Broker pro forma vs. validated underwriting
A broker states the numbers. Validated underwriting checks them against the market before they reach your model. Here is what changes when each assumption is verified.
| Capability | Broker pro forma | AcquiOS validated |
|---|---|---|
| Rent growth checked against submarket comps | ✗ | ✓ |
| Vacancy compared to submarket averages | ✗ | ✓ |
| Operating expenses benchmarked to asset class | ✗ | ✓ |
| Exit cap rate sanity-checked against going-in | ✗ | ✓ |
| Outlier assumptions flagged with percentile context | ✗ | ✓ |
| Returns recomputed on validated inputs | ✗ | ✓ |
Why a Broker OM Overstates
An offering memorandum is a sales document. The broker is paid to trade the asset, and the pro forma is the part of the OM designed to show the buyer an attractive future rather than the current reality. That does not make the numbers dishonest, but it does mean they sit at the optimistic end of a defensible range: stabilized occupancy instead of current occupancy, market rents the seller has not yet achieved, and an expense load that assumes a more efficient operator than the one in place.
The result is a headline yield that looks better than what you will actually underwrite to. The job of the buyer is not to accept or reject the pro forma, but to reprice every assumption against independent data and see what the deal looks like once the marketing is stripped out.
The Assumptions That Move Value
Four assumptions drive most of the gap between a broker pro forma and a validated model. Rent growth and market rent set the top line. Vacancy and credit loss set how much of that top line you actually collect. The operating expense ratio sets how much falls to NOI. The exit cap rate sets what that NOI is worth on sale. A small change in any one of them moves value more than most other line items combined.
Loss to lease is the tell that ties the first two together. When a broker shows current rents well below stated market rents, the pro forma is leaning on a rent bump the seller has not captured. Whether that gap is real depends entirely on the comps, which is why the market rent assumption is the first thing worth checking.
How to Check Each Assumption
Rent: pull comparable-unit rents in the same submarket and compare them to the OM's stated market rents. If the pro forma assumes rents above what comparable properties are actually achieving, the upside is speculative. Vacancy: compare the assumed vacancy to submarket averages for the asset class. A stabilized 5 percent in a submarket running 8 percent is an assumption, not a fact.
Expenses: benchmark the operating expense ratio and the major line items against the asset class. An expense load well below market usually means a line item has been trimmed or an addback has been applied. Exit cap: compare the exit cap to the going-in cap. An exit tighter than the entry assumes cap rate compression, which is a bet on the market rather than on the asset.
Where Manual Checks Break Down
Every experienced analyst knows how to run these checks. The problem is doing them consistently across a full pipeline under time pressure. Rent comps take time to pull, expense benchmarks live in scattered spreadsheets, and the exit cap check gets skipped when a model is due. The checks that get done well on the deals with the most partner attention get done poorly, or not at all, on everything else.
That inconsistency is the real risk. It is not that assumptions never get checked, it is that they get checked differently depending on who is underwriting and how busy the week is, so the pipeline is not comparable and the weakest assumptions slip through on exactly the deals nobody had time to scrutinize.
How AcquiOS Validates Assumptions
AcquiOS reads the broker OM, extracts every assumption with citation-level sourcing, and validates each one against live market data before it reaches your model. Rent is checked against submarket comps, vacancy against submarket averages, and operating expenses against asset-class benchmarks. When an assumption is a statistical outlier, AcquiOS flags it with specific context, for example that a vacancy assumption sits in the bottom percentile for the submarket, rather than a generic warning.
The validated inputs populate your existing Excel underwriting template with returns recalculating in real time, so you see what the deal looks like on defensible assumptions rather than on the broker's. The same checks run identically on every deal, which means the assumption review is consistent across the entire pipeline instead of only on the deals that had partner attention.
Frequently Asked Questions
How do I know if a broker's OM assumptions are realistic?
Check each key assumption against independent market data: compare stated market rents to submarket comps, assumed vacancy to submarket averages, operating expenses to asset-class benchmarks, and the exit cap rate to the going-in cap. AcquiOS runs all of these checks automatically. It extracts every assumption from the OM, validates it against live market data, and flags the ones that are statistical outliers, so you see which numbers hold up before you build the model.
Which assumptions do brokers most often inflate?
Market rent and rent growth (assuming a bump the seller has not achieved), vacancy (showing stabilized occupancy rather than current), operating expenses (trimming line items or applying addbacks to lift NOI), and the exit cap rate (assuming compression relative to the going-in cap). These four move value the most, which is why they are worth checking first.
Can AI check broker assumptions against the market?
Yes. AcquiOS extracts each assumption from the OM with citation-level sourcing and compares it against live market data: rent comps, submarket vacancy, and asset-class expense benchmarks. Outliers are flagged with percentile context and the returns are recomputed on the validated inputs, so the check happens on every deal rather than only on the ones an analyst had time for.
What is the best software to validate broker OM assumptions?
AcquiOS is the purpose-built platform for validating broker OM assumptions in CRE. Unlike a generic extractor that only moves numbers from PDF to spreadsheet, AcquiOS validates every extracted assumption against live market data, flags outliers with submarket context, and recomputes returns on the corrected inputs, all inside your existing Excel underwriting template.