Using Data to Predict Arrears Before They Happen

Introduction: Why Proactive Arrears Management Matters

Arrears are one of the most pressing challenges facing housing providers today. Whether you’re part of a local housing association, support vulnerable tenants in supported housing, or manage hundreds of student units — unpaid rent can lead to service cuts, tenancy breakdowns, and significant administrative strain. What makes matters harder is when you only know there’s a problem after it’s already happened.

The ability to predict arrears before they happen could transform the way we manage tenancies. It’s not just about protecting revenue — it’s about identifying vulnerability earlier, intervening faster, and reducing the need for formal enforcement or eviction procedures.

But for many housing providers, the right data doesn’t exist in the right form — or it does, but it lives in five different systems and ten spreadsheets. Over the past decade working with housing teams of all sizes, I’ve seen firsthand how reliance on outdated tools and siloed information blocks efforts to become more predictive and proactive.

The Real Challenges Housing Teams Face

Before we jump into data models and predictive tools, it’s worth surveying the on-the-ground realities that housing providers deal with every day. These are the systemic barriers that prevent teams from being truly effective in arrears prevention.

Manual Workarounds

Many income recovery teams still rely on spreadsheets, paper notes from phone calls, and standalone databases built up over years. That means frontline officers spend hours every week chasing information, duplicating effort, or relying on memory to spot trends. There’s little time left for quality tenant engagement.

Legacy Systems

Older housing management systems often weren’t designed with integration or analytics in mind. They might record transactional data — such as the balance or a missed payment — but they don’t give you broader insight into tenant behaviour or risk. Worse still, many legacy systems make it difficult to export usable data at all.

Disconnected Systems

Housing data is now spread between multiple systems: housing management platforms, CRM tools, rent accounting software, tenant portals, and sometimes even third-party systems like Universal Credit portals or council support services. These systems rarely talk to each other, which means tenancy officers are constantly switching contexts. Connecting the dots — for example, recognising that a missed maintenance appointment and a dropped benefit claim might signal future rent issues — becomes near impossible.

Resource Pressures

With growing workloads and diminishing budgets, many housing organisations are operating in firefighting mode. The focus is on responding to tenants who are already in arrears, completing compliance audits, or following rent escalation processes. There’s rarely time (or tooling) to step back and spot preventable arrears before they crystalise.

Changing Tenant Needs

The financial landscape for tenants has also shifted. More residents now work in precarious or low-income work. There’s a higher reliance on digital forms of communication, which not all tenants are confident using. And the cost-of-living crisis adds continual pressure. These changes demand more agile, data-informed responses from housing providers — but the systems in place haven’t kept up.

What Does It Mean to “Predict Arrears”?

Predicting arrears doesn’t mean having a crystal ball. It means using existing data points to surface risk indicators before rent is missed. You can then prioritise support, outreach, or referral early — perhaps even preventively.

Some examples of predictive indicators include:

• New tenants missing their first rent payment
• Tenants with recent welfare benefit changes or deductions
• Reduced service or portal engagement over time
• Delays in responding to communications
• Increased reports of disrepair or anti-social behaviour
• Historic patterns — e.g., tenants who usually go into arrears around the holiday season

Alone, none of these data points means a tenant is guaranteed to fall into arrears. But combined, in the right context, they become powerful indicators. With the right models or business rules, these patterns can be flagged automatically — giving your team a lead on prevention days or even weeks before any payment is missed.

Modern Tools that Make This Possible

If your current tools don’t make this level of insight possible, you’re not alone. But things are changing — and affordable, practical solutions are becoming more accessible, even to small teams.

Business Intelligence Platforms

Tools like Power BI or Tableau can link to your housing management system and surface indicators on an easy-to-read dashboard. You don’t need machine learning to start — instead, begin with basic rules: missed payments, support plan expiry, length of arrears streaks, etc. These platforms let you visualise risk in ways that are scalable and shareable across your organisation.

Integrated Housing Management Systems

Some modern housing platforms now offer in-built analytics functions. Look for systems that allow you to create custom rules, integrate data from multiple sources (e.g., benefits, income, support), and trigger alerts or workflows when risk thresholds are reached.

Automation and Workflows

It’s not just about insight — it’s acting on it. Setting up automated communications (e.g., a customised SMS when a tenant misses two consecutive transactions), or generating support referrals through low-code platforms like Power Automate, can help reduce the time it takes to respond to risk.

Cross-Team Collaboration Platforms

Tools like Microsoft Teams or Trello (connected with tenancy and arrears data) can help multi-disciplinary workers around the tenant collaborate. When arrears, support needs, or engagement issues show up, the platform can prompt a shared action plan — ensuring no one falls through the cracks because “it wasn’t my job”.

Implementing Predictive Arrears Prevention in Stages

Going from reactive to predictive won’t happen overnight. But you can begin incrementally. Here’s a staged approach many housing teams have used successfully:

1. Audit Existing Data

Map what data you already collect, where it sits, and how complete or recent it is. Common sources include rent payment logs, CRM interactions, Universal Credit records, and tenancy history. Typically, you’ll find your teams already have rich information — it just lives in disparate places.

2. Define Basic Risk Indicators

Work with front-line staff to develop a shared understanding of early warning signs. These don’t need to be technical. It could be “first-time UC applicants”, “low engagement tenants”, or “tenants in supported housing with changed support status”. Create a simple scoring system or traffic light indicator as a starting point.

3. Build or Buy Simple Dashboards

Use tools that your teams are comfortable with — Excel, Power BI, or another reporting layer — to start tracking your risk cohort. This gives you visibility. Some organisations even print these and review them weekly at team huddles.

4. Act on the Insight

Risk prediction is only useful if it leads to intervention. Develop pathways — custom templates for calls or letters, soft referral to tenancy sustainment services, or proactive check-ins — and empower staff to act on early-stage risk, not just formal arrears cases.

5. Constant Iteration

Listen to your team. You’ll soon find that some indicators work, while others don’t. Build a feedback loop, and iterate on your risk model quarterly. As your systems become more connected, you can refine the model and introduce predictive capabilities like informed intervention recommendations.

Closing Thoughts: Data Can Be a Safety Net, Not Just a Scorecard

Predictive arrears management isn’t about penalising tenants — it’s about seeing them more clearly, earlier. When used ethically and strategically, data can surface vulnerability, aid tenancy sustainment, and free up officer time to focus on human support, not admin cycles.

Many teams feel overwhelmed when thinking about data analytics — but the truth is, the raw ingredients are already in your day-to-day work. By starting small and building smart systems around the risks you already see, you can relieve pressure, improve tenancy outcomes, and ultimately keep more people in stable housing.

If you need help implementing technology into your organisation or want some advice — get in touch today at info@proptechconsult.uk