The First Chicago Method is a valuation framework that uses three distinct financial scenarios to project a startup's future worth. Rather than predicting a single outcome, investors model a conservative case (worst reasonable scenario), a base case (most likely outcome), and an optimistic case (best reasonable scenario). The method then applies probability-weighted calculations to arrive at a blended valuation, giving investors a more nuanced view of potential returns and downside risk.
How It Works
The process begins with building detailed financial projections for each scenario, typically spanning 5-10 years. For the conservative case, assume slower growth, higher customer acquisition costs, and potential market challenges. The base case reflects realistic assumptions based on comparable companies and market conditions. The optimistic case models aggressive growth, strong unit economics, and successful market penetration.
Once you've projected revenue, expenses, and cash flow for each scenario, apply probability weights—often 25% conservative, 50% base, 25% optimistic, though these vary by risk profile. Calculate the discounted cash flow or exit multiple for each scenario, then sum the probability-weighted values to determine fair entry valuation.
Why It Matters for Investors
Early-stage investing requires managing extreme uncertainty. A single-point valuation ignores the reality that startups rarely follow projections exactly. The First Chicago Method acknowledges this variability and forces disciplined scenario planning. It helps you avoid anchoring on best-case narratives while still capturing upside potential.
This approach also facilitates better portfolio management. By understanding the range of outcomes, you can size positions appropriately and set realistic return targets. It's particularly valuable for seed-stage and Series A investments where volatility is highest.
Example
Suppose you're evaluating a SaaS startup requesting $1 million at a $5 million post-money valuation. You model three scenarios with exit multiples applied at year 7:
Conservative: $20M exit value → $2M investor payout (25% probability)
Base Case: $50M exit value → $5M investor payout (50% probability)
Optimistic: $100M exit value → $10M investor payout (25% probability)
Probability-weighted value: ($2M × 0.25) + ($5M × 0.50) + ($10M × 0.25) = $5.75M. If the implied return at current valuation meets your 10x target, proceed; if not, negotiate better terms.
Key Takeaways
- Three scenarios replace single-point estimates, reducing valuation bias and anchoring errors
- Forces explicit assumptions about market adoption, competition, and unit economics across different outcomes
- Helps you set position size and return targets aligned with realistic risk-adjusted expectations
- Works best paired with other methods like comparable company analysis and venture capital method for triangulation