By: Pip Deely
At the NEW INC incubator at the New Museum of Contemporary Art in New York City, I work alongside 80 creative visionaries at the intersection of art, technology and design. Artists working in virtual reality, or experimenting with 3D printing, sit alongside startups developing everything from digital photography watermarking tools to fashion e-commerce platforms. You don’t have to look much further than the workstation to see technology transforming the way artists create artworks today. But when it comes to the question of how the business of art will be disrupted – the answer to that begins with data.
In my first few months as a resident member of NEW INC, I interviewed a number of leading dealers and auction house specialists about how technology has disrupted their businesses or how it might in the future. The nearly-unanimous answer? Commercially-available price databases.
Over the past two decades, the aggregation of auction price data like that from Invaluable has radically reshaped the way business is conducted in the art world, shrinking margins dramatically among dealers who once were the sole players at auctions and could reasonably expect to sell a purchased work to a retail client at a large markup. No longer.
Now, auctions are more accessible and popular than ever to both the professionals and the consumers who can bid from home or stay connected anywhere with mobile apps like Invaluable’s. And with price data in hand like Invaluable’s price archive, consumers know exactly how much a specific work sold for and when.
How will data continue to transform the business of art in years to come? To answer that question, let’s look at some of the early startups that are leveraging art data on behalf of the buyer in innovative ways.
The Magnus app, released in April 2016 bills itself as the “Shazam for art.” Snap a photo of an artwork, and in anywhere from a few moments to a few days, the app will return the artwork’s key data along with price information sourced from their community of contributors. In practice, the image recognition technology that powers the app itself is not revolutionary. But crowdsourcing art prices and publishing the private price data is a radical approach; pricing on the primary market (among galleries) has traditionally been kept close to the vest.
It’s no surprise that the “About” page on the Magnus website includes the section, “Why are my prices online?” It’s a question that more and more galleries are going to be asking. Available on the iOS platform in some countries and soon for Android, it remains to be seen whether Magnus catches on as an app. But one takeaway is clear: people are looking for gallery and dealer data.
While the ArtAdvisor product is not public yet, it promises to be one of the more scientific approaches to leveraging data to predict everything from valuations to who might be the next art star or which artist you might be interested in. Founded by Hugo Liu, an MIT PhD who developed taste-sensing algorithms at tech startup Hunch before its sale to eBay, and Lucas Zwirner, son of mega-dealer David Zwirner, ArtAdivsor blends a healthy dose of artworld access with high-powered data science.
According to Liu, ArtAdvisor is “powered by machine-learning algorithms that work with large sets of data about artists to better understand their cultural significance, and make predictions about their trajectories….ArtAdvisor attends to what tastemakers are saying and seeks to model and simulate the insights that drive the best tastemakers’ intuitions about art.” Exactly how ArtAdvisor’s analytic engine gets packaged for public consumption is yet-to-be-seen- but get used to automated tools and predictions. If Facebook can show you ads based on your Amazon browsing history, you can bet that startups like ArtAdvisor are going to be suggesting which artists you should check out.
Provocatively named “Sellyoulater” at launch in 2014, ArtRank publishes online quarterly indexes around fairs and auctions with predictions of who might be the next art star within certain price brackets. While focused on only the hottest young talents of the day to start, ArtRank has recently expanded to identifying undervalued “blue chip” artists from older generations. According to ArtRank’s site, the “algorithm is intent on assessing the intrinsic value of an artwork, not its survival value.”
First developed in 2012, ArtRank says their algorithm is “comprised of six exogenous components: Presence, Auction results, market Saturation, Representation and Social mapping (PASSRS).” A network of dealers, auction houses, collectors, advisers, and journalists supply the company’s data.
Demonized by some for the categorization of artists in categories such as Sell/Peaking (“Liquidate” was a category that attracted widespread ire and has since been removed)- it has earned a place among the most disruptive firms to apply art data in new ways.
About the Author
Pip Deely is a technologist residing at NEW INC, the technology incubator at the New Museum of Contemporary Art in New York. Recent projects focus on new innovations in exotic asset lending, including automated asset valuation and blockchain-based ownership records. Pip is a Chartered Alternative Investment Analyst, and consultant to a number of data science and art technology startups. Follow him on Instagram and Twitter.