Why ARV Fails (And the Methodology That Survives Bad Comps)
ARV is the most-quoted number in fix and flip underwriting and the most-failed one. The failures are not random. They follow a small set of repeatable patterns — and each pattern has a methodology fix.
Six reasons ARV fails, in order of frequency.
1. The comp set is contaminated with one outlier
Five comps. Four cluster between $440–470/sqft. One sold at $620/sqft because the seller's brother bought it cash with no inspection. The mean of the five is $498/sqft. The median is $462. The mean drags your ARV up 8% on a single bad data point.
Fix: use the median, not the mean. Median absorbs single outliers without bias. The institutional standard is the median price-per-sqft of 5+ comps.
2. Comps include flips that have not yet sold
Active listings are not comps. Pending sales are not comps. Only closed sales are comps. Operators who comp against active listings underwrite to the seller's hope, not the buyer's reality.
Fix: closed-only. Sort comps by sale date, not list date.
3. Renovation parity is missing
A "renovated" comp at $480/sqft may have had $35k more in finishes than you are budgeting. The price-per-sqft of the comp reflects finishes you are not paying for. Your finished flip will sell below comp.
Fix: pull listing photos for every comp. Match finish tier. If a comp has quartz, hardwood, and shaker cabinets and you are installing laminate counters and engineered wood, do not use the comp at full value — haircut 15–25%.
4. The neighborhood line is invisible
Subject is at 1402 Maple. Comps are at 1808 Maple and 1903 Maple. The street is the same. The neighborhood is not — the elementary school district line runs between them, and the higher-rated school is on the other side. Same street, different submarket, different $/sqft.
Fix: overlay the school district map on your comp pull. Treat district boundaries as hard comp boundaries.
5. Time decay on stale comps
A 9-month-old comp from a different rate environment is not the same data point as a 60-day-old comp. Rate moves, buyer appetite shifts, and submarkets re-price faster than operators notice. A 6-month-old comp in a falling market overstates current ARV.
Fix: 90-day window. If you have fewer than 3 comps inside 90 days, the submarket is illiquid — your ARV confidence is low and the buyer-pool exit risk is higher. Underwrite at 90–95% of base ARV.
6. Square footage mismatch larger than 10%
A 2,400 sqft comp sold at $200/sqft does not mean your 1,400 sqft subject sells at $200/sqft. Smaller homes sell at a higher $/sqft because the floor of buyer demand (kitchen, bath, mechanicals) is fixed regardless of size. Within ±10% sqft, the parity holds. Outside ±10%, it breaks.
Fix: filter comps to subject sqft ± 10% before computing $/sqft.
The institutional method, in one paragraph
Pull 5+ closed comps within 0.5 miles of subject, sold in the last 90 days, within ±10% of subject square footage, with finish tier matched to subject finish budget, on the same side of school district and neighborhood boundary lines. Take the median price-per-sqft. Multiply by subject sqft. That is base ARV. Apply a 0–10% confidence haircut for thin or scattered comp sets. The result is institutional ARV.
Operators using this method tend to land much closer to eventual sale price than operators using rule-of-thumb price-per-sqft. On a $500k ARV, the gap between a tight median-method comp set and a casual price-per-sqft assumption is routinely $15–25k — which is the difference between a profitable flip and a break-even one.
What DealIntel does
DealIntel pulls comps automatically against the six filters above, computes confidence-weighted ARV with median methodology, and flags any deal where the comp set is too thin to underwrite at full ARV. The confidence score on every DealIntel ARV is the operator-facing version of this entire post.
Try it on a deal: run the free ARV calculator, or read the long-form how to calculate ARV post.
Related reading
- ARV definition
- Comparable sales definition
- The 70% rule definition
- How to analyze a fix and flip deal
- 10 reasons flips lose money
Keep reading
- How to Analyze a Fix and Flip Deal (The Institutional Workflow)A step-by-step workflow for underwriting a fix and flip deal the way an institutional capital allocator would — ARV from a confidence-weighted comp set, MAO from the 70% rule, stress-tested rehab budget, full carry math, and a pre-mortem before the offer goes in.
- Fix & Flip Red Flags Checklist (25 Things to Inspect Before You Sign)A pre-offer red flags checklist for fix and flip operators — structural, mechanical, legal, market, and financing red flags that should trigger a renegotiation or a walk. Built from the 25-point Kill List DealIntel runs on every property.
- 10 Reasons Fix and Flips Lose Money (Ranked by How Often We See Them)Most failed flips do not fail for exotic reasons. They fail for the same ten reasons, in roughly the same order, every cycle. Here is the ranked list — and the institutional discipline that prevents each one.
Matt Abadi is the founder of DealIntel. He leads the development of the platform's six-strategy underwriting engine, 25-point Kill List, and Monte-Carlo financial model — the institutional analysis stack DealIntel applies to every fix and flip deal. DealIntel was founded in 2025 with the central thesis that knowing when not to invest is the most valuable number on the page.