Pre-sale Estimates Research Articles

Presale Estimates and Auction Prices in Indian Art Market: Accuracy, Determinants and Motivations

Presale Estimates and Auction Prices in Indian Art Market: Accuracy, Determinants and Motivations

Biased Auctioneers

ABSTRACTWe 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.

Investing in Art: The Informational Content of Italian Painting Pre-Sale Estimates

As the size of the art market increases and a growing number of investors are attracted by the high returns, the amount and quality of information available to market participants becomes increasingly relevant, especially for less experienced investors. One of the most relevant information sources in the art market is the price estimate provided by auction houses, that is the price that auctioneers believe a piece of art might bring at auction. Auction houses are regarded as providing additional valuable information to market participants. Thus pre-sale estimates could be useful reference points in the art valuation process, driving operators' investment and divestment decisions. However, as the price of each unique artwork is affected by inconstant and intangible factors, estimates are usually expressed as a range within which the experts forecast the final price will fall. The informational content of such estimates can be examined along two dimensions: the uncertainty and the accuracy of estimates in predicting sale prices. We test for any systematic differences in predicting hammer prices using a sample of 1,975 sales of Italian paintings which were sold all over the world at least twice during the 1985-2006 period. Three results emerge from the empirical evidence. First, pre-sale estimates are not good predictors of final sale prices. Second, uncertainty and accuracy in price prediction decreases and increases, respectively, when Italian paintings are auctioned in Italy, thus revealing a Country effect. Finally, the informational content of estimates is affected by past prices, thus revealing an anchoring effect.

Machines and Masterpieces: Predicting Prices in the Art Auction Market

We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique - neural networks - to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers' pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts' systematic biases in expectations formation - and identify ex ante situations in which such biases are likely to arise.

Experience and Brokerage in Asset Markets: Evidence from Art Auctions

AbstractFocusing on the art market, where auction houses act as brokers between art sellers and buyers, we investigate whether more experienced brokers achieve better performance as information providers and matchmakers. We find that houses with artist‐specific experience are more likely to achieve a sale and provide more precise pre‐sale estimates. These findings suggest that experience in a specific market segment plays an important role for brokers to reduce illiquidity and opacity in markets with asymmetric information. Our analysis also shows that specific experience matters above and beyond other house‐specific factors, including reputation and market power.

On the relationship between reserve prices and low estimates in art auctions

Using a unique hand-collected dataset comprising all auctions held by a major Italian auction house in Italy from 1994 to 1997 and including confidential information on reserve prices, we find empirical evidence in support of several studies in cultural economics literature that reserve prices are set in a range of approximately 75 % of low presale estimates.

Experience and Brokerage in Asset Markets: Evidence from Art Auctions

Focusing on the art market, where auction houses act as brokers between art sellers and buyers, we investigate whether more experienced brokers achieve better performance as information providers. We use a unique data set of auctions of Italian paintings in various houses around the world, and we measure experience as the number of times an auctioneer has auctioned the artworks of a certain artist in a given location. We find that more experienced auction houses (i) are more likely to sell and (ii) provide more precise pre-sale estimates. These findings suggest that experience plays an important role for brokers to reduce illiquidity and opacity in markets with asymmetric information.

Asking prices, selling prices, and anchoring effects

Purpose– Commercial auctions of cultural goods are typically brokerage arrangements where potential buyers may consider pre-sale estimates (PSEs) in bidding. The economic theory suggests that PSEs should provide honest guidance – winning bids should, on average, equal PSEs – but available research from fine art and antique auctions finds otherwise. The authors examine this relationship for commercial auctions of fine wine – items which, unlike fine art and antiques, are widely traded so that bidders may more knowledgably interpret PSEs.Design/methodology/approach– The paper examines the relationship of PSEs to winning bids econometrically for an iconic wine widely traded in several Chicago wine auctions, a novel setting for this analysis.Findings– The relationship of winning bids to PSEs differs significantly between two neighboring auction houses, perhaps reflecting differences in how they do business; neither’s PSEs have a straightforward relationship to winning bids; PSEs have an influence on winning bids independent of a lot’s characteristics, reflecting perhaps an anchoring effect; the analysis suggests broad bidding strategies (with counter-strategies implied) and might guide auction house lot selection and ownership if more complete data became available.Originality/value– The role and reliability of PSEs in auctions of other cultural goods, representing most of the literature, has limited application to auctions of fine wine whose markets differ categorically from those of other cultural goods.

