How much is a commercial property really worth?

For decades, the answer has been a blend of flawed inputs. Sale prices from comparables months out of date. Rateable values that lag the market. Gut feel from local agents that doesn’t scale.

But what if you could answer that question with granular, property-level financial data — across 2.5 million commercial properties, every day, at the click of a button?

At Doorda, our Commercial Real Estate dataset doesn’t just tell you what a property is — it gives you the financial and valuation infrastructure to understand what an asset is worth, how it performs, and where the opportunity really lies. This is data-driven property valuation at scale — a modern approach to Commercial Real Estate Valuation that moves beyond estimates and into evidence.

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The Financial Foundation: What’s in the Dataset

Our financial and valuation variables form the connective tissue between a property’s physical characteristics and its true market value — the bedrock of any rigorous asset valuation methodology. Here’s what we cover:

Variable	What It Tells You	Valuation Role
rental_value	The estimated annual rental value of the property	Core input for income capitalisation and yield analysis
business_rates	The official rateable value from the Valuation Office Agency	Benchmark for cost burden and comparable valuation
average_cost_m2_rent	The cost per square metre of rent	Enables valuation benchmarking across property types
location_size_m2	The total size of the property	Critical for per-square-metre valuation metrics

Taken together, these four variables allow you to answer questions that traditional property data simply cannot:

• What’s the rental yield per square metre across my portfolio vs. the market?
• How does this property’s rates burden compare to similar assets in the same local authority?
• Which sub-categories of commercial property offer the best value per square metre?
• Where are the valuation anomalies — properties under-rented or over-valued relative to peers?

This is modern Commercial Real Estate Valuation — not guesswork, but property-level financial intelligence backed by 2.5 million data points.

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Rental Value Intelligence: The Income Side of the Equation

Rental value is the single most important metric for anyone investing in, lending against, or insuring commercial property. It’s the foundation of yield calculations, loan-to-value ratios, and income-based valuation models.

Our dataset captures rental values across millions of properties — from small workshops renting for a few thousand pounds a year to major distribution centres commanding six-figure annual rents.

What this enables for Commercial Real Estate Valuation:

Portfolio-Level Yield Analysis

By combining rental_value with location_size_m2, you can calculate rental yield per square metre across any segment of your portfolio — by local authority, by sub-category, by occupant sector, or by property size band. This is investment property valuation at portfolio scale.

Valuation Anomaly Detection

Spot properties materially under- or over-rented compared to the average for their category and location. These anomalies represent either risk (overvalued assets) or opportunity (undervalued assets ripe for repositioning) — a critical function in any property valuation analysis workflow.

Income Forecasting

Build bottom-up rental income models using actual per-property rental values, not blended averages. Model the impact of lease expiries, rent reviews, and vacancy scenarios with property-level precision — a cornerstone of asset-level valuation discipline.

Valuation Benchmarking

Compare average_cost_m2_rent across similar properties to understand whether an asset is performing at, above, or below market. Our data spans 512 sub-categories and 120 property category descriptions, giving you the granularity to compare like-for-like — comparative market analysis at its finest.

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Business Rates: The Cost Side of the Equation

Business rates are often the single largest fixed cost for a commercial occupier — and one of the most opaque. Yet rates data is often overlooked in Commercial Real Estate Valuation, even though it directly impacts net operating income and therefore capital value.

Our dataset brings clarity and comparability to this critical financial variable.

What the data enables for valuation:

Occupant Cost Burden Analysis

Understanding the business rates burden on your occupants helps you assess their financial resilience — and therefore the quality and security of the income stream. A property with rates disproportionate to its rental value may indicate future occupancy risk, directly affecting its valuation for insurance or portfolio purposes.

Portfolio Tax Exposure

Aggregate business_rates across your portfolio to understand total rates exposure, identify appeals opportunities, and model the impact of revaluation cycles — essential for accurate commercial property appraisal.

Comparative Cost Benchmarking

How does the rates burden on your retail units compare to similar properties in neighbouring local authorities? Are there jurisdiction-level disparities you can use to attract tenants? This is valuation comparables analysis applied to the cost side of the equation.

