Based on the Stanford GSB 2026 Search Fund Study (Case E-967)
Every searcher eventually faces the same question at a term sheet: is this a normal deal, or am I about to overpay for the wrong company? Stanford's 2026 Search Fund Study provides the closest thing the industry has to an answer key, with benchmark data across hundreds of acquisitions. Here is what a search fund acquisition actually looks like today, what it costs, how it is structured, and which deal characteristics the data links to better outcomes.
The median company acquired by a search fund in the most recent cohort looks like this: roughly $8.1 million in revenue, $2.5 million of EBITDA, a 25% EBITDA margin, about 30 employees, growing EBITDA at 12% a year, purchased for $16.0 million at a 6.2x EBITDA multiple.
Three things stand out against history. First, prices have risen: the $16.0M median purchase price is the second-highest on record, and the median across all acquisitions ever is $13.5M. Second, multiples softened even as prices rose: 6.2x is down from roughly 7x in the two prior cohorts, meaning searchers are buying somewhat larger earnings streams rather than paying more per dollar of profit. Third, the target quality bar has held: margins around 25% and double-digit EBITDA growth remain the standard.
The dispersion matters as much as the median. Purchase prices in the dataset run from $1M to over $150M, with 38% of all deals above $16M and only 10% below $5M. If your pipeline is full of sub-$5M targets, you are fishing where few search deals actually close, often because the fixed costs of the model (board, diligence, debt) do not scale down well.
Industry mix has been stable for years: services lead, followed by software, with tech-enabled services and healthcare perennially strong. The one genuine shift in the latest data is education, particularly credentialing and vocational training, which posted its highest-ever number of acquisitions.
Geographically, California and Texas tied for the most new acquisitions in 2024-25, followed by Florida, Ontario, Alberta, Arizona, Massachusetts, and New York. And 52% of searchers buy in the same state or region where they searched, a persistent pattern that reflects how much proximity helps in sourcing, diligence, and seller trust.
From the start of the search, the median acquisition closes at month 20. The path there typically runs through 2.5 signed letters of intent, meaning the modal experience includes at least one busted deal, most often killed by something discovered in due diligence (cited by 79% of searchers), a valuation gap with the seller (45%), or insufficient investor support (40%).
Plan for the busted-deal cost, financially and emotionally. Diligence on a deal that dies is not wasted; it is tuition. But searchers who model only the happy path run out of runway and morale exactly when the second, better deal appears.
The study links several acquisition-stage decisions to eventual investor outcomes, and they are worth internalizing before you negotiate:
Debt at acquisition is associated with higher returns. Deals that used leverage outperformed unlevered deals on both returns and public-market-equivalent measures. The mechanism is the classic one: when a business has understandable, predictable profits, sensible debt amplifies equity returns. The qualifier is the important part; leverage against volatile earnings amplifies in both directions.
High recurring revenue is associated with higher ROI, IRR, and PME. Contracted, repeatable revenue gives a first-time CEO stable cash flow and, crucially, time to improve the business before results must show. If two targets are otherwise comparable, the one with recurring revenue is worth a higher multiple than the spread between them suggests.
Services businesses show higher returns. The largest category is also, on the data, the best-performing one on average.
Seller alignment is common and useful. Structures that keep the seller economically engaged, notes, earn-outs, or rolled equity, remain standard tools for bridging valuation gaps and de-risking the transition, and the long-duration-enterprise cohort in the study shows how normal this has become: 63% of those first acquisitions included equity to sellers.
Is 6.2x too much? The historical arc is instructive. The 2008-09 cohort bought at a median of 4.9x during the financial crisis and produced the best acquisition rates and some of the strongest outcomes on record. Multiples then climbed for over a decade, peaking around 7.3x in 2020-21, before easing to 6.2x in the current cohort.
But the study's power-law analysis adds a caution about extrapolating golden-era returns: funds that returned 10x or more skewed heavily toward older vintages (median acquisition year of 2012) and smaller purchases ($4.0M median versus $6.7M for everyone else). Buying bigger companies at higher multiples than the legends did is the current reality; underwriting to the legends' returns from those entry points is how investors and searchers get disappointed. Underwrite the deal in front of you at today's prices, and let the compounding, not the entry multiple, do the heroic work.
Because 79% of busted deals die on diligence discoveries, the highest-leverage work happens before you sign. Four questions to answer as well as possible pre-LOI:
Is the revenue what it appears to be? Recurring versus reoccurring versus one-time, contract terms, churn, and the customer concentration table. Most fatal discoveries live here.
Do the financials survive contact with an accountant? Owner add-backs, cash versus accrual quirks, and working capital patterns. If the seller's books are informal, price the QoE surprise in advance.
Why is the seller really selling? Retirement and health are fine answers. A quietly deteriorating market position is not, and it rarely appears in the CIM.
Will your investors fund this specific deal? Two in five searchers lost a deal to their own cap table. Socialize the target early, know each investor's real appetite, and line up the debt conversation in parallel rather than in sequence.
The benchmark search acquisition in 2026 is a $16M purchase of a durable, 25%-margin services or software business with 30-odd employees, bought at 6.2x EBITDA around month 20 of the search, financed with sensible leverage, and often with the seller keeping some skin in the game. Deals that match that profile, especially the recurring-revenue and prudent-debt parts, are the ones the forty-year data most consistently rewards. Know the benchmarks cold, and you will recognize both the fair deal worth stretching for and the flattering one worth walking away from.
Part of the Five Experts series on the acquisition phase of the ownership journey. See also our guide to the search phase and our breakdown of Stanford's 2026 Search Fund Study.
