The impact of color palettes on the prices of paintings


We emphasize that color composition is an important characteristic of a painting. It impacts the auction price of a painting, but it has never been considered in previous studies on art markets. By using Picasso’s paintings and paintings of Color Field Abstract Expressionists sold in Chrisite’s and Sotheby’s auctions in New York between 1998 and 2016, we demonstrate the method to analyze color compositions: How to extract color palettes from a painting image and how to measure color characteristics. We propose two measures: (1) the surface occupied by specific colors, (2) color diversity of a painting composition. Controlling for all conventional painting and sale characteristics, our empirical results find significant evidence of contrastive paintings, i.e., paintings with high diversity of colors, carrying a premium than equivalent artworks which are performed in monochromatic style. In the case of Picasso’s paintings, our econometric analysis shows that some colors are associated with high prices.

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  1. 1.

    In several studies, the subject of a painting (landscape, portrait, etc.) or the number of figures [in the case of figurative works] has been considered as price explanatory variables (Etro and Pagani 2012, 2013; Etro and Stepanova 2016, 2017). But these studies focus on the Old Masters where the classification of subjects is unambiguous. Modern Art, in turn, presents a puzzle for defining subjects. A possible solution is to use the artist’s age [under the assumption that paintings done in the same year are close in style] (Galenson 2000; Hodgson 2011; Hellmanzik 2009).

  2. 2.

    Neuroscientists who studied human reaction to the abstract art confirm that color characteristics of paintings play crucial role (Mallon et al. 2014).

  3. 3.

    We thank our referee for pointing this out to us. Later version of this paper is published as Pownall and Graddy (2016).

  4. 4.

    Further discussion of Pownall (2014) follows in the next section.

  5. 5.

    The price is equal to the auction hammer price plus the buyer’s premium. The buyer’s premia are included as these differ from period to period and, more importantly, between auction houses.

  6. 6.

    In the period 1942–1978, Christian Zervos produced 34 volumes of catalogue raisonnee, in which most of Picasso’s works are registered. This registration is considered to be a proof of authenticity, and it is assumed to influence prices. In our case, Christie’s and Sotheby’s only auction authentic works and in the Provenance indicate, apart from Christian Zervos catalogue raisonnee, other important art catalogs. So the variable “mentioned in more than 2 art books” means that the painting is mentioned not only in Christian Zervos catalogue raisonnee but in some other art catalogs. Pre-auction catalog information refers to extremely prominent and influential publications. It is reasonable to expect that Christie’s and Sotheby’s use valuable catalog space to report the fact that the piece has been reproduced in a book only if it is perceived as an important work of reference. We expect such references to have a positive effect on prices.

  7. 7.

    Works on paper and collages are usually drafts of his oil paintings with prevailing use of black and white colors to sketch the objects, so we do not want to consider these works in our analyses of Picasso’s color palette.

  8. 8.

    In a small number of cases, auction catalogs on Web sites are not available for free. Alternative solutions are the online databases and

  9. 9.

    As color brightness and gradients are rounded off, it is not critical that there may be differences in the brightness of an image due to the amount of external light hitting the object. We also do not need high image resolution, i.e., a larger amount of pixels (a color is associated with each pixel), because we round off colors to the principal ones. Actually, the color quantizing algorithm is a workhorse tool in computer science used to reduce the memory weight of an image (image resolution) while preserving its color characteristics.


  1. Agnello RJ, Pierce RK (1996) Financial returns, price determinants, and genre effects in American art investment. J Cult Econ 20(4):359–383

    Article  Google Scholar 

  2. Anfam D (1990) Abstract expressionism (world of art). Thames & Hudson, London

    Google Scholar 

  3. Ashenfelter O, Graddy K (2003) Auctions and the price of art. J Econ Lit 41(3):763–787

    Article  Google Scholar 

  4. Beggs A, Graddy K (1997) Declining values and the afternoon effect: evidence from art auctions. Rand J Econ 99:544–565

    Article  Google Scholar 

  5. Boyatzis CJ, Varghese R (1994) Children’s emotional associations with colors. J Genetic Psychol 155(1):77–85

    Article  Google Scholar 

  6. Brun L, Trémeau A (2003) Color quantization. In: Sharma G (ed) Digital color imaging handbook. CRC Press, Boca Raton, pp 589–638

    Google Scholar 

  7. Chanel O, Gérard-Varet L-A, Ginsburgh V (1996) The relevance of hedonic price indices. J Cult Econ 20(1):1–24

    Article  Google Scholar 

  8. Cimbalo RS, Beck KL, Sendziak DS (1978) Emotionally toned pictures and color selection for children and college students. J Genetic Psychol 133(2):303–304

    Article  Google Scholar 

  9. Czujack C (1997) Picasso paintings at auction, 1963–1994. J Cult Econ 21(3):229–247

    Article  Google Scholar 

  10. Deng X, Hui SK, Hutchinson JW (2010) Consumer preferences for color combinations: an empirical analysis of similarity-based color relationships. J Consum Psychol 20(4):476–484

    Article  Google Scholar 

  11. Etro F, Pagani L (2012) The market for paintings in italy during the seventeenth century. J Econ Hist 72(02):423–447

    Article  Google Scholar 

  12. Etro F, Pagani L (2013) The market for paintings in the venetian republic from renaissance to rococò. J Cult Econ 37(4):391–415

