Case Study #1: Standardizing Value

Any seasoned NFT traders will tell you that an essential step in evaluating a potential flip is to look at recent sales history. Picture this:

  • You see an item with a rare trait just got listed for 1.09Ξ and the floor is 0.94Ξ. Intuitively, you feel like it there can be an opportunity to flip here.
  • Now you scramble and find the following:
    • 48 minutes ago an NFT with this trait sold for 1.21Ξ
    • 3 hours ago an NFT with this trait sold for 1.27Ξ
    • 5 hours ago an NFT with this trait sold for 1.32Ξ
  • With the next lowest listing with this trait sitting at 1.35Ξ now. Should you go for it?

The answer to this question is complex, but let’s focus on just one key detail for now: the floor may be different now than at all these past times of sale.

To make these data meaningful, we are standardizing prices with a “floor deviation metric”. Instead of comparing the prices of current listings directly to past sale prices, let’s compare their floor deviation:

Current Listings Floor & Deviation
Current Listings Floor & Deviation
Historic Sales Floor & Deviation
Historic Sales Floor & Deviation

In this case, we can instantly see that there is no market consensus for an above-floor price for this trait through its historical sales floor deviations. This means the current listings are probably overpriced. Even if you buy a seemingly good deal that’s just 15% above the floor and attempt to resell it for 35% above the floor, a whole 6% floor deviation lower than the next lowest listing (41%), it probably still will not sell.

Alternatively, here is an example of a trait with a much more obvious above-floor market value:

Suppose you see one of these for 5% above the floor, then well, perhaps it’s your lucky day. 🙂

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