Three scenarios, three probabilities and one discount rate. We trace the method back to the bank that built it, value a US pre-seed startup with it to the dollar, and show the mistake that makes many versions of it count failure twice.
The First Chicago method values a startup by building a success, a sideways and a failure scenario, estimating the company’s exit value in each, discounting those values to today and weighting them by the probability of each scenario. The weighted sum is the valuation. It is a scenario-based alternative to the venture capital method.
The First Chicago method is not one of the five methods Equidam runs (Scorecard, Checklist, the VC method, DCF with long-term growth and DCF with multiples). Our guide to startup valuation methods to use and avoid lists it among the methods to avoid, and we hold to that for the method used on its own to set a price. Its central idea, making the chance of failure explicit, is sound, and it survives inside the methods we do use.
What is the First Chicago method, and where does it come from?
The method is named after First Chicago Corporation, the Chicago bank holding company. In the words of a 2011 guide from the Global Entrepreneurship Institute, “The ‘First Chicago Method’ was developed at First Chicago Corporation’s venture capital group” (GEI, 2011, archived). No source we found credits a single inventor.
Madison Dearborn Partners, whose founders came from the bank’s venture arm, traces its history to the early 1980s, when they built “a $2.6 billion management buyout and venture capital portfolio at First Chicago Venture Capital”, and became an independent firm in 1992 (Madison Dearborn).
Business schools learned the method from a Harvard Business School note by Daniel Scherlis and William Sahlman, “A method for valuing high-risk, long-term investments: the ‘venture capital method'”, note 9-288-006, copyright 1987 and revised since (Stanford library record; Stout, 2018). Summaries of that note name the three scenarios success (dividends, then an IPO), sideways (the company survives and pays dividends but never lists) and failure (bankruptcy, with whatever the assets recover) (Reinfeld, HEC Paris thesis, 2018, pp.38-39).
The original was designed for investments made through redeemable preferred stock, so it counts payouts to the investor during the holding period, such as redemptions, as well as the exit (Schreyer, 2009, in German; Achleitner and Lutz, 2005). Modern write-ups usually call the scenarios best, base and worst case and drop the interim payouts, because venture-backed startups rarely pay any.
Is the First Chicago method the same as the venture capital method?
No, although several popular pages use the names as synonyms. The GEI guide credits Sahlman with the VC method and First Chicago’s venture group with the other. Achleitner and Lutz present the First Chicago method as an alternative to the VC method, with “conceptional advantages” but “a more complex valuation process” (Achleitner and Lutz, 2005).
They differ in where the risk of failure goes. The VC method discounts the exit value of a success at a very high target rate, while the First Chicago method gives the bad outcomes scenarios of their own. The common mistake, covered below, is to do both at once.
How does the First Chicago method work?
The modern version has five steps.
- Define three scenarios (success, sideways and failure, or best, base and worst case), each with its own consistent forecast up to the investor’s expected exit.
- Estimate the exit value in each scenario, usually as revenue or EBITDA in the exit year times a multiple suited to that scenario.
- Discount each exit value, plus any payouts before exit, back to today.
- Weight each present value by its scenario’s probability and add them up. The probabilities must sum to 100%.
- Compare the value of the stake on offer with the cash the investor puts in.
With p for probability, r for the discount rate and n for years to exit:
Value today = Σ pᵢ × [Exit valueᵢ / (1 + r)ⁿ + discounted payoutsᵢ]
First Chicago method example: how would a US pre-seed startup be valued?
Take Frostline, a hypothetical US pre-seed company building wireless temperature sensors and monitoring software for refrigerated food shipments. We valued the same company in our Scorecard and Checklist articles. It has two full-time founders, a working prototype, no revenue, a pilot with one regional refrigerated-freight carrier and a large capital need for manufacturing.
