How to Benchmark Your PE Fund Against Preqin Data

The five-step benchmarking workflow

A private equity benchmark is a reference point built from the performance of comparable funds, and Preqin PE fund benchmarking takes five decisions rather than one lookup. Define the peer universe by vintage year, strategy, geography, fund type and size. Choose between a Preqin peer benchmark and a Preqin index. Compare net IRR, TVPI, DPI, RVPI, public market equivalent (PME) and cash-flow pacing rather than a single headline return. Test whether the sample is large enough and the data complete enough to support the claim. Then present the result with the filters and methodology attached. Preqin states that, as of June 2026, its Benchmarks and Indices cover 140,000+ customisable peer group benchmarks and 65 reporting-grade indices across six asset classes, so the constraint is selection discipline, not availability.

Choosing the right Preqin output

Fix the fund’s identity before using filters

Every filter you apply is an assertion about what the fund is. Write those assertions down before you open the platform, because retrofitting the definition to a flattering result is the most common failure in this process.

• Vintage year: the year the fund began investing or held its first close, which anchors the comparison to a shared market environment. See what vintage year means in fund performance.
• Strategy: buyout, growth equity or venture capital, kept distinct rather than merged into a single private equity bucket.
• Geography: the fund’s target region, not the domicile of the management company.
• Fund size: a lower-mid-market vehicle and a large-cap fund face different entry multiples, leverage and exit routes.
• Return basis: net of fees, carry and expenses if the benchmark is net, which it usually is for LP-facing comparisons.
• Reporting date: the valuation date of your fund’s numbers and of the benchmark, which must match.

Preqin uses “private capital” to include private equity, venture capital, real estate, infrastructure, natural resources and private debt. If you mean corporate buyout, filter for it and say so.

Peer benchmark or index

Preqin draws a distinction between the two, and it affects what you can claim. Peer benchmarks support flexible fund-level comparison across strategy, geography and vintage, including quartile rankings and J-curve analysis. The indices are described by Preqin as reporting-grade and frozen, underpinned by validated cash-flow data, and they offer both money-weighted IRR and time-weighted return views.

Use a peer benchmark to answer whether a manager is strong relative to comparable managers. Use an index to answer what private capital delivered in a period for allocation, policy benchmarking or a board pack. An index is a poor answer to a manager selection question, whereas a tightly filtered peer group is a poor answer to an allocation question.

The similarity versus sample-size trade-off

Preqin Academy identifies the central problem in peer group construction: the funds must be similar enough for the comparison to mean something, and numerous enough for the comparison to be credible. Those two requirements pull against each other, and every additional filter buys comparability at the cost of statistical weight.

The commercial conflict sits here. A GP tightening filters to European mid-market healthcare buyout funds of a single vintage may end up in the top quartile of eight funds. An LP widening the universe to all European buyout funds of that vintage gets a more robust distribution but a looser like-for-like fit. Neither party is wrong on method. They are optimising for different outcomes.

Build the peer group outward rather than inward. Start with asset class and strategy, add vintage, add geography, then stop. Add fund size only if the remaining sample still supports quartile breaks. Preqin’s product materials describe a video workflow in which custom benchmarks required at least four selected funds, although that detail dates from 2019 and should be verified against the current platform before you rely on it. Four funds would not survive an LP’s due diligence question regardless.

Worked workflow: a 2014 vintage European buyout fund

Preqin’s product video describes filtering for 2014 vintage buyout funds targeting Europe, using strategy, geographic focus, vintage and point-in-time filters, then drilling into constituent funds and exporting the output. The sequence below follows that workflow. It illustrates the process only and produces no performance figures, because the source supplies none.

1. Select private equity benchmarks within the private capital universe.
2. Set strategy to buyout.
3. Set vintage to 2014.
4. Set geographic focus to Europe.
5. Fix the point in time to match your fund’s reporting date.
6. Record the number of constituent funds before reading any result.
7. Note the statistical measure in use: median, mean, weighted average or pooled IRR.
8. Read net IRR, TVPI, DPI and RVPI across quartile breaks rather than the median alone.
9. Add a PME comparison if fund-level cash flows support it.
10. Drill into constituents where available to confirm the sample contains genuine comparables.
11. Export the table and save the filter set alongside it.
12. Write one paragraph justifying the peer group before the result circulates.

Step twelve is the one that survives contact with an investment committee. A quartile rank without its filter set is an assertion, not evidence.

