What Is the Dead Deal Tax in Commercial Real Estate?
The dead deal tax is the cumulative analyst time a CRE acquisitions team spends evaluating deals that ultimately get killed. With 10 to 15 new offering memorandums arriving per week and roughly 80% dying at initial screening, teams lose 4 to 6 hours of analyst time weekly on deals that will never close. It is one of the largest hidden operational costs in commercial real estate acquisitions.
The 80/20 Rule of Deal Screening
Every experienced acquisitions professional knows the ratio: roughly 80% of the deals you evaluate will be killed. Only about 20% advance past initial screening to serious underwriting. Of those, only a fraction make it to LOI, and fewer still close.
The critical insight is that the operational cost is not in the 20% that advance. It is in the 80% you have to touch before killing. Each of those deals requires an analyst to open the offering memorandum, skim the rent roll, check the location, verify it against the buy box, and confirm it does not fit. That takes 20 to 30 minutes per deal.
This is a concept that operators have felt for years, even though it lacked a formal name. One platform CEO managing $2.4B in assets described it precisely: the cost is not the deals you pursue. It is the 8 to 12 deals per week that consume 20 to 30 minutes each before dying.
Quantifying the Dead Deal Tax
The dead deal tax is measurable. Once you know your deal flow volume, kill rate, and time per screening decision, the math is straightforward.
// Weekly dead deal tax
New OMs per week: 12
Kill rate at screening: 80%
Deals killed: 12 x 0.80 = ~10 deals
Time per kill decision: 25 minutes
Weekly dead deal tax: 10 x 25 min = 4.2 hours
// Annual cost
4.2 hours/week x 52 weeks = 218 hours/year
At $50/hr fully loaded analyst cost = $10,900/year per analyst
// For a team with 2 analysts
$10,900 x 2 = $21,800/year on deals that die
These numbers are conservative. Firms with higher deal flow (20+ OMs per week) or more complex asset classes (where screening takes longer) pay a proportionally larger dead deal tax. Deloitte's commercial real estate outlook reports that institutional buyers are increasingly focused on operational efficiency in their acquisition processes as deal competition intensifies.
The Hidden Cost: Opportunity, Not Just Time
The dollar cost of the dead deal tax is significant, but the opportunity cost is worse. When analysts spend 4 to 6 hours per week on deals that die, those are hours not spent on deeper underwriting of the deals that fit.
The result is incomplete pipeline coverage. Teams evaluate maybe 40% of their deal flow thoroughly and skim or skip the rest. The deals they skip are often the "cuspy" or marginal ones that could outperform with closer analysis. A deal that looks marginal from the OM summary might be a strong acquisition once you extract the actual rent roll data and run the numbers.
The question is not "how many hours can we save?" The question is: what if the best deal of the quarter is sitting in the pile you never opened?
Where the Screening Time Goes
Breaking down the 20 to 30 minutes per kill decision reveals where time is consumed and where it can be reclaimed.
| Activity | Time | Automatable? |
|---|---|---|
| Open OM, find key metrics | 5 to 8 min | Yes, via document extraction |
| Check geography against buy box | 2 to 3 min | Yes, automated matching |
| Verify unit count and deal size | 2 to 3 min | Yes, extracted from OM |
| Quick cap rate sanity check | 3 to 5 min | Yes, computed from extracted data |
| Skim rent roll for red flags | 5 to 8 min | Partially (extraction yes, judgment no) |
| Log decision, update CRM/tracker | 3 to 5 min | Yes, with workflow integration |
The majority of screening time goes to data extraction and comparison, not judgment. An analyst does not need 25 minutes to decide a deal does not fit. They need 25 minutes to find and verify the information required to make that decision.
How to Reduce the Dead Deal Tax
Reducing the dead deal tax means compressing the time between OM arrival and kill decision, without sacrificing decision quality. Three strategies work in combination.
Strategy 1: Define a Clear Buy Box
A documented buy box with specific parameters (asset class, geography, unit count, cap rate range, vintage) lets analysts kill non-qualifying deals on the first data point that fails, rather than reviewing the entire OM before deciding. This alone can reduce screening time from 25 minutes to 5 to 10 minutes per deal.
Strategy 2: Automate Data Extraction
The biggest time sink in screening is finding the key metrics buried in a 60-page OM PDF. Tools that extract unit count, asking price, NOI, occupancy, and cap rate automatically eliminate the data-hunting phase entirely. Analysts receive a structured summary instead of a PDF to skim.
Strategy 3: AI-Assisted Triage
Combining automated extraction with buy box matching creates a triage layer that pre-sorts incoming deals into "fits," "does not fit," and "borderline." Analysts focus their 25-minute reviews on the borderline deals where judgment actually matters. The clear kills and clear advances are handled in seconds.
McKinsey's real estate practice has published research showing that firms adopting technology-assisted deal screening processes achieve 30% to 50% higher deal throughput per analyst without increasing headcount.
The Sunk Cost Trap
The dead deal tax creates a secondary problem: sunk cost bias on kill decisions. When a team has already invested 2 hours underwriting a deal, they are psychologically inclined to advance it further, even if the numbers are weak. The thinking is "we have already spent the time, we might as well see it through."
This is how mediocre deals consume disproportionate resources. A deal that should have been killed in 5 minutes at screening advances to LOI, consumes 40 hours of analyst time, and dies anyway at investment committee. The dead deal tax compounds when kill decisions are delayed.
Tools that provide clear, data-backed kill signals help teams overcome this bias. When the data objectively shows a deal does not fit, it is easier to walk away than when the decision relies purely on human judgment colored by sunk cost.