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

Author

Listed:
  • Kirill S. Glavatskiy

    (The University of Sydney)

  • Mikhail Prokopenko

    (The University of Sydney)

  • Adrian Carro

    (University of Oxford
    Banco de España)

  • Paul Ormerod

    (University College London, and Algorithmic Economics Ltd.)

  • Michael Harré

    (The University of Sydney)

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.

Suggested Citation

  • Kirill S. Glavatskiy & Mikhail Prokopenko & Adrian Carro & Paul Ormerod & Michael Harré, 2021. "Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large-scale agent-based model," SN Business & Economics, Springer, vol. 1(6), pages 1-21, June.
    • Handle: RePEc:spr:snbeco:v:1:y:2021:i:6:d:10.1007_s43546-021-00077-2
    DOI: 10.1007/s43546-021-00077-2
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    Cited by:

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    2. Bondita Robidas & Subrata Borgohain Gogoi, 2024. "Economic analysis of transportation of crude oil of Upper Assam Basin through pipeline," SN Business & Economics, Springer, vol. 4(8), pages 1-30, August.
    3. Bardoscia, Marco & Carro, Adrian & Hinterschweiger, Marc & Napoletano, Mauro & Popoyan, Lilit & Roventini, Andrea & Uluc, Arzu, 2024. "The impact of prudential regulations on the UK housing market and economy: insights from an agent-based model," Bank of England working papers 1066, Bank of England.
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    5. Mérő, Bence & Borsos, András & Hosszú, Zsuzsanna & Oláh, Zsolt & Vágó, Nikolett, 2023. "A high-resolution, data-driven agent-based model of the housing market," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).

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    More about this item

    Keywords

    Agent-based modeling; Housing market; Herding; Simulations; Price dynamics; Boom–bust cycles;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D10 - Microeconomics - - Household Behavior - - - General
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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