From Bricks to Bytes: What 30 Years in Commercial Property Taught Me About Valuing What You Can't See

I spent most of my career in commercial property. I worked with institutional landlords, investment trusts, and pension fund managers. We valued buildings: office blocks in central London, retail parks, industrial warehouses, hotels. We knew how to walk a property, assess its condition, analyse the rental covenant of its tenants, compare it to recent transactions of similar buildings, and calculate its yield.

It was a regulated profession. We followed RICS standards (Royal Institution of Chartered Surveyors). We knew the three valuation approaches — cost, market, income — and we knew which to apply in which circumstances. We had market data. We had comparable transactions. We had understandable, predictable depreciation curves. A 30-year-old office building was worth less than it was at 15 years. A building's value could reasonably be predicted from its location, tenant quality, lease length, and rental yield.

Then I transitioned into valuing intangible assets — technology capital, customer relationships, data, organisational knowledge. At first, it seemed a different universe. How could you value something you could not walk through, touch, or inspect? How could you compare it to market transactions when markets barely existed? How could you predict its useful life when depreciation curves were invisible?

I realised, after several years of struggling with the conceptual differences, that the fundamentals are not different at all. Valuing an intangible asset uses the exact same frameworks as valuing commercial property. The tools transfer perfectly. You just have to know where to look.

The Three Valuation Approaches: Property and Intangibles

In property valuation, we learned the three approaches early and used them constantly:

The cost approach: What would it cost to rebuild this building? If a commercial office building is worth less than the cost to build it, something is wrong (either the building is obsolete, or the land is very valuable). If it is worth more, there is market scarcity.

The market approach: What have comparable buildings sold for recently? If a similar building in the same neighbourhood sold for £50 million per floor, and this building has comparable quality and tenancy, it should sell for approximately £50 million per floor.

The income approach: What rental income does this building generate? If annual rental is £2 million and comparable properties sell at 5x annual rental (a "5x cap rate"), the building is worth £10 million. A 4x cap rate (compressed yields in a tight market) would imply £8 million.

We knew that in a liquid market with many comparable transactions, the market approach dominated. In a scarce market with few comparables, we relied more on the income approach. The cost approach was a reality check — buildings where value exceeded replacement cost by more than 50% were suspect.

Now I value intangible assets. The three approaches are identical.

Cost approach for intangibles: How much did it cost to build this technology, train this team, develop this dataset? A software platform that cost £20 million to develop but is generating £50 million in revenue is not necessarily overvalued — it is just undervalued at creation. But if it was expensive to build and is now competing against cheaper alternatives, cost is a floor, not a value.

Market approach for intangibles: What have comparable technology assets, customer bases, data assets sold for? If a similar SaaS company with comparable revenue and customer retention sold for 8x annual revenue, and your company has equivalent characteristics, it should sell for 8x annual revenue.

Income approach for intangibles: What returns does this asset generate? If a proprietary dataset generates £2 million in annual licensing revenue and is expected to do so for 5 years, and comparable data assets command a 3x revenue multiple, it is worth £6 million.

The structures are identical. The asset being valued is different, but the logic is the same.

Lesson 1: Comparables Are Everything (and Harder to Find)

When I was valuing a commercial office building in central London, finding comparables was straightforward. I could look at every commercial building sold in the same postcode in the past two years. I could adjust for location, condition, tenant quality, and lease length. Three months of comp analysis would produce a valuation range that was usually within 10-15% of market.

Valuing intangible assets requires the same discipline, but comparables are scarcer. There is no equivalent of "sold 10 similar properties in the same area this month." There are occasional M&A transactions where the acquirer discloses purchase price allocation (how much they paid for goodwill vs. IP vs. customer relationships), but these are rare and often not directly comparable.

However, I learned that this scarcity is temporary. In property, we have 100+ years of transaction data. In intangibles, we have perhaps 10-15 years of serious market data. As M&A in technology, data, and AI accelerates, comparable transaction data is accumulating.

The approach I use is the same I used in property:

Primary comparables: M&A transactions involving similar assets. If a SaaS company with customer relationships similar to mine sold at £8 per £1 of annual revenue, that is a comparable. If a proprietary technology platform sold at 3x annual product revenue, that is a comparable.

Secondary comparables: Transactions in related categories. If software IP typically sells at 2-4x annual software revenue, and my IP is software, I can use that range as a reality check.

Proxy comparables: Market prices for similar assets sold through different channels. If comparable pre-trained AI models are licensed for £1-5 million annually, that informs what a proprietary model might be worth.

