The thesis analyzes the impact of short-term rental regulations in Amsterdam, focusing on the introduction of the 30-day annual limit for renting entire apartments through platforms such as Airbnb. Using a combination of descriptive statistics and regression models based on the Difference-in-Difference (DiD) approach, the study compares pre and post regulation trends to assess the effects on property availability, demand, and pricing. The analysis incorporates multiple regression models to control for various factors, including time-fixed effects and property-specific trends, allowing for a more nuanced understanding of the regulation's impact. Preliminary results indicate a significant reduction in listings and rentable days, along with an increase in revenue per night, particularly for properties directly affected by the regulation.