A leveraged loan trades and you get a mark. A direct loan to a mid-market business does not trade, and the mark comes from a model, a comp set, and a fair amount of judgment. That single difference is why benchmarking a private credit portfolio is a genuinely harder problem than benchmarking a public bond fund, and why so many firms end up benchmarking against very little.
The comparison problem
Ask a portfolio manager how their fund's leverage or spread compares to the market and the honest answer is often anecdotal: a few recent deals they saw, a conversation with a banker, a data provider's published median. None of those sources are wrong, but none of them are a systematic view of the manager's own book relative to a defined universe of comparable credits.
Why internal data alone is not enough
A firm's own deal history is a useful but incomplete benchmark. It tells you how your underwriting has drifted over time, but not how it compares to the market you are competing in for the same borrowers. Without an external reference point, a portfolio can quietly re-rate its own risk tolerance over several years and never notice, because every new deal looks reasonable relative to the last one.
Building a workable benchmarking framework
A credible benchmark needs three things: a defined comparable universe, consistent metric definitions, and enough deal volume to be statistically meaningful.
- Segment the comparable universe by sector, EBITDA size band, and structure type (unitranche, first lien, second lien, mezzanine).
- Normalize adjustments before comparing multiples of EBITDA, since add-back conventions vary widely between sponsors.
- Track spread, leverage, and covenant package together, since any one metric in isolation can be misleading.
- Refresh the benchmark on a rolling basis rather than annually, since private credit terms move meaningfully within a single vintage.
The role of structured deal data
Benchmarking gets dramatically easier once a firm's own historical deals exist as structured records rather than a folder of memos. Extracting leverage, pricing, covenant terms, and sector classification from every closed deal, consistently, turns three years of transaction history into a usable internal dataset on day one, without waiting for a third-party provider to catch up.
A benchmark is only as good as the consistency of the data feeding it. A single well-defined metric across five hundred deals beats twenty metrics across fifty.
Applying the benchmark to underwriting decisions
Once a firm has a working benchmark, it becomes a genuine underwriting tool rather than a reporting afterthought.
- New deal terms get compared to the relevant comparable set before the credit committee meets, not after.
- Portfolio construction decisions can target explicit relative value, adding exposure where the firm's own pricing looks attractive against the benchmark.
- LPs and consultants get a defensible answer when they ask how the manager's underwriting compares to peers, backed by data rather than assertion.
The direction this is heading
As more private credit activity gets captured in structured form, benchmarking will look increasingly like the public market practice it has always lagged behind: less anecdote, more distribution. Firms that build the internal data discipline now will be the ones with a credible answer when an LP asks the question that used to get a shrug.
