One of the most crucial obstacles to the growth of NFTs is that they are still a relatively new asset class. This makes buying and purchasing them challenging, as it can be difficult to estimate their value accurately.

The difficulty in gauging the value of an NFT arises due to their subjective nature. Given that NFTs are generally tied to digital art in some form, the biggest/most important factor affecting their price is their rarity, or on how unique the concept behind them is.

Further complicating this process of price estimation is the fact that there are those who seek to inflate the value of NFTs. They achieve this through practices such as wash trading, where an NFT is repeatedly sold between two or more than two accounts at increasing prices.

The situation may seem dire, especially considering that as per bitsCrunch analysis, 46% of all the NFTs volume in Ethereum is affected by wash trading and carry inflated price tags. But can the value of an NFT be accurately estimated?

Assessing NFT Value — How is Price Estimation Useful?

First of all, it is important to understand that NFTs come with an inherent component of price volatility. It is difficult to estimate the value of an NFT given this volatility, as well as due to disparities in trading volumes for NFTs across collections.

Factors such as the NFT collection, its trading volume and history, the last traded price, and who bought it all factor into estimations of its price.

Studying these many parameters before a trade is very beneficial.

It not only verifies that an NFT is genuine but also protects users from wash trading, floor sweeping and many other practices that may manipulate NFT prices. It also ensures that they buy NFTs from verified wallets and do not fall prey to those with ulterior motives.

While collecting, curating and sifting through this data can be tedious for those who constantly purchase and trade NFTs, there is a simpler solution for NFT price estimation — bitsCrunch’s most accurate AI price estimation.

Data-Driven — How Can NFTs Be Accurately Estimated?

Estimating the price of an NFT need not be tedious. Indeed, it need not even be difficult, as long as one is using the right tools.

We studied the data from over 2200 NFT collections, covering more than 30 million NFTs overall! And from this ocean of data, a clear-cut solution to price estimation for NFTs emerged.

bitsCrunch has an AI-driven machine learning tool for assessing the value of NFTs. Our solution builds on a simple concept to bring advanced data and analytics right to a user’s fingertips.

It is aimed at facilitating informed decisions regarding NFT purchases and trades, while also creating value in other ways. By studying the many useful parameters of an NFTs journey, finding an estimated value to pay for it becomes as simple as clicking a few buttons!

Let’s see our AI model in action with an example case study of token id 1137 from popular BAYC.

As you can see in the example above, we have predicted the price of the token ID 1137 as 77.33ETH with a price interval having a lower bound value of 61.66ETH and a higher bound value of 168.94ETH along with the last updated timestamp of the prediction.

Broadly speaking, an NFT has three important data points to analyse:

• Transaction History: Historical sales data from the time an NFT was minted.
• Buy/Sell Intent: Off-Chain prices for the NFT as listed in different marketplaces.
• Traits: Information from an NFT’s Smart Contract that shows any assigned benefits or value to it.

Our baseline price estimation models utilize these data points and present them as broad categories of price drivers.

• Collection Drivers: Our models gauge the interest in a specific collection as well as in collections similar to the one in question.
• NFT Sales Drivers: Analyzing its sales history provides further insights into its value.
• NFT Rarity Drivers: Like its sales history, looking into any unique traits an NFT possesses can help determine an estimated price for it.

There are additional parameters that are looked at, such as date-derived metrics, the type of marketplace where the NFT is being sold, and any bundles associated with the NFT.

Once the baseline price is established, the real work begins!

To manage factors that baseline models — generated with tree-based algorithms — cannot account for, there are robust models that are built on top of them to create a clearer picture. For instance, baseline models cannot account for a collection’s wide price range and price volatility.

Our high-level machine learning models account for these disparities by simulating different transformations to the target NFT and using the one that is closest to its actual distribution.

With a lot of firepower, our in-house wash trading detection suite creates strong signals that boost the accuracy of our prediction estimates.

As a result, it is simple to gauge the estimated price of any NFT, while also generating a price interval for which it can be traded. Our tools also help understand the impact of key price drivers, and their contribution to an NFT’s value.

Accuracy and Security (Going Forward)

Our efforts at creating the perfect price estimation model for NFTs do not stop here. The roadmap for further improvements is aimed at profiling wallets that hold NFTs. Using the feedback we have received on our predictions to further train our machine learning models is also on the horizon.

Clearly, gauging the price of an NFT is not as much of a hassle as one would believe. What is important is that it is done using the right tools and methods.

Our price estimation tools have got you covered on both fronts. Visit UnleashNFTs to find out more, and begin your NFT trading journey today!

For more interesting updates on the latest NFT trends, follow bitsCrunch!