Date of Submission

Spring 2024

Academic Program

Economics and Finance

Project Advisor 1

Taun Toay

Abstract/Artist's Statement

This paper examines the interplay between commercial and residential real estate markets within the United States and assesses their impact on financial stability. Employing Hyman Minsky’s Financial Instability Hypothesis alongside a robust Ordinary Least Squares (OLS) regression analysis, this research identifies how fluctuations in the commercial real estate sector can influence the stability of residential markets. The study reveals a pronounced correlation where trends and activities in the commercial sector often foreshadow developments in the residential market, serving as early indicators of potential instabilities or downturns.

Our findings highlight the commercial sector's role as a barometer for residential market conditions, suggesting that commercial real estate dynamics are predictive of shifts that could lead to bubbles or market crashes. Particularly, the econometric modeling results indicate a tangible risk of bubble formation in both markets, driven by speculative investment and external economic factors. This insight underscores the need for vigilant market monitoring and timely policy interventions to mitigate these risks.

The implications of this research are significant for policymakers, investors, and stakeholders, who can use these insights to devise strategies that enhance market foresight and resilience. By recognizing the leading indicators from the commercial real estate market, it is possible to anticipate major market corrections and implement preventive measures to mitigate the impact on the broader economy. This paper contributes to the body of economic literature by providing a detailed empirical foundation for the predictive interconnections between commercial and residential real estate markets and offers recommendations for future policy interventions aimed at stabilizing and sustaining the United States real estate market.

Open Access Agreement

Open Access

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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