How to Value a Pre-Revenue Startup: 4 Methods Compared

Valuing a pre-revenue startup means putting a number on a company that has no revenue history, no earnings, and often no product in the market. The four methods that dominate practice in 2026 are the Berkus Method, the Scorecard (Payne) Method, the Venture Capital Method, and Risk Factor Summation — each triangulates a different piece of the same missing puzzle. Run them side by side and you get a defensible pre-money range instead of a single guess an investor will haggle down.

Before the frameworks, anchor to the market. Carta's Q3 2025 State of Private Markets report puts the median seed pre-money at $16M on a $4M median raise, with post-money hitting $24M by Q4. That headline number is heavily distorted by AI: AI-focused seed rounds average $17.9M pre-money, a 42% premium over the broader market, per AngelList's 2025 data. If you are not building foundation-model infrastructure, valuing yourself off the $24M median is how founders lose 30% of their cap table before they finish writing the term sheet.

The four methods for valuing a pre-revenue startup

All four methods below share one assumption: a pre-revenue company has value only to the extent it has eliminated risk. That framing comes from Dave Berkus himself and it should guide how you present any valuation to an investor. You are not selling a forecast. You are selling risk that has been retired.

1. The Berkus Method

Dave Berkus, a California angel who has backed more than 100 early-stage companies, built this framework in the mid-1990s. It assigns up to $500,000 to each of five risk categories, capping the pre-money at $2.5M. The five categories:

Sound idea (basic value, product risk)
Prototype (technology risk)
Quality management team (execution risk)
Strategic relationships (market risk)
Product rollout or sales (production risk)

Score each on a 0 to $500K scale. A team with a working prototype, a signed design partnership with a known enterprise buyer, and no early sales might land at $500K + $500K + $400K + $400K + $0 = $1.8M pre-money.

Where it fits: Very early angel and pre-seed rounds under $2.5M pre-money. The $2.5M cap makes it structurally useless for AI startups or hot verticals where seed pre-money starts at $10M and up. Most practitioners now flex the per-category cap to $1M or $2M to keep the method usable in a 2026 market, but that is a house rule, not the original.

Practical takeaway: Use Berkus as your floor. If Berkus says $1.5M and a VC offers you $8M, you know exactly which risks you have not yet retired and how much each one is worth.

2. The Scorecard (Payne) Method

Angel investor Bill Payne formalized this method in 2001 and it is now the default framework taught by the Angel Capital Association. Unlike Berkus, Scorecard is top-down: start with the regional median pre-money for pre-revenue companies at your stage, then adjust up or down based on how you compare on seven weighted factors.

The 2019 revision of the ACA weights:

Strength of the management team — 30%
Size of the opportunity — 25%
Product / technology — 15%
Competitive environment — 10%
Marketing, sales channels, partnerships — 10%
Need for additional investment — 5%
Other (customer feedback, hot sector, etc.) — 5%

Step by step:

Set the baseline. For 2026, if you are a non-AI B2B SaaS company at seed, that anchor is roughly $16M pre-money (Carta Q3 2025 median). For pre-seed the anchor is closer to $10M pre-money (AngelList 2025).
Score each factor as a percentage of the comparable company. 100% is average, 150% means you are meaningfully better than the median, 75% means worse.
Multiply the score by the weight and sum. A sum of 1.05 means 5% above the median. A sum of 0.85 means 15% below.
Multiply the sum by the baseline. 0.85 × $16M = $13.6M pre-money.

Practical takeaway: Scorecard is the method most seed investors actually run in their head during a pitch. If you cannot honestly score yourself above 100% on team and opportunity size, you are asking for a discount before you open your mouth.

3. The Venture Capital Method

Professor Bill Sahlman introduced the VC Method in a 1987 Harvard Business School case; it is still on the HBS teaching syllabus today. It works backward from an exit, not forward from risk factors. Because it uses only four inputs, VCs run it in a spreadsheet before the founder finishes the deck.

The formula:

Terminal Value = projected exit-year revenue × industry exit multiple
Post-money Valuation = Terminal Value ÷ target ROI
Pre-money Valuation = Post-money − investment amount

Worked example. You are raising a $2M seed. You project $25M in ARR by year 5. Public SaaS multiples in 2026 sit around 6-8x forward revenue (public comps like Snowflake, HubSpot, and Datadog), so use 7x — terminal value $175M. Your VC targets 10x returns over 5 years on a seed check (roughly 58% IRR). Post-money = $175M ÷ 10 = $17.5M. Pre-money = $17.5M − $2M = $15.5M.

