Artnome Wants to Predict the Price of a Masterpiece. The Problem? There’s Only One. – Built In

Buying a Picasso is like buying a mansion.

Theres not that many of them, so it can be hard to know what a fair price should be. In real estate, if the house last sold in 2008 right before the lending crisis devastated the real estate market basing todays price on the last sale doesnt make sense.

Paintings are also affected by market conditions and a lack of data. Kyle Waters, a data scientist at Artnome, explained to us how his Boston-area firm is addressing this dilemma and, in doing so, aims to do for the art world what Zillow did for real estate.

If only 3 percent of houses are on the market at a time, we only see the prices for those 3 percent. But what about the rest of the market? Waters said. Its similar for art too. We want to price the entire market and give transparency.

We want to price the entire market and give transparency.

Artnome is building the worlds largest database of paintings by blue-chip artists like Georgia OKeeffe, including her super-famous works, lesser-known items, those privately heldand artworks publicly displayed. Waters is tinkering with the data to create a machine learning model that predicts how much people will pay for these works at auctions. Because this model includes an artists entire collection, and not just those works that have been publicly sold before, Artnome claims its machine learning model will be more accurate than the auction industrys previous practice of simply basing current prices on previous sales.

The companys goal is to bring transparency to the auction house industry. But Artnomes new model faces the old problem: Its machine learning system performs poorly on the works that typically sell for the most the ones that people are the most interested in since its hard to predict the price of a one-of-a-kind masterpiece.

With a limited data set, its just harder to generalize, Waters said.

We talked to Waters about how he compiled, cleaned and created Artnomes machine learning model for predicting auction prices, which launched in late January.

Most of the information about artists included in Artnomes model comes from the dusty basement libraries of auction houses, where they store their catalog raissons, which are books that serve as complete records of an artists work. Artnome is compiling and digitizing these records representing the first time these books have ever been brought online, Waters said.

Artnomes model currently includes information from about 5,000 artists whose works have been sold over the last 15 years. Prices in the data set range from $100 at the low end to Leonardo DaVincis record-breaking Salvator Mundi a painting thatsold for $450.3 million in 2017, making it the most expensive work of art ever sold.

How hard was it to predict what DaVincis 500-year-old Mundi would sell for? Before the sale, Christies auction house estimated his portrait of Jesus Christ was worth around $100 million less than a quarter of the price.

It was unbelievable, Alex Rotter, chairman of Christies postwar and contemporary art department, told The Art Newspaper after the sale. Rotter reported the winning phone bid.

I tried to look casual up there, but it was very nerve-wracking. All I can say is, the buyer really wanted the painting and it was very adrenaline-driven.

The buyer really wanted the painting and it was very adrenaline-driven.

A piece like Salvatore Mundi could come to market in 2017 and then not go up for auction again for 50 years. And because a machine learning model is only as good as the quality and quantity of the data it is trained on, market, condition and changes in availability make it hard to predict a future price for a painting.

These variables are categorized into two types of data: structured and unstructured. And cleaning all of it represents a major challenge.

Structured data includes information like what artist painted which painting on what medium, and in whichyear.

Waters intentionally limited the types of structured information he included in the model to keep the system from becoming too unruly to work with. But defining paintings as solely two-dimensional works on only certain mediums proved difficult, since there are so many different types of paintings (Salvador Dali famously painted on a cigar box, after all). Artnomes problem represents an issue of high cardinality, Waters said, since there are so many different categorical variables he could include in the machine learning system.

You want the model to be narrow enough so that you can figure out the nuances between really specific mediums, but you also dont want it to be so narrow that youre going to overfit.

You want the model to be narrow enough so that you can figure out the nuances between really specific mediums, but you also dont want it to be so narrow that youre going to overfit, Waters said, adding that large models also become more unruly to work with.

Other structured data focuses on the artist herself, denoting details like when the creator was born or if they were alive during the time of auction. Waters also built a natural language processing system that analyzes the type and frequency of the words an artist used in her paintings titles, noting trends like Georgia OKeeffe using the word white in many of her famous works.

Including information on market conditions, like current stock prices or real estate data, was important from a structured perspective too.

How popular is an artist, are they exhibiting right now? How many people are interested in this artist? Whats the state of the market? Waters said. Really getting those trends and quantifying those could be just as important as more data.

