2026-09-23T00:51:24Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1518432023-08-24T03:26:41Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Rahmandad, Hazhir
Zhang, Tianyi
Sloan School of Management
2023-08-23T16:12:56Z
2023-08-23T16:12:56Z
2023-06
2023-07-14T20:01:20.659Z
https://hdl.handle.net/1721.1/151843
This research strives to explore and simulate the dynamics of regional real estate markets within the United States using the system dynamics methodology. Building upon the original model by John Sterman, the study expands it by introducing new structures related to construction-in-progress, unit prices, alternative funds, and sales. The model undergoes calibration utilizing historical data from 1975 to 2021, with a focus on its capacity to simulate key parameters such as start rate, construction-in-progress rate, construction rate, and price. Although calibration fitness demonstrates a reliable match for trends, it exhibits limitations in representing dynamics over short time periods and seasonality. Utilizing the calibrated model, the study generates forecasts for future real estate market trends under three scenarios: baseline, standard growth, and elevated interest rate. The forecast results emphasize the influential role of space demand, the effect of interest rates on prices, and the reinforcing feedback loop of future prices. The study highlights potential avenues for model enhancement and establishes a foundation for subsequent research.
S.M.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Application of A System Dynamic Model on U.S. Regional Real Estate Industry
Thesis
application/pdf
Master
Master of Science in Management Studies
Publication
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<Abstract>This research strives to explore and simulate the dynamics of regional real estate markets within the United States using the system dynamics methodology. Building upon the original model by John Sterman, the study expands it by introducing new structures related to construction-in-progress, unit prices, alternative funds, and sales. The model undergoes calibration utilizing historical data from 1975 to 2021, with a focus on its capacity to simulate key parameters such as start rate, construction-in-progress rate, construction rate, and price. Although calibration fitness demonstrates a reliable match for trends, it exhibits limitations in representing dynamics over short time periods and seasonality. Utilizing the calibrated model, the study generates forecasts for future real estate market trends under three scenarios: baseline, standard growth, and elevated interest rate. The forecast results emphasize the influential role of space demand, the effect of interest rates on prices, and the reinforcing feedback loop of future prices. The study highlights potential avenues for model enhancement and establishes a foundation for subsequent research.</Abstract>
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