The Bear Market is Coming, But Don't Overlook PawnHouse's Pricing Oracle in the NFT Infrastructure Field

TOYKO, June 26, 2022 /PRNewswire/ -- Recently, with NFTs popularity in decline, the market bubble is in the process of clearing up. Blockchain investors are again turning their attention to building and reinforcing the NFT market's infrastructure. A robust pricing mechanism that evaluates an NFT's worth and guarantees liquidity will help restart the bull market. Eventually, a solid NFT pricing mechanism will emerge with Pawnhouse's continued building.

At present, NFT oracles in the market are divided mainly into two categories:

1.  Data-driven

Through data mining and modeling analysis, machine learning algorithms and expert systems get used to complete the NFT asset price predictions.

2.  Price-generating

With the crypto world as the foundation and the community as the core, the artificial price feeding method involves a massive collection of information provided by users and aggregating the data to form an effective price reference system.

Since the NFT market is still in its early stages and the data volume of the overall industry is insufficient, price information in the current market is scattered and disordered. As a result, the price information of a single asset gets primarily concentrated in the single digits. And because there are many types of projects, the types of assets are different, and the popularity factors of each project are varied.

Traditional auctions can effectively increase the selling price of NFT assets, but can also dramatically increase the difficulty of implementing data-driven price oracles in the industry. Compared with artificial intelligence and machine learning that uses big data in the traditional Web2 space, NFT price data is more difficult to quantify and label due to insufficient amounts of information in the market.

In contrast, the price generation mechanism has more substantial flexibility, and has the potential ability to strongly regulate the market. Moreover, it can even provide price references for NFT assets lacking data support that has not been published.

PawnHouse is an oracle project with price information generation aggregation and an information reward mechanism. The project adopts the community-provided price information method, providing NFTs with price information of 100-300 values per item. The project also gives a reasonable price range (price corridor) through model calculations.

Pawnhouse employs a familiar economic reward mechanism in the crypto world to incentivize more users to participate in the provision of price information. At the same time, the oracle mechanism of PawnHouse is more conducive to increasing the publicity and community activity of NFT projects, and provides a more participatory community pricing tool for NFT asset projects.

PawnHouse conducted a three-month social experiment with its community at the beginning of this year, verifying its model's effectiveness. This experiment aims to predict the DEX price of BTC at a certain point in time. The experiment predicted more than 1,700 times in total. Compared with the final price, the minimum deviation of the price information provided by users is only 0.39 USDT, and the average prediction difference is about 1.3%.

After verifying the feasibility of its mechanism, the PawnHouse project team completed the beta version development of the NFT Oracle. Currently, it has provided price predictions for the assets of more than 20 NFT projects and made price predictions for 28 assets. In addition, after an on-chain query, more than 300 addresses provided price information and more than 6,000 price data points.

PawnHouse said it had completed the closed-loop payment operation model of NFT projects. In the future, it will open up cooperation and prepare for the development of a new contract structure so that the pricing function can be serviced in batches and establish the foundation for Simultaneous Multiple Round Actions (SMRA).

Contact: 

Clarissa Stone
[email protected]

SOURCE PawnHouse

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