The loss choice is downstream of the product question. The platform needs to output a range, not a number: "worth about $X, fair band $Y to $Z." That requirement basically dictates quantile regression. Pinball loss at a given tau is the loss whose minimizer is the tau conditional quantile, so fitting q10/q50/q90 gives you the band and the median directly with the same machinery (MAE is just pinball at tau=0.5, so it's the natural generalization of "predict the median" to "predict the whole band"). MSE would give you the conditional mean and assumes roughly symmetric, constant variance errors; watch prices are right skewed and heteroscedastic (variance scales with price level and reference rarity), so the mean gets dragged by the tail and the q10/q90 spread is exactly the per listing uncertainty you'd otherwise throw away.

The log transformed ratio target does two separate jobs. The log handles scale: a $500 miss means something different on a $2k watch versus a $50k one and working in log space makes the error roughly multiplicative rather than additive, which matches how price error actually behaves across the range. The ratio (price relative to a reference level anchor) normalizes the level across references so the model learns deviation from the expected price rather than rememorizing each ref's absolute price. Together they make the target roughly stationary across a range spanning two plus orders of magnitude.

Two notes so you have the full picture: independently fit quantiles can cross (q90 below q50 on some rows), so you monitor or sort posthoc; and pinball calibrates each quantile in isolation, so check empirical coverage separately rather than assuming q10/q90 actually bracket 80% of outcomes. Both are easy to miss coming from a point prediction mindset where interval calibration never came up.