building-venture-return-models

name	building-venture-return-models
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

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]