Methodology note — Reading sell-through rate

This is the June 18, 2026 entry in our rotating methodology series — sixteen explainers cycling through the statistical concepts and data assumptions that sit behind every Bagonomics research piece. The series is meant to be read once and referenced often. Today's entry covers reading sell-through rate.

Sell-through rate is one of the cleaner liquidity signals we publish. It is defined simply: the number of listings that closed inside a window divided by the number of listings that were active at any point in the window. A sell-through of eighty percent means four out of every five listed bags found a buyer inside the period. A sell-through of twenty percent means most listings sat unfilled.

We compute sell-through at the variant level on a 30-day rolling window. The window length matters: a 7-day window has too few observations to be informative for variants with thin listing flow, while a 90-day window would smear together periods where the demand-supply balance shifted. The 30-day window is the operational compromise.

A high sell-through — sixty percent and above — is the footprint of strong demand against constrained supply. New listings clear quickly; the marginal listing finds a buyer before the price has to drop. Variants in this regime are also typically the variants where premium-to-retail is positive and elevated. The two signals are consistent: when boutique allocation cannot keep up with demand, the secondary absorbs the overflow at premium prices and high turnover.

A moderate sell-through — thirty to sixty percent — is the most common reading across our universe. Most variants live here. Demand and supply are roughly in equilibrium; the marginal listing finds a buyer eventually, after some negotiation, at a price close to the median.

A low sell-through — below thirty percent — flags a variant where supply is exceeding demand. Listings sit; sellers either chase the market down on price or pull the bag and try again later. Variants in this regime typically also show widening asking-to-sold spreads and softening median price trajectories.

The interpretation has limits. Sell-through measures the rate at which listings convert; it does not directly measure the absolute volume of demand. A variant with two listings and one sale has a sell-through of fifty percent. A variant with two hundred listings and one hundred sales has the same sell-through. The two variants are in very different liquidity regimes despite the matching rate. We always pair sell-through with the underlying listing count in our published tables for this reason.

There is also a selection effect specific to luxury handbags. The variants with the strongest demand are also the variants where sellers are most likely to pull a listing the moment they receive a competing offer they prefer over the listed channel. Some real demand-driven transactions therefore do not show up as listing-conversions inside our window. The sell-through reading underestimates the true clearing rate for these variants. We do not have a clean way to correct for this and we surface it as a known limitation rather than a quantitative adjustment.

For a reader trying to read the market, sell-through is most useful as a relative measure rather than an absolute one. A change in sell-through for a given variant over time is informative — accelerating sell-through means demand is firming; decelerating sell-through means demand is softening. A cross-section sell-through ranking is informative within the same model line, where the listing-pull selection effect is roughly comparable across variants. Cross-brand comparisons of absolute sell-through numbers should be read with caution because platform composition and listing-pull behavior differ from brand to brand.

Where this comes up in our research

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Methodology

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