"Today’s renters are different. The rise of self-employment, the gig economy, and alternative living arrangements means many people simply don’t “fit the form” anymore. As a result, landlords are rejecting qualified tenants, not because they’re risky, but because the system can’t see them properly"
- Sarah Wernér - Husmus
The UK rental market is on the cusp of a radical shift. As the Renters’ Rights Bill moves closer to becoming law, landlords and letting agents are being asked to adapt to a more tightly regulated and inclusive system. But regulation alone won’t be enough to fix the biases baked into traditional tenant referencing. That requires a technological rethink.
For decades, landlords and agents have relied on outdated metrics - credit reports, PAYE employment, and salary multiples - to judge a tenant’s reliability. These signals are often poor predictors of actual rental behaviour, and worse, they disproportionately exclude people who would make perfectly good tenants: freelancers, recent graduates, carers, immigrants, and anyone with a non-standard financial footprint.
At Husmus, we believe it's time for not just a more accurate approach, but a more inclusive one too, built not on assumptions, but on data.
Why bias persists in tenant selection
Traditional referencing was designed for a time when employment was linear and financial histories were easy to categorise. Today’s renters are different. The rise of self-employment, the gig economy, and alternative living arrangements means many people simply don’t “fit the form” anymore. As a result, landlords are rejecting qualified tenants, not because they’re risky, but because the system can’t see them properly.
This not only hurts tenants, it increases void periods, narrows the applicant pool, and exposes landlords to more risk, not less. The very tools designed to protect property income are, ironically, reducing it. And insurance, often viewed as a safety net, frequently doesn’t step in either, either due to coverage gaps or because tenants are excluded by the very referencing criteria that insurance policies require.
What does data-led inclusivity look like?
We’ve developed an AI-powered assessment model that analyses behaviour, not just background. With tenant consent, our system looks at income and payment patterns, lifestyle indicators, and true affordability to create a fuller picture of each applicant’s ability to sustain a tenancy. It's fast, accurate (97% so far), and crucially, it’s inclusive.
Our tech doesn't just say yes or no. It helps landlords understand why a tenant is a good fit, even if their credit history or employment status would traditionally raise red flags. It’s a smarter, fairer way to screen tenants - and when paired with our rent guarantee insurance, it removes the need for arbitrary cut-offs entirely.
This kind of risk transparency also opens the door to new protections. Insurance products can now be more closely aligned with actual rental risks - unpaid rent, property damage, breakdowns - not abstract creditworthiness. That means landlords are no longer forced to rely on deposits, guarantors, or high up-front costs to feel secure.
Inclusivity isn’t risky. In fact, it’s safer
There’s a common fear that being more inclusive means opening landlords up to more risk. In reality, our data shows the opposite.
Every single tenant claim paid out by rent guarantee insurers had previously passed a credit history check. Similarly, the majority of tenants who later defaulted on rent had clean CCJ records at the point of application - the very metric most landlords and agents treat as a binary go/no-go decision.
This isn't an indictment of due diligence - it's evidence that we're measuring the wrong things. A clean credit history tells us about past financial behaviour, not future capacity to pay rent. A tenant might have an exemplary credit score today but lose their job tomorrow, or face unexpected medical bills, or experience a relationship breakdown.
The data suggests we need to shift from backwards-looking financial archaeology to forward-looking affordability modelling. Instead of asking 'Have they paid bills before?' we should be asking 'Can they realistically sustain this rent level given their income volatility and essential outgoings?'
Many tenants excluded by conventional checks go on to be excellent renters. We reduce false negatives while keeping false positives low, and if life does happen, our insurance picks up the slack.
With Husmus referencing, our clients pay for success. This means if a tenant fails, we will keep referencing new tenants until someone is successful. Moreover, because we’re the same company underwriting the risk, our incentives are aligned with our clients. It’s a system designed for trust, not box-ticking.
Where next for the rental market?
The Renters’ Rights Bill is a step in the right direction, but it won’t create meaningful change if the industry keeps using outdated tools. Technology can take us further, towards a rental ecosystem where bias is designed out, access is widened, and risk is managed with insight, not instinct.
The question isn’t whether the industry is ready for data-led inclusivity. It’s whether we can afford to wait any longer.