Does the sun ‘shine’ on art prices?

This paper examines how variation in mood influences subjective risk and hence auction prices for art in London during the period 1990–2007. The private value of an object is closely related to taste and mood which is proxied for by the variation in weather. Using a unique data set that includes presale estimates for paintings sold through Sotheby's and Christie's auction houses as well as weather data for London from the British Atmospheric Data Centre we find that the lower part of the price distribution is populated with paintings with a relative high private value, whereas in the upper part, prices are driven primarily by the common value characteristics. Our findings have important implications for collectors and investors in the art market.

Investing in Art and the Informational Content of Pre-Sale Estimates

Investing in Art and the Informational Content of Pre-Sale Estimates

The Impact of Reservation Prices on the Perceived Bias of Expert Appraisals of Fine Art

We examine whether expert appraisals provided to bidders before major art auctions are unbiased indicators of value. Despite a strong grounding in theory, this aspect of optimal auction design has been frequently challenged in previous empirical research, particularly in the market for fine art. We adopt a valuation benchmark that incorporates sellers’ reserve prices as well as high bids, and recognize censoring of works that fail to make reserve. Although the auction houses never divulge reserve prices, we exploit the fact that they can be observed indirectly via their impact on buy-in rates. Using the set of French Impressionist paintings brought to auction from 1985 to 2001, we estimate the distribution of reserve prices, establish their link to a proper valuation benchmark, and isolate the selection bias due to bought-in works on the perceived market value of fine art works. After controlling for the impact of reservation prices, and considering all works brought to auction, we find no evidence of bias in the experts’ pre-sale estimates.

The collateral value of fine art

In this paper, we examine the effect of implicit seller reserves on the estimation of value-at-risk based on historical asset sales data. We direct our examination toward how and whether fine art might prove an appropriate form of loan collateral for banks and other financial institutions. Using a data set of French Impressionist paintings brought to auction from 1985 to 2001, we control for the effect of works that are bought in-house to construct a distribution of potential sale values that corrects for sample selection bias. It turns out that the downside risk surrounding deviations of auction prices from expert presale estimates depends critically on how buy-ins are incorporated. If downside risk is assessed solely on historical experience with successful auction sales, the data appear to support loan-to-value ratios between 50% and a 100% larger than loan-to-value ratios that countenance the existence of seller reserves. The auction process, however, is quantifiable and can reveal the necessary risk information required for loan consideration.

Do art specialists form unbiased pre-sale estimates? An application for Picasso paintings

This work investigates whether art specialists provide good predictors of realized prices for Picasso paintings. A sample selection model is proposed to represent the decision of the seller and the price equation. The model is applied to data on 675 Picasso paintings for the period 1975–1994. It is found that the two auction houses, Sotheby's and Christie's, have given good predictions for the works that have been sold. However, for the unsold works, it would have been possible to give estimates better than those given by the salerooms. As a consequence they could perhaps have sold more paintings than they actually did.

Art experts and auctions Are pre-sale estimates unbiased and fully informative?

SummaryPublic sales art catalogues include low and high pre-sale price estimates by experts. This makes it possible to analyze whether pre-sale estimates are unbiased predictors of realized prices. Unbiasedness is tested using a sample of some 1,600 lots of English silver auctioned by Christie’s and Sotheby’s. Results show that estimates are slightly (but significantly) biased and that experts do not use all the information that is available to them when they make their estimates.

