You get an LOI on a mid-size value-add and the broker’s pro forma looks like a home run. Your partner wants a yes-or-no by the end of the day. This is where CRE underwriting mistakes kill deals: not in the math, but in the assumptions you accept because time is short.
The core idea
Most CRE underwriting mistakes are assumption errors, not spreadsheet math. If you underprice the rehab, overstate immediate occupancy, or lean on stale comp data, the model will look great until cash starts leaving the property. Underwrite costs and realities first; income second. That flip in sequence is the difference between a viable deal and a surprise loss. For background on how lender and investor approaches to underwriting differ, see What Is Underwriting in Real Estate? Lender vs. Investor Explained.
1) Money goes out before money comes in: underwrite rehab and hidden costs first
What sellers and brokers call a “light rehab” often hides walls, roofs, and mechanicals that need work. The most expensive underwriting mistake is underestimating construction and soft costs. Build reserves for overruns in your model. Use the rule of thumb that deals should survive a 20% construction overrun in the budget—if they don’t, they’re not firm enough.
How to spot it fast:
- Compare the rehab line to the property’s age and tenant profile. If the rehab is tiny for an old asset, flag it immediately.
- Ask for contractor ballpark pricing on big-ticket items first (roof, HVAC, structure). If you can’t price those, don’t assume rent upside will save the deal. Also ensure you’re organizing property docs and inspections so scope and bids are documented.
- Look for missing soft costs: permitting, temporary power, testing, third-party inspections, and tenant relocation. Brokers routinely undercount these.
2) Revenue assumptions that forget reality: occupancy, rent, and STR costs
Inflated occupancy and aggressive rent ramps destroy returns. Two patterns repeat: assuming immediate full occupancy after a rehab, and copying a top-of-market ADR or rent growth without submarket proof.
Short-term rentals are a specialized trap. Underwriters often ignore STR-specific operating costs. Cleaning fees, platform fees, management, and higher reserves add up. Expect STR operating expense ratios to run much higher than long-term rentals—underwriters should model STR reserves and fees explicitly (industry discussions often cite STR op-ex in the 35–50%+ range of revenue).
How to spot it fast:
- Don’t enter one static occupancy or ADR number. Sloped occupancy—month-to-month or year-by-year—tells the story. If the model uses a single value for all years, it’s lying to you.
- Check comp stay length and seasonality for STRs. If comps show big seasonality and the model is flat, push back. Also make sure you can read a rent roll like an underwriter to validate unit-level assumptions where applicable.
- Run a downside case with conservative rents and a slower rent ramp. If the IRR or DSCR collapses, that’s a red flag.
3) Data and process errors that feel tactical but are strategic
Bad data and sloppy spreadsheet processes are common. Two mistakes stand out: stale comps and spreadsheet hygiene failures.
Stale comp data creates cap-rate and rent mispricing. Markets change at different speeds by submarket. If your comps are from a different micro-market or older than recent tenant turnover, the pro forma will overpromise.
Spreadsheet mistakes are easier to miss. Mirroring values by copy-paste, typing over source cells, or leaving single-value inputs where arrays belong causes hidden bugs. These aren’t academic problems—these errors change your monthly cash flow and can invalidate lender assumptions at financing. Consider underwriting workflow software to prevent spreadsheet errors and centralize inputs to reduce human mistakes.
How to spot it fast:
- Open the workbook and trace the key inputs. If rents, occupancy, or capex are typed in many places, consolidate them to single source cells.
- Ask for comp metadata: transaction dates, unit-level rents, and tenant credit where relevant. If the broker can’t provide it, the comp is thin.
- Stress test formulas: change one input (like a 10% lower rent) and see if the outputs update logically across the sheet. If something doesn’t move, there’s a formula problem.
4) Exit assumptions and sponsor narratives: don’t confuse hope with an exit plan
Setting an exit cap rate at or below your going-in yield is a common way to inflate reversion value. That’s wishful thinking. You need a defendable exit thesis that matches submarket liquidity and buyer demand. If your exit depends on a better cap rate and there’s no comp to support it, the model is brittle.
Also watch sponsor narratives: promises that trouble will be “made right” or that sponsor goodwill will cover shortfalls create legal and governance risk. Distressed situations need clarity, not theater. Underwrite to governance realities—don’t assume sponsors will fix bad cash flow out of pocket.
