4 indicators to analyze the supply and demand of NFT collections
Data Source: Footprint Analytics — NFT Market Overview
In a previous article, 5 indicators for smarter NFT trading, we looked at 5 general indicators for analyzing sentiment and performance in the NFT market. This time, we’re analyzing NFT collectibles in terms of price behavior, holder patterns, rarity characteristics, and leaderboards to understand supply and demand.
Indicators are not fixed rules for trading. They are a set of tools to build a comprehensive picture of projects and trends.
Price is one of the most basic indicators for evaluating NFTs. There are 5 metrics one could look at.
Sale prices allow you to track the behavior of whales and see median, average, and floor prices.
Most NFT buyers focus on the highest, average and floor price of a collection before buying. These metrics allow you to see at what price an item is likely to experience upside resistance. For example, the chart below shows that many users are getting into Azuki at prices concentrated between 10 and 25 ETH.
The floor price is the lowest price listed for the entire series of NFTs. Footprint Analytics shows that the BAYC series NFT has a floor price of 71 ETH, which is the lowest price listed for a BAYC NFT in the market.
The breakeven price for an NFT includes all the fees that must be paid, such as floor price, gas, creator fee, and platform fee, to recuperate the original cost. By determining this, you can set a reasonable and profitable sales price.
The mint price of an NFT allows you to calculate the total revenue earned by a collection.
The royalty amount is the amount of royalties earned per transaction, and not every NFT will have royalties. This price indicator is also primarily a calculation of the total revenue earned by the NFT.
The rarity of an NFT collection refers to how scarce a collectible or NFT is based on certain characteristics. These features include the physical characteristics of the NFT, such as color and design.Toanalyze an NFT collection for this indicator, a trader needs to look at the NFT’s rarity score along with other useful information such as price, volume, and liquidity. This indicator is tied to the intrinsic supply and demand of an asset.
An example of an extremely rare collection is Azuki, there is only 1 collectible with 5 traits. Conversely, a “common” set has 6 to 11 features, with a total of 9,999 NFTs. Note that rarity is also an important indicator within collections, with rare traits increasing relative value compared to the rest of the collection.
However, rarity is far from being a 1-to-1 comparison. BAYC and CryptoPunks have the same rarity, but there are significant differences in their utility and vision.
An important parameter used to determine the value of the NFT is the number of addresses holding it, a metric used to determine the size of the community surrounding the project. This helps to sell the project to a larger number of potential purchasers.
Indicators such as which addresses are blue chip holders and whale holders can be assessed through the indicators of holders.
Holders of at least one other blue-chip NFT, assess whether the NFT is popular with blue-chip holders, a higher percentage of blue-chip holders indicates a strong holder base.
Addresses with more than $1 million in assets are marked as whales. As shown in the figure below, different whale NFT holders can own 1 or more NFTs, and their transaction volume will not increase because of the number of NFTs they own.
Use the leaderboard to find the best performing wallet addresses and track their behavior. The metrics analyzed are as follows:
- Profits and Losses: Ranking of addresses with the highest profits
- Number of NFTs held: Ranking of addresses with the most NFTs
- Value of NFTs held: Ranking of addresses with the most valuable NFTs held
- Volume of buys and sells: Ranking of addresses with the highest volume of buys and sells
Summary
Supply and demand is a core part of analyzing the value of any asset. Luckily, by being on-chain, NFTs give us a transparent picture of the supply, while other metrics can be used to infer demand.
This piece is contributed by Footprint Analytics community.
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