Explore the Library
Home Financial Library Books About Contact
Library / Deal Analysis / The Assumptions Behind Every Model
Deal Analysis · Concept Guide

The Assumptions Behind Every Model

Two investors can run the same spreadsheet and reach opposite decisions — not because the math changed, but because the assumptions did. A pro-forma is only ever as honest as the handful of inputs you feed it. Here is how to set each one so the model tells you the truth.

Matt NunnMatt Nunn · Founder, Builders Finance
11 min read

Key Takeaways

  • A pro-forma does not predict the future. It makes an argument from assumptions — and precision (a lot of decimal places) is not the same thing as accuracy (inputs that hold up).
  • In most STR underwriting models, only a handful of assumptions dominate the result — the nightly rate, the occupancy, and the financing terms. Get those honest and the remaining inputs have a much smaller effect on the decision.
  • Assumptions rarely fail at random. They fail optimistically, for three recurring reasons — anchoring on someone else's number, survivorship bias in your comp set, and false precision that dresses a guess up as a measurement.
  • There is a repeatable way to handle every assumption — Source it, Haircut it, Record it, Stress it — and most of underwriting discipline is just applying those four moves to the two or three inputs that decide the answer.
  • This is the "how to think" guide. Its siblings handle the mechanics of each input — revenue, comp tools, costs, and returns.

The model is an argument, not a forecast

Every underwriting model is perfectly honest. It tells you the exact truth about the assumptions you fed it — and nothing about the property itself.

A spreadsheet hands you a clean number — 18.5% cash-on-cash — and the cleanliness makes it feel like a fact. It is not a fact. It is the mechanical consequence of the assumptions you chose. Move one of them — occupancy from 70% to 62% — and the "fact" slides several points. The arithmetic never lied; it did exactly what you told it to with the inputs you gave it.

That is the trap of a model: the precision is real and entirely borrowed. Rigor does not live in the third decimal place of the output. It lives in the sourcing of the inputs. A projection carried to the dollar reads as diligence, but it is only ever as good as the assumption you were least sure of when you typed it.

The assumptions that actually matter

In most STR underwriting models, only a handful of assumptions dominate the result — the nightly rate, the occupancy, and the financing terms. The remaining inputs usually have a much smaller effect on the final decision.

It helps to sort the inputs into three buckets:

  • Revenue — average daily rate, occupancy, and how both move across a seasonal year.
  • Cost — the operating-expense stack, and the maintenance and capital reserve most models forget.
  • Capital — the down payment, the interest rate and term, and the upfront cash to furnish and equip the property before it earns a dollar.

Within those, a small number carry the outcome. A perfect estimate of your streaming-subscription line will not rescue a deal built on a fantasy occupancy. So spend your attention where it changes the answer — the rate, the occupancy, and the debt — and don't polish the trivia. (The mechanics of the revenue inputs live in Estimating STR Revenue Without Fooling Yourself; the full cost stack in What STRs Actually Cost to Run.)

Where assumptions go wrong — almost always in the same direction

Assumptions rarely fail at random. They fail optimistically, and for three recurring reasons.

Anchoring. The first number you see sets the frame, and every later adjustment quietly orbits it. The seller's pro-forma, the comp tool's projection, the agent's "it could do nine a month" — once that figure is in your head, your "conservative" estimate tends to land a polite haircut below it rather than where the evidence actually points. Seller and broker pro-formas are marketing documents, not financial projections; treat them as the seller's opening argument, never your baseline. The fix is order of operations: build the number from your own inputs before you look at anyone else's, then compare.

Survivorship bias. The comps you can see are the listings that survived. The ones that underperformed and got delisted, and the owner who quietly sold at a loss, are not in the data set. A market-average occupancy is an average of the survivors — a figure that, by construction, excludes every listing your property might turn out to resemble in a bad year.

False precision. "$8,437 a month" hides the truth that the real range is something like $6,000 to $10,000 depending on a season you don't control. A number to the dollar disguises a guess as a measurement — and a guess you've disguised from yourself is one you'll defend instead of test.

◆ Builders Finance Principle · No. 04

"Every assumption gets a source and a haircut."

An input you can't trace to evidence is a wish. And evidence drawn from mature listings still has to be discounted for the year you will actually have. Source it, then haircut it — that discipline, applied to the two or three inputs that decide the answer, is most of what separates a model you can defend from one you're hoping about.

The four moves — The Builders Finance Underwriting Method

Builders Finance handles every number in a deal the same four ways: Source it, Haircut it, Record it, Stress it — the Builders Finance Underwriting Method. They are not four ideas — they are one process, and every underwriting decision runs through it. Most of the rest of this guide is those four moves in detail; the flagship STR Deal Underwriting Manual runs the same four moves across a whole deal.

        Evidence
           │   ① SOURCE   — trace every input to real data,
           ▼               not the seller's or the tool's headline
       Assumption
           │   ② HAIRCUT  — discount for the year you'll
           ▼               actually have, not the comp set's best
   The Assumptions Ledger
           │   ③ RECORD   — value, source, and haircut for each,
           ▼               on one page you can defend and revisit
         Model
           │   ④ STRESS   — move the two or three big inputs
           ▼               to their realistic downside
        Decision

One idea, read top to bottom: an assumption only earns its place in the model after it has been sourced, discounted, written down, and stress-tested.

