Building LP Fund Valuation Scorecards from Raw Data

Allen took on this engagement as one of his primary accounts at TresVista, supporting an LP endowment fund that invested across multiple VC and PE funds globally. The fund's investment process was largely relationship-driven, but the client needed a quantitative layer to confirm whether a target fund's historical performance held up against expectations.

His job was to build that layer from scratch. He owned the full workflow: receiving raw performance datasets from the client, building historical Excel models, and producing an interactive dashboard that served as the final scorecard. The scorecard tracked IRR, MOIC, time-to-investment, and per-asset value creation across each fund's history.

Setting the Right Benchmarks

The harder part of the work was calibration. Each fund type required a different benchmark: a VC investing in early-stage startups carries a structurally higher loss ratio than a growth equity or buyout fund. Allen had to research what normal looked like for each fund type and stage before he could assess whether a target fund was performing well or poorly.

He used the StepStone SPI database to pull comparable fund performance data, matching by sector, geography, and fund type. This gave him a defensible baseline for each scorecard rather than applying a single standard across structurally different vehicles.

Delivering the Decision Tool

The final output was an interactive dashboard that let the client see, at a glance, whether the target fund's historical performance met the benchmarks Allen had set. The scorecard covered multiple investment criteria: deployment pace, loss ratios, return multiples, and term structure. It was designed to confirm or challenge the narrative the GP had presented in conversations with the LP.

Allen worked across VC, growth equity, private equity, and buyout fund types over the course of this engagement, logging over 1,000 hours of due diligence support for this client alone.