Where it breaks. The output is only as honest as the inputs. If your ARR forecast is wishful, the pre-money is wishful. Sequoia and a16z partners have publicly discussed applying 20x to 30x ROI targets on seed checks in 2024-2025 to correct for base-rate optimism; using a 10x target when your investor uses 25x explains 60% of every negotiation gap you will ever see.

Practical takeaway: Build the VC Method spreadsheet before the meeting. Then hand the investor a version with three ROI scenarios (10x, 20x, 30x) and let them pick — you have anchored the conversation on their number, not fought it.

4. Risk Factor Summation

Developed by the Ohio TechAngels, Risk Factor Summation extends Berkus from 5 categories to 12. Start with a comparable regional pre-money (same baseline as Scorecard) and adjust ±$250K or ±$500K per factor depending on whether each risk is well-managed (+), average (0), or elevated (−).

The twelve risks: management, stage of the business, legislation/political, manufacturing, sales & marketing, funding/capital raising, competition, technology, litigation, international, reputation, and potential exit. A startup with a first-time solo founder in a heavily regulated vertical (say, digital health) might see −$500K on management, −$500K on legislation, and −$250K on litigation before the model adds a dollar for anything positive.

Practical takeaway: Risk Factor Summation is the method to use when your business has non-obvious risks Berkus and Scorecard both miss — regulation, litigation exposure, geopolitical concentration. Use it as a sanity check on the other three.

Comparing the four methods head to head

The point of running all four is to triangulate a range, not average a number. Here is how a hypothetical non-AI B2B SaaS startup at pre-seed with a working prototype, two founder-market-fit signals, and one paying design partner might look:

Berkus: $1.8M pre-money — capped, low signal for anything above $2.5M
Scorecard: $11M pre-money — 1.10 sum × $10M pre-seed baseline
VC Method: $13M pre-money — $150M terminal, 10x target, $2M raise
Risk Factor Summation: $9.5M pre-money — $10M baseline minus $500K for competitive risk in a crowded category

Range: $9.5M to $13M once you set Berkus aside as a floor test. That is your defensible ask. Anchor at $12M, accept $10M, walk at $8M.

Practical takeaway: Do not average the four outputs. Investors see averaged numbers as unforced compromises. Present a range with the two most credible methods (usually Scorecard and VC Method for anything above $5M pre-money) and reference the other two as sanity checks.

Which method to use when

The right method depends on stage, sector, and what you are trying to prove:

Friends-and-family or first angel check under $1M raise: Berkus, full stop. Anything more elaborate will not survive investor scrutiny.
Standard pre-seed or seed round, non-AI: Scorecard as your primary, VC Method as your check. Both use the same market anchor, which is a feature — the anchor is the argument.
AI or hot-sector seed round: VC Method with a 20-25x ROI target and a public comps multiple pulled the week of the raise. Do not use Berkus.
Regulated verticals (health, fintech, defense): Add Risk Factor Summation. The other three methods systematically under-price regulatory and litigation risk.
Anytime you are challenged by a founder who "just knows" the number: Run all four in a spreadsheet model. The exercise itself surfaces which risks are actually unretired.

Practical takeaway: The methods are not competitors. They are lenses. A founder who walks into a term sheet negotiation having run all four has already won the conversation about process, which is half the deal.

From framework to term sheet

The failure mode in every one of these methods is the same: founders build them once, in a rush, on the back of a napkin, and never update the assumptions. Then they hand a VC a stale number six months later. The fix is not more theory. It is a live spreadsheet model with each method wired to the same underlying inputs — market baseline, comps multiple, ROI target, factor scores — so that when one assumption changes, all four outputs update.

That is exactly what a ready-made valuation template gives you: a pre-built Excel spreadsheet model with Berkus, Scorecard, VC Method, and Risk Factor Summation running side by side, sourcing from a single set of inputs. Instead of rebuilding the math every time you talk to a new investor, you change three cells and the range updates. You walk into the room with a defensible pre-money, a downside, and a ceiling — and you spend the meeting negotiating the deal, not the arithmetic. That is the difference between a founder who gets valued and a founder who values themselves.