The firm
Crown MedRealty Partners is a fast-growing, vertically integrated real estate investment firm headquartered in Kansas City. The firm acquires and manages medical office buildings (MOBs) and ambulatory surgery centers (ASCs) nationally.
The problem
With 100+ properties under management and 20+ acquisitions per year, the acquisitions team screens far more deals than it closes. Every opportunity follows the same sequence: read the OM, organize the files, review the rent roll, populate the firm's proprietary Excel pro forma by hand, solve the going-in price against JV hurdle rates, build sensitivity tables, draft an IC memo. Most deals get passed on. But each one still requires the full analytical workup before the team can make that call.
The hours spent on deals that go nowhere are hours not available for sourcing and negotiation. The capital partner expects institutional-grade deliverables. The team needs to move fast. Those two demands pull in opposite directions.
Why this is hard to solve
The bottleneck is not summarizing an OM or drafting a memo. The hard part is operating inside the firm's actual financial model. Crown's pro forma is a multi-tab Excel workbook built over years. It has named ranges, a GP/LP waterfall, firm-specific conventions, and an output format the capital partner expects. The team uses this model to compare every deal against the rest of the portfolio. Replacing it with a new template would break reporting compatibility and force reconciliation on every deal.
What Apers does differently
Learns the model once, operates inside it on every deal
Apers XL-2 Modeling Engine maps the firm's pro forma models: every assumption input, the waterfall logic, the cell dependencies, the output format. On subsequent deals, Apers operates inside the same workbook without re-reading its structure. Every cell modification is logged with before-and-after values. The acquisitions team can open the populated model, verify any assumption, and adjust cells by hand.
This extends across asset types. A net lease single-tenant deal and a multi-tenant MOB require different model configurations. Apers adapts the population logic to match each asset's structure while staying inside the same pro forma, so every deal remains an apples-to-apples comparison.
Catches inconsistencies before the model even runs
Before populating the model, Apers cross-references every document in the deal room (OM, leases, financials) against each other. On one deal, it identified a building square footage discrepancy between the OM and the lease documents. On another, it flagged unsigned lease signature pages and a renovation date conflict between two sections of the same OM. These are issues the team had caught during formal due diligence, weeks into the process. Apers surfaces them at screening.
Solves the price with existing models
Apers iterates through the model: populating assumptions from the OM, solving the going-in price against the target levered IRR, checking whether the solved price clears each layer of the JV waterfall, adjusting, and re-solving.
When assumptions require human judgment, the system pauses and surfaces structured decision points. On one single-tenant sale-leaseback, it presented three: base-case lease term selection from a quoted range, holding period, and treatment of landlord expense leakage under an absolute triple-net structure. The acquisitions team answered each via button selection. The system then resumed, solved the price, generated a sensitivity table (going-in cap rate vs. levered IRR), and composed an IC memo.
TIME TO IC MEMO
Total time from email receipt to completed memo, populated model, and sensitivity analysis: ~15 minutes, down from weeks by solving manually.
Human in charge. Stops where it should.
Investment judgment stays with the team. After underwriting, Apers pauses. The acquisitions team reviews the memo and model, applies market knowledge, and decides whether to advance. Automation resumes only when the team initiates the next action.
Results
| Workflow | Before | After |
|---|---|---|
| OM to IC memo | Days to weeks | ~15 minutes |
| Model population | 30–60 min per deal | Supervised Autonomy, fully logged |
| Inconsistency detection | During DD (weeks) | During screening (minutes) |
| Sensitivity analysis | Built separately | Generated with underwriting |
| Team time | Weighted toward data entry | Weighted toward sourcing and negotiation |
A lean team with this infrastructure can sustain institutional analytical quality across a high volume of acquisitions without the G&A load of a larger shop. The competitive advantage that earned the deal access in the first place gets reinforced rather than diluted as volume grows.
CLIENT NOTE
Crown MedRealty Partners is an enterprise client of Apers. This case study reflects workflows demonstrated during onboarding and pipeline setup.