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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
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    References listed on IDEAS

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    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.
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    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).
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    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.
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    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.

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