The Future of Private Rented Sector Enforcement: Why Local Authorities Must Adopt Data-Led Approaches

Local authorities across the United Kingdom face unprecedented challenges in regulating the private rented sector. The combination of limited resources, complex legislation, and an increasing number of rogue landlords operating under the radar has made traditional, reactive enforcement models unsustainable. With the implementation of the Renters' Rights Act 2025 and new investigatory powers now available to council officers, the shift towards intelligence-led, data-driven enforcement is no longer optional. It is essential.

This article explores the scale of the unlicensed House in Multiple Occupation (HMO) crisis, the growing issue of council tax fraud, and how innovative HMO discovery tools and data analytics are transforming local authority enforcement strategies.

The Scale of the Unlicensed HMO Crisis

The private rented sector has grown significantly over the last decade, and with it, the number of properties converted into HMOs. While many landlords operate within the law, a substantial proportion evade licensing requirements. This evasion not only deprives local councils of vital licensing revenue but also puts vulnerable tenants at risk of living in overcrowded, unsafe, and hazardous conditions.

Recent statistics highlight the magnitude of the problem. The Local Authority Housing Statistics for 2023 to 2024 estimated there were 472,823 HMOs in England [1]. Of those, 131,061 were subject to mandatory licensing, yet only 93,319 were actually licensed [1]. This reveals a compliance gap of nearly 30 per cent for mandatory HMOs alone. In some areas, the disparity is even more alarming. Reports indicate that in certain London boroughs, there may be up to ten times as many unlicensed HMOs operating covertly as there are on the official register [2].

Despite these high numbers, local authorities struggle to keep pace. The number of new HMO licences granted by councils across Great Britain fell by 5.9 per cent in 2024 [3]. Relying solely on tenant complaints or ad hoc inspections is insufficient to identify hidden HMOs. Council officers need robust HMO discovery tools to pinpoint properties that require investigation before harm occurs.

The Financial Impact of Council Tax Fraud

Alongside the challenge of unlicensed HMO enforcement, local authorities are grappling with significant revenue losses due to council tax fraud. Single Person Discount (SPD) fraud is particularly prevalent. In April 2025, the fraud prevention organisation Cifas reported that nearly one in six people admitted to dishonestly claiming the Single Person Discount [4].

When properties are illegally sublet or converted into unlicensed HMOs, the occupants often fail to register for council tax, or the primary tenant falsely claims a single occupancy discount. This deprives local authorities of millions of pounds in revenue that could be reinvested into frontline services. The National Audit Office has highlighted that the use of data analytics could save the government up to £6 billion per year by reducing fraud and error [5]. However, a House of Commons committee report published in March 2026 found that the use of data analytics to tackle fraud remains "underdeveloped" across the UK public sector [6].

By integrating council tax data with HMO licensing compliance data, local authorities can cross-reference records to identify discrepancies. A property claiming a Single Person Discount but exhibiting multiple financial footprints, multiple utility connections, or multiple tenancy deposit registrations is a prime candidate for investigation.

The Renters' Rights Act 2025: A Catalyst for Change

The landscape of private rented sector enforcement shifted dramatically with the Renters' Rights Act 2025. This landmark legislation, which received Royal Assent in October 2025, is the most significant reform of residential tenancies since 1988 [7]. Crucially for local authorities, it introduces significant new investigatory powers and stronger enforcement mechanisms.

As of 27 December 2025, authorised local housing authority officers gained the ability to require information from landlords, agents, and third parties to support investigations [8]. Officers can now enter business premises to seize documents and, most importantly, access council tax information, housing benefit systems, and tenancy deposit protection scheme data for enforcement purposes [8].

"This data-matching capability fundamentally transforms enforcement from reactive, complaint-driven approaches to proactive, intelligence-led strategies. Authorities can identify unlicensed properties, detect potential exploitation, and build comprehensive pictures of landlord portfolios and compliance histories."

The maximum civil penalty for housing offences has been increased from £30,000 to £40,000 [8]. To support councils in exercising these new powers, the government allocated over £18 million to local authorities in the 2025 to 2026 financial year, with a further £60 million available from 1 May 2026 [9]. Councils must utilise this funding to adopt modern data solutions that maximise the impact of their enforcement teams.

Transitioning to Data-Led Enforcement

The traditional approach to finding hidden HMOs often involves officers physically walking streets, looking for visual clues such as multiple satellite dishes or overflowing bins. This method is resource-intensive and highly inefficient. The future of private rented sector enforcement relies on sophisticated data analytics that can surface risk before a complaint is ever made.

Data-led enforcement involves aggregating multiple datasets to create a comprehensive risk profile for every residential property within a local authority's jurisdiction. This approach was recently piloted by North East Lincolnshire Council. In March 2026, their Cabinet Working Group reviewed a data science activity report aimed at supporting the identification of small, non-licensable HMOs [10]. The pilot used a risk-ranked evidence base to inform targeted validation activity, ensuring a more proportionate use of housing team resources [10].

The North East Lincolnshire pilot recommended a phased approach: first, validating the highest-risk properties through desktop checks and targeted visits; second, sampling medium-risk properties to refine model precision; and third, feeding inspection results back into the model to create a continuous intelligence loop [10]. This iterative methodology is a blueprint for any council seeking to modernise its enforcement capability.

