing doesn’t resemble a factory floor and is unlikely to employ lots of advanced technology – not just yet, anyway – but there is a valuable lesson to take from the private sector: data-driven predictive maintenance beats reactive fixes. Repairs will become more efficient, tenants will experience fewer disruptions and assets will perform for longer.

No matter the level of technology available, taking a more predictive approach to maintenance rather than a reactive or periodically planned approach is driven by having reliable asset data. Effective standards need to be in place so that the data is consistent and usable. This doesn’t require state-of-the-art technologies, it can be managed via much more simple software.

Fixing data quality and fragmentation

Yet the reality for many in social housing is stark: data remains more of a barrier than enabler.

The same LocalDigital study found only 45.5% of housing providers trust their own data. Over half admitted to encountering data-quality issues within the past year.

These problems often stem from fragmented systems. Information lives in silos where maintenance teams, housing officers, finance departments and contractors all record and access data differently. In some cases, critical information sits in individual spreadsheets or even in paper files, with no consistent standard for entry or format. This is all creating barriers to improvement.

Without integration, data can’t be shared or analysed effectively to produce workable insights on cost- and efficiency-savings. Many landlords are instead still hampered by legacy IT systems, high volumes of manual data entry – which is invariably prone to human error – and a shortage of data literacy among staff.

For all the promise of digital transformation, the foundation simply isn’t ready yet.

Treating data as a strategic asset

To unlock its potential, housing providers must shift how they view data. It’s not an operational necessity, but a strategic asset.

We’ve highlighted the role of data in predictive maintenance above, but its role in improving operational efficiency extends from there. Landlords can use data to optimise staff allocation and resources. When requests for repairs come in, providers can ensure the right engineer, tools and materials are dispatched, which will increase the chance of a fix on the first visit.

Data can be used to ensure compliance by tracking and scheduling health and safety checks, gas certificates and energy-performance certificates (EPCs) inspections.

It’s also useful in strategic decision making, such as using demographics, location, and tenant data to make smarter decisions about where to invest in new housing developments or refurbishments.

Insights for a better tenant experience

Aside from operational improvements, the other main objective of data management in social housing is improving the tenant experience.

By analysing tenant information, landlords can identify those with specific support needs (mobility, vulnerability, financial hardship) and plan interventions accordingly. They can work with tenants towards proactive arrears prevention by analysing payment patterns and engagement history to spot early signs of rent-payment issues and step in before situations deteriorate.

They can also take a proactive approach to managing tenant enquiries or complaints, anticipate issues, streamline contact-centre operations and boost tenant satisfaction. It shifts from reactive case-handling to proactive care.

Building a single view of the tenant and property

In retail, we’re used to brands knowing who we are: purchase history, online behaviour, in-store interactions. We all now know and accept that this information will feed into tailored communications and offers, or we can opt out.

Imagine a similar system where every tenant’s history is also visible in one place: all repairs, communications, rent payments, support needs. And how about a single record of every home’s condition, maintenance history and asset-type? With that combined view, landlords would gain full visibility and be in a far better position to deliver personalised and joined up services, unlocking substantial savings.

At its heart, all it takes is the correct approach to data management.

To get there, start with the basics: data collection, consistency, integration and governance. Equip your team with the right software to take care of this for you. Break down silos and integrate data across departments. Introduce robust data governance with clear roles, standards, and ownership, and investment in training and data literacy among staff.

Civica Housing Management is a cloud-based platform that delivers comprehensive housing, asset and property management capabilities to housing associations - providing them with the visibility they need to ensure those in need are protected and supported at all times.

AI is only as good as the data that feeds it

Once these foundations are in place, the door opens for the next wave: AI, predictive analytics and operational transformation.

Predictive analytics offers the potential to help maintenance teams pre-empt breakdowns. Natural language processing can enhance contact centres by automating simple queries. Machine learning can spot rent payment patterns that indicate financial distress long before arrears occur.

Over time, these systems will become integral to daily operations, improving accuracy, speeding up responses and allowing staff to focus on more complex or sensitive tasks.

However, there’s a clear caveat: AI is only as good as the data it uses. If inputs are inconsistent, incomplete or inaccurate, then outputs become unreliable. It’s time to make a start on improving data maturity now so that the sector is ready to take advantage of these emerging technologies.

By rethinking data management and embracing the application of AI, housing providers can shift from firefighting to foresight - predicting problems rather than reacting to them. They can optimise every pound spent, improve service, minimise disruption and extend asset life.

And crucially, they can deliver a tenant experience that respects people’s time, needs and aspirations.