How 4 ETA Insider Guests Financed Their Acquisitions
The Polsky Center’s ETA Insider podcast has spent 10 years interviewing search fund operators, self-funded searchers, and ETA investors from the Chicago Booth network and beyond. We matched 4of their guests to actual SBA loan filings — the banks, rates, terms, and deal sizes that made their acquisitions possible.
R.M. · LenderHawk · Updated 2026-04-09
Best Deal
P + 1.00%
Nick Oberhouse got the cheapest loan in the dataset — a $500K Village Bank and Trust loan for DAWGS (Door and Window Guard Systems, Inc.).
Listen to the episode →Highest Spread
P + 2.00%
A 100 bps gap between the best and worst deal in the cohort. Same podcast, same advice ecosystem, dramatically different pricing.
Top Lender
1 of 4
Regions funded the most ETA Insider deals. Top 5 banks fund 100% of all matched deals.
Deal Size Reality
50%
of matched deals are under $500K. Median: $500K. ETA podcasts talk big, but most Booth searchers land in the lower middle market.
Small Loans, Big Spreads
+150 bps
Sub-$250K deals pay ~150 bps more over prime than $2M+ deals. Search fund acquisitions carry more risk premium, and smaller deals get hit hardest.
The Long Game
0 of 4
guests took 25-year SBA terms — meaning real estate came with the deal. The other 100% took 10 years or less.
Brian O’Connor has been interviewing acquisition entrepreneurs out of Chicago Booth for a decade. The ETA Insider podcast is one of the longest-running shows in the search fund space — and unlike most, it draws from a single network where searchers, investors, and operators all know each other.
We went looking for the financing behind the stories.
The SBA publishes detailed loan-level data on every 7(a) and 504 loan it guarantees. We matched 4 of those records to specific ETA Insider episodes — connecting the guest, the business, and the actual loan terms. Below is what we found.
The first takeaway: even within a tight-knit ETA community, the spread gap is enormous. Nick Oberhouse got P + 1.00% from Village Bank and Trust. The most expensive deal? P + 2.00%. That’s a 100 basis point spread between the best and worst. The network doesn’t equalize pricing. The specific lender and the specific deal do.
The Best Deals: P + 1.00%
| Guest | Business | Loan | Lender | Spread |
|---|---|---|---|---|
| Nick Oberhouse | DAWGS (Door and Window Guard Systems, Inc.) | $500K | Village Bank and Trust | P + 1.00% |
| Keith Burns | ETA | $453K | Glacier | P + 1.50% |
| Jake Levine | ROBO | $1.2M | Regions | P + 1.75% |
| Gabe Barkley | MHW | $100K | Adirondack | P + 2.00% |
The Highest Spreads: P + 2.00%
| Guest | Business | Loan | Lender | Spread |
|---|---|---|---|---|
| Gabe Barkley | MHW | $100K | Adirondack | P + 2.00% |
| Jake Levine | ROBO | $1.2M | Regions | P + 1.75% |
Why Only 4 SBA Matches Out of 93 Episodes?
This is the real story. ETA Insider guests are disproportionately sponsored search fund operators who raise equity from institutional investors, not self-funded searchers who use SBA loans. The podcast covers the full ETA spectrum, and most of that spectrum doesn’t touch SBA lending.
Compare this to Acquiring Minds, where we matched 129 deals. Acquiring Minds skews toward self-funded searchers who use SBA 7(a) loans as their primary acquisition vehicle. ETA Insider skews toward the institutional end: sponsored searches backed by investor equity, PE-adjacent strategies, and international deals.
The 4 matches we did find are the guests who went the SBA route, and their data is real. But the absence of SBA data for most guests is itself a signal about how the search fund world is financed.
| Guest | Business / Role | Likely Financing |
|---|---|---|
| Graham Weaver | Alpine Investors | PE fund (institutional) |
| JT Fitzgerald | Kingsway Financial Services | Public company / permanent capital |
| Reyes Florez | Platform Accounting Group | PE-backed roll-up |
| Marc Shiffman | SMS Assist (CEO) | Venture / institutional equity |
| Andrei Papayanopulos | Arpa Capital (Mexico) | International search fund equity |
| Brian Vanderheyden | Richmond Alarm Company | Traditional search fund equity |
| Aizik Zimerman | J Blanton Plumbing | Search fund / unknown |
| Marcela Fernandez | Colorado Home Services | Partner search / unknown |
Financing type inferred from episode context. These guests may have used SBA loans that we couldn’t match, or financing structures that don’t appear in our SBA dataset.
Regions funds 1 in 4 ETA Insider guests.
