--- name: building-venture-return-models language: en description: Constructs venture return models with entry valuation, follow-on reserve, multiple scenario exits, and portfolio-level fund math. Use when modeling VC returns, calculating fund economics, or projecting portfolio outcomes. tags: - modeling - venture-capital - portfolio - valuation metadata: author: casemark practice_areas: - Venture Capital - Seed/Series Investing - Startup Ecosystems document_types: - Model skill_modes: - Modeling --- # Building Venture Return Models ## When To Use - Modeling expected returns for a new fund or vintage year - Evaluating a prospective deal's impact on portfolio-level fund math - Sizing follow-on reserves and determining pro-rata allocation strategies - Preparing fund economics exhibits for LP reporting or fundraising decks - Stress-testing portfolio outcomes under varying exit timing, valuation, and dilution assumptions ## Inputs To Gather - **Fund parameters**: Fund size, management fee rate and structure (on committed vs. invested), carry percentage, preferred return / hurdle rate, GP commit percentage - **Portfolio construction**: Target number of investments, initial check size range, stage focus (pre-seed / seed / Series A+) - **Entry deal terms**: Pre-money valuation, round size, ownership target, instrument type (priced equity, SAFE, convertible note), discount/cap terms if applicable - **Follow-on strategy**: Reserve ratio (e.g., 1:1, 2:1 follow-on to initial), pro-rata rights, expected follow-on rounds and dilution per round - **Exit assumptions**: Target exit multiples (base / upside / downside), expected hold period by scenario, exit modality (M&A vs. IPO vs. secondary) - **Loss/write-off rate**: Historical or assumed percentage of portfolio companies returning 0-1x - **Recycling policy**: Whether and how much the fund recycles management fees or early proceeds ## Workflow 1. **Set fund-level parameters** - Define fund size, fee structure (typical: 2% on committed capital during investment period, stepping down thereafter), carry split, and hurdle rate - Calculate investable capital after fees (e.g., $100M fund → ~$80-85M deployable over fund life) [VERIFY fee assumptions against actual LPA terms] - Determine GP commit amount and any co-invest sidecar capacity 2. **Build portfolio construction model** - Allocate investable capital across initial checks and follow-on reserves - Map target ownership at entry for each stage bucket (e.g., 10-15% at seed, 7-10% at Series A) - Model expected dilution per subsequent financing round (typical: 15-25% per round) [VERIFY against current market dilution data] - Calculate fully-diluted ownership at exit after projected dilution rounds, accounting for pro-rata follow-on participation 3. **Model individual deal economics** - For each representative investment, compute entry ownership, follow-on investment amounts, and resulting cost basis - Apply scenario-based exit valuations: - **Downside**: 0-1x return (write-off / acqui-hire) - **Base case**: 3-5x gross MOIC - **Upside**: 10-30x+ gross MOIC (fund returners) - Account for liquidation preferences, participation rights, and any ratchets that affect payout waterfall - Convert gross deal MOIC to net proceeds after accounting for dilution and preference stack 4. **Aggregate to portfolio-level fund math** - Apply a return distribution across the portfolio (e.g., power-law: 50% return 0-1x, 20% return 1-3x, 20% return 3-10x, 10% return 10x+) - Sum gross portfolio proceeds and compute gross fund TVPI and IRR - Run the GP/LP waterfall: return of contributed capital → preferred return → catch-up → carried interest split - Calculate net TVPI, net IRR, and DPI at projected exit timelines - Determine fund-returner threshold — what exit valuation a single company needs to return 1x the fund 5. **Sensitivity and scenario analysis** - Vary key drivers independently: loss rate (±10%), median exit multiple (±2x), hold period (±2 years), dilution per round (±5%) - Build a scenario matrix (bear / base / bull) with coherent macro assumptions per scenario - Identify which 2-3 variables have the highest sensitivity on net IRR and net TVPI - Test J-curve profile by modeling cash flow timing (drawdowns, distributions, net cash position by year) 6. **Document and present** - Summarize all key assumptions in a dedicated assumptions table - Present core outputs: net TVPI, net IRR, DPI trajectory, and fund-returner analysis - Include sensitivity tables and tornado charts for the highest-impact variables - Flag all [VERIFY] items for review against actual LPA terms, market benchmarks, or GP-provided data ## Output - **Assumptions table**: Fund size, fees, carry, portfolio construction, dilution schedule, exit timing, loss rate - **Deal-level schedule**: Per-company entry, follow-on, ownership, and scenario exit proceeds - **Portfolio summary**: Gross TVPI, gross IRR, return distribution histogram - **Waterfall output**: Net TVPI, net IRR, DPI, GP carry, LP net proceeds - **Sensitivity matrix**: Net IRR / TVPI under varied loss rates, exit multiples, hold periods, and dilution - **J-curve / cash flow schedule**: Year-by-year drawdowns, distributions, and net cash flow to LPs ## Quality Checks - Investable capital + total fees + GP commit = fund size (balance check) - Sum of initial checks + follow-on reserves = investable capital allocation - Ownership-at-exit correctly reflects cumulative dilution from all subsequent rounds minus pro-rata participation - Waterfall math reconciles: LP distributions + GP carry + unreturned capital = total gross proceeds - Net IRR is computed on actual cash flow timing, not simplified annual averages - Power-law return distribution is realistic — a single company should not drive >50% of gross returns unless explicitly modeled as a concentrated-bet strategy - All jurisdiction-dependent tax treatment (e.g., QSBS exclusion, long-term capital gains rates) marked with [VERIFY] - Management fee offset or rebate provisions reflected accurately per LPA terms [VERIFY]