A Fast Literature Search Engine based on top-quality journals, by Dr. Mingze Gao.
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- Please kindly let me know [mingze.gao@mq.edu.au] in case of any errors.
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This paper uses stamp catalogue prices to investigate the returns on British collectible postage stamps over the period 1900-2008. We find an annualized return on stamps of 7.0% in nominal terms, or 2.9% in real terms. These returns are higher than those on bonds but below those on equities. The volatility of stamp prices approaches that of equities. Stamp returns are impacted by movements in the equity market, but the systematic risk of stamps remains low. Stamps partially hedge against unanticipated inflation. Estimates of average after-cost returns for individual investors show that stamps may rival equities in terms of realized performance.
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We present an infinite-horizon model of endogenous trading in the art auction market. Agents make purchase and sale decisions based on the relative magnitude of their private use value in each period. Our model generates endogenous cross-sectional and time-series patterns in investment outcomes. Average returns and buy-in probabilities are negatively correlated with the time between purchase and resale (attempt). Idiosyncratic risk does not converge to zero as the holding period shrinks. Prices and auction volume increase during expansions. Our model finds empirical support in auction data and has implications for selection biases in observed prices and transaction-based price indexes.
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Real estate—housing in particular—is a less profitable investment in the long run than previously thought. We hand-collect property-level financial data for the institutional real estate portfolios of four large Oxbridge colleges over the period 1901–1983. Gross income yields initially fluctuate around 5%, but then trend downward (upward) for agricultural and residential (commercial) real estate. Long-term real income growth rates are close to zero for all property types. Our findings imply annualized real total returns, net of costs, ranging from approximately 2.3% for residential to 4.5% for agricultural real estate.
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Using historical price records for Bordeaux Premiers Crus, we examine the impact of aging on wine prices and the long-term investment performance of fine wine. In line with the predictions of an illustrative model, young maturing wines from high-quality vintages provide the highest financial returns. Past maturity, famous châteaus deliver growing non-pecuniary benefits to their owners. Using an arithmetic repeat-sales regression over 1900–2012, we estimate a real financial return to wine investment (net of storage costs) of 4.1%, which exceeds bonds, art, and stamps. Returns to wine and equities are positively correlated. Finally, we find evidence of in-sample return predictability.
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This paper investigates the impact of equity markets and top incomes on art prices. Using a newly constructed art market index, we demonstrate that equity market returns have had a significant impact on the price level in the art market over the last two centuries. We also find evidence that an increase in income inequality may lead to higher prices for art. Finally, the results of Johansen's cointegration tests strongly suggest the existence of a long-run relation between top incomes and art prices.
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Real and private-value assets—defined here as the sum of real estate, infrastructure, collectibles, and noncorporate business equity—compose an investment class worth an estimated 84 trillion in the U.S. alone. Furthermore, private values can affect pricing in many other financial markets, such as that for sustainable investments. This paper introduces the research on real assets and private values that can be found in this special issue. It also reviews recent advances and highlights new research directions on a number of topics in the real assets space that we believe to be particularly important and exciting.
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We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and nonvisual object characteristics. We find that higher automated valuations relative to auction house presale estimates are associated with substantially higher price‐to‐estimate ratios and lower buy‐in rates, pointing to estimates' informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers' prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
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