Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large-scale agent-based model

Kirill S. Glavatskiy (), Mikhail Prokopenko, Adrian Carro, Paul Ormerod and Michael Harré ()
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Kirill S. Glavatskiy: The University of Sydney
Mikhail Prokopenko: The University of Sydney
Adrian Carro: University of Oxford
Paul Ormerod: University College London, and Algorithmic Economics Ltd.
Michael Harré: The University of Sydney

SN Business & Economics, 2021, vol. 1, issue 6, 1-21

Abstract: Abstract Urban housing markets, along with markets of other assets, universally exhibit periods of strong price increases followed by sharp corrections. The mechanisms generating such non-linearities are not yet well understood. We develop an agent-based model populated by a large number of heterogeneous households. The agents’ behavioral rules are consistent with the concept of bounded rationality. The model is calibrated using several large and distributed datasets of the Greater Sydney region (demographic, economic and financial) across three specific and diverse periods since 2006. The model is not only capable of explaining price dynamics during these periods, but also reproduces the novel behavior actually observed immediately prior to the market peak in 2017, namely a sharp increase in the variability of prices. This novel behavior is related to a combination of trend-following aptitude of the household agents (herding) and their collective propensity to borrow. Trend-following behavior is found to be essential in replicating market dynamics.

Keywords: Agent-based modeling; Housing market; Herding; Simulations; Price dynamics; Boom–bust cycles (search for similar items in EconPapers)
JEL-codes: C15 C63 D10 E37 G17 R31 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s43546-021-00077-2

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