Assume Frostline raises $3,000,000 now, in 2026, and that any exit comes at the end of 2034, eight years out, which is the typical time to exit in our required-ROI model. Frostline will pay no dividends, so the method reduces to exit values. Every forecast below is an assumption.
| Scenario | What happens by 2034 | Revenue 2034 | EBITDA margin | EBITDA | Multiple | Exit value | Probability |
|---|---|---|---|---|---|---|---|
| Success | Becomes a leading cold-chain monitoring provider and is sold | $300,000,000 | 25% | $75,000,000 | 10× | $750,000,000 | 10% |
| Sideways | Survives as a small niche hardware business | $20,000,000 | 10% | $2,000,000 | 6× | $12,000,000 | 30% |
| Failure | Runs out of money before reaching scale | n/a | n/a | n/a | n/a | $0 | 60% |
We use EBITDA multiples because Equidam’s VC method does, with the multiple taken from Damodaran’s data on listed companies (2026 sample report, pp.17 and 29). A small, slow-growing company earns a lower multiple, and in a failed hardware startup creditors and preferred investors take whatever the assets fetch, so failure is zero.
How do you assign probabilities to the scenarios?
Many published examples treat the worst case as the least likely outcome. Venture data say otherwise. Across first early-stage rounds from 1985 to 2009, “about 55 percent of startups that received venture capital over this period were terminated at a loss, and only 6 percent of them returned more than five times their investment” (Kerr, Nanda and Rhodes-Kropf, 2014).
For success we anchor on our own data. Equidam’s required-ROI model uses a 10.23% success rate for development-stage companies, from averaged Dealroom and PitchBook data (Required ROIs of the VC method; our VC method explainer). A company with a prototype and no revenue would typically be at the development stage, so we use 10%.
For failure we use 60%. Kerr’s data put losses at 55% of venture-backed companies. The survival curve our DCF methods use for a newly incorporated US company has 51.41% still operating in year eight, so about half are gone (sample report, appendix p.24), and US data on all new establishments point higher: 50.2% were still operating after five years and 34.7% after ten (BLS Business Employment Dynamics, Table 7). Both cover every kind of new business, from shops to software. We take the top of that range because Frostline has to raise a lot of money for manufacturing before it earns anything. Sideways takes the remaining 30%.
What discount rate should you use in the First Chicago method?
With failure in its own scenario, the rate should match what investors expect to earn across all outcomes, so we use 22% a year, the rate implied by our own model. The required-ROI table starts from a 5× target for an early-stage fund, and 5× over eight years is 1.2228 a year, or about 22% (Required ROIs of the VC method). It is also close to what early-stage investors earned. Damodaran reports 21.4% a year for early-stage VC over the 20 years to 2007, “well below the 50-70% target returns” (Damodaran, 2009).
Later rounds will dilute today’s shareholders, the new investor included. Our model assumes development-stage investors keep 52% of their stake to exit (Required ROIs of the VC method), and we apply that to both surviving scenarios.
The calculation, step by step
- Discount factor: 1.22⁸ = 4.9077
- Success: $750,000,000 × 0.52 / 4.9077 = $79,466,843, weighted at 10% = $7,946,684
- Sideways: $12,000,000 × 0.52 / 4.9077 = $1,271,469, weighted at 30% = $381,441
- Failure: $0 × 60% = $0
- Post-money value: $7,946,684 + $381,441 + $0 = $8,328,125
- Pre-money value: $8,328,125 – $3,000,000 = $5,328,125
Now compare with the round. At an $8,000,000 pre-money valuation ($11,000,000 post-money), the investor gets 27.3%, worth $2,271,307 on this model, less than the cash going in. To break even on expected value, the investor needs 36.0% ($3,000,000 / $8,328,125), which is what a $5,328,125 pre-money gives.
How does that compare with the Scorecard, the Checklist and today’s pre-seed valuations?
Frostline came out at $8,465,625 on the Scorecard method and $7,097,500 on the Checklist method. The First Chicago result of $5,328,125 is 37% below the Scorecard figure and 25% below the Checklist one.
The Scorecard and Checklist compare the company with real rounds in its market, while the First Chicago result rests on three numbers we chose: a $750M success exit, a 10% chance of reaching it and a 22% discount rate.
For context, here is the typical pre-seed valuation by region in our Valuation Delta H1 2026. These come from valuations founders ran on Equidam while preparing to raise, not from prices of closed rounds.
| Region | Typical pre-seed valuation, H1 2026 |
|---|---|
| Middle East | $5.56M |
| United States | $5.16M |
| South East Asia | $3.84M |
| Europe | $3.30M |
| Oceania | $3.26M |
| Africa | $2.99M |
| Latin America | $2.40M |
Frostline’s First Chicago result lands near the US figure, partly because of the assumptions we chose, as the next table shows.
