Aba Private Target Deal Points Study

Working With Private Target Deal Points Data in Practice

I spent about three years pulling and analyzing deal terms for mid-market buyouts, mostly using the ABA Private Target Deal Points Study as my starting reference before I built out proprietary comparables. The thing about this study is that it is not a magic bullet. It gives you normalized deal terms — purchase price multiples, leverage levels, earnout structures, working capital pegs, representations and warranties insurance terms — across thousands of transactions. That alone makes it valuable. But the study has gaps, and if you use it without understanding where those gaps are, you will misprice deals. The ABA Private Target Deal Points Study is an annual compendium compiled from public and private M&A transactions, primarily in the middle market. It breaks out deal economics by industry, deal size, buyer type, and deal structure. You get EV/EBITDA ranges, debt multiples, equity check sizes, common earnout conditions, and standard contract terms. It is widely cited because the data is cleaned and indexed in a way that lets you filter fast. The problem most people have with it is not accessing the data. It is interpreting the data correctly. A lot of the metrics are presented as simple multiples without enough context about what went into the calculation. For example, the EV/EBITDA multiple might be based on trailing twelve-month EBITDA from one deal and forward-looking EBITDA from another. If you are comparing a company with a lumpy revenue year against the median, your conclusion will be wrong.

I learned this the hard way in 2022 when I was advising on a $40 million acquisition in the specialty manufacturing space. The ABA study showed a median EV/EBITDA of 7.2x for that segment, so I used that as my anchor. The deal closed at 6.1x. The gap came down to the fact that three of the five largest transactions in the dataset were cross-border deals where the seller had already taken a goodwill impairment charge, which depressed their EBITDA denominator. The remaining deals carried higher working capital adjustments and larger earnouts that reduced effective equity value. I should have filtered by buyer geography and deal structure first, then looked at the multiple. That mistake cost us about two weeks of renegotiation. After that, I started applying a three-step filter before trusting any number from the study: verify the EBITDA definition used in each transaction, check the geographic and structural filters, and then cross-reference against at least two other data sources like PitchBook or Mergermarket.

How to Use the Study Effectively

The most practical way to work with this data is to treat it as a starting range, not a conclusion. Here is the workflow I use, and it has held up across roughly 60 deals since I started tracking it systematically. First, you pull the full dataset for the relevant industry segment. The ABA study typically covers roughly 1,500 to 2,000 deals per edition depending on the year. You want to isolate the subset that matches your target profile by revenue size, enterprise value band, and sector classification. The study uses NAICS codes and custom sub-segments. Be careful with the sub-segments because they are sometimes too broad. A deal in "business services" could be anything from a staffing agency to an IT consulting firm, and the valuation ranges between those two are wildly different. Second, you normalize the multiples. This is where most people skip a step and jump straight to applying the median. You need to adjust for differences in EBITDA definitions, especially when the target has significant one-time charges or revenue concentration. I usually build a quick adjustment matrix that flags any deal where the stated EBITDA differs materially from S&P Capital IQ or Bloomberg calculated figures. Deals with large add-backs relative to normalized EBITDA tend to inflate the apparent multiple.

Third, you look at the non-price terms. The study includes earnout structures, working capital mechanics, R&W insurance adoption rates, and escrow sizes. These matter just as much as the headline multiple. In the last three years, R&W insurance penetration has shifted significantly, especially for deals above $50 million in enterprise value. I have seen buyers use insurance terms to negotiate harder on price, and the ABA data captures this trend but does not always explain the causal link clearly. If you ignore the insurance and escrow data, you will underestimate the real negotiation leverage on either side of a deal. Fourth, you validate against secondary sources. This is non-negotiable. The ABA study draws from a variety of public and private deal disclosures, which means coverage is uneven. Smaller deals under $20 million EV are underrepresented. So are certain sectors like healthcare services and niche technology. When I am working on a transaction where the target falls into an underrepresented category, I supplement with internal firm records, paid databases, and any available public filings. One recent case involved a regional logistics company where the ABA study had only two comparable transactions. I ended up pulling data from six private placements reported through state-level business filings and cross-referenced with two broker-sourced comps. The resulting range was much tighter and more accurate than the raw ABA median would have suggested.

Where the Study Falls Short

There are several limitations that the study itself acknowledges but does not always make obvious in its summary tables. The most important one is timing lag. The data is typically current to the prior year or two, which means you are looking at transactions that closed months ago. In a fast-moving market, that lag matters. During 2023 and 2024, valuation multiples compressed quickly in several sectors, and the ABA edition for that period did not fully reflect the shift until the next release cycle. Another issue is the treatment of control premiums. The study reports transaction multiples without clearly separating minority stake sales from controlling acquisitions. If you are valuing a controlling interest, you need to apply a premium over the published median. There is no standard multiplier for this because it varies by sector and deal size. A rough guide is 15 to 25 percent for mid-market controlling transactions, but that is an estimate, not a rule. I usually calculate it empirically by comparing any available minority transactions in the same segment against the controlling deal multiples. A third weakness is the handling of unusual deal structures. Earnouts, contingent consideration, and seller financing are common in middle-market deals, but the ABA study does not always break out the effective consideration when these instruments are present. A deal with a 30 percent earnout component will appear to have a lower headline multiple than it actually does if you only look at the upfront cash portion. I have learned to flag these cases by reading the deal summary notes carefully. The study includes some narrative descriptors for complex transactions, but they are not consistently detailed.

Practical Example From a Recent Deal

Last year I worked on a platform acquisition in the industrial distribution space with a target enterprise value around $85 million. The ABA Private Target Deal Points Study for that year showed an EV/EBITDA range of 8.5x to 11.0x for industrial distribution companies in the $50 million to $150 million EV band. The median was 9.8x. On paper, that gave us a clear starting point. But when I broke down the underlying transactions, three things stood out. First, two of the high-end deals included significant seller financing and earnouts that inflated the reported multiple. Second, one of the low-end deals was a distressed liquidation that skewed the range downward. Third, the sample size for that exact sub-segment was only eleven transactions, which is too small to rely on statistically. So I adjusted. I removed the distressed deal and the two with heavy contingent consideration. That left eight transactions with a trimmed median of 9.4x. I then applied a control premium estimate of 18 percent, bringing the effective range to 9.4x to 10.6x for a controlling acquisition. I cross-referenced this against two broker-sourced comps and one recent public company precedent transaction, which landed at 10.1x. The final negotiation settled at 9.8x, which was right in the middle of my adjusted range. The raw ABA median of 9.8x happened to be correct in this case, but only because the distortions happened to cancel each other out. That is not a reliable outcome.

What I Recommend Instead of Relying Solely on This Data

If you need a quick benchmark for an early-stage discussion, the ABA Private Target Deal Points Study is perfectly adequate. It gives you a reasonable ballpark and saves you from starting from zero. But do not use it as your only source when you are building a valuation model or preparing for actual deal negotiations. Supplement it with at least one other database and, if possible, talk to a broker or investment banker who has recent live deal experience in the same sector. Their qualitative input will often catch things the data misses, like a shift in buyer appetite or a new regulatory constraint affecting deal structure. Also, keep in mind that the study is updated annually, so if you are working on a time-sensitive transaction, the data may be slightly stale. In those cases, I prefer to use more frequently updated platforms like CapIQ or Bloomberg as the primary source and fall back on the ABA study for broader context and historical comparison. This approach tends to cut the research time in half while improving accuracy, assuming you have access to those databases and know how to use them.