Another type of data included in the model is unstructured data which, as the name might suggest, is a little less concrete than the structured items. This type of data is mined from the actual painting, and includes information like the artworks dominant color, number of corner points and if faces are pictured.

Waters created a pre-trained convolutional neural network to look for these variables, modeling the project after the ResNet 50 model, which famously won the ImageNet Large Scale Visual Recognition Challenge in 2012 after it correctly identified and classified nearly all of the 14 billion objects featured.

Including unstructured data helps quantify the complexity of an image, Waters said, giving it what he called an edge score.

An edge score helps the machine learning system quantify the subjective points of a painting thatseem intuitive to humans, Waters said. An example might be Vincent Van Goghs series of paintings of red-haired men posing in front of a blue background. When youre looking at the painting, its not hard to see youre looking at self portraits of Van Gogh, by Van Gogh.

Including unstructured data in Artnomes system helps the machine spot visual cues that suggest images are part of a series, which has an impact on their value, Waters said.

When you start interacting with different variables, then you can start getting into more granular details.

Knowing that thats a self-portrait would be important for that artist, Waters said. When you start interacting with different variables, then you can start getting into more granular details that, for some paintings by different artists, might be more important than others.

Artnomes convoluted neural network is good at analyzing paintings for data that tells a deeper story about the work. Butsometimes, there are holes inthe story being told.

In its current iteration, Artnomes model includes both paintings with and without frames it doesnt specify which work falls into which category. Not identifying the frame could affect the dominant color the system discovers, Waters said, adding an error to its results.

That could maybe skew your results and say, like, the dominant color was yellow when really the painting was a landscape and it was green, Waters said.

Interested in convolutional neural networks?Convolutional Neural Networks Explained: From Pytorch to CNN

The model also lacks information on the condition of the painting, which, again, could impact the artworks price. If the model cant detect a crease in the painting, it might overestimate its value. Also missing is data on an artworks provenance, or its ownership history. Some evidence suggests that paintings that have been displayed by prominent institutions sell for more. Theres also the issue of popularity. Waters hasnt found a concrete way to tell the system that people like the work of OKeeffe more than the paintings by artist and actor James Franco.

Im trying to think of a way to come up with a popularity score for these very popular artists, Waters said.

An auctioneer hits the hammer to indicate a sale has been made. But the last price the bidder shouts isnt what theyactually pay.

Buyers also must pay the auction house a commission, which varies between auction houses and has changed over time. Waters has had to dig up the commission rates for these outlets over the years and add them to the sales price listed. Hes also had to make sure all sales prices are listed in dollars, converting those listed in other currencies. Standardizing each sale ensures the predictions the model makes are accurate, Waters said.

Youd introduce a lot of bias into the model if some things didnt have the commission, but some things did.

Youd introduce a lot of bias into the model if some things didnt have the commission, but some things did, Waters said. It would be clearly wrong to start comparing the two.

Once Artnomes data has been gleaned and cleaned, information is input into the machine learning system, which Waters structured into a random forest model, an algorithm that builds and merges multiple decision trees to arrive at an accurate prediction. Waters said using a random forest model keeps the system from overfitting paintings into one category, and also offers a level of explainability through its permutation score a metric that basically decides the most important aspects of a painting.

Waters doesnt weigh the data he puts into the model. Instead, he lets the machine learning system tell him whats important, with the model weighing factors like todays S&P prices more heavily than the dominant color of a work.

Thats kind of one way to get the feature importance, for kind of a black box estimator, Waters said.

Although Artnome has been approached by private collectors, gallery owners and startups in the art tech world interested in its machine learning system, Waters said its important this data set and model remain open to the public.

His aim is for Artnomes machine learning model to eventually function like Zillows Zestimate, which estimates real estate prices for homes on and off the market, and act as a general starting point for those interested in finding out the price of an artwork.

When it gets to the point where people see it as a respectable starting point, then thats when Ill be really satisfied.

We might not catch a specific genre, or era, or point in the art history movement, Waters said. I dont think itll ever be perfect. But when it gets to the point where people see it as a respectable starting point, then thats when Ill be really satisfied.

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Artnome Wants to Predict the Price of a Masterpiece. The Problem? There's Only One. - Built In