Econometric Fine Art Valuation by Combining Hedonic and Repeat-Sales Information

Abstract

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 real estate, houses in a neighborhood. Where the object itself has previously sold, an alternative is to base an estimate on the previous sale price. The combination of these approaches has been employed in real estate price index construction (e.g., Jiang et al. 2015); in the present context, we treat the use of these different sources of information as a forecast combination problem. We first optimize the hedonic model, considering the level of aggregation that is appropriate for pooling observations into a sample, and applying model-averaging methods to estimate predictive models at the individual-artist level. Next, we consider an additional stage in which we incorporate repeat-sale information, in a subset of cases for which this information is available. The methods are applied to a data set of auction prices for Canadian paintings. We compare the out-of-sample predictive accuracy of different methods and find that those that allow us to use single-artist samples produce superior results, that data-driven averaging across predictive models tends to produce clear gains, and that, where available, repeat-sale information appears to yield further improvements in predictive accuracy.

Non‐Fungible Tokens (NFTs): A Review of Pricing Determinants, Applications and Opportunities

“…The coefficients ßt can be estimated using the OLS method. The RSR model allows to overcome some of the limitations of the hedonic regression model as it predicts that the characteristics of each product are constant over time, thus avoiding the problem of attribute misspecification (Galbraith and Hodgson, 2018). On the other hand, however, the RSR model is affected by some severe issues: (i) it restricts the amount of data analyzed, as it is simply based on products sold more than once and, thus, disregards all single sales.…”

Building a Model for Financial Management of Digital Technologies in the Areas of Combinatorial Effects

“…Scientists cannot but notice the loss of balance in economic development, the turbulence of emerging trends, and the variability of basic conditions and preconditions. The authors relied on the basic concepts of mathematical modeling for the digital economy proposed in the works of contemporary economists (Galbraith and Hodgson 2018;De Gaetano 2018;Yakovlev et al 2019). It is the use of the mathematical apparatus within the framework of evaluating the economic phenomena that contributes to the most effective analysis of their essential characteristics and provisions.…”