How to spot it fast:
- Compare your exit cap assumption to recent closed sales in the same submarket and vintage. If you can’t find comps that back the math, price the exit conservatively.
- Look at the upside drivers. If the model relies on operational miracles (lower churn, zero concessions, higher rents with no upgrade), it’s a stretch.
- Read the sponsor language closely. Any informal assurance that isn’t written into the governing documents is smoke. Treat it as such.
Mini case: a value-add that failed the basic sequence
We underwrote a mid-level asset where the broker’s pro forma had a small rehab line and a fast rent ramp. The spreadsheet showed good returns. We dug into the scope and discovered the rehab excluded two roof replacements and a major HVAC package. The contractor bids pushed the cost up. Even factoring a moderate rent ramp, the deal didn’t absorb the added capital without a hefty equity injection.
What went wrong: the team had priced rent upside before pricing the roof and mechanicals. The rehab number was accepted as gospel. That single assumption—ignoring hidden capex—turned a touted acquisition into a near-term cash drain. We walked. That was the right call.
What to actually do—practical rules
- Price the roof, envelope, and mechanicals before you price rent upside. If you can’t get a contractor ballpark, assume a meaningful overrun and test the deal against it.
- Model occupancy as a slope, not a single input. For STRs, include cleaning, platform, and management fees explicitly and use a higher op-ex ratio.
- Centralize inputs in the spreadsheet. One cell per assumption. No duplicate hard-coding.
- Stress test exits. Run a conservative cap-rate case and a slow-rent-growth case to see how sensitive equity returns are to realistic moves in the market.
- Document sponsor promises. Anything not in the LPA or purchase docs should be considered non-binding for underwriting purposes.
Where a CRE-specific tool helps
Stale Excel inputs are the exact pain a centralized underwriting view solves: keep one set of stored assumptions, recalc metrics live, and create a read-only underwriting report you can share with partners so numbers don’t diverge across inboxes. That reduces copy-paste errors and forces one source of truth for NOI, IRR, and DSCR calculations.
- Store assumptions and recalc NOI/IRR live in a single underwriting view to reduce manual copying and hidden formula errors.
- Generate a shareable, read-only underwriting report so your exit and rent assumptions are the same document everyone reviews.
For an example platform that keeps underwriting assumptions together and supports shareable reports, track your acquisitions pipeline in CREflow.
Key takeaways
- Underwriting fails on bad assumptions—price the fix before you price the upside.
- Build a buffer: deals must survive a construction overrun; assume one when you can’t verify costs.
- Model revenue realistically—sloped occupancy and STR-specific op-ex matter.
- Clean data and spreadsheet hygiene are as important as market comps.
FAQ
How big a rehab overrun should I assume?
Assume your budget needs to survive a 20% construction overrun unless you’ve verified bids and scope with contractors. If the deal collapses with that buffer, it isn’t robust.
Should I treat STRs and long-term rentals the same in underwriting?
No. STRs have different cost profiles. Model cleaning, platform, and management fees and expect higher operating expense ratios—industry discussions point to STR op-ex running in the 35–50%+ range of revenue in many cases. Treat STRs as a different product, not a minor variant.
What’s the fastest way to test a broker pro forma?
Trace the key inputs, force a conservative rent and occupancy case, and test a meaningful capex overrun. If the cash flow and DSCR don’t hold under those moves, reject the pro forma as optimistic.
When is it okay to rely on sponsor assurances?
Only when the assurances are written into the governing documents. Verbal promises or side conversations are not underwriting inputs. Underwrite to the legal agreement, not the pitch.
Before You Underwrite This, Check This:
- Have you priced roof, structural, and mechanical scopes with contractors or added a 20% overrun buffer?
- Is occupancy modeled as a slope and not a single value across years?
- Did you centralize assumptions into single source cells in the spreadsheet?
- Have you modeled an exit cap that’s defendable by submarket comps?
- Are sponsor promises documented in purchase or governance documents?
For a process-level playbook to make these checks repeatable across deals, consider How to Build Repeatable Commercial Real Estate Acquisitions.
Try CREflow’s free commercial real estate underwriting calculator to model NOI, cap rate, DSCR, and IRR without a spreadsheet.