Learn how comparable retail listings are used to estimate a vehicle's market value and why comp quality matters.
What Are Retail Comps?
Retail comps (comparable listings) are similar vehicles currently listed for sale at dealerships. By analyzing these listings, you can estimate what a vehicle is worth in the retail market.
How Comps Are Matched
The comp matching algorithm scores each potential comparable on:
- Year proximity — Same year is ideal, +/- 1 year is acceptable
- Mileage range — Within 20% of the subject vehicle's mileage
- Trim match — Exact trim match scores highest
- Geographic distance — Closer comps reflect local market conditions
Why Comp Count Matters
- 10+ comps: High confidence — the estimate is reliable
- 5-9 comps: Medium confidence — reasonable but verify
- 1-4 comps: Low confidence — treat estimates with caution
- 0 comps: No data — you're flying blind
Outlier Removal
RunsList uses IQR (interquartile range) outlier removal to filter noise. This means abnormally high or low listings don't skew the median retail value.
Retail Low, Median, and High
- Retail Low: 25th percentile — a conservative estimate
- Retail Median: 50th percentile — the most likely selling price
- Retail High: 75th percentile — optimistic, for vehicles in excellent condition
Always base your max bid on the retail low or median, not the high. Build in margin for the unexpected.
Frequently Asked Questions
What is the main takeaway from How Retail Comps Work: Understanding Vehicle Valuation?
Learn how comparable retail listings are used to estimate a vehicle's market value and why comp quality matters.
How should dealers use this before bidding?
Use the guidance before sale day to compare retail comps, estimate landed cost, set a max bid, and avoid vehicles where fees, reconditioning, title status, or buyer demand do not support the risk.
Who is this article written for?
This article is written for auto dealers, brokers, wholesalers, and flippers working on deal scoring who need faster auction decisions backed by comps, margin math, and risk flags.