You just toured a 120-unit value-add deal. The broker’s pro forma has RUBS revenue jumping 40% from in-place, and your partner wants to know if the number holds up. You need to anchor a stabilized utility recovery figure before the LOI goes out tomorrow morning.
You know how to work the analysis. You’ve done it dozens of times: pull the T-12, back into the recovery rate, cross-check against the OM, factor in the local utility landscape. The problem isn’t the methodology. It’s that you’re juggling three other deals and this one needs a defensible number by end of day.
That’s exactly what this task is built to fix.
What This Task Does
You upload a historical operating statement for the property. That’s the only required input. If you have an offering memorandum, broker pro forma, RUBS comps, or any other supporting documents, you can add those too. You can also paste in commentary about how the property currently handles RUBS or any constraints on increasing recovery.
From there, the Real Estate Analyst (with Memory) extracts every relevant data point, calculates in-place RUBS performance metrics, and works through a structured analytical methodology to arrive at a recommended stabilized UW RUBS figure. It shows you exactly how it got there, positioned relative to in-place with a clear justification for any delta.
The whole process takes roughly 5 minutes of your time. The AI does the rest.
Who This Task Is For
Any CRE professional who needs a defensible RUBS assumption for a multifamily deal and doesn’t want to spend an hour building it manually every time.
This task is built for:
- Acquisitions analysts who need to underwrite RUBS revenue on new multifamily deals quickly and accurately
- Asset managers who want to benchmark in-place RUBS performance and identify upside in their existing portfolio
- Investment committee members who want a clear, defensible utility recovery assumption before approving a deal
- Brokers who want to validate or pressure-test a seller’s RUBS pro forma before putting it in front of buyers
In short: if you already have a T-12 and a multifamily deal, this task gives you a fully underwritten RUBS figure you can stand behind.
Why It Matters
RUBS is one of the most common line items in multifamily underwriting, and one of the easiest to get wrong. The difference between a 55% and an 80% recovery rate can swing NOI by tens of thousands of dollars on a mid-size deal. That’s not a rounding error; that’s a different purchase price.
You already know this. Every experienced multifamily professional understands that utility recovery is a real lever, not a throwaway line item.
The problem is that building a properly anchored RUBS assumption takes time. You need to pull the T-12, isolate utility expense and reimbursement revenue, calculate the implied recovery rate, cross-reference against the OM or broker pro forma, and then make a judgment call on where the stabilized figure should land. That’s 10 minutes on a clean deal, longer when the data is messy or scattered across multiple documents.
Without a tool like this, the RUBS line either gets rushed (plug in a number and move on) or deprioritized (deal with it later, except later never comes before the LOI deadline). Either way, you’re leaving value on the table or, worse, underwriting to a number you can’t defend.
This task takes a 10-minute manual process and compresses it to 5. You get the same analytical rigor, the same sensitivity analysis, the same diligence checklist, just without the time tax. That’s the multiplier.
What the Output Looks Like
The RUBS analysis generated by this task includes:
- An in-place RUBS performance table with total units, occupancy, T-12 utility expense, T-12 RUBS revenue, per-unit metrics, and implied recovery rate
- A clearly stated recommended stabilized UW RUBS figure (per unit per month and per year) with delta justification relative to in-place
- A recovery-rate sensitivity table showing how the stabilized figure moves across 55%, 65%, recommended, 80%, and 90% scenarios
- Prioritized diligence items with specific questions to confirm and how each answer would affect the UW figure
- An optional IC memo assumption narrative, available on request after the initial analysis is delivered
The output is not a rough estimate pasted into a cell. It’s a structured, methodology-driven analysis with sensitivity context, the kind you’d expect from a senior analyst who’s underwritten hundreds of multifamily deals.
CRE Agents is a platform built for commercial real estate professionals who want to move faster without cutting corners. Task #[TASK_NUMBER] is just the beginning.
Frequently Asked Questions About Underwriting Multifamily RUBS With AI
Yes, and the task is designed with that expectation. The output gives you a recommended stabilized figure with full transparency into how it was derived, including the in-place metrics it anchored to and the delta justification. The sensitivity table lets you stress-test the number across recovery rate scenarios before committing to it. Think of it as a first draft from a sharp analyst: solid enough to act on, easy to adjust if your deal-specific knowledge suggests a different landing point.
The output follows the same analytical methodology a senior acquisitions analyst would use: in-place benchmarking, recovery rate calculation, sensitivity analysis, and prioritized diligence items. It’s structured to slot directly into an IC memo or lender package. The optional assumption narrative feature can even draft the write-up for you in IC memo format. The numbers are only as strong as the data you feed in, so make sure your T-12 is clean and complete.
Absolutely. Each run takes about 5 minutes, so you can underwrite RUBS on a dozen deals in the time it used to take to do one. The task works with whatever documents you have available: just a T-12, or a T-12 plus an OM, broker pro forma, and property-specific commentary. That flexibility makes it practical for early screening (when you only have a T-12) and deep underwriting (when you have the full package). Run it on every deal in your pipeline and you’ll start spotting patterns in recovery rates across your target markets.