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

Journal of Risk and Financial Management, 14(9), 423. https://doi.org/10.3390/jrfm14090423

The University of Auckland Business School Research Paper Series

Posted: 30 Nov 2021

See all articles by William Cheung

William Cheung

The University of Auckland Business School; The University of Auckland

Julian TszKin Chan

Bates White Economic Consulting

Sijie Li

University of Pittsburgh

Edward Chung Yim Yiu

University of Auckland Business School

Date Written: 2021

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. Full paper available at https://doi.org/10.3390/jrfm14090423

Keywords: unsupervised machine learning, natural language process, non-local buyers, anchoring biases, information asymmetry, repeat-sales estimates

Suggested Citation

Cheung, William and Chan, Julian TszKin and Li, Sijie and Yiu, Edward Chung Yim, Anchoring and Asymmetric Information in the Real Estate Market: A Machine Learning Approach (2021). Journal of Risk and Financial Management, 14(9), 423. https://doi.org/10.3390/jrfm14090423, The University of Auckland Business School Research Paper Series, Available at SSRN: https://ssrn.com/abstract=3973212

William Cheung (Contact Author)

The University of Auckland Business School ( email )

12 Grafton Rd, OGG Building
Private Bag 92019
Auckland Central, Auckland 1142
New Zealand
210487282 (Phone)
1010 (Fax)

HOME PAGE: http://https://www.business.auckland.ac.nz/people/profile/william-cheung

The University of Auckland ( email )

Auckland, 1010
New Zealand
099234819 (Phone)
1142 (Fax)

HOME PAGE: http://https://www.business.auckland.ac.nz/people/profile/william-cheung

Julian TszKin Chan

Bates White Economic Consulting ( email )

1300 Eye Street NW
Suite 600
Washington, DC 20005
United States

Sijie Li

University of Pittsburgh

Edward Chung Yim Yiu

University of Auckland Business School ( email )

12 Grafton Rd
Private Bag 92019
Auckland, 1010
New Zealand

HOME PAGE: http://https://unidirectory.auckland.ac.nz/people/profile/edward-yiu

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