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A Model of Trading in Unique Durable Assets

Author

Listed:
  • Stefano Lovo

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

  • Christophe Spaenjers

    (HEC Paris - Recherche - Hors Laboratoire - HEC Paris - Ecole des Hautes Etudes Commerciales)

Abstract

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.

Suggested Citation

  • Stefano Lovo & Christophe Spaenjers, 2014. "A Model of Trading in Unique Durable Assets," Working Papers hal-01993374, HAL.
    • Handle: RePEc:hal:wpaper:hal-01993374
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    Cited by:

    1. 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.
    2. Shen, Lily & Ross, Stephen, 2021. "Information value of property description: A Machine learning approach," Journal of Urban Economics, Elsevier, vol. 121(C).

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