IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v33y2020i7p2898-2936..html
   My bibliography  Save this article

Asymmetric or Incomplete Information about Asset Values?

Author

Listed:
  • Crocker H Liu
  • Adam D Nowak
  • Patrick S Smith
  • Stijn Van Nieuwerburgh

Abstract

We provide a new framework for using text as data in empirical models. The framework identifies salient information in unstructured text that can control for multidimensional heterogeneity among assets. We demonstrate the efficacy of the framework by reexamining principal-agent problems in residential real estate markets. We show that the agent-owned premiums reported in the extant literature dissipate when the salient textual information is included. The results suggest the previously reported agent-owned premiums suffer from an omitted variable bias, which prior studies incorrectly ascribed to market distortions associated with asymmetric information. (JEL D82, G14, R00)Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Suggested Citation

  • Crocker H Liu & Adam D Nowak & Patrick S Smith & Stijn Van Nieuwerburgh, 2020. "Asymmetric or Incomplete Information about Asset Values?," The Review of Financial Studies, Society for Financial Studies, vol. 33(7), pages 2898-2936.
  • Handle: RePEc:oup:rfinst:v:33:y:2020:i:7:p:2898-2936.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhz096
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Walter D'Lima & Timothy Komarek & Luis A. Lopez, 2023. "Risk perception in housing markets: Evidence from a fighter jet crash," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(4), pages 819-854, July.
    2. Shen, Lily & Ross, Stephen, 2021. "Information value of property description: A Machine learning approach," Journal of Urban Economics, Elsevier, vol. 121(C).
    3. Marcus T. Allen & Justin D. Benefield & Ronald C. Rutherford, 2023. "Co-Listing Strategies: Better Transaction Outcomes?," The Journal of Real Estate Finance and Economics, Springer, vol. 67(3), pages 517-544, October.
    4. Paul M. Anglin & Yanmin Gao, 2023. "Value of Communication and Social Media: An Equilibrium Theory of Messaging," The Journal of Real Estate Finance and Economics, Springer, vol. 66(4), pages 861-903, May.
    5. William N Goetzmann & Christophe Spaenjers & Stijn Van Nieuwerburgh, 2021. "Real and Private-Value Assets [Gendered prices]," The Review of Financial Studies, Society for Financial Studies, vol. 34(8), pages 3497-3526.
    6. Daniel Broxterman & Tingyu Zhou, 2023. "Information Frictions in Real Estate Markets: Recent Evidence and Issues," The Journal of Real Estate Finance and Economics, Springer, vol. 66(2), pages 203-298, February.
    7. Geoffrey K. Turnbull & Bennie D. Waller & Scott A. Wentland, 2022. "Mitigating agency costs in the housing market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(3), pages 829-861, September.
    8. Chris Cunningham & Kristopher Gerardi & Lily Shen, 2022. "The Good, the Bad, and the Ordinary: Estimating Agent Value-Added Using Real Estate Transactions," FRB Atlanta Working Paper 2022-11, Federal Reserve Bank of Atlanta.
    9. Crocker H. Liu & Patrick S. Smith, 2023. "School quality as a catalyst for bidding wars and new housing development," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(4), pages 785-818, July.
    10. Luis Arturo Lopez & Shawn J. McCoy & Vivek Sah, 2022. "Steering consumers to lenders in residential real estate markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(6), pages 1596-1641, November.

    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:rfinst:v:33:y:2020:i:7:p:2898-2936.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sfsssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.