Industry surveys and benchmarks: Investment banks, consulting firms, and industry groups publish surveys of valuation multiples by sector. A SaaS company with 90%+ net revenue retention typically commands higher multiples than one with 80% NRR. These benchmarks constrain the valuation range.

The most valuable comps in my property days came from transactions I had worked on myself — I knew the actual prices, condition, and terms. In intangible assets, the equivalent is transactions where I have had access to detailed due diligence — the actual revenue figures, customer metrics, technology assessment, and purchase price allocated to each asset.

Lesson 2: Subjective Judgement Is Necessary and Defensible

One of the hardest conversations I had when transitioning from property to intangibles was with analysts who wanted "objective" valuations. In property, I explained, there is no objectivity — there is only rigorous subjectivity.

A comparable property is never truly comparable. One is on a slightly better street. One has slightly better tenants. One was sold in a down market, another in an up market. A property valuer must make subjective adjustments: "This building is 10% better located, so it is worth 8% more. This building has slightly weaker tenants, so I discount it by 5%." The adjustments are based on experience and market data, but they are fundamentally subjective.

Yet the valuations were rigorous and defensible. I could explain every adjustment. A buyer could challenge a specific assumption, but could not dismiss the methodology. Courts accepted property valuations because they were transparent about where judgement was applied.

Intangible asset valuation requires the same transparency. A SaaS company's customer asset is worth the lifetime value of a typical customer, multiplied by the customer base. But what is "typical"? What is the expected churn rate, expansion rate, and gross margin retention rate? These require assumptions.

For a technology company with a 10-year history, I can extract actual churn and expansion from customer data. For a younger company, I must benchmark against comparable companies. The assumption is subjective, but if I can cite the benchmark and explain why this company is above or below it, the valuation is defensible.

In property, I learned to segment assumptions into three categories:

Observable facts: Actual rental data, tenant credit quality, lease length. These are not subjective. I can defend them with evidence.

Market-based assumptions: Cap rates, exit yields, terminal value assumptions. These come from market comparables and am defensible by reference to the market.

Company-specific assumptions: How this company's organisational quality, management depth, or competitive moats differ from average. These are subjective but defensible with evidence.

The same segmentation applies to intangible assets. A valuation is defensible if you clearly separate observable facts from market-based assumptions from company-specific judgements.

Lesson 3: Risk and Uncertainty Must Be Embedded, Not Ignored

In property valuation, we dealt with uncertainty through scenario analysis and risk adjustment. A property with uncertain planning permission was worth less than an identical property with secure zoning. A building with a tenant on a 3-year lease was worth less than an identical building with a 10-year lease (because the income was less secure).

We embedded risk into our discount rates and terminal assumptions. We did not assume away risk.

Valuing intangible assets requires the same discipline. A proprietary technology platform is worth more if it is defensively patented than if it is not (risk of competitive copying is lower). A customer base is worth more if customers have high switching costs than if they do not (risk of churn is lower). A dataset is worth more if it is protected by exclusive contracts than if it is not (risk of commoditisation is lower).

The risk adjustments I use in intangible asset valuation are direct translations from property valuation:

Lease term risk: In property, a shorter lease meant lower value. In intangibles, a shorter contract with a customer or licensee means lower value. If a customer signs a 1-year renewable contract, the revenue is less secure than if they sign a 5-year contract.

Tenant covenant risk: In property, a stronger tenant (lower probability of default) meant higher value. In intangibles, stronger customers (lower probability of churn, lower default risk) mean higher value.

Market obsolescence risk: In property, a building type that was becoming obsolete (single-tenant retail spaces, as ecommerce displaced shopping centres) was worth less, despite current cash flows. In intangibles, technology that is becoming obsolete (databases that AI will replace) are worth less despite current cash flows.

Refinancing and extension risk: In property, we worried about lease expiry — the income might not be renewable at the same rate. In intangibles, we worry about model obsolescence, customer switching, or technology replacement.

I learned in property that risk-adjusted returns are the foundation of value. A risky asset must have higher expected returns to justify the investment. A safe asset can justify lower returns.

The same principle applies to intangible assets. A proprietary technology with a strong moat can justify a lower discount rate in valuation (more certain future returns). A fragile technology with weak protection requires a higher discount rate (more uncertain returns).

Lesson 4: Terminal Value Is Critical and Contentious

The most important (and most debated) part of an intangible asset valuation is terminal value — the value of the asset beyond the explicit forecast period.

In property valuation, terminal value is relatively straightforward. A building will not decay significantly in 30 years. It will still command rent. You can forecast a perpetual income stream (or assume the building is sold at year 30 and apply a residual value assumption). The terminal value is explicit and debatable, but the logic is clear.