Sub-Category Valuation Insights

Rates vary dramatically by property type. A casino paying £83,728 in business rates has a very different cost structure to a warehouse paying £2,269. Our data lets you normalise and compare across categories for more accurate real estate asset valuation.

Cost Per Square Metre: The Efficiency Metric

average_cost_m2_rent is where financial and physical data converge — and where the most actionable valuation insights live.

A property may look cheap on total rent but expensive per square metre. Conversely, a large property with a modest total rent may represent exceptional value for an occupant that can utilise the space effectively.

Use cases in Commercial Real Estate Valuation:

• Space efficiency scoring — Which properties in your portfolio deliver the best value per square metre? A key input for any data-driven property valuation model.
• Occupant fit analysis — Does the occupant’s business model support the cost per square metre they’re paying? Cross-reference with SIC code for sector-level norms — valuation by use in practice.
• Lease negotiations — Benchmark average_cost_m2_rent across comparable properties in the same postcode sector to strengthen negotiation positions using real valuation data sources.
• Development feasibility — Model whether redeveloping or subdividing a property makes financial sense based on achievable rent per square metre — highest and best use analysis grounded in data.

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What This Data Unlocks: Three Practical Applications

1. For Investors & Asset Managers

Combine rental_value, business_rates, and average_cost_m2_rent with physical variables like location_size_m2, number_of_floors, and basement_present to build a comprehensive valuation scoring model for every property in your portfolio.

Identify which assets are driving returns — and which are dragging on performance — with property-level precision. This is data-driven valuation in action.

2. For Commercial Lenders

Use rental value and cost-per-square-metre data to stress-test loan portfolios. Which properties would struggle to maintain debt service coverage if rental values fell by 10%, 20%, or 30%? How does the rates burden affect net operating income?

Make lending decisions based on actual financial data, not assumptions — a modern approach to commercial real estate valuation appraisal that reduces risk.

3. For Occupiers & Tenants

Businesses searching for new premises can use average_cost_m2_rent and rental_value to compare options across geographies and property types. Is it cheaper per square metre to take industrial space in Swansea or Manchester? How do rental values for similar properties compare across different local authorities?

Find the right space at the right price — backed by property valuation data that levels the playing field.

The Broader Picture: Financial Data as Part of a Complete Commercial Real Estate Valuation Proposition

Financial and valuation data doesn’t exist in a vacuum. Its power multiplies when combined with the other variables in the Commercial Property CP01 dataset — creating a truly holistic Commercial Real Estate Valuation platform.

Layer	Variables	How It Connects to Value
Physical	location_size_m2, number_of_floors, floor_levels, basement_present	Size and layout underpin rental value per square metre
Occupant	occupant_name, occupant_sic_code_1, occupation_date, n_years_occupant_address	Occupant quality and tenure influence income stability and therefore capital value
Compliance	companies_house_compliance, n_disqualified_directors, fsa_hygiene_score	Regulatory risk affects the quality of rental income and valuation for insurance
Spatial	location_internals_m2 (space usage map), seats	How space is used determines its value to specific occupants — valuation by function
Location	latitude, longitude, local_authority, output_area_2021, lsoa_2021	Geography drives comparable valuation and market-based valuation

A property that looks financially strong on paper — good rental value, reasonable rates burden — may reveal hidden risks when overlaid with occupant compliance data. Conversely, a property that appears modest on financial metrics alone may be an exceptional value when paired with a long-tenured, financially stable occupant.

Financial and valuation data is the engine. But the full dataset is the vehicle for true Commercial Real Estate Valuation.

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The Bottom Line

In a market where every basis point of yield matters and every pound of cost affects net operating income, having property-level financial and valuation data at your fingertips isn’t a luxury — it’s a competitive necessity. This is the new standard for Commercial Real Estate Valuation.

Our dataset covers 2.5 million commercial properties across England, Wales, Scotland, and Northern Ireland, with financial variables that let you analyse, benchmark, and optimise at a scale and granularity that simply isn’t available from traditional Commercial Real Estate valuation data sources.

The question isn’t whether you can afford to use better financial data. It’s whether you can afford not to.