Evergreen
Pricing Guides
Foundational methodology for pricing resale assets. These guides explain how to read our market data and apply it to real listing decisions.
How to Price Items for Resale
→A practical framework: sold comparables over asking prices, P25–P75 ranges over single numbers, and how condition and platform economics shape your final list price.
Sold Prices vs. Listed Prices
→Why asking prices are inflated 20–40% above actual sale prices, and why our reports are built from cleaned multi-platform listing data rather than single-platform asks.
Common Resale Pricing Mistakes
→Five errors that cost sellers money: chasing outlier listings, ignoring platform fees, misjudging condition, holding out on cold-demand items, and treating the median as the only number that matters.
How Condition Affects Resale Value
→Deadstock vs. worn: why condition is the largest variable within a single product's price range, illustrated with the Dunk Low Panda's $57 P25–P75 spread.
How Color Affects Resale Value
→Why the colorway — not the silhouette — often drives resale premium. A real contrast between the Jordan 1 Lost & Found (+86%) and the Dunk Low Panda (−20%).
How Rarity Affects Resale Value
→Production volume relative to demand — not age — is the real driver of rarity premiums. The Dunk Low Panda proves that hype without scarcity collapses.
How Seasonality Affects Resale Pricing
→Release calendars, holiday gifting, and back-to-school shift demand. An honest look at what we know — and what we are still collecting data to verify.
Instant Resale Value Estimates
→ResalePriceGuide for depth, Flip411 for speed. How photo-based instant estimates and auto-generated listings fit into the resale workflow.
Is It Worth Reselling?
→A two-number decision framework: premium over retail and demand signal. Real examples show when the math works (Jordan 1 Lost & Found) and when it does not (Dunk Low Panda).