How sensitive is the result?
Every input is a guess about 2034, so the result moves a lot. Changing one assumption at a time:
| Change | Pre-money value |
|---|---|
| Base case | $5,328,125 |
| Success probability 15% instead of 10% (failure 55%) | $9,301,467 |
| Success probability 5% instead of 10% (failure 65%) | $1,354,783 |
| Success exit $500M instead of $750M | $2,679,230 |
| Discount rate 30% instead of 22% | $2,010,477 |
Five points of probability move the result by about $4M either way, and nothing in Frostline’s current state says whether its chance of a $750M exit is 5%, 10% or 15%.
Why does a 50-70% discount rate double-count failure?
Venture capital target rates are high because they already assume most investments fail. Damodaran lists typical target rates of 50-70% for start-ups, 40-60% at first stage, 35-50% at second stage and 25-35% at bridge or IPO stage, and writes that the target rate “incorporates the likelihood that the business will fail” (Damodaran, 2009). A survey of 885 VCs notes that early-stage funds “may calculate IRRs from ‘if successful’ scenarios” (Gompers, Gornall, Kaplan and Strebulaev, NBER working paper 22587).
A rate like that belongs on the success case alone. Add a 60% failure scenario on top and you charge for failure twice. Reinfeld’s summary of the original says the same from the other side: “Instead of using discount rates as high as 70%, one can capture the risk of failure” with scenarios (Reinfeld, 2018, p.39).
Here is Frostline with the same scenarios and weights, discounted at 60%, the middle of the start-up range:
- Discount factor: 1.60⁸ = 42.9497
- Success: $750,000,000 × 0.52 / 42.9497 × 10% = $908,040
- Sideways: $12,000,000 × 0.52 / 42.9497 × 30% = $43,586
- Post-money value: $951,625 (the components are rounded)
- Pre-money value: $951,625 – $3,000,000 = -$2,048,375
A company raising $3M comes out worth less than nothing before the money arrives, and the post-money value is about one ninth of the 22% result, because (1.22 / 1.60)⁸ = 0.114. Some recent guides warn that a high rate on every scenario charges for the same risk twice, but none we found shows how large the error is.
So choose one treatment of risk. Either model failure explicitly and discount at a rate close to what investors earn across a portfolio, or use one success case and a target rate that already includes failure. Damodaran prefers the first (Damodaran, 2009).
How does it relate to Equidam’s VC method, DCF and PWERM?
Our VC method takes the second route. It divides an exit value by (1 + required ROI)ⁿ, and the required ROI already contains the success rate and expected dilution. At the development stage, a 5× fund target divided by a 10.23% success rate gives 48.88×, and dividing by the share investors keep after dilution (0.52, rounded in the table) gives 93.43×, which over eight years is 76.32% a year (Required ROIs of the VC method).
Apply that to Frostline’s success exit over the same eight years: 1.7632⁸ = 93.414, and $750,000,000 / 93.414 = $8,028,763 post-money, or $5,028,763 pre-money. That is within $300,000 of the First Chicago result, because each method prices failure once, in a different place. Our VC method works like a First Chicago model with a success case and a zero-value failure case, with the probability folded into the rate. (In the product, the exit value comes from the last projected year’s EBITDA and is discounted over the projection period; we used eight years here to show the arithmetic.)
Our two DCF methods take the first route. They “weight the projected free cash flow to equity by the probability the startup will survive” before discounting (2026 sample report, p.18). Survival probabilities come from Eurostat and US Bureau of Labor Statistics data by country (p.31), so the failure weight is data and nobody picks it by hand. The US sample company’s DCF uses a 17.16% discount rate (p.18), far below the 76.32% (p.20) its VC method uses at the development stage, because the chance of failure is handled separately, through survival rates, while the VC rate also carries dilution and the fund’s target return. As we wrote on startup survival rates, survival rates work best as part of scenario analysis rather than as single-point estimates.
The closest living relative of the First Chicago method is the probability-weighted expected return method (PWERM) used in 409A and fund valuations. Mercer Capital describes it as value “based on the probability-weighted present value of various future outcomes” (Mercer Capital, 2019). A December 2025 draft update to the AICPA’s cheap-stock guide calls it the scenario-based method and notes that “discount rates may vary by scenario” (Equity Methods, 2026).