Federal Offshore Oil and Gas Lease Bonus Bid Rejection Policy: Past and Present

Summary Policy concerning rejection of bids for U.S. government Policy concerning rejection of bids for U.S. government federal offshore oil and gas leases offered to the market recently has been changed. Technical analyses presaging this change are given here. An initial technical assessment of the new policy compared with the old is presented. presented. Introduction In 1968, the U.S. government began preparing presale estimates of record for offshore oil and gas leases offered to the market. Previously, informal estimates, now not of record, were made, resulting in the rejection of 5.8% of the leases that otherwise would have been issued. After 1968, the rejection rate more than doubled! The policy that led to that rejection rate has recently been changed. The change parallels a previous study and follows that study closely in time. The purpose of this work is twofold. The issues and technical study that underlie the policy change are traced and summarized. Also, technical assessments based on data under the new policy are made. Fair Market Value and Government Estimates of Lease Value The presale estimates prepared and recorded by the U.S. government were used to decide which leases to issue and which should have all bids rejected. The objective written in law is that the government should obtain fair market value for leases issued. Under the old policy, these presale estimates were considered necessary "to assure [that] the Federal government receives fair market value for lands leased....."That policy position became an argued issue elsewhere as well as in the previous technical study. Opposite that position was the viewpoint that fair market value is determined only by cash, or equivalent, exchanges between actual willing sellers and buyers. Government estimates, never required to meet that test, could not be considered to affect fair market value. So argued those opposing the old policy position. In March 1983, the U.S. Dept. of Justice issued a statement that more or less administered a coup de grce to the old policy vis-…-vis fair market value and government estimates: "Our position… is that the high bid on a tract will generally represent fair market value for that tract, absent collusion or other irregularities in the bidding process." This Dept. of Justice position is implicit in the new rejection policy. What Is an Offered Lease Worth? The next question is obvious. To whom? The answer relevant to rejection policy is the owners of the leases offered. The owners are, of course, the collected citizens. The issue is: what is the value of an offered lease to the collected citizens as owners of that lease? The question can be answered mathematically as follows. .....................(1) JPT p. 1951

U.S. Offshore Oil and Gas Lease Royalty Bidding

U.S. offshore oil and gas lease royalty bids were analyzed. Bids appear to be normally distributed. Using a simple investment model, the trade-off between bonus and royalty bids for an investor was examined. Higher royalties can result in lost recovery when (1) marginal deposits are not developed and (2) productive leases are shut in earlier. Introduction U.S. offshore oil and gas leases were issued exclusively as a result of bonus bids from 1954 to 1974. Since then, leases have been offered and issued to royalty bidders in two sales, although bonus bidding has remained the predominant system. With the emergence of royalty bidding, we undertook to examine the character of the royalty bids that have been submitted and to assess the impact of royalty bidding on subsequent production. In this study, we examined the statistical distribution of royalty bids. Government presale estimates of royalty bid leases were compared with royalty bids received; the results of this comparison are contrasted to the corresponding finding with bonus bids. We used a simple investment model to predict the trade-off between royalty and bonus that predict the trade-off between royalty and bonus that a bidder would bid. Finally, we examined how royalty affects recovery in two ways:decisions not to initiate production anddecisions to cease production that would continue if the royalty were production that would continue if the royalty were lower. Statistical Analyses of Royalty Bids The two sales in which leases were offered by royalty bidding were held Oct. 15, 1974, in Louisiana and Oct. 27, 1977, in Alaska. Of the 10 such leases offered in the Louisiana sale, eight received a total of 57 bids and all were issued. Through 1979, six of the eight leases were relinquished. Of the two leases still active, one had production starting in 1979. In the Alaska sale, 46 leases were offered to royalty bids. Of these, 30 received a total of 98 bids, and all leases receiving bids were issued. Through 1979, none of these Alaska royally bid leases were relinquished or had any production. Detailed data about these leases, the bids, and bidders are available. Fig. 1 is a plot shown on normal probability graph paper of the bids received for lease OCS No. G2905 paper of the bids received for lease OCS No. G2905 (Oct. 15, 1974, Louisiana sale). Thirteen bids were received for this lease, the largest number of bids for any of the royalty bid leases. The linearity of the plotted points in Fig. 1 is typical and suggested the plotted points in Fig. 1 is typical and suggested the hypothesis that royalty bids for leases may be normally distributed. Analogously, bonus bids have been treated as log-normally distributed. Taking the royalty bids as normally distributed, the pooled standard deviation of the bids on the 32 leases with two or more bids in both sales was 13%. Detailed statistical results have been given elsewhere. That result provides a crude but pragmatic measure of the disagreement between bidders' royalty bids.