Record it: The Assumptions Ledger

The most useful artifact in underwriting is not the model. It is the one-page ledger of the assumptions behind it.

For every core assumption, The Assumptions Ledger captures three things: the value you used, where it came from, and the haircut you applied and why. It takes ten minutes and it changes the character of the whole exercise — from a number you produced into a set of claims you can stand behind.

AssumptionValue usedSourceHaircut / basis
ADR (net of cleaning & tax)$2858 comps, same bed count & sub-marketmedian; top and bottom outliers dropped
Occupancy62%market ≈ 70%, minus first-year ramp−8 pts for ramp-up and conservatism
Operating costs$24,000 / yrbottom-up stack, not a % of revenueincludes a maintenance + capex reserve
Interest rate7.0%current lender quotesstress-tested at +1 point
Upfront furnishing$45,000itemized OS&E budgetcounted in the cash-on-cash denominator

The Assumptions Ledger does three things a bare model can't. It makes the underwrite auditable — you, a partner, or a lender can see exactly what the answer rests on. It makes it defensible — you can explain every input instead of pointing at a spreadsheet. And it makes it revisable — once you own the property, you replace each assumption with your actuals and learn precisely where your judgment ran hot, which is how the next underwrite gets sharper.

Put it to work

The STR Assumptions Ledger

Not a blank table you could rebuild in Excel in five minutes, but a real working tool: the core assumptions pre-filled by category, plain-language haircut guidance for each, evidence prompts so you record where every number came from, a confidence rating per assumption, built-in stress-test reminders, and a "review after purchase" column to check your assumptions against reality once you own the property.

Free. No spam. Unsubscribe anytime.

Stress it: break the model on purpose

Before you trust a model, break it on purpose. Move each dominant assumption a realistic amount and watch what happens to the answer.

Run occupancy down eight to ten points, pull the rate back ten to fifteen percent, and bump the interest rate up a hundred basis points — all three of the levers that carry the outcome. If cash-on-cash stays positive and the deal still covers its debt after that, the assumptions have genuine margin of safety, and the base case is a floor you can live on. If a mild, entirely plausible move flips it from "buy" to "pass," then the base case was riding on optimism — and now you know it, before your money is in.

Stress-testing also tells you where to spend your remaining research time. If the answer barely flinches when you change the cleaning-cost line but swings hard on occupancy, occupancy is the assumption to go get right. (That is exactly the trail into the break-even occupancy and revenue guides.)

The common mistake

underwriting to a single number instead of a range. A property doesn't "do $85,000" — it does somewhere between a bad year and a good one, and when you buy it you buy all of those years. Model the conservative end and treat anything above it as upside, not as the plan. Owners who underwrite to the optimistic point estimate have quietly bought the good year and are hoping the others never show up.

Your action plan

  1. Source — list the assumptions that matter (rate, occupancy, seasonality, the cost stack, the reserve, and the financing terms) and trace each to real evidence: a comp set you built yourself, current financing quotes, a bottom-up cost stack — never the seller's or the tool's headline. Clean the revenue inputs first — strip cleaning-fee revenue and occupancy taxes before you compute a rate or an occupancy.
  2. Haircut — discount market data 10–15% for a first-year listing, and more if your comp set is thin.
  3. Record — write The Assumptions Ledger: value, source, and haircut for each assumption, on one page.
  4. Stress — move the two or three dominant inputs (occupancy, rate, interest rate) to their realistic downside and confirm the deal still holds. Then, once you own it, replace each assumption with your actuals and note where your judgment ran hot — that review is how the next underwrite gets sharper.

The bottom line

A pro-forma can never be more honest than the assumptions inside it, and those assumptions have a gravitational pull toward optimism. You don't beat that with a fancier model. You beat it with four moves applied to the numbers that matter: source every input, haircut it for the year you will actually have, record it where you can defend it, and stress it before you trust it. Do that, and the model stops being a story you tell yourself and becomes a decision you can defend.

Matt Nunn
About the author

Matt Nunn is the founder of Builders Finance. He has spent two decades working with the financial side of real estate businesses, and started Builders Finance to give short-term-rental operators the financial systems, frameworks, and plain-language education that most hosting advice skips over. Builders Finance publishes educational content for STR owners and is not a substitute for advice from your own qualified tax professional.

Continue learning

The STR Financial Bible

the complete financial system for short-term-rental operators, from underwriting a deal to keeping the books to the exit.

Explore the book →
The Builders Finance Underwriting Method

Source it — trace every number to real evidence, not a headline.
Haircut it — discount for the year you’ll actually have; round revenue down, costs up.
Record it — value, source, and haircut, in The Assumptions Ledger.
Stress it — move the numbers that matter to their downside before you trust them.

Note (method now locked)

The four-move framework is now canonical: The Builders Finance Underwriting Method (Source → Haircut → Record → Stress). Definition lives in the standalone spec (The Builders Finance Underwriting Method). This guide applies it and carries the method footer above; the flagship manual introduces and owns it. Principle "Every assumption gets a source and a haircut" is the Method's flagship Principle (No. 04).

Educational information only — not individualized tax, legal, or investment advice. The worked example is an illustrative model, not a projection or a recommendation.

The Informed Operator

Get the weekly email.

A weekly email on the financial side of short-term rentals — what changed, why it matters, and what owners should understand.

No spam. Unsubscribe anytime.