Intangible assets are trickier. A technology platform might be obsolete in 10 years. A dataset's value might appreciate or depreciate unpredictably. A customer base might exist in perpetuity or might face technical disruption.

I learned in property that terminal value accounting for 60-70% of total valuation is normal. Small changes in terminal assumptions (1% difference in perpetual growth rate) can create 20-30% differences in valuation.

The same is true for intangibles. A proprietary model might be worth £10 million in explicit value (years 1-5) but £40 million in terminal value (years 6-perpetuity), if you assume it remains competitively defensible. If you assume it is obsolete after 5 years, terminal value is near zero.

In my property experience, I learned to be disciplined about terminal assumptions:

• Explicit period: 5-10 years of detailed forecasting based on observable data
• Terminal value assumption: A steady-state assumption about perpetual growth or exit value
• Sensitivity analysis: Show what happens if terminal growth is 0%, 1%, or 2% — this reveals how much the valuation is exposed to terminal assumptions

I apply the exact same discipline to intangible asset valuation. A SaaS company's customer asset valuation is explicit for years 1-5 (based on current churn, expansion, and margins), then terminal value is based on a steady-state assumption (maybe NRR of 105%, maybe churn of 5% annually). Sensitivity analysis shows that a 5% variation in steady-state assumptions creates 30-50% variation in valuation.

Lesson 5: Depreciation and Useful Life Are Not What They Seem

In property, we understood that not all assets depreciate equally. A prime central London office building might hold its value for 50 years. A retail mall hit by ecommerce decline might lose 50% of its value in 10 years.

Depreciation depends on:

• Structural durability: How long the asset physically lasts
• Functional obsolescence: How long the asset remains fit for purpose
• External obsolescence: How long the market wants what this asset provides

A beautiful historic building in a prime location might last 200 years structurally, but if markets no longer want that building type, its value declines faster.

Intangible assets have similar, complex depreciation patterns:

Technology capital: A proprietary platform might functionally degrade quickly (competitors release better alternatives), even if it technically works perfectly. Expected useful life: 3-10 years depending on pace of change in the market.

Customer relationships: Customers might renew contracts indefinitely (perpetual asset), or might churn quickly if a competitor's product is better. Expected useful life: highly variable, depends on switching costs.

Data assets: A dataset might appreciate (becomes more valuable as it grows), plateau (reaches maturity), or depreciate (becomes obsolete as newer data methods emerge). Expected useful life: highly uncertain.

In property, we did not assume linear depreciation. A building's value might decline steeply in the first 5-10 years (capital improvements wear off), then more slowly (if maintained), then potentially faster if technological change overtakes it.

Intangible assets follow the same pattern. A software platform created at high cost might depreciate 20% in year 1 (as bugs are discovered, better alternatives emerge), 10% in years 2-5 (as market matures), then 5% in years 6-10 (if it remains defensible).

The key insight from property: Do not assume straight-line depreciation for intangibles. Understand how the asset will actually lose value over time, and model depreciation accordingly.

Putting It Together: The Valuation Framework

After 30 years in property and a decade in intangibles, I use a unified framework that applies to both:

This framework works for property. It works for intangibles. The discipline is the same.

Why This Matters for Growing Businesses

For a founder or CEO preparing a company for investment, acquisition, or PE exit, the lesson from property valuation is clear: make your intangible assets visible, measurable, and comparable.

In property, we would never value a building without walking it, understanding its tenants, and comparing it to market. We would document every assumption.

In intangibles, too many founders and management teams present their value to investors as a "story" — "we have great technology, loyal customers, brilliant team." No numbers. No structure. No comparables.

The highest-value preparation is the opposite: Document your technology capital (what does it do, how is it defensible, what revenue does it enable). Measure your customer relationships (what is your churn, expansion, lifetime value, how do you compare to peers). Quantify your organisational capital (what are your operational metrics, how much better than competitors). Identify your data assets (what proprietary data do you own, what value does it create).

Then, compare yourself to market. Find comparable companies. Show how you are similar, or how you differ. Apply three valuation methodologies. Show your work. Make your intangible assets as transparent as a property surveyor makes a building.

The companies that do this achieve premium valuations at exit. Not because the assets are worth more, but because buyers can finally see what they are paying for.

Mark Hillier is Co-Founder and Chief Commercial Officer of Opagio. He brings 30+ years of experience advising businesses through growth, scaling, and successful PE exits. His client roster includes Legal & General, AEW UK Investment Management, and Salmon Harvester. At Opagio, Mark leads go-to-market strategy and client acquisition across the SME and investor markets.