What can resale prices tell designers about fashion trends?

Resale prices and second-hand search data tell designers which existing products consumers still want after the first sale: which silhouettes hold or gain value, which archive styles are returning and which brands are regaining desirability. Unlike social media visibility, a resale price reflects what someone was willing to pay. That makes second-hand data a valuable complement to conventional trend forecasting, especially for carry-over decisions and archive reissues, though it has clear limits outside the luxury and premium segments.

Why is resale data becoming a trend signal?

The second-hand market has grown large enough to matter. ThredUp's 2025 Resale Report, as reported by Retail Dive, projects the US second-hand apparel market to reach 74 billion dollars by 2029 and the global market 367 billion dollars, and found that a record 58 percent of US consumers bought second-hand apparel in 2024. The State of Fashion 2026 report by McKinsey and The Business of Fashion names resale as one of its ten themes and expects second-hand fashion to grow two to three times faster than the first-hand market through 2027.

As more transactions move to resale platforms, those platforms accumulate structured data on prices, sell-through times, searches and wish lists for specific brands and models. Some publish annual reports based on that data, which designers and merchandisers can read as a view of long-term desirability rather than launch-season hype.

What does resale data reveal that other trend data does not?

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What did resale platforms report in 2025?

The RealReal's 2025 resale report, published in September 2025, gives examples of the kind of signals available. It reported resale value increases for classic pieces including the Hermès Birkin 30 (15 percent), the Louis Vuitton Speedy (13 percent), the Goyard Saint Louis tote (18 percent) and Van Cleef & Arpels Alhambra jewellery (20 percent). It named Isabel Marant its comeback brand of the year, with growth of 54 percent year on year, and reported strong momentum for American designers such as Diotima (up 152 percent).

The same report points to revivals: it cites Hervé Léger bandage dresses, up 106 percent, as part of a nostalgia for 2010s fashion, and nearly 30 percent more members searched vintage inventory than a year earlier. It also showed how events move demand, reporting a 488 percent search surge linked to Jonathan Anderson's departure from Loewe. These figures are platform-specific and describe The RealReal's members, not the whole market.

How can designers and merchandisers use resale signals?

1. Identify value-retaining styles. Models whose resale prices stay close to, or exceed, the original price are candidates for continuity programmes rather than seasonal replacement.
2. Mine the archive deliberately. When resale searches for an era rise, review your own archive for styles that match, and test a limited reissue.
3. Watch competitors' brand heat. Rising second-hand demand for a competitor can indicate a shift in desirability before it appears in first-hand sales.
4. Check durability and quality. Styles that resell well often share construction and material qualities; feed that back into specifications.
5. Use your own take-back or resale data. Brands running their own resale programmes have direct, model-level data on what customers return and what sells again.

Where does resale data mislead?

• Segment bias: liquid resale markets exist mainly for luxury, premium and some sportswear brands; for basics and mass-market items, too few pieces are resold to draw conclusions.
• Time lag: resale reflects products from past seasons, so it signals durable desire and revivals rather than entirely new trends.
• Platform bias: each platform has its own member base, pricing policies and authentication rules, and reports are also marketing documents.
• Supply effects: a price rise can reflect scarcity, for example after a brand reduces production, rather than rising demand.
• Event spikes: celebrity moments or designer changes create short-lived search surges that may not last.

Designers should therefore read resale signals as one input among several. A model that resells well tells a team that something about it has lasted, whether the shape, the material or the brand story, but not which of those factors matters most. That interpretation still needs design judgement, and ideally a comparison with first-hand sales and return data for the same model.

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How does AI help turn resale data into design input?

Resale listings are messy: user-generated descriptions, inconsistent naming and photos of varying quality. Image recognition and language models can help match listings to specific models, extract attributes such as colour and material, and track price changes over time. The same techniques used by trend platforms to read social images can be applied to resale listings. The output is most useful when mapped to the brand's own product codes and attributes, so that a designer can see, for example, that a particular jacket block from three seasons ago now resells above its original price in two colours but not in a third.

Used this way, resale data does not replace forward-looking trend forecasts. It adds a backward-looking, money-weighted view of what has lasted, which is exactly the information many collections lack when deciding what to keep, what to bring back and what to stop making.