Reading the metrics against each other

Net IRR is the most quoted figure and the least stable. It is sensitive to the timing of calls and distributions, and commentary comparing private equity datasets has noted that TVPI statistics between Preqin and MSCI-Burgiss look remarkably similar while IRRs tend to run higher in Preqin, with IRR described as noisy and fragile. Treat that as qualified analytical commentary rather than settled fact, but do not build a case on IRR alone. The IRR versus MOIC comparison covers why multiples and rates answer different questions.

• TVPI: total value to paid-in, capturing realised and unrealised value together.
• DPI: distributions to paid-in, the cash actually returned, and the metric LPs scrutinise hardest in a slow exit market.
• RVPI: residual value to paid-in, the portion still held at manager-determined marks.
• PME: private performance measured against a public index with cash-flow timing respected.
• Horizon IRR: return over a defined recent window, useful for isolating momentum against inception IRR.

The DPI-to-RVPI split is the honest test of a strong TVPI. A fund at top-quartile TVPI carried mostly in RVPI is asserting valuation rather than delivering liquidity, which is why fair value adjustments deserve attention before the quartile rank does. Never compare a gross fund return against a net benchmark, a mismatch covered in the gross versus net IRR breakdown.

Adjusting the read for fund age

A 2022 vintage judged on DPI will look poor by construction. Early-life funds sit in the drawdown phase, where fees and unrealised positions depress returns before value emerges, which is the mechanism behind the J-curve. Preqin supports J-curve trajectory and cash-flow momentum analysis, including how quickly capital is called and returned for a strategy or manager.

For young funds, weight TVPI, deployment pace and call profile against peers. For mature funds, weight DPI and inception IRR. For funds in the middle, horizon IRR against inception IRR shows whether recent performance is improving or decaying.

Data quality checks that change the conclusion

Preqin sources performance data through GP-to-LP reports, Freedom of Information Act requests, GP contributions, public filings, listed firm disclosures, annual reports and proprietary research. Coverage of summary performance is wider than coverage of fund-level cash flows. An academic study of private equity real estate reported that over half the Preqin funds in its sample lacked detailed fund-level cash flows, a finding specific to that sample rather than a general figure for all private equity. The same literature notes that risk-adjusted performance measurement usually requires cash-flow-level data.

The practical consequence is that PME and risk-adjusted analysis run on a narrower subset than headline benchmark tables. If cash flows are absent, PME is not available and should not be implied. Commentary on private equity datasets has also flagged that usable samples shrink once filters and performance-data requirements are applied together, which is a reason to check constituent counts rather than assume them.

Confirm the statistical basis too. A median IRR, a pooled IRR and a size-weighted average tell different stories about the same universe, and switching between them mid-analysis is a reporting error.

GP and LP use of the same table

Preqin notes that LPs use benchmarks to evaluate fund risk-return profiles, GPs may use them in marketing materials, and service providers apply them according to client objectives. Same data, opposed incentives.

A GP preparing for fundraising should run the peer group an LP would run, not the one that flatters the deck, and be ready to defend the filters. An LP receiving a GP benchmark should ask four questions: which filters produced it, how many funds it contains, whether the returns are net, and what the DPI quartile looks like on its own. If a manager cannot produce the constituent count, the ranking is unusable. Whether outperformance reflects skill or market beta is a separate question addressed in the private equity alpha discussion, and PME is the closest available test.

Red flags in a benchmark presentation

• Vintage band widened by two or three years without explanation.
• Strategy bucket merging buyout with growth or venture.
• Gross fund returns shown against a net peer set.
• Quartile rank quoted with no sample size.
• PME cited without naming the public index or confirming cash-flow coverage.
• Benchmark reporting date more recent than the fund’s own valuation date.
• Custom peer set assembled after the fund’s numbers were known.

Conclusion

Access to Preqin settles nothing. A defensible benchmark comes from filters fixed before the result is visible, a sample large enough to bear quartile breaks, and a metric set that separates cash returned from value asserted.

The decision rule is simple: if you would not accept the peer group when it produced an unfavourable rank, you cannot use it when it produces a favourable one. LPs reconcile GP-supplied benchmarks against their own peer sets and other providers, and a peer group that only works in one direction is the fastest way to lose credibility in a re-up conversation.

P.S. If fund performance analysis is part of your week, check out our Premium Resources for financial models, PE & VC databases and more tools to help you advance your career.

Sources

• Preqin Private Market Benchmarks & Indices | BlackRock
• Preqin Private Equity Performance Benchmarks | BlackRock
• Lesson 7.1: Introduction to Private Capital Benchmarks
• How to Use Market Benchmarks in Preqin Pro
• Benchmarking Private Equity Performance When Fund-level Cash Flows are Missing | The Journal of Real Estate Finance and Economics | Springer Nature Link
• Navigating Private Equity Data: What the Main Datasets Actually Contain