Key Components of Effective HMO Discovery Tools

To successfully implement a data-led strategy, local authorities require access to specialised software that can process complex datasets. Effective HMO discovery tools typically incorporate the following elements:

By synthesising these data points, council officers can generate a prioritised list of properties that exhibit a high probability of being unlicensed HMOs. This allows enforcement teams to focus their limited time and resources on the most egregious offenders, rather than conducting speculative inspections.

Practical Steps for Local Authorities

Councils looking to transition to a data-led enforcement model should consider the following steps as a starting point.

• Conduct a data audit. Before investing in new technology, councils should assess what data they already hold. Many authorities have rich datasets across housing, council tax, benefits, and environmental health that are simply not being used together. Establishing a clear picture of available data is the essential first step.
• Invest in UPRN coverage. The Unique Property Reference Number is the backbone of effective cross-departmental data matching. Councils should prioritise improving UPRN coverage and data hygiene to ensure that records from different systems can be accurately linked to the correct property.
• Establish cross-departmental working groups. Data-led enforcement works best when housing, fraud, council tax, and anti-social behaviour teams share intelligence. Formalising this collaboration through a dedicated working group or data-sharing protocol is a practical way to break down silos.
• Adopt a risk-ranked approach. Not all potential unlicensed HMOs carry the same level of risk. A risk-ranked model allows councils to prioritise properties where the evidence for unlicensed operation is strongest and where tenant welfare concerns are greatest. This ensures proportionate use of enforcement resources.
• Use the new investigatory powers proactively. The Renters' Rights Act 2025 grants councils the power to access tenancy deposit data, council tax records, and housing benefit information for enforcement purposes. These powers should be used systematically as part of a data-led workflow, not merely in response to individual complaints.

How OccupID Supports Local Authority Enforcement

OccupID provides the definitive data solution for local authorities seeking to modernise their enforcement strategies. Our platform is designed specifically to meet the needs of UK council officers, offering unparalleled visibility into the private rented sector.

By leveraging advanced data analytics, OccupID empowers local authorities to identify hidden HMOs, detect council tax fraud, and enforce licensing compliance with unprecedented accuracy. As the Renters' Rights Act 2025 grants councils greater investigatory powers, OccupID provides the essential intelligence required to turn those powers into tangible results. We enable councils to transition from reactive complaint handling to proactive, intelligence-led enforcement, ensuring safer housing for tenants and maximising revenue recovery for local authorities.

To find out how OccupID can support your enforcement team, request a free demonstration today.

References

1. [1] Local Government Lawyer. "From the front line of HMO licensing." Local Government Lawyer, November 2025. https://localgovernmentlawyer.co.uk/housing-law/315-housing-features/98934-from-the-front-line-of-hmo-licensing
2. [2] Accommodation for Students. "Renters' Rights Bill will worsen the number of unlicensed HMOs." Accommodation for Students, August 2025. https://www.accommodationforstudents.com/student-landlord-guides/4579
3. [3] Landlord Today. "Annual drop in number of HMO licenses granted by councils." Landlord Today, May 2025. https://www.landlordtoday.co.uk/breaking-news/2025/05/annual-drop-in-number-of-hmo-licenses-granted-by-councils/
4. [4] Cifas. "Cifas warns about growing Council Tax fraud with 1 in 6 admitting to dishonestly claiming Single Person Discount." Cifas Newsroom, April 2025. https://www.cifas.org.uk/newsroom/single-person-discount-growing-2025
5. [5] National Audit Office. "Using data analytics to tackle fraud and error could save government billions." NAO Press Releases, July 2025. https://www.nao.org.uk/press-releases/using-data-analytics-to-tackle-fraud-and-error-could-save-government-billions/
6. [6] Global Government Forum. "UK government use of data analytics to tackle fraud underdeveloped, MPs say." Global Government Forum, March 2026. https://www.globalgovernmentforum.com/uk-government-use-of-data-analytics-to-tackle-fraud-underdeveloped-mps/
7. [7] Browne Jacobson. "Renters' Rights Act 2025: Practical guide for local housing authorities." Browne Jacobson Insights, January 2026. https://www.brownejacobson.com/insights/renters-rights-act-2025-local-housing-authorities-guide
8. [8] BCLP Law. "Navigating the Renters' Rights Act 2025: key changes and practical implications." BCLP Law, November 2025. https://www.bclplaw.com/en-US/events-insights-news/navigating-the-renters-rights-act-2025-key-changes-and-practical-implications.html
9. [9] Public Sector Executive. "Councils backed to support renters as new protections come into force." Public Sector Executive, April 2026. https://www.publicsectorexecutive.com/articles/councils-backed-support-renters-new-renter-protections-come-force
10. [10] North East Lincolnshire Council. "Cabinet Working Group Houses of Multiple Occupation/Selective Licencing: HMO Data Science Activity." North East Lincolnshire Council, March 2026. https://www.nelincs.gov.uk/meetings/selective-licensing-and-houses-of-multiple-occupancy-cabinet-working-group-2/4-hmo-data-science-activity/