The first time most analysts value a private company, they apply the same methodology they would to a public one. The DCF runs, the comps table gets built, the output looks defensible. Then the questions start — and the model doesn’t hold up.
Private company valuation isn’t a variation of public company valuation. It’s a structurally different problem. The inputs are harder to get, the adjustments are non-standard, and the sources of error are different. A model that ignores those differences produces a number. It doesn’t produce a valuation.
Why standard methodology breaks down for private companies
The mechanics of a DCF are identical whether you’re valuing a public or a private company. What changes is every significant input.
Start with the discount rate. In a public company DCF, beta comes from observed market data — you unlever the company’s historical beta, strip out financial leverage, and get a measure of business risk. For a private company, there is no market-observed beta. You are working with proxies: the unlevered betas of comparable public companies, relevered to reflect the private company’s capital structure. That process introduces estimation error at every step, and that error compounds through the entire valuation.
Then there is the size premium. Public company betas are calibrated to the return behavior of listed equities. Private companies — particularly small and mid-market businesses — carry incremental return risk that the base CAPM model doesn’t capture. The size premium adjusts for this. The company-specific risk premium (CSRP) adjusts for factors unique to the business: customer concentration, key-person dependency, single-product exposure, lack of audited financials. Both require judgment calls that public company models never ask you to make.
What happens when analysts skip these adjustments: the discount rate is understated, the DCF output overstates value, and the investment committee asks why the implied multiple is two turns above every precedent transaction in the sector.
The three adjustments private company models always require
Before any valuation methodology is applied, the financial statements of a private company need to be normalized. This is not optional — it is the prerequisite for a model that produces a defensible number.
Owner compensation normalization. Private company owners frequently run personal expenses through the business — vehicles, insurance, family salaries, above-market compensation to related parties. A business generating $2M in reported EBITDA may be generating $2.8M in normalized EBITDA once those items are removed and replaced with a market-rate management cost. The difference is not cosmetic. At a 6x multiple, it is $4.8M of enterprise value. The normalization has to be documented line by line, with each adjustment sourced to the financial statements and the rationale stated explicitly.
Related-party transaction cleanup. Private companies frequently transact with related entities at non-market terms — below-market rent paid to a landlord entity owned by the same family, above-market consulting fees to an owner’s side business. Each transaction needs to be identified, the market-rate equivalent established, and the adjustment made before EBITDA is calculated. A model that doesn’t do this is valuing the business as it is operated, not as it would be operated under new ownership.
Working capital normalization. Public companies report working capital quarterly. Private companies — particularly those without audited financials — may report it annually, inconsistently, or not at all. The working capital peg in a transaction model is one of the most contested items in any deal. Establishing a normalized working capital baseline requires reconstructing the seasonality of receivables and payables from bank statements, aged AR schedules, and management accounts. It is time-consuming. Skipping it produces a working capital adjustment at close that neither side anticipated.
How to build a WACC for a private company
A bank-grade WACC for a private company requires explicit handling of four components that public company models treat as given.
Unlevered beta from public proxies. Select a set of publicly traded companies in the same business — not the same industry broadly, but the same business model, revenue mix, and customer type. Unlever each company’s beta using its capital structure. Take a median or trimmed mean. This is your proxy for the business risk of the company you’re valuing. The selection of the proxy set is a judgment call, and it will be challenged — document the selection criteria and the exclusions.
Size premium. The Duff & Phelps / Kroll data is the standard reference. Size premium is applied based on market capitalization decile for public companies; for private companies, it is applied based on revenue, EBITDA, or enterprise value as a proxy. The premium ranges from approximately 2% to 6% depending on company size. Use the data. Don’t estimate it.
Company-specific risk premium (CSRP). This is the adjustment most models either ignore or apply without documentation. A CSRP of 1%–4% is typical for small to mid-market private companies with identifiable risk factors. Each factor — customer concentration above 20% to a single customer, key-person dependency, single-product revenue, absence of audited financials — should be quantified and documented. An undocumented CSRP adjustment is the first thing an investment committee will ask you to justify.
Cost of debt. Private companies often carry debt at variable rates, on terms that reflect their private credit profile rather than public market pricing. Use the actual cost of existing debt for the capital structure as it stands. For a pro forma capital structure post-acquisition, use current market rates for the relevant credit quality and loan type.
Here is what this means in practice: a private company that looks comparable to a public peer trading at 8x EBITDA may warrant a discount rate 300–500 basis points higher, which at a 5% terminal growth rate translates to a DCF output 15%–25% below the public peer’s implied multiple. That gap is not a modeling error. It is the correct answer.
