Introduction

Lil Nouns Dao has sold over 1000 NFTS so far at auction, with an NFT being auctioned off to eager investors on their website every 15 minutes. Each NFT grants the holder the ability to take part in the governance of their DAO but as is usual with most NFT collections people are looking out for traits on the secondary market.

Each NFT is generated randomly from a number of traits, with there being 2 background traits, 30 body traits, 137 accessory traits, 234 head traits and 21 glasses traits. You can play around with the Lil Nouns traits here : Lil Nouns Playground.

Not accounting for backgrounds, Lil Nouns glasses are the only trait to not have a trait valuing over 1.5ETH. Due to fewer traits, it's likely that Glasses' traits are more evenly spread out over NFTs that have sold on the market within 24 hours. This could lead to a lower average for some traits as a trait is less likely to have one large buy, as opposed to many buys that vary in value. The highest value trait we see is for Glasses trait 15 Lil Nouns with this trait we see 1.31 ETH on average.

Accessory 93 commands the largest value of an accessory trait at the market, 2.4ETH on average one of only two traits in this dashboard that command over 2ETH on average.

The lowest value Lil Noun trait, if you sell at market on average, is Head trait 130 commanding a meagre 0.38 ETH, head trait 88 sits in second with 0.42 ETH on average. With a number of other head traits sitting below 0.5ETH, showing that a bad head treat can have the largest negative effect on price out of all the traits. Head trait 70 nets investors 2.4ETH on average, with head trait 11 in second with 1.97ETH.

Here are all the Lil Nouns sold within 24 hours of Mint and how much they each sold for, Lil Noun 545 went for 2.4 ETH, with Lil Noun 353 going for 2 ETH. There is a large variation in price among Lil Noun NFTs sold on the market, with some going for less than 0.5ETH, with NFT 1579 going for 0.38 ETH.

If your Lil Noun has the body type 13 you are in luck with NFTs with that body type fetching an avg of 1.63 ETH within 24 hours of mint. Body type 23 fetches 1.5ETH on average, with a number of other Body traits above the 1ETH value avg. If you get body trait 6 you are looking at 0.575ETH on the open market, with it being the least valuable body trait a Lil Noun can have.

We can see that the majority of Lil Nouns sold on the Market go for a higher fee than mint price, with some going for upwards of 0.4ETH more than mint price. The highest positive fee is 353 which went for 0.95 eth more than auction price. With the biggest loss being tokenid 693 which went for 0.4ETH left. Those who sold around the 0 mark and were in profit may or may not have been happy with the gains made due to the gas fees on secondary marketplaces for selling, which could have taken them into a loss.

Background 1 = 1688.40 USD, Background 0 = 1765.14 USD

Lil Nouns have only 2 backgrounds, with the background 0 commanding more than background 1 at 0.95 ETH a 4.4% increase on background 0 0.91 ETH average price paid.

Methodology

• For the chart Lil Nouns Sold Within 24 Hours of Mint, I used the Lil Nouns mint date and looked for Lil Nouns that had sold at Auction within 24 hours. Matching them based on TokenID which is the unique ID of each NFT.

• For the chart Lil Nouns Sold Within 24 Hours Price Difference On Market, I used the Lil Nouns mint date and looked for Lil Nouns that had sold at Auction within 24 hours. Matching them based on TokenID which is the unique ID of each NFT. I subtracted the value of Mint NFTs from the value of Auctioned NFTs to find the price difference in ETH.

• For the charts Average Price Of Lil Nouns 'Insert Trait' Trait at Market I found the data for the traits associated with a Lil Noun. I used the Lil Nouns mint date and looked for Lil Nouns that had sold at Auction within 24 hours. I then calculated the average price of the relevant trait associated with a Lil Nouns NFT for those NFTs that had sold within 24 hours. I multiplied the ETH price by the value of ETH in USD calculating the average of that value to get the AVG_USD. The size of bubbles in the scatter graphs are based off of the AVG_USD price.