The housing and rental market has been a volatile and unpredictable environment ever since the COVID-19 pandemic. This study is an examination of the post-pandemic housing market dynamics across ten United States metropolitan areas, comparing five technology-centric hubs including San Francisco, San Jose, Seattle, Austin, and Cambridge with five service-oriented economies which includes Las Vegas, Orlando, San Antonio, Phoenix, and Tampa. I obtained the data on Zillow and focused on two variables: Home Value Index (ZHVI) and Zillow Observed Rent Index (ZORI) spanning 2019 through 2024. I conduct a difference-in-differences (DiD) analysis in addition to an event-study decomposition, placebo testing, and advanced time-series forecasting. The DiD regression estimates a treatment effect of +6.75 percentage points in rent growth for technology hubs relative to service metros in the post-2023 period (p < 0.001). However, checks for robustness which includes event-study pre-trends and placebo date tests reveal violations of the parallel trends assumption, precluding causal interpretation. Results are therefore reported as descriptive correlations. I also utilized STL decomposition, SARIMAX modeling, Prophet forecasting, and gradient-boosted machine learning to confirm that metro type accounts for a substantial share of predictive variation in rent trajectories. The results suggest that return-to-office mandates, remote-work persistence, and migration patterns interact in complex ways to shape rental recovery heterogeneity. The convergence of urban–suburban price gradients and the decline in price-to-rent ratios point to a structural repricing of urban housing markets that extends beyond the immediate pandemic shock.