This study aims to quantify the price elasticity of short-term rentals (STRs) across various districts and accommodation types in Santiago, Chile, analyze the relationship between accommodation capacity and occupancy rates, assess the impact of price changes on occupancy, and develop effective, data-driven pricing strategies. Data were collected via web scraping from Airbnb listings between October 21 and October 31, 2024, covering three key seasonal periods in 2025: January (peak summer), May (low autumn), and July (peak winter). The variables analyzed included nightly rates, bed capacity, accommodation types (houses, apartments, hotels, and guesthouses), and estimated occupancy rates. Findings reveal that price elasticity varies significantly between districts and accommodation types, with some areas exhibiting high sensitivity to price changes, particularly during peak seasons. For instance, districts like Las Condes and Downtown Santiago showed greater responsiveness to price adjustments, while Providencia and Vitacura demonstrated more stable demand regardless of pricing fluctuations. Accommodation types also played a critical role; apartments and guesthouses were more price-sensitive compared to houses and hotels. The Random Forest model highlighted the importance of digital reputation metrics, such as the number of reviews and average ratings, in influencing occupancy rates. Properties with higher ratings and more reviews consistently maintained higher occupancy, underscoring the significance of a positive online presence. Seasonal analysis showed occupancy rates peaking in January and July, aligning with summer and winter tourist influxes, respectively. Conversely, May experienced a noticeable dip in occupancy, reflecting the impact of seasonality on STR performance. The findings validate five hypotheses, confirming the pivotal roles of price elasticity, digital reputation, and tailored pricing strategies in optimizing STR performance. Based on these insights, the study proposes implementing flexible pricing models that adjust rates in real time according to demand fluctuations, particularly in price-sensitive districts. Furthermore, it recommends encouraging hosts to enhance their digital reputation by actively seeking positive reviews and maintaining high ratings. Finally, the study suggests introducing targeted promotions during low-demand seasons, such as offering discounted rates or value-added services, to attract guests and ensure steady occupancy across all periods.