Building comps without public peers
The comparable company analysis methodology assumes you have public companies to compare against. For private companies in niche industries, that assumption frequently fails.
What replaces it:
Precedent transaction analysis. M&A transactions involving private companies are disclosed in varying degrees of detail — through regulatory filings, press releases, and proprietary databases. Precedent transaction multiples reflect control premiums and deal-specific terms, which means they are not directly comparable to a minority DCF value without adjustment. But they are the closest available market evidence for how the business has been priced by buyers with access to full information. They are the anchor.
Sector proxy comps. When no direct private transaction data exists, the public company comps set serves as a ceiling — adjusted downward for the illiquidity discount. The illiquidity discount for private company equity ranges from approximately 15%–35% depending on size, marketability of the business, and time-to-exit assumptions. The discount is applied to the implied equity value, not the enterprise value. Document the basis for the discount applied; it will be questioned.
Adjusted private multiples. Industry associations, business brokers, and mid-market M&A advisors publish private company transaction multiple data by sector — BizBuySell, DealStats, and sector-specific sources. These are less rigorous than public market data but useful as a cross-check. They are supporting evidence, not primary methodology.
What a defensible private company valuation model looks like
The output of a private company valuation model needs to be structured differently from a public company model — because the audience is different and the scrutiny is different.
In a public company context, you are presenting a valuation to an investment committee that can verify your inputs against observable market data. In a private company context, every significant input is an estimate — and the committee knows it. The model’s credibility depends on documentation, not precision.
What that means structurally:
Every normalization adjustment is its own line item with a source reference. Every WACC component — proxy beta selection, size premium source, CSRP factors and amounts — is documented in a dedicated assumptions tab. The DCF output is presented alongside the precedent transaction range and the adjusted comps range in a football field chart. The sensitivity table shows the output across WACC and terminal growth rate assumptions broad enough to encompass the genuine uncertainty in the inputs.
A private company valuation that presents a point estimate without a range is not a valuation. It is a number that will be challenged and replaced.
If you’re working on a private company transaction or valuation that requires a model built to withstand investment committee scrutiny — let’s talk through what that looks like.
The mistake we see most often in private company models
What we see consistently when reviewing private company models built by analysts without specific private company experience: the normalization step is either skipped or done incompletely, and the WACC is built using a public company beta without size or company-specific adjustments.
The result is a model that looks technically correct — the DCF runs, the balance sheet ties, the sensitivity tables move in the right direction — but produces an output that overstates value by 20%–40%. That error surfaces when the model is compared against precedent transactions. At that point, the analyst is asked to explain the gap, and the explanation requires rebuilding the model from the WACC down.
The discipline that prevents this is building the normalization and the WACC construction before touching the DCF. Those two components determine whether the output is in the right range. The rest of the model refines within that range.
FAQ — Financial Modeling for Private Companies
How do you value a private company without public market data?
Private company valuation uses three approaches: a DCF built with a private-company-adjusted WACC (including size premium and CSRP), precedent transaction analysis from comparable M&A deals, and sector proxy comps adjusted downward for illiquidity. All three outputs are cross-referenced in a football field chart.
What discount rate should I use for a private company DCF?
Build the WACC using unlevered betas from comparable public companies, relevered to the private company’s capital structure. Add a size premium (typically 2%–6% based on company size) and a company-specific risk premium (1%–4%) for identifiable risk factors. Do not use a public company WACC without these adjustments.
What is the illiquidity discount for private company valuation?
The illiquidity discount applied to private company equity typically ranges from 15%–35%, depending on company size, business marketability, and time-to-exit assumptions. It is applied to implied equity value, not enterprise value. The basis for the discount selected must be documented explicitly.
What adjustments does a private company financial model require before valuation?
Three normalizations are required before any valuation methodology is applied: owner compensation normalization (removing above-market or personal expenses), related-party transaction cleanup (restating non-market-rate transactions to market terms), and working capital normalization (establishing a seasonally adjusted baseline from management accounts).
What comes next
Private company financial modeling is one of the areas where practitioner knowledge diverges most sharply from what standard training covers. The DCF methodology is the same. The inputs, the adjustments, and the documentation discipline are structurally different — and the gaps show up under deal pressure, not in the model itself.
If you want to build private company valuation models that hold up in an investment committee or a due diligence process, that is exactly what our training covers — built around how these models are actually constructed on live transactions, not sanitized case studies.