Regions funded 1 of the 4 deals we matched — 25.0% of the cohort, more than any other bank. The top 5 banks fund 100% of all matched ETA Insider deals. The other 0 deals are spread across -1 different banks.
Regions’s average spread to ETA Insider guests is P + 1.75%. There’s a reason search fund operators gravitate to specific lenders.
ETA Insider guest share vs SBA-wide market share. Which lenders over-index among searchers?
What Booth Searchers Actually Pay
Median matched ETA Insider deal: $500K. 50% of matched deals are under $500K. Only 0 are over $4M.
The ETA conferences and case studies lean toward the bigger deals. But the median Booth searcher, the one who goes through the grind of a 2-year search and closes something real, is buying a $500K business.
Small loans pay more. Booth searchers are no exception.
These are some of the most networked acquirers in ETA — people with Booth connections, search fund investor backing, and access to the best advisors in the space. And yet their SBA pricing tracks the same curve as everyone else.
Small SBA loans get worse pricing across the board. The network gets you a better process, better diligence, better advice. It does not get you a better spread. The lender + the deal size + the specific banker determines that.
Median spread over prime by loan size. ETA Insider guests vs all SBA 7(a) borrowers since 2015.
Spread Over Time
Average spread over prime by loan approval year. Strips out prime rate noise to show the actual lender pricing trend.
See which lenders offer better pricing
LenderHawk shows real spreads from 2,500+ lenders, based on real SBA lending data.
Search Lenders →Every Matched Deal
4 SBA loan filings matched to ~4 ETA Insider episodes. Sorted by loan amount.
| Guest | Business | State | Loan Amount↓ | Rate | Spread | Term (mo) | Lender | Episode | Conf. |
|---|---|---|---|---|---|---|---|---|---|
| Jake Levine | ROBO | TX | $1.2M | 7.0% | P + 1.75% | 168 | Regions Bank | #85 | 95% |
| Nick Oberhouse | DAWGS (Door and Window Guard Systems, Inc.) | — | $500K | 4.8% | P + 1.00% | 120 | Village Bank and Trust | #61 | 85% |
| Keith Burns | ETA | — | $453K | 7.0% | P + 1.50% | 126 | Glacier Bank | #84 | 85% |
| Gabe Barkley | MHW | — | $100K | 5.3% | P + 2.00% | 84 | Adirondack Bank | #77 | 85% |
Methodology
We matched podcast guests to real SBA loan records using a multi-strategy pipeline:
- Exact borrower name — normalized business name from episode descriptions matched against the SBA borrower name field (confidence: 95%)
- Guest name as borrower — sole proprietors sometimes file under their personal name, scoped by state and year (confidence: 80%)
- State + year + loan amount — when episode mentions deal size, matched within 5% tolerance (confidence: 70%)
- State + year + lender — when guest names their bank on-air (confidence: 75%)
Each match includes a confidence score. Only matches at 60%+ confidence are shown. Multiple matches per guest are ranked by confidence, with the best shown first.
Data sources: real SBA 7(a) loan data (FY2010-present), ETA Insider episode descriptions from polsky.uchicago.edu. Deal details extracted via Claude Haiku with structured prompts.
Not every ETA Insider guest used SBA financing. Many search fund acquisitions use equity from investors, seller financing, or conventional bank debt. Some SBA loans from recent episodes may not be in our dataset yet because the SBA updates quarterly.
Brian — this one’s for you and the Polsky Center.
You’ve built one of the longest-running ETA podcasts in the world. We wanted to add the one thing the episodes don’t always cover: the financing. If you want the full dataset as CSV, want a row corrected, or want to discuss any of this, reach out at founders@lenderhawk.com.
— Founders
Frequently Asked Questions
How accurate are these matches?
Each match has a confidence score based on the matching strategy used. High-confidence matches (85%+) are based on exact business name matches. Medium-confidence matches (70-84%) use combinations of state, year, and loan amount. We only show matches at 60%+ confidence.
Why don't all ETA Insider guests appear?
Many search fund acquisitions use investor equity and conventional debt rather than SBA financing. Self-funded searchers are more likely to use SBA loans. Some show notes don't contain enough structured data to make a confident match, and the most recent episodes may not have SBA data yet.
Where does this loan data come from?
The SBA makes detailed loan-level data public for every 7(a) and 504 loan. This includes loan amount, interest rate, term, lender, location, industry, and loan performance status. It's public record.
How does this compare to Acquiring Minds?
We have a similar analysis for Acquiring Minds, which tends to have more self-funded searchers using SBA loans. ETA Insider has a higher proportion of sponsored/institutional search fund operators, which means a different financing profile.