The Art Market: An Econometric Model

Presentation by Jordan Pock

Outline

•Motivation

•Background Information

•Literature Review

•Economic Theory

•Data and Methodology

•Results and Analysis

•Conclusion

Motivation

•What is the value of an experience good?

•How do we place an economic value on cultural goods?

•What if economists are able to predict trends in the art market?

•What are the factors that determine the price of a piece of art?

•Can economists identify trends and market inefficiencies?

•Can we earn higher than average return on investment?

The Economics of Art

                               

                    

Understanding the Art Market

Created by three sub-markets

•Primary

•Secondary

•Auction market

Importance of Auction Houses

•Components of the sale

VARIABLE AND THEIR PREDICTED RELATIONSHIPS

1. Age of artist at his death ( - )

2. Age of the artist at completion of painting ( - )

3. Number of auction records ( - )

4. Number of painting records ( - )

5. Page number of reproduction in catalogue ( - )

6. Lot number at auction ( - )

Hypothesis

7. Size ( + )

8. Low presale estimate ( + )

9. High presale estimate ( + )

10. Signed ( + )

11. Dated ( + )

12. Day sold (undetermined)

13. Month sold (undetermined)

14. Style (undetermined)

15. Location of sale (undetermined)

16. Country of origin (undetermined)

VARIABLE AND THEIR PREDICTED RELATIONSHIPS

Literature Review

1. The Supply and Demand of Art Works•Regression Analysis of Wine (Ashenfelter 1989)

•Order of Auction sales (Beggs et. Al. 1997)

•State of the Economy (Candela and Sorcu 2001)

2. The Risk and Return of Art as an Investment

•Risk of Holding art as an Investment (Chanel et al. 1994) (Anderson 1974)

3. Transaction and Information Costs of Art

•Costs of art vs. traditional investments (Matsumoto 1994)

•Increased venues for trade reduce costs (Wilke 2000)

                              

                     

Literature Review- continued

Economic Theory

1. The Demand for Art

•Far fewer buyers than sellers

•Wealthy individuals, private collectors, or museums

2. The Supply of Art

•Unlimited number of painting by an unlimited number of artists

3. Art as a Normal Good

•Differentiated good

•Substitutability

4. The Economics of Risk and Return

Economic Theory- continued

Data

The Data Set:

194 Points from two online sources

•(www.askart.com and

www.artprice.com)

The period 1830 to 1910

•Based on location of artistic movements

•Stylistic movements

Methodology

Multivariate Regression Analysis

•Ordinary least squares

•Relates Y to a series of independent X’s

Price = F(deathage, complage, paintage, auctrecs, paintrecs, dasold, mosold, yearsold, size, lowestm, highestm, newyork, lotno, reprodpg, espress, preraph, symbol, aesthetc, impress, signed, dated, nethrlnd, france, england, norway, austria)

Results and AnalysisDependent Variable: PRICEMethod: Least SquaresIncluded observations: 193

Variable Symbol Coefficient Std. Error t-Statistic Prob.

Constant C -21608271 27099637 -0.797364 0.4263Age of Painting PAINTAGE 4463.101 4535.966 0.983936 0.3264Age of Artist at Death DEATHAGE 11221.45 4303.283 2.60765 0.0099*Year Sold YRSOLD 10579.45 13533.51 0.781722 0.4354Size SIZE 16.77475 14.86543 1.128441 0.2606High Presale Estimate HIGHESTM 0.832163 0.013819 60.21951 0*Expressionism EXPRESS -776917.6 281219.2 -2.762676 0.0063*Preraphealism PRERAPH -760453.4 350777.4 -2.167909 0.0315*Symbolism SYMBOL -937382.1 316597.1 -2.960804 0.0035*Aesthetism AESTHETC -469889.6 279269.9 -1.682564 0.0942*

R-squared 0.958484 Mean dependent var 1193663Adjusted R-squared 0.956443 S.D. dependent var 3174859S.E. of regression 662605.1 Akaike info criterion 29.69617Sum squared resid 8.03E+13 Schwarz criterion 29.86522Log likelihood -2855.68 F-statistic 469.4431Durbin-Watson stat 2.183428 Prob(F-statistic) 0

Results and Analysis

Price= -2.2E+07 + 4463* PAINTAGE + 11221.45 * DEATHAGE + 10579* YRSOLD + 16.77 SIZE + .83 * HIGHESTM + -776918 * EXPRESS + .0035 * PRERAPH + .0942 * AESTHETC

T-Statistics

•Less than impressive

•What does that mean?

•Multicollinearity- within age variables and estimates

R-Squared

•.958

•Is that a good thing?

ConclusionsSignificant Variables•Style

•Age of the artist at his death

•High presale estimate

Search for market inefficiencies is elusive at best•High presale estimate

•Variables that outperform the market

Limitations•Repeat sales information not available

•Subject matter, condition are not included

Expansions•Larger data set

•Additional variables

•Accuracy and influence of presale estimates