Automated Compliance Checking: How It Works

Traditional PRS enforcement is reactive: a tenant complains, an officer investigates, and action follows if a breach is found. This model catches only the properties where tenants are willing and able to complain, leaving the majority of non-compliant properties undetected. Studies suggest that only 10-20% of tenants experiencing poor conditions ever contact their council. Automated compliance checking flips this model. By ingesting data from multiple sources and checking every PRS property against compliance criteria automatically, councils can identify non-compliant properties before any complaint is received. The Renters' Rights Act 2025 and the PRS Database make this approach more practical than ever, providing the national dataset that automated systems need.

The process follows a clear pipeline: 1. Data ingestion: The system pulls data from multiple sources. These include the EPC register (energy ratings), council tax records (property and occupant data), the licensing database (licence status), the PRS Database (registration and compliance declarations), Land Registry (ownership), and police data (crime and ASB). 2. Property matching: All data is matched to a single property record, typically using the UPRN as the primary key. This creates a comprehensive profile for each property. 3. Compliance rules: The system applies a set of rules reflecting legal requirements. For example: "If property is PRS and EPC rating is F or G and no MEES exemption registered, then MEES breach." Or: "If property has five or more occupants forming two or more households and no HMO licence, then licensing offence." 4. Risk scoring: Properties with compliance failures are scored based on severity, number of breaches, landlord history, and area risk factors. This produces a ranked enforcement priority list. 5. Output: The system generates alerts for enforcement officers, populates case management queues, and produces management dashboards showing compliance rates across the borough.

The main compliance rules that can be automated include: Licensing compliance: - Mandatory HMO licence required (five or more occupants, two or more households) - Additional HMO licence required (if scheme in force) - Selective licence required (if scheme in force) - PRS Database registration required (from late 2026) - PRS Ombudsman membership required (from late 2026) Energy efficiency: - Valid EPC required for all PRS lettings - EPC rating of E or above required (MEES) - EPC validity (within 10 years) Safety compliance: - Gas safety certificate (annual, declared on licence or PRS Database) - Electrical safety certificate (five-yearly EICR) - Smoke and carbon monoxide alarms (as required by regulations) Property conditions: - Known HHSRS Category 1 hazards from previous inspections - Outstanding improvement notices or prohibition orders - Complaints history Not all of these can be fully automated today, as some require physical inspection. But automated checking can flag properties where non-compliance is likely, directing officers to inspect the highest-risk properties first.

Automated compliance checking is only as good as its data. Common data quality issues that affect accuracy include: Stale data: An EPC that shows an F rating may have been followed by improvements, but if no new EPC was commissioned, the system still shows a breach. Officers must verify before taking action. Matching errors: Where UPRNs are not available and address matching is used, false matches (two different properties confused) and missed matches (the same property not linked) reduce accuracy. Incomplete data: Not all PRS properties appear in every dataset. A property with no EPC on the register may be owner-occupied and never needed one, or it may be an unlicensed PRS property with no certificate. To manage these issues, automated systems should: - Assign confidence scores to matches, not just binary pass/fail - Allow officers to verify and override automated flags - Learn from officer feedback to improve accuracy over time - Be transparent about data sources and freshness A well-designed system with good data quality can achieve a false positive rate of 10-20%, meaning 80-90% of flagged properties genuinely require attention. This is far more efficient than random or complaint-driven inspection.

Councils considering automated compliance checking should plan for: Data infrastructure: All relevant data sources must be accessible, ideally via APIs. The data audit and integration work described in our API integration article is a prerequisite. Rule maintenance: Compliance rules change as legislation evolves. The system must be configurable, allowing rules to be updated without developer involvement. The Renters' Rights Act 2025 introduces several new requirements that will need to be added as they come into force. Officer training: Automated systems change the officer's role from finding non-compliance to verifying and acting on it. Training should cover how the system works, how to interpret risk scores, and how to handle false positives. Governance: Automated decisions that trigger enforcement action raise fairness and transparency questions. Councils should publish their compliance checking methodology and ensure officers exercise human judgment before formal action is taken. Cost: Purpose-built automated compliance checking platforms range from £20,000 to £200,000 depending on scope and complexity. However, the civil penalty income from a single identified breach (up to £30,000) can justify the investment. The £18.2 million enforcement fund provides a funding route for councils that can demonstrate the enforcement benefit.