Analyzing Art Market Performance: Price DriversInvestment Returns over Decades

Buying Beauty: Analyzing the Price Drivers and Investment Returns in the Art Market

Abstract

investigates the factors influencing prices and investment performance within the art market, drawing on an expansive dataset of over one million auction transactions for pntings and works on paper. We construct a price index using hedonic regression analysis to determine that artworks have appreciated by a modest 3.97 annually in real U.S. dollars between 1957 and 2007a performance similar to corporate bonds, albeit with significantly higher risk.

A repeat-sales regression performed on a subset of the data confirms the robustness of our price index formulation. Our quantile regressions highlight that the average rate of appreciation and volatility are disproportionately high in more expensive brackets of artworks. Additionally, we uncover discrepancies in historical returns across different mediums and artistic movements.

Finally, we demonstrate how indicators of high-income consumer sentiment and art market sentiment effectively predict trs within the art pricing landscape.

Keywords: Artwork, auctions, hedonic regression analysis, investments, repeat-sales regressions, sentiment

JEL Classification: Z11, G11, E21

This comprehensive study provide insights into the dynamics of the art market, shedding light on its historical performance and potential as an investment vehicle.

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The analysis encompasses a wide range of data from international auctions conducted over several decades, offering unprecedented depth in assessing price determinants and investment returns. The constructed price index through hedonic regression techniques illustrates that despite the fluctuations within this market, artworks have appreciated at a moderate pace of 3.97 per year in real terms between 1957 and 2007. This performance aligns with that observed in corporate bond investments but comes with a substantially higher risk profile.

The validity of our index is further substantiated by conducting repeat-sales regressions on a subset of the dataset, which confirms its reliability and applicability across various artworks over time. Quantile regression analysis reveals that while average returns are generally lower in less expensive brackets, volatility ts to increase significantly as prices rise.

Moreover, we observe nuanced differences in historical return performance based on medium e.g., oil pntings vs. lithographs and artistic movement periods e.g., impressionism versus contemporary art. These findings shed light on the complexity of the market structure and provide a more detled perspective for potential investors.

Lastly, our study validates the predictive capabilities of high-income consumer confidence indicators along with broader sentiment measures that capture market expectations as strong predictors for shifts in art pricing trs. This insight underscores the importance of monitoring economic conditions alongside traditional art market indices when assessing investment opportunities.

is designed to offer a robust foundation for understanding both the historical and current dynamics within the art market, providing valuable information for investors seeking knowledge about this niche asset class.

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