    Article  Google Scholar 

  13. Etro F, Stepanova E (2016) Entry of painters in the amsterdam market of the golden age. J Evolut Econ 26(2):317–348

    Article  Google Scholar 

  14. Etro F, Stepanova E (2017) Art collections and taste in the spanish siglode oro. J Cult Econ 41(3):309–335

    Article  Google Scholar 

  15. Galenson DW (2000) The careers of modern artists. J Cult Econ 24(2):87–112

    Article  Google Scholar 

  16. Galenson DW (2011) Old masters and young geniuses: the two life cycles of artistic creativity. Princeton University Press, Princeton

    Google Scholar 

  17. Hagtvedt H, Brasel SA (2017) Color saturation increases perceived product size. J Consum Res 28:439–449 (ucx039)

    Google Scholar 

  18. Hellmanzik C (2009) Artistic styles: revisiting the analysis of modern artists careers. J Cult Econ 33(3):201–232

    Article  Google Scholar 

  19. Hemphill M (1996) A note on adults’ color-emotion associations. J Genetic Psychol 157(3):275–280

    Article  Google Scholar 

  20. Higgs H, Worthington A (2005) Financial returns and price determinants in the australian art market, 1973–2003. Econ Rec 81(253):113–123

    Article  Google Scholar 

  21. Hodgson DJ (2011) Age-price profiles for canadian painters at auction. J Cult Econ 35(4):287

    Article  Google Scholar 

  22. Labrecque LI, Milne GR (2012) Exciting red and competent blue: the importance of color in marketing. J Acad Mark Sci 40(5):711–727

    Article  Google Scholar 

  23. Landau EG (2005) Reading abstract expressionism: context and critique. Yale University Press, New Haven

    Google Scholar 

  24. Mallon B, Redies C, Hayn-Leichsenring GU (2014) Beauty in abstract paintings: perceptual contrast and statistical properties. Front Hum Neurosc 8:49–70

    Article  Google Scholar 

  25. Niblack CW, Barber R, Equitz W, Flickner MD, Glasman EH, Petkovic D, Yanker P, Faloutsos C, Taubin G (1993) Ibm research project: Qbic project—querying images by content, using color, texture, and shape. In: IS&T/SPIE’s symposium on electronic imaging: science and technology. International Society for Optics and Photonics, pp 173–187

  26. Orchard MT, Bouman C et al (1991) Color quantization of images. IEEE Trans Signal Process 39(12):2677–2690

    Article  Google Scholar 

  27. Pesando J, Shum PM (1996) Price anomalies at auction: evidence from the market for modern prints. In: Ginsburgh V (ed) Economics of the arts: selected essays. Elsevier, Amsterdam, pp 113–134

    Google Scholar 

  28. Pesando JE (1993) Art as an investment: the market for modern prints. Am Econ Rev 92:1075–1089

    Google Scholar 

  29. Pownall RA, Graddy K (2016) Pricing color intensity and lightness in contemporary art auctions. Res Econ 70(3):412–420

    Article  Google Scholar 

  30. Pownall RAJ (2014) Pricing colour intensity in contemporary art. In: Proceedings of the international conference of the association for cultural economics

  31. Puccinelli NM, Chandrashekaran R, Grewal D, Suri R (2013) Are men seduced by red? the effect of red versus black prices on price perceptions. J Retail 89(2):115–125

    Article  Google Scholar 

  32. Sandler I (1976) The triumph of american painting: a history of abstract expressionism. Harper & Row, London

    Google Scholar 

  33. Scorcu AE, Zanola R (2011) The right price for art collectibles: a quantile hedonic regression investigation of picasso paintings. J Altern Invest 14(2):89–99

    Article  Google Scholar 

  34. Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32

    Article  Google Scholar 

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Corresponding author

Correspondence to Elena Stepanova.

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The author declares that they have no conflict of interest.

Additional information

All images of artworks used in the paper are obtained from free online database



See Tables 3, 4 and Figs. 4, 5, 6, 7, 8.

Table 3 Descriptive statistics (Picasso’s paintings sold in New York in 1998–2016)
Table 4 Descriptive statistics (Color Field Abstract Expressionists paintings sold in New York in 1998–2016)
Fig. 4

Distribution of the diversity of the painting colors (Picasso and Color Field Abstract Expressionists artworks). (Color figure online)

Fig. 5

Example of paintings that belong to the blue-teal cluster. (Color figure online)

Fig. 6

Example of paintings that belong to the orange cluster. (Color figure online)

Fig. 7

Fixed effects of Picasso’s working periods (Table 1) and the number of artworks from each period in our dataset. Note: The reference period, the Blue and Rose Period (1902–1906), is set up to 1. Bars indicate the number of works belonging to a particular working period. The works that belong to the blue-teal cluster are in blue, the works that belong to the orange cluster are in orange, the rest of the works from a particular period that do not belong to neither of the two clusters are in gray. (Color figure online)

Fig. 8

Price indexes of Picasso’s paintings and paintings of Color Field Abstract Expressionists sold in New York. Note: Reference period is 1998–1999, and it is set up to 1. (Color figure online)

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Stepanova, E. The impact of color palettes on the prices of paintings. Empir Econ 56, 755–773 (2019).

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  • Art markets
  • Hedonic pricing
  • Picasso
  • Rothko
  • Visual perception
  • Color
  • Color quantizing

JEL Classifications

  • Z11
  • C810