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Machine learning, human experts, and the valuation of real assets

Author

Listed:
  • Aubry, Mathieu
  • Kräussl, Roman
  • Manso, Gustavo
  • Spaenjers, Christophe

Abstract

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.

Suggested Citation

  • Aubry, Mathieu & Kräussl, Roman & Manso, Gustavo & Spaenjers, Christophe, 2019. "Machine learning, human experts, and the valuation of real assets," CFS Working Paper Series 635, Center for Financial Studies (CFS).
    • Handle: RePEc:zbw:cfswop:635
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    References listed on IDEAS

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    1. Orley Ashenfelter & Kathryn Graddy, 2011. "Sale Rates and Price Movements in Art Auctions," American Economic Review, American Economic Association, vol. 101(3), pages 212-216, May.
    2. Stefano Lovo & Christophe Spaenjers, 2018. "A Model of Trading in the Art Market," American Economic Review, American Economic Association, vol. 108(3), pages 744-774, March.
    3. BAUWENS, Luc & GINSBURGH, Victor, 2000. "Art experts and auctions are pre-sale estimates unbiased and fully informative?," LIDAM Reprints CORE 1485, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Alan Beggs & Kathryn Graddy, 2009. "Anchoring Effects: Evidence from Art Auctions," American Economic Review, American Economic Association, vol. 99(3), pages 1027-1039, June.
    5. David Genesove & Christopher Mayer, 2001. "Loss Aversion and Seller Behavior: Evidence from the Housing Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(4), pages 1233-1260.
    6. Jianping Mei & Michael Moses, 2005. "Vested Interest and Biased Price Estimates: Evidence from an Auction Market," Journal of Finance, American Finance Association, vol. 60(5), pages 2409-2435, October.
    7. Orley Ashenfelter & Kathryn Graddy, 2011. "Sale Rates and Price Movements in Art Auctions," American Economic Review, American Economic Association, vol. 101(3), pages 212-216, May.
    8. Erik Brynjolfsson & Tom Mitchell & Daniel Rock, 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 43-47, May.
    9. Clare McAndrew & James L Smith & Rex Thompson, 2012. "The impact of reserve prices on the perceived bias of expert appraisals of fine art," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 235-252, March.
    10. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    11. Arthur Korteweg & Roman Kräussl & Patrick Verwijmeren, 2016. "Does it Pay to Invest in Art? A Selection-Corrected Returns Perspective," The Review of Financial Studies, Society for Financial Studies, vol. 29(4), pages 1007-1038.
    12. Lee, Yong Suk & Sasaki, Yuya, 2018. "Information technology in the property market," Information Economics and Policy, Elsevier, vol. 44(C), pages 1-7.
    13. Edward L. Glaeser & Michael Scott Kincaid & Nikhil Naik, 2018. "Computer Vision and Real Estate: Do Looks Matter and Do Incentives Determine Looks," NBER Working Papers 25174, National Bureau of Economic Research, Inc.
    14. Dimson, Elroy & Spaenjers, Christophe, 2011. "Ex post: The investment performance of collectible stamps," Journal of Financial Economics, Elsevier, vol. 100(2), pages 443-458, May.
    15. Luc Renneboog & Christophe Spaenjers, 2013. "Buying Beauty: On Prices and Returns in the Art Market," Management Science, INFORMS, vol. 59(1), pages 36-53, February.
    16. Milgrom, Paul R & Weber, Robert J, 1982. "A Theory of Auctions and Competitive Bidding," Econometrica, Econometric Society, vol. 50(5), pages 1089-1122, September.
    17. Orley Ashenfelter & Kathryn Graddy, 2003. "Auctions and the Price of Art," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 763-787, September.
    18. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    19. Glaeser, Edward L. & Nathanson, Charles G., 2017. "An extrapolative model of house price dynamics," Journal of Financial Economics, Elsevier, vol. 126(1), pages 147-170.
    20. repec:pri:cepsud:214ashenfelter is not listed on IDEAS
    21. Michael Bailey & Ruiqing Cao & Theresa Kuchler & Johannes Stroebel, 2018. "The Economic Effects of Social Networks: Evidence from the Housing Market," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2224-2276.
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    Cited by:

    1. Wayne Xinwei Wan & Thies Lindenthal, 2023. "Testing machine learning systems in real estate," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(3), pages 754-778, May.
    2. Stephen Sheppard, 2021. "Image Content, Complexity, and the Market Value of Art," Department of Economics Working Papers 2021-08, Department of Economics, Williams College.
    3. Alexander Arimond & Damian Borth & Andreas Hoepner & Michael Klawunn & Stefan Weisheit, 2020. "Neural Networks and Value at Risk," Papers 2005.01686, arXiv.org, revised May 2020.
    4. Shen, Lily & Ross, Stephen, 2021. "Information value of property description: A Machine learning approach," Journal of Urban Economics, Elsevier, vol. 121(C).
    5. David Chambers & Elroy Dimson & Christophe Spaenjers, 0. "Art as an Asset: Evidence from Keynes the Collector," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(3), pages 490-520.
    6. Barth, Andreas & Laturnus, Valerie & Mansouri, Sasan & Wagner, Alexander, 2021. "ICO analysts," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242429, Verein für Socialpolitik / German Economic Association.
    7. Lily Shen & Stephen L. Ross, 2019. "Information Value of Property Description: A Machine Learning Approach," Working papers 2019-20, University of Connecticut, Department of Economics, revised Sep 2020.
    8. Guan‐Yuan Wang, 2023. "The effect of environment on housing prices: Evidence from the Google Street View," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 288-311, March.
    9. Wan, Wayne Xinwei & Lindenthal, Thies, 2022. "Towards accountability in machine learning applications: A system-testing approach," ZEW Discussion Papers 22-001, ZEW - Leibniz Centre for European Economic Research.

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    More about this item

    Keywords

    asset valuation; auctions; experts; big data; machine learning; computer vision; art;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

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