Proactive vs Reactive Enforcement: Building a Balanced Approach

Most council enforcement teams operate primarily on a reactive basis, responding to tenant complaints as they arrive. While complaint-driven enforcement is essential, relying on it exclusively means enforcement outcomes are determined by who complains rather than where the worst conditions exist. Research consistently shows that tenants most vulnerable to poor housing conditions, including those with language barriers, insecure immigration status, low incomes, or fear of retaliatory eviction, are the least likely to complain. A purely reactive model therefore systematically under-serves the tenants who most need protection. It also creates a skewed picture of the PRS landscape, as complaint patterns reflect complainant demographics rather than actual housing conditions. Only around 2% of non-compliant landlords face enforcement action under reactive models, leaving 98% of non-compliance unaddressed.

Proactive enforcement uses data and intelligence to identify non-compliance without waiting for complaints. This approach offers several advantages: it reaches properties where tenants do not or cannot complain; it enables systematic coverage of the PRS stock rather than random complaint-driven sampling; it generates higher volumes of enforcement cases, which increases civil penalty income and builds deterrence; it identifies patterns of non-compliance by landlord, area, or property type that inform strategic enforcement decisions; and it demonstrates to government and elected members that the team is maximising the impact of enforcement funding. The government's allocation of £18.2 million for PRS enforcement in 2025/26 explicitly supports the development of proactive enforcement capacity, recognising that reactive approaches alone cannot achieve the policy objective of improving PRS conditions across the board.

The optimal approach is not purely proactive or purely reactive but a balanced model that allocates defined proportions of team capacity to each. A common split is 60% reactive (handling incoming complaints, maintaining triage and response standards) and 40% proactive (data-driven screening, targeted inspections, area-based operations). The exact split depends on local factors including complaint volumes, team size, data availability, and political priorities. To protect proactive capacity from being consumed by reactive demand spikes, some councils designate specific officers or days for proactive work. Others set a minimum proactive caseload that is maintained regardless of complaint volumes. The key is making the proactive allocation explicit in the enforcement policy and reporting proactive outcomes separately from reactive outcomes so that both streams receive appropriate attention.

Proactive enforcement requires a targeting methodology to focus limited resources on the properties and landlords most likely to be non-compliant. Data signals that indicate higher non-compliance risk include: properties with expired or missing gas safety certificates; EPC ratings below the minimum E standard; properties with multiple previous complaints even if resolved; landlords with previous enforcement history across any property; areas with high concentrations of older, lower-quality housing stock; properties let through management companies with poor compliance records; and HMO indicators without corresponding licences. Combining multiple risk signals into a composite score creates a prioritised list for proactive investigation. Starting with the highest-risk properties maximises the return on proactive enforcement investment and generates early results that demonstrate the value of the approach.

To maintain the balanced model, teams need metrics that capture the impact of both reactive and proactive enforcement. For reactive enforcement, key metrics include: average response time by priority category; percentage of cases resolved within target timescales; tenant satisfaction with the complaint process; and compliance rate following enforcement action. For proactive enforcement, key metrics include: number of properties screened per quarter; non-compliance detection rate (percentage of screened properties with confirmed issues); number of enforcement actions arising from proactive screening; civil penalty income from proactive cases; and estimated improvement in housing conditions across the PRS stock. Reporting both streams side by side to senior leadership demonstrates that proactive enforcement is not a discretionary add-on but a core enforcement function that generates measurable improvements.