Doorda’s Commercial Real Estate Data UK provides comprehensive, granular insights into over 2.5 million commercial real estate locations across the United Kingdom. This dataset combines property characteristics, occupancy information, financial metrics, and business intelligence to deliver a complete picture of the commercial real estate landscape.

Our data is sourced directly from 100s of Local Authorities and dozens of national ones providing you with a single view, covering everything from high-street retail units to industrial warehouses and office complexes. With records updated every month, this single dataset product offers comprehensive market intelligence for Commercial Real Estate across the UK.

Doorda’s Commercial Real Estate Product Contains

2,504,526	Commercial Real Estate Locations	100% Coverage
1,799,911	Occupant Names	72% Coverage
1,938,509	Rental Estimates	77% Coverage

What sets this dataset apart is that Commercial Real Estate Data UK typically requires stitching together multiple disparate sources — property characteristics, rental valuations, business rates, Companies House compliance records, FSA hygiene scores, gambling licences, and geospatial data. This dataset consolidates all of those into a single, pre-joined dataset, eliminating the need for complex ETL pipelines, manual cross-referencing, or wrestling with mismatched schemas.

Every property record comes enriched with its occupant and ownership details, financial metrics like estimated rental costs and average cost per m², compliance flags, and even internal floor-level breakdowns. This means you can go from asking a question to getting an answer in seconds — whether you’re analysing a portfolio for risk, evaluating site suitability, or researching market trends. It’s built for speed of insight, not just volume of data.

Pull the data on demand, or integrate directly into your applications via our SDK.

Key Features of Commercial Real Estate Data UK

Rich Property Attributes

• Detailed commercial real estate classifications (category, sub-category)
• Physical characteristics (floor levels, basement presence, size)
• Geographic coordinates with source attribution

Financial Intelligence

• Rental values (median: £9,990, mean: £33,202)
• Business rates (median: £3,844, mean: £10,069)
• Property sizes in square meters (median: 82m², mean: 355m²)

Occupancy Insights

• Current occupant names and company numbers
• Occupation dates and duration at address
• SIC codes for business classification
• Occupant source verification (Ratepayer, Name Over Door, Commercial Owner)
• Owner-occupier identification

Business Intelligence with Commercial Real Estate Data UK

• Companies House compliance status
• Registered company counts at each address
• Director disqualification information
• FSA hygiene scores for food businesses
• Gambling license information

Use Cases

Commercial Real Estate Investment & Portfolio Optimisation

Investors and fund managers can benchmark assets against comparable properties using rental values, business rates, and size data. Cross-referencing occupancy with Companies House compliance and director records enables due diligence. Portfolio managers can track competitor holdings, identify overexposed segments, and support valuation models for fund reporting.

Example: A REIT evaluating retail warehouses across the Midlands benchmarks rental values against the dataset median of £9,990, cross-references occupant creditworthiness via SIC codes, and identifies owner-occupied vs tenanted assets — informing acquisition and exit strategies.

Retail & Hospitality Expansion Planning using Commercial Real Estate Data UK

Retailers and hospitality operators can map competitor locations, identify underserved postcodes, and filter for optimal property sizes and categories. Franchise developers can pinpoint sites by rental value bands and footfall-adjacent categories. Performance benchmarking against similar sites ensures new openings are set up for success.

Example: A restaurant chain planning 50 new UK sites filters for “Shop & Premises” units between 100–200m² in low-competition postcodes with rental values under £20,000 and strong FSA hygiene scores — reducing site identification from weeks to hours.

Credit Risk & Insurance Assessment

Lenders and insurers can enrich risk models with property and occupancy intelligence using the Commercial Property Data. Credit teams verify business locations, cross-check company numbers against director disqualification records, and assess sector concentration risk. Insurers evaluate property size, sub-category, business rates exposure, and occupancy history to underwrite policies based on real-world data.

Example: A lender processing a £2M loan for a manufacturer validates the trading address, checks disqualified directors at the property, reviews SIC code industry risk, and assesses local market saturation — all from a single property record.

B2B Sales Intelligence & Account-Based Marketing Leveraging Commercial Real Estate Data UK

B2B teams can build targeted prospect lists filtered by SIC code, property size, occupation tenure, and geography. Sales teams prioritise accounts by estimated turnover, rental value bands, and owner-occupier status. Marketers enrich profiles using occupant company numbers to identify group structures and map office footprints for ABM campaigns.

Example: A SaaS provider targeting manufacturers builds a list of 12,000 “Workshop & Premises” occupants with manufacturing SIC codes, sizes over 500m², and rentals above £25,000. Enriched via Companies House, they launch an ABM campaign across 3,200 accounts achieving a much higher conversion rate.

Frequently Asked Questions about Commercial Real Estate Data UK