Algorithmic price forecasts are now a routine feature of consumer decision environments, yet little is known about how they shape market dynamics beyond individual choice. This research examines how publicly visible housing price forecasts influence belief persistence and market adjustment. Using metro-level Zillow Home Value Index (ZHVI) data combined with national housing price trends from Federal Reserve Economic Data (FRED) spanning 2000–2025, we document an informational environment characterized by smooth national price narratives alongside highly heterogeneous local price dynamics. We argue that this structure allows algorithmic price forecasts to function as belief anchors, reinforcing seller expectations and delaying adjustment when local conditions diverge from national narratives. Descriptive evidence reveals substantial dispersion in local price growth across markets despite shared national regimes, highlighting systematic opportunities for forecast misalignment. We conceptualize housing inventory as an aggregate behavioral outcome reflecting collective decisions to wait rather than revise beliefs. By reframing algorithmic forecasts as active components of belief formation rather than passive information, this research advances understanding of how algorithmic transparency can generate persistent market frictions in consumer markets