Cube vs. Mosaic for a Commercial Debt Advisory Shop

When rates are falling or expected to fall, refinancing activity tends to pick up as borrowers look to lock in better terms, while rising or uncertain rates tend to push origination volume down as fewer deals pencil out at the buyer's required return. A forecast built on a flat month-over-month growth assumption ignores this cyclicality entirely and will look wrong almost every quarter.

With the 10-year Treasury yield near 4.44%¹, build your near-term pipeline assumptions off the current rate level and its recent trend, not off a multi-year average that smooths over exactly the cyclicality that actually drives your business.

A deal in early-stage discussion with a borrower carries a much lower probability of actually closing than one with signed terms and a lender committed, and treating every pipeline entry as equally likely to close will overstate near-term fee revenue. Apply stage-based conversion rates to each deal, refreshed against your own historical close rate by stage rather than a rule of thumb borrowed from a different market cycle.

Revisit those conversion rates whenever the rate environment shifts meaningfully, since a deal's probability of closing at a given stage isn't fixed, it moves with financing conditions the same way total deal volume does across the broader lending market.

If you or your team already track deal-level probability, expected fee, and closing timeline in a spreadsheet, Cube's approach of syncing that spreadsheet against your CRM or deal-tracking system keeps the model where it's understood, without forcing a rebuild inside a new interface during a period when deal flow is already unpredictable enough.

This tends to be the more natural fit for a smaller shop where the principal or a single analyst owns the pipeline model directly and wants it kept current without extra administrative overhead.

Once you have several originators each building their own book of business, a dashboard rolling up pipeline probability and expected fees across the whole team can surface which originators are carrying the shop's near-term revenue and which are light on deal flow. Confirm in a demo that Mosaic can track deal-level probability and fee structure with the granularity your specific deals need, since commercial debt deals vary enough in structure that a generic template may not capture the nuance.

A dashboard view also makes it easier to spot when total shop-wide pipeline is becoming concentrated in just one or two originators, which is a real business risk worth surfacing early rather than discovering it when someone leaves.

Run your annual fee forecast at more than one rate scenario, since a meaningful move in rates during the year can shift deal volume enough to change your revenue outlook materially even if your win rate on individual deals stays constant. A single-scenario forecast built at the start of the year tends to look stale within a quarter or two once rates move.

Share the range, not just a single number, with anyone relying on the forecast, whether that's a lender partner, a landlord, or your own compensation planning, so a rate move doesn't blindside decisions that were made assuming the baseline case would hold.

Use these checks to keep the fee forecast honest:

• Run the annual fee forecast at more than one rate scenario, since a meaningful rate move can change deal volume even when your win rate on individual deals holds steady.
• Apply stage-based conversion rates to each deal, refreshed against your own historical close rate by stage rather than a rule of thumb.
• Set near-term pipeline assumptions from the current rate level and its recent trend, not from a multi-year average that smooths over the cycle.
• Share the full range of outcomes with your team instead of one number, and revisit the scenarios once rates move meaningfully during the year.

Jirav's driver-based approach is useful when you're deciding whether to hire another originator, since that decision should be tested against realistic ramp-up time, a new originator typically takes months to build a productive pipeline, and against what rate environment you expect during that ramp period, not just against your current, possibly cyclically elevated, deal flow.

A hire that looks easily justified against today's strong pipeline can look considerably riskier once you model their ramp-up against a rate environment that's already showing signs of turning, which is exactly the scenario a driver-based model is built to surface.