Modeling the Feedback of AI Price Estimations on Actual Market Values

TL;DR

This paper models how AI-generated real estate price estimates influence actual market values, revealing feedback loops that can cause hyper-inflation and proposing simulation-based mitigation strategies.

Contribution

It introduces a theoretical game model of AI price estimations interacting with homeowners and validates feedback effects through simulations.

Findings

AI estimation feedback correlates with market inflation

Restricting estimation availability can reduce feedback loops

Simulation confirms the impact of AI estimates on real estate inflation

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.