This paper investigates the importance of including data on new housing supply in Dynamic Stochastic General Equilibrium (DSGE) models in forecasting the Great Financial Crisis (GFC), focusing on the U.S. While existing models have added a financial sector and real estate sector, they have largely overlooked housing supply. I develop an extended DSGE model that includes both the financial sector and endogenous housing supply and show that forecasting accuracy significantly improves when data on new houses is included. Robustness checks confirm the importance of these additions to the model. The findings highlight the necessity of combining model extension and housing supply data for accurate forecasting during economic crises. I identify negative housing demand shocks and escalating adjustment costs as primary drivers of the GFC, propagating into the real economy and accelerating through the financial sector. Additionally, this paper addresses the zero lower bound challenge in modeling forward guidance using a regime change approach.
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