Comparing every model year against its own model's normal output of listings, sorted by how bad that year's complaint record is relative to the same nameplate. Both columns come from the same 5,824 model years, so they're directly comparable.
| Complaints vs model average | Relative supply | Relative price | Model years |
|---|---|---|---|
| 3x or more | 1.464 | 1.005 | 346 |
| 1.5–3x | 1.307 | 0.977 | 984 |
| 0.7–1.5x | 1.113 | 0.979 | 1,446 |
| Under 0.7x | 0.864 | 0.982 | 3,048 |
Seventy percent more of them for sale
A model year carrying three times its nameplate's usual complaint volume puts 1.464x the normal number of cars on the market. The quietest years put out 0.864x. That's a 69% difference in how often the car turns up for sale.
The price column, over the identical model years, reads 1.005, 0.977, 0.979 and 0.982. No ordering, no gradient — the worst tier prices marginally above the best.
Two things are happening at once
Owners leave. A car that keeps going wrong gets sold. Not at a discount — the owner still believes it's worth what the guides say — but sooner than it otherwise would have been.
Buyers don't know. Complaint records aren't in listings. Without that information nobody bids the price down, so supply rises while the asking price holds.
The result is a market where the signal exists but has nowhere to express itself except in volume.
The averages behind the tiers
The worst tier averages 540 complaints per model year. The quietest averages 46 — a twelvefold difference in the underlying record, producing a rounding error in price and a 69% swing in supply.
It's worth being clear about what this is not. Across different models, complaints do move price — high-complaint models sell below their class. It's within a single nameplate that the market stops discriminating. The buyer prices the badge and the year comes free.
Why this is useful to you
Supply is visible and free to check. If you're looking at a model and one particular year has noticeably more examples for sale than the years either side, that's worth a question.
It's a rough indicator, not proof — a strong original sales year also produces more used listings a decade later. But combined with the complaint record it's a clear signal, and the effect can be extreme: the 2018 RAM 2500 holds 54% of its model's complaints across fourteen years.
How to use it when shopping
Compare listing counts across adjacent years. Same model, three consecutive years — if the middle one has half again as many for sale, find out why.
Abundant supply is negotiating leverage regardless. Whatever caused it, more examples means you can walk away.
Check the complaint record by year, not by model. The gap between adjacent years of the same car can be fourfold.
Then check the individual car. A bad model year is a population statistic; whether this particular one had the problem fixed is a question about a VIN. A VIN check shows the open campaigns and title history for that vehicle.
Frequently asked questions
Do problem model years sell for less?
No. Model years with three times their model's average complaints price at 1.005 of their class against 0.982 for the quietest years — no gradient at all. What changes is supply, which runs 1.464x normal against 0.864x.
Why doesn't the price drop on a bad model year?
Because buyers can't see the problem. Complaint records don't appear in listings, so nobody bids the price down — while owners quietly sell earlier than they otherwise would.
Can I spot a bad model year from listings alone?
Roughly. Compare listing counts across three consecutive years of the same model — if the middle one has noticeably more for sale, it's worth checking the complaint record. A strong original sales year can produce the same effect.
How different can adjacent model years be?
Dramatically. The 2018 RAM 2500 accounts for 54% of its model's complaints across fourteen model years, with 2,489 against 670 for the 2017 — the same truck one year earlier.
Denis Kataev is a serial solopreneur and the founder of ForCar. With 15+ years in software engineering and 10 years in SEO, he builds data-driven products end to end — backed by a sharp eye for design. At ForCar he mines proprietary vehicle datasets, turning raw numbers into buying advice you can actually trust.
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