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

Ka Shing Cheung, Julian TszKin Chan, Sijie Li and Chung Yim Yiu
Additional contact information
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

JRFM, 2021, vol. 14, issue 9, 1-22

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.

Keywords: unsupervised machine learning; natural language process; non-local buyers; anchoring biases; information asymmetry; repeat-sales estimates (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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