PWERM adds two things. It splits each scenario’s value across share classes, so liquidation preferences count (EisnerAmper). And valuers calibrate it to the last round, solving for the discount rate “such that the total present value of the probability-weighted future equity values is equal to the equity value implied by the most recent financing round” (Stout). Used this way, scenarios allocate a price the market has already set.
Why do we list the First Chicago method as one to avoid?
Our use-and-avoid guide calls it “potentially useful as a thought exercise” but warns that “generating these distinct scenarios can be arbitrary and often lacks practical insight.” Its strength is that it makes the chance of failure explicit instead of hiding it in a rate, and Achleitner and Lutz credit it with “conceptional advantages” over the VC method (Achleitner and Lutz, 2005). Frostline shows the weaknesses.
The scenarios are hand-picked. Nothing ties a $750M success exit to Frostline today; another analyst could defend $400M or $1.5B just as well.
The probabilities describe the average company at a stage. Data can anchor them, but the chance of a large win is the hardest weight to observe for one company, and it drives the answer. The method also fits pre-revenue companies worst, since Frostline’s entire value here comes from a 2034 income statement we wrote.
The discount rate is easy to get wrong, and the usual mistake turns a $5.3M result into a negative number.
And one weighted number hides where the value sits. In Frostline’s case, 95% of the post-money value ($7,946,684 of $8,328,125) comes from the 10% success scenario.
The underlying logic still holds, because venture outcomes are a few large wins and many losses. We capture that with inputs nobody picks per company, stage-level success rates in the VC method and country-level survival rates in the DCF methods, next to two qualitative methods that compare the company with real rounds.
How should a founder use the First Chicago method?
As a way to test your own story before an investor does:
- Write down what has to be true in each scenario (customers, margins, capital raised) before putting a number on it.
- Put a realistic weight on failure. In the Kerr data, 55% of venture-backed companies lost their investors money.
- Use one treatment of risk. If you model failure, do not also discount at 50-70%.
- Keep the result next to a Scorecard, Checklist or VC-method view, and when they disagree, find out why before your first investor meeting.
Equidam lets you build the scenarios without the weighting step. You can save a copy of a valuation and walk in “with two or three scenarios ready: a base case, an optimistic case, and a conservative case,” each rerun through all five methods (Help Center: Valuation Scenarios). The product does not blend them into one probability-weighted number. Each scenario keeps its own report, so the range stays visible.
First Chicago vs Scorecard, Checklist, Berkus and Risk Factor Summation: what is the difference?
Scorecard, Checklist and Risk Factor Summation start from recent rounds in the market, and Berkus from fixed amounts. The First Chicago method starts from forecasts, which puts it closer to DCF and the VC method. Our overview of the five methods covers the ones Equidam runs.
| Method | Starting point | How it adjusts | Limit on result | Fits best |
|---|---|---|---|---|
| Scorecard | Regional average valuation | Weighted scores on 6 criteria | None built in | Pre-revenue and early stage |
| Checklist | Country maximum valuation | Share earned on 5 weighted criteria | The maximum | Pre-revenue and early stage |
| Berkus | Zero | Up to $500K per element, 5 elements | $2M pre-revenue, $2.5M after roll-out | Pre-revenue only |
| Risk Factor Summation | Regional average valuation | ±$250K per step on 12 risks | None built in | Pre-revenue, as a cross-check |
| First Chicago | Scenario forecasts | Probability weights on success, sideways and failure (best, base, worst) | None built in | Companies with forecastable outcomes |
| VC method (Equidam) | Exit value from EBITDA × multiple | Discounted at a required ROI that includes success rate and dilution | None built in | Early stage with a credible exit case |
What is the next step?
Inside an Equidam valuation, success rates and survival rates price the risk the First Chicago method tries to capture, and five methods test each other, using data from more than 160,000 companies valued (Equidam internal data) and 30,000+ public comparables across 90+ countries and 600+ industries (Data Sources).
The Help Center guide to required ROIs shows how those success rates become a required return. When you want your own company run through all five methods, with scenarios you can save and compare and a report you can share with investors, you can compare Equidam plans.