Statistical methods are widely used for valuation (prediction of the value at sale or auction) of a unique object such as a work of art. The usual approach is estimation of a hedonic model for objects of a given class, such as paintings from a particular school or period, or in the context of...

We study the impact of colors of paintings on prices in the art auction market and incorporate color attributes of non-figurative paintings in pricing models. A one standard deviation increase in the percentages of blue (red) hue leads to premiums of 10.63% (4.20%). We also conduct laboratory...

We study price determinants and investment performance of art based on a vast sample of transactions around the world over the past 60 years. Art has appreciated at a real (nominal) annual return of 2.49% (6.24%). Higher art returns are reached for paintings at high-end of the price...

In article the concept of work of art value, that employs system approach to the auction market, has been presented. While treating the market as a system, respective elements of the system and their relationships have been identified. Sellers, buyers, auction institution as an agent, and works...

While there are no significant investment characteristics that inhibit art from being considered as an asset, a major hurdle has long been the lack of a systematic measure of its financial performance. Due to its heterogeneity (each piece is different) and its infrequency of trading (the exact...

This paper contains a new review of the research of the last decade that has been designed to shed light on how the art auction system works, what it indicates about price formation, and how well it performs. We begin with a short description of the mechanics of the auction system and then...

We provide evidence for the behavioral biases of anchoring and loss aversion in paintings sold at auction. We find that anchoring is more important for items that are resold quickly, and that the effect of loss aversion increases with the time that a painting is held. This evidence contributes...

We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher...

We analyze organization of auctions and bidding strategies with a unique dataset on Paris auctions between 700s and 800s. Prices reflect the objective features of the paintings and of the sale, and they reveal a substantial death effect, with upward jumps in the years after the death of the...

Aggregate art price patterns mask a lot of underlying variation — both in the time series and in the cross-section. We argue that, to increase our understanding of the market for aesthetics, it is helpful to take a micro perspective on the formation of art prices, and acknowledge that each...