IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v14y2021i9p423-d629208.html
   My bibliography  Save this article

Anchoring and Asymmetric Information in the Real Estate Market: A Machine Learning Approach

Author

Listed:
  • Ka Shing Cheung

    (Department of Property, The University of Auckland, 12 Grafton Road, Auckland 1142, New Zealand)

  • Julian TszKin Chan

    (Bates White Economic Consulting, 2001 K Street NW, North Building, Suite 500, Washington, DC 20006, USA)

  • Sijie Li

    (Freddie Mac, 8200 Jones Branch Drive, McLean, VA 22102, USA)

  • Chung Yim Yiu

    (Department of Property, The University of Auckland, 12 Grafton Road, Auckland 1142, New Zealand)

Abstract

Conventional wisdom suggests that non-local buyers usually pay a premium for home purchases. While the standard contract theory predicts that non-local buyers may pay such a price premium because of the higher cost of gathering information, behavioral economists argue that the premium is due to buyer anchoring biases in relation to the information. Both theories support such a price premium proposition, but the empirical evidence is mixed. In this study, we revisit this conundrum and put forward a critical test of these two alternative hypotheses using a large-scale housing transaction dataset from Hong Kong. A novel machine-learning algorithm with the latest technique in natural language processing where applicable to multi-languages is developed for identifying non-local Mainland Chinese buyers and sellers. Using the repeat-sales method that avoids omitted variable biases, non-local buyers (sellers) are found to buy (sell) at a higher (lower) price than their local counterparts. Taking advantage of a policy change in transaction tax specific to non-local buyers as a quasi-experiment and utilizing the local buyers as counterfactuals, we found that the non-local price premium switches to a discount after the policy intervention. The result implies that the hypothesis of anchoring biases is dominant.

Suggested Citation

  • Ka Shing Cheung & Julian TszKin Chan & Sijie Li & Chung Yim Yiu, 2021. "Anchoring and Asymmetric Information in the Real Estate Market: A Machine Learning Approach," JRFM, MDPI, vol. 14(9), pages 1-22, September.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:9:p:423-:d:629208
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/14/9/423/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/14/9/423/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chang, Chuang-Chang & Chao, Ching-Hsiang & Yeh, Jin-Huei, 2016. "The role of buy-side anchoring bias: Evidence from the real estate market," Pacific-Basin Finance Journal, Elsevier, vol. 38(C), pages 34-58.
    2. George A. Akerlof, 1970. "The Market for "Lemons": Quality Uncertainty and the Market Mechanism," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 84(3), pages 488-500.
    3. Zumpano, Leonard V & Elder, Harold W & Baryla, Edward A, 1996. "Buying a House and the Decision to Use a Real Estate Broker," The Journal of Real Estate Finance and Economics, Springer, vol. 13(2), pages 169-181, September.
    4. Shen, Lily & Ross, Stephen, 2021. "Information value of property description: A Machine learning approach," Journal of Urban Economics, Elsevier, vol. 121(C).
    5. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    6. Poh Har Neo & Seow Eng Ong & Yong Tu, 2008. "Buyer Exuberance and Price Premium," Urban Studies, Urban Studies Journal Limited, vol. 45(2), pages 331-345, February.
    7. Hua Sun & Seow Ong, 2014. "Bidding Heterogeneity, Signaling Effect and its Implications on House Seller’s Pricing Strategy," The Journal of Real Estate Finance and Economics, Springer, vol. 49(4), pages 568-597, November.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Ka-Shing Cheung & Chung-Yim Yiu & Yihan Guan, 2022. "Homebuyer Purchase Decisions: Are They Anchoring to Appraisal Values or Market Prices?," JRFM, MDPI, vol. 15(4), pages 1-13, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahlfeldt, Gabriel M. & Heblich, Stephan & Seidel, Tobias, 2023. "Micro-geographic property price and rent indices," Regional Science and Urban Economics, Elsevier, vol. 98(C).
    2. Andres Liberman & Christopher A. Neilson & Luis Opazo & Seth Zimmerman, 2019. "Equilibrium Effects of Asymmetric Information on Consumer Credit Markets," Working Papers 2019-7, Princeton University. Economics Department..
    3. 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.
    4. 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.
    5. Crocker H. Liu & Adam Nowak & Patrick S. Smith, 2018. "Does the Asset Pricing Premium Reflect Asymmetric or Incomplete Information?," Working Papers 18-06, Department of Economics, West Virginia University.
    6. Jorge Mejia & Shawn Mankad & Anandasivam Gopal, 2019. "A for Effort? Using the Crowd to Identify Moral Hazard in New York City Restaurant Hygiene Inspections," Information Systems Research, INFORMS, vol. 30(4), pages 1363-1386, December.
    7. Jermain C. Kaminski & Christian Hopp, 2020. "Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals," Small Business Economics, Springer, vol. 55(3), pages 627-649, October.
    8. Assaf Razin & Efraim Sadka & Chi-Wa Yuen, 1999. "An Information-Based Model of Foreign Direct Investment: The Gains from Trade Revisited," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 6(4), pages 579-596, November.
    9. Tisdell, Clem, 2014. "Information Technology's Impacts on Productivity, Welfare and Social Change: Second Version," Economic Theory, Applications and Issues Working Papers 195701, University of Queensland, School of Economics.
    10. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    11. Konduru, Srinivasa & Kalaitzandonakes, Nicholas G. & Magnier, Alexandre, 2009. "GMO Testing Strategies and Implications for Trade: A Game Theoretic Approach," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49594, Agricultural and Applied Economics Association.
    12. König, Philipp J. & Pothier, David, 2018. "Safe but fragile: Information acquisition, sponsor support and shadow bank runs," Discussion Papers 15/2018, Deutsche Bundesbank.
    13. Andrea Attar & Thomas Mariotti & François Salanié, 2021. "Entry-Proofness and Discriminatory Pricing under Adverse Selection," American Economic Review, American Economic Association, vol. 111(8), pages 2623-2659, August.
    14. Reynolds, Travis & Kolodinsky, Jane & Murray, Byron, 2012. "Consumer preferences and willingness to pay for compact fluorescent lighting: Policy implications for energy efficiency promotion in Saint Lucia," Energy Policy, Elsevier, vol. 41(C), pages 712-722.
    15. Ginger Zhe Jin & Andrew Kato & John A. List, 2010. "That’S News To Me! Information Revelation In Professional Certification Markets," Economic Inquiry, Western Economic Association International, vol. 48(1), pages 104-122, January.
    16. Ritu Agarwal & Michelle Dugas & Guodong (Gordon) Gao & P. K. Kannan, 2020. "Emerging technologies and analytics for a new era of value-centered marketing in healthcare," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 9-23, January.
    17. Villas-Boas, Sofia B, 2020. "Reduced Form Evidence on Belief Updating Under Asymmetric Information," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt08c456vk, Department of Agricultural & Resource Economics, UC Berkeley.
    18. Yaofeng Fu & Ruokun Huang & Yiran Sheng, 2017. "Labor Contract Law -An Economic View," Papers 1702.03977, arXiv.org.
    19. Ghosh, Suman, 2007. "Job mobility and careers in firms," Labour Economics, Elsevier, vol. 14(3), pages 603-621, June.
    20. Eunsoo Kim & Suyon Kim & Jaehong Lee, 2021. "Do Foreign Investors Affect Carbon Emission Disclosure? Evidence from South Korea," IJERPH, MDPI, vol. 18(19), pages 1-14, September.

    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:gam:jjrfmx:v:14:y:2021:i:9:p:423-:d:629208. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.