Non-fungible tokens (NFTs) are allowing more artists and creators to realize their potential, while investors, fans, and collectors are joining the foray. With the emergence of fractional NFTs, even more people across the globe can gain access to the NFT market. 

Despite their success, NFTs are quite exclusive, so to speak. Their scarcity and uniqueness have been the main focus, but there is a lack of liquidity, as the market is limiting access to the wider audience, which includes retail investors, fans and small collectors.

To ensure more liquidity and give more investors the opportunity to get exposure to NFTs, many creators and NFT issuers are turning to NFT fractionalization – a trend that is getting traction and may transform the emerging industry.

What Are Fractional NFTs and What Are Their Benefits?

NFTs were designed to be unique and scarce – that’s their selling point. 

However, if there is an increased demand for these tokens on the other side, we get an illiquid and unbalanced market, with only a few investors being able to buy valuable NFTs. The majority of NFT trading is conducted through auctions, which directly points to the lack of liquidity.

Thankfully, there are fractional NFTs, which democratize access to the NFT space.

During the fractionalization of an NFT – the token is locked into a smart contract that splits it into multiple parts that represent fungible tokens. Fungibility is the ability of a token to be exchanged with other tokens of the same type. While NFTs are still not fungible in their entirety, an asset can be broken up into smaller parts represented by fungible tokens, and that can bring in more investors.

For example, think about the Mona Lisa. What if it was tokenized as an NFT? Leonardo da Vinci’s masterpiece is unique and original, and an NFT would be the only kind of token to represent it on a blockchain. While the painting is priceless, it’s worth noting that it has the highest ever insurance value for a painting, which would be about $1 billion when adjusted for inflation. However, this kind of artwork would be sold only through an auction, and its price could surge well above $1 billion.

If sold as a single NFT, there would be only a couple of investors capable of buying it, including individuals like Elon Musk or Jeff Bezos, investment funds, and governments. However, if a masterpiece like the Mona Lisa is tokenized through an NFT and then fractionalized, then it can be owned by multiple investors, just as public companies are owned by multiple stakeholders.

In the future, NFTs can be fractionalized to be made of more NFTs, meaning that the internal parts would have unique values by themselves. 

For example, speaking of the Mona Lisa, each share of it can represent a certain portion of the painting. Thus, a token representing a part of the face would be more expensive than one representing the background. If a piece of internal NFT increases in price, then this would influence the price of the primary or main NFT.

Such an approach could be used in digital art, online games, or fantasy sports, among others.

In their present form, fractional NFTs have several benefits:

• They allow multiple investors to co-own a piece of digital art, real estate, or any other digital or physical thing tokenized as an NFT. This would bring more liquidity in NFT secondary markets.
• Artists and NFT creators can monetize their work more efficiently and easier.
• Investors can buy only a fraction of expensive artworks represented by NFTs, thus getting exposure to a market that has been difficult to access. With fractional NFTs, retail investors can find better opportunities to generate profits or preserve value.

The arts and collectibles markets would be the first beneficiaries of fractional NFTs, given that they operate with assets that are so illiquid.

Bringing in Liquidity to NFT Market with Fractional NFTs

Algorand supports the creation of fractional NFTs and is the only decentralized network that is mathematically proven to be forkless.

The blockchain project founded by MIT Professor Silvio Micali is ideal for NFT issuers who want valuable, tokenized assets to reach a wider audience. Now, you can buy fractions of NFTs representing valuable objects. This ensures more diversification for investors and more convenience for NFT creators.

Developers can tokenize digital and physical goods through Algorand Standard Assets (ASA) functionality, which allows for the creation of digital assets in Algorand’s Layer-1 blockchain – a high-performing network that is rapid, secure, and forkless. ASAs functionalities can be customized in smart contracts based on specific needs.

ASA supports both fungible and non fungible tokens, which enables the creation of fractional NFTs. You can use Algorand to build an NFT and then divide it into any number of parts represented by fungible assets. A good example from the above is the Mona Lisa, which could be fractionalized and deployed as an NFT within moments on the Algorand Blockchain.

Moreover, Algorand protects your NFTs through a unique protocol that relies on the Pure Proof of Stake (PPoS) algorithm, which makes the network unforkable, decentralized, and secure. Thus, your NFT can never be duplicated as a result of a fork or replicated in any way, and will not lose it’s value due to any replication that may happen on other blockchains.

Algorand provides a full range of developer tools to customize fungible and non-fungible tokens. 

To learn more about minting NFTs on Algorand, check out the following resources:

1. Building NFTs on Algorand 
2. Build and deploy Non-Fungible Tokens with Laravel & IPFS
3. Algorealm - a simple NFT game that teaches you how to build NFTs with embedded royalty payments on Algorand. 
4. Watch the following video the covers Stablecoins & NFTs on Algorand:

Start Building

Algorand is a truly revolutionary technology poised to unleash a borderless economy and accelerate the evolution of frictionless finance. 

If you are interested in getting started building blockchain applications, watch our webinar on How to Build Applications on Algorand:

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