How Traditional Fine Art Pricing May Be Reinforcing Historical Biases

And How You Could Easily Price Your Artwork Without Bias

By Ebonique Boyd, the co-founder of Budget Collector, a company creating an artificial intelligence art adviser.

Do you ever wonder how Damian Hirst sells his artwork at sometimes more than $4 million for one piece? When you may be struggling to sell your own artwork at even $200? Why do some people seem to so very easily make a living selling artwork, while others still have to consider themselves merely a hobbyist?

I have been considering these unanswered questions during my research interviews with fine artists. I’ve learned so much about the art market over the last few years, and I was most bothered by traditional fine art pricing and future prediction models. I looked through several ways that art advisers predict future artwork prices, and I found that those models tend to reinforce historical biases that seem to give preference to the same type of artworks that were previously sold.

In some instances, institutional biases are difficult to quantify, but it can almost be too readily apparent in other cases. We’ve all seen artist friends who “made it” and to find out later that the deck was stacked for them as they are the nephew of a prominent collector or for another artist to make it because their work is almost an exact facsimile of some other famous artists. From my research, these instances that seem so commonplace are likely derived from a codified system that favors such occurrences. The system that codifies this type of institutional bias is through the pricing methods that many art advisers and galleries use; whether it be the repeat sales approach or the hedonic method, both tend to create a system that favors nepotism and unoriginality. You can read more about those pricing methods in our upcoming study.

Another factor related to pricing would be the general art ecosystem’s tendency to be required to pay for pricing transparency and financial insights into fine art’s appreciation or depreciation. Our company’s research found that middle-class consumers were 60% more likely to purchase fine art if given more insight into pricing. We could have simply used the traditional pricing models to provide artificial intelligence (AI) insights to consumers. However, we believed those models were exclusive, dated, and biased. So, instead, we decided to develop our own model from scratch.

Thankfully, both co-founders were accepted into a new Stanford University program that encourages black women to dive into solving complex artificial intelligence problems. It has been an opportunity to discover whether it would be possible to create a more inclusive pricing model that would allow more artists, regardless of their background, to be priced fairly and accurately without the biases of more traditional models.

However, we need the Teaching Artists Guild members and their supportive friends’ help. Although we have many collectors who signed up for our research study with a budget between $200 to $15,000, we do not have many artists who have signed up to participate. Our study is designed to determine what pricing method will sell more artwork at the highest price. Our research goal is to have more artists make a living and steady wage. To participate, sign-up here:



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