Statistical Analyses of Solo and Joint Bids for Federal Offshore Oil and Gas Leases

Abstract This study of Outer Continental Shelf (OCS) bid data, plus a critical analysis of other such studies, was made to determine the impact of joint bidding on competitiveness of OCS lease sales, It concludes that no class of joint bids has been shown to reduce the level of competition. Banning joint bidding by two or more major oil companies did result in an abrupt increase in the number of pint bids that included one major. Introduction Sealed, competitive bids for U.S. offshore oil and gas leases are classed as either solo or joint bids. Solo bids are submitted by one bidder with 100-percent ownership. Joint bids are submitted by several bidders who divide ownership among themselves. The pragmatic question that triggered this study was, "Is there a kind of solo or joint bid whose occurrence tends to decrease the number of sealed, competitive bids?" Such a bid would lower the level of competition. This study reports the results of a statistical analysis to measure the impact of joint bidding on the level of competition in sales of U.S. oil and gas leases. The study first presumed that the level of competition increases as the number of competing bids increases. This presumption while not unassailable, also was not unreasonable. Three previous studies of solo and joint bidding were reviewed first, revealing that conclusions drawn by two of the studies are statistically unsupported. Our study of the pragmatic question found no consistent correlation supporting a positive answer to the question. The U.S. policy regulation proscribing joint bids involving two or more majors tended to broaden the proportion and number of bids involving majors. REVIEW OF PREVIOUS STUDIES OF FEDERAL OFFSHORE SOLO AND JOINT BIDS Joint bidding for U.S. offshore oil and gas leases has been seated in previous studies of which three will be reviewed in detail. GASKINS AND VANN Gaskins and Vann computed values of the ratio of the sum of the highest bids to the sum of the U.S. presale estimates, Fmax/est, for leases that presale estimates, Fmax/est, for leases that received the same number(s) of bids. Precise definition of Fmax/est is given in the Nomenclature. Gaskins and Vann observed that values of F increased with n, from which they concluded the "government gets a larger percentage of its estimated value when there are more bidders." For the March 28, 1974, sale, Gaskins and Vann calculated Fmax/est for four different categories of highest bids:all bids,bids in which only nonmajors were involved,bids in which one or more majors were involved, andbids in which Mobil Oil Corp. was a participant. (No list was given of which bidders are classed as major.) Values of Fmax/est when majors and/or Mobil were involved in the highest bid were more often lower than for the other categories of highest bids. From this, Gaskins and Vann concluded that the "data support the hypothesis that major oil companies, and Mobil in particular, were able to attain lower winning bids..." We recalculated values of F,../est for the March 28, 1974, sale. These are shown in Table 12 along with comparable values of Fm../mean and Fmean/est. The agreement between values of Fmax/est presented by Gaskins and Vann and in Table 1 is excellent in most cases. Some of the differences, however, may be explained by differing definitions of majors. We considered these eight companies as major: Amoco International Oil Co., British Petroleum Ltd., Chevron U.S.A. Inc., Exxon Corp., Gulf Oil Corp., Mobil Oil Corp., Shell Oil Co., Texaco Inc. Other differences may be caused by disagreements in source data and/or computations.