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Modeling the Feedback of AI Price Estimations on Actual Market Values

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  • Viorel Silaghi
  • Zobaida Alssadi
  • Ben Mathew
  • Majed Alotaibi
  • Ali Alqarni
  • Marius Silaghi

Abstract

Public availability of Artificial Intelligence generated information can change the markets forever, and its factoring into economical dynamics may take economists by surprise, out-dating models and schools of thought. Real estate hyper-inflation is not a new phenomenon but its consistent and almost monotonous persistence over 12 years, coinciding with prominence of public estimation information from Zillow, a successful Mass Real Estate Estimator (MREE), could not escape unobserved. What we model is a repetitive theoretical game between the MREE and the home owners, where each player has secret information and expertise. If the intention is to keep housing affordable and maintain old American lifestyle with broad home-ownership, new challenges are defined. Simulations show that a simple restriction of MREE-style price estimation availability to opt-in properties may help partially reduce feedback loop by acting on its likely causes, as suggested by experimental simulation models. The conjecture that the MREE pressure on real estate inflation rate is correlated with the absolute MREE estimation errors, which is logically explainable, is then validated in simulations.

Suggested Citation

  • Viorel Silaghi & Zobaida Alssadi & Ben Mathew & Majed Alotaibi & Ali Alqarni & Marius Silaghi, 2024. "Modeling the Feedback of AI Price Estimations on Actual Market Values," Papers 2405.18434, arXiv.org.
    • Handle: RePEc:arx:papers:2405.18434
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    File URL: http://arxiv.org/pdf/2405.18434
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