Fire engines and ambulances trade from five to seven figures, and the tools that appraise them publish no accuracy figure. That is not unusual and it is not a criticism. Almost nobody in appraisal publishes one.
This is that number. A comparable-sales estimator, back-tested on a time-based split, reported with the error bar it actually needs rather than the one that would look better.
Describe a fire engine or an ambulance. The repository's comps model runs in your tab, against the same 903 cached listings the back-test used, and gives you a number, the range it actually needs, and the ten trucks it reached that number from.
The estimate is a weighted average of those ten trucks in log space, and the range comes from how much they disagree. When the ten are a mixed bag, the range is enormous and the number means very little, which is the honest thing an appraisal tool can tell you and the thing none of them do.
Ask it for something the set barely has, an aerial from the 1980s, and watch the comparables stop resembling what you typed. That is the moment to distrust it, and you can only see it because the comparables are on the page.
Absolute percentage error on the held-out set, by percentile. The median is the number an appraisal tool would quote if it quoted one. The right tail is the number that costs someone a deal.
Mean error is 60.7% and median error is 36.0%. The gap is entirely the right tail: the worst single prediction on the test set put $95,786 on a pumper asking $10,000. Quoting the mean overstates the typical case, and quoting the median hides the case that matters.
The time-based split is the primary one: it trains on older listings and tests on newer, which is the order the world arrives in. Splitting the same data at random cuts mean error sharply for all three fitted models, by 9 to 20 points, and leaves median error slightly worse for two of them. It flatters the tail, not the typical case, which is why both are on the toolbar.
An estimate with a range attached is only useful if the range is honest. Nominal coverage assumes the errors are log-normal. Actual coverage is measured on the held-out set, and the width is what a seller would be shown.
The intervals are honest and they are enormous. Every band covers more than its nominal rate, so the model is not overconfident. Covering nine cases in ten takes a range whose top is 6.8 times its bottom, which on a $50,000 truck is roughly $19,000 to $130,000.
That is the real product question. A range that wide is close to useless to a seller, and it is the correct answer given this data. Narrowing it needs closed sale prices, which is the thing the whole exercise does not have.
Median error by slice, so a buyer can tell which quotes to distrust. Cheap vehicles are the hard ones.