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House Prices as a Result of Trading Activities: A Patient Trader Model

Author

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  • Ralf Korn

    (TU Kaiserslautern)

  • Bilgi Yilmaz

    (Middle East Teachnical University)

Abstract

We present a new modeling approach for house price movements as a consequence of the trading behavior of market agents. In our modeling approach, all agents are assumed to assign a personal threshold value to a (standardized) house and update the threshold value permanently by a continuous-time filtering procedure based on observing the quoted house prices and the resulting price movements. The traders then trade according to a threshold price strategy (try to sell if the personal threshold value is lower, try to buy if the personal threshold value is higher than the actually quoted house price). Our modeling approach and its resulting characteristics are illustrated via numerical examples that highlight certain realistic constellations between various traders.

Suggested Citation

  • Ralf Korn & Bilgi Yilmaz, 2022. "House Prices as a Result of Trading Activities: A Patient Trader Model," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 281-303, June.
    • Handle: RePEc:kap:compec:v:60:y:2022:i:1:d:10.1007_s10614-021-10149-y
    DOI: 10.1007/s10614-021-10149-y
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    References listed on IDEAS

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    Cited by:

    1. Bilgi Yilmaz, 2024. "Housing GANs: Deep Generation of Housing Market Data," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 579-594, July.
    2. Rosa Drift & Jan Haan & Peter Boelhouwer, 2024. "Forecasting House Prices through Credit Conditions: A Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3381-3405, December.

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