Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach

Abstract

In this article, we demonstrate that a quantal response statistical equilibrium approach to the US housing market with the help of the maximum entropy method of modeling is a powerful way of revealing different characteristics of the housing market behavior before, during and after the recent housing market crash in the US. In this line, a maximum entropy approach to quantal response statistical equilibrium model (QRSE) is employed in order to model housing market dynamics in different phases of the most recent housing market cycle using the S&P Case Shiller housing price index for 20 largest- Metropolitan Regions, and Freddie Mac housing price index (FMHPI) for 367 Metropolitan Cities for the US between 2000 and 2015. Estimated model parameters provide an alternative way to understand and explain the behaviors of economic agents, and market dynamics by questioning the traditional economic theory, which takes assumption for the behavior of rational utility maximizing representative agent with self-fulfilled expectations as given.

A Maximum Entropy Model of Bounded Rational Decision-Making with Prior Beliefs and Market Feedback

“…Ömer [22][23][24] applies QRSE to housing markets (which we also use as a validating example), modelling the change in the U.S. house price indices over several distinct periods, and explaining dynamics of growth and dips. Yang [25] applies QRSE to a technological change, modelling the adoption of new technology for various countries over multiple years and successfully recovering the macroeconomic distribution of rates of cost reduction.…”

Statistical Equilibrium Methods in Analytical Political Economy

“…This model was first used to explain the equalization of profit rates (Scharfenaker and Foley, 2017) from a classical Smithian perspective by explicitly modeling the entry and exit decisions of firms and how they stabilize the profit rate distribution into a statistical equilibrium by generating negative feedbacks. It has also been used to model induced technical change (Yang, 2018b), fluctuations in housing markets (Ömer, 2018, 2020), asset price fluctuations (Blackwell, 2018; Scharfenaker, 2020), and international competition in labor markets (Wiener, 2020). The main idea behind the QRSE model is to consider a system in which an outcome, $$x \in \mathcal {X}$$, is brought into statistical equilibrium by the purposive actions, $$a^{\prime }\in \mathcal {A}$$, of participants in an institutional structure that generates negative stabilizing feedbacks.…”

Technological Change and Market Conditions: Evidence from Bitcoin Fork

“…Te fndings mostly suggested that the Bitcoin market was rife with randomness, unpredictability, and disorder [20,21]. However, prior studies on long-run equilibrium are still limited for the cryptocurrency market in comparison to other fnancial markets, such as stock [22,23], energy [19,24,25], and real estate [17,26,27]. In particular, to the best of our knowledge, no single study has been conducted to assess the long-run equilibrium in the new and old fork markets by using the concept of entropy.…”