Art Innovative Systems for Value Tagging

Art Innovative Systems for Value Tagging

Laurel Powell, Anna Gelich, Zbigniew W. Ras
DOI: 10.4018/978-1-7998-3473-1.ch080
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Abstract

Prices of artworks are rather arbitrary. Artists use word of mouth and galleries to learn about pricing. Also, the professionals in the art market are searching the internet for information about prices of comparable artworks to the ones they plan to sell, but it is not very helpful. Existing systems do not use data analytics but human experts to evaluate fine art pieces and make recommendations. The system discussed in this article, called ArtIST, is based on big data analytics. Using the artist's name, appraisal of the piece of art is done by a personalized recommender system built from the data describing similar artists and similar art pieces including information about their sales. To evaluate an art piece using ArtIST, the user needs to submit the same information about the work as is required by existing art appraisal tools or websites.
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Introduction

Consumers entering the art market are faced with an environment that gives them a vast number of options, but a limited amount of useful knowledge. While data on the current market are widely available, the necessary context to understand this ecosystem is much harder to effectively obtain. How can consumers know what artworks are good purchases without hiring an external consultant or conducting extensive research on their own? How can artists set the ideal price for their work, when this price is driven by numerous outside factors?

Today, artwork evaluations are conducted by human experts. Collectors with high budgets may hire highly priced art consultants and dealers to select artworks for them. For other buyers and sellers some online services exist, and they tend to rely heavily on extensive datasets of past sales. With these services, either the artists themselves or hired experts find comparable works and use these to determine a market price. These comparables are similar works that have already been sold and can be used to provide a basis for pricing. However, using these services can be time-consuming and expensive.

The art market is a unique problem area for modeling prices. Artworks are unique and their prices are driven by aesthetic and emotional considerations, as well as simple financial ones. Artworks seldom follow traditional economic models and need to be considered differently (Beckert, 2013; Velthuis, 2005). An artist seeking information on pricing his/her own artworks is faced with a challenging task. Gallerists sometimes recommend prices based on their own personal market experiences or on their professional contacts. Sometimes, these judgements are highly subjective and are based on emotional considerations about what price the work should be to fit with the narrative that the price is conveying to a buyer (Velthuis, 2005).

The nature of the art market has changed significantly since the 1940s becoming an “unpredictable financial roller coaster” (Shnayerson, 2019). Artists or art galleries have become brands, where buyers use the brands as a guarantee for quality (Pendergast, 2014). That creates a situation which restricts the number of participants on the top level as well as conditions where the phenomenon of promotion plays a crucial role in the artist’s success. In the reality of the 21st century art market, there are no longer artists who try to hide themselves from the social aspect of their artistic careers. The numerous artist accounts on Instagram and Facebook are clear examples of this statement. Artists, art galleries, and art dealers provide significant information to the Internet to increase an artist’s popularity. Active and established artists have personal websites and Wikipedia pages. If an artist does not have a Wikipedia page with many references, he/she is not considered an established artist. Galleries often create the promotion for emerging artists. It is almost impossible for an established artist today to not have a considerable amount of information on the Internet. Emerging artists often try to follow the same strategy to provide as much as possible about their art and themselves.

The complexity of the market is exacerbated by the role of “superstars” (McAndrew, 2017). Consumers looking to reduce the amount of work that they must do to find a quality piece of art look for artists with well-known names and established reputations. This concentrates sales in a very small subset of artists and raises their prices accordingly.

An artist may experiment with using the Internet as a resource to help him/her determine a price for his/her artwork. However, while the amount of information available is vast, sifting through that information to find a comparable work is much more of a challenge. This can create problems for artists, who may overprice their work and lose sales, or underprice their work and diminish their market potential. Artists must take care to ensure that their prices are appropriate to the current stage of their career, so that they can fit into their role in the current market (Velthuis, 2005). Being perceived as too concerned with making money on artworks can be a significant hindrance to an artist’s career (Velthuis, 2005). In contrast, pricing too low can make an artist’s work less appealing from an investment perspective.

Key Terms in this Chapter

Classifier: A model that can be used to place objects into discrete categories based on some set of features. Classifiers are trained on datasets.

Knowledge-Based System: A recommender system that uses knowledge of the application area to make suggestions to users. The recommendations use domain knowledge to offer recommendations that fulfill the constraints provided by the user.

Primary Art Market: Works of art that are being sold by either the original artist or a direct intermediary. This is in contrast to the secondary art market which consists of artworks that are being sold by collectors or other purchasers.

Recommender system: A system that aids in decision making by providing users with suggestions. These suggestions are developed based on past information or domain knowledge.

Feature: An attribute or characteristic of an object that can be used to develop a classifier.

Emerging Artist: An emerging artist is an artist that has had some professional development as an artist. However, they are not widely recognized and are not at the potential height of their career.

Established Artist: An established artist is an artist that has a solid reputation as an artist among art critics, art buyers, galleries, and other stakeholders in the art world. They generally can sustain themselves through their art and are represented by leading art galleries and art museums.

Contemporary Art Market: In the context of this work, the contemporary art market consists of artists, art dealers, art collectors or other buyers working in or near the present day.

Confusion Matrix: A table that represents the error rate of a classifier.

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