{"id":23979333,"url":"https://github.com/nagar2nd/airbnb-property-management-optimization","last_synced_at":"2026-03-01T16:01:32.873Z","repository":{"id":259208149,"uuid":"876599024","full_name":"Nagar2nd/Airbnb-property-management-optimization","owner":"Nagar2nd","description":"This project aims to analyze Airbnb’s dataset to optimize rental strategies, enhance customer satisfaction, and maximize revenue for property owners. 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Room Type Popularity by Neighborhood Group\n- **Finding:** Entire homes and private rooms are the most popular room types across all neighborhoods, with the highest concentration in Manhattan and Brooklyn.\n- **Implication:** Shared rooms have a negligible presence, indicating lower demand.\n\n### 2. Instant Bookability by Room Type\n- **Finding:** A large portion of entire homes and private rooms are available for instant booking, especially in Manhattan, enhancing convenience for guests.\n- **Implication:** Hotel rooms and shared rooms show limited instant bookability options, suggesting a potential area for growth.\n\n### 3. Cancellation Policy by Room Type\n- **Finding:** The most common cancellation policies for entire homes and private rooms are flexible and moderate, providing guests with flexibility.\n- **Implication:** Strict cancellation policies are less common but significant for higher-priced listings, reflecting host strategies.\n\n### 4. Host Listings by Average Price\n- **Finding:** Hosts with multiple listings tend to charge higher prices, particularly those with more than 50 properties. Manhattan hosts generally have the highest average prices.\n- **Implication:** This suggests that established hosts can leverage their experience and reputation to command higher prices.\n\n### 5. Correlation of Price and Reviews\n- **Finding:** The correlation between price and review rate per month is almost negligible (-0.0046), indicating that pricing does not significantly impact reviews.\n- **Implication:** Other factors, such as guest experience or location, likely play a larger role in determining guest satisfaction.\n\n### 6. Neighborhood Analysis\n- **Finding:** Popular neighborhoods like Manhattan and Brooklyn host the majority of listings, with Queens and Staten Island contributing significantly fewer.\n- **Implication:** Manhattan sees the highest average prices, followed by Brooklyn, indicating a premium associated with these areas.\n\n### 7. Additional Correlations\n- **Price vs. Availability:** Higher-priced properties often have lower availability, suggesting that premium listings are either more exclusive or booked further in advance.\n- **Price vs. Ratings:** No strong correlation was found between listing price and the rating received, implying that lower-priced properties can achieve high ratings with good service.\n\n### 8. Seasonality Trends\n- **Finding:** There is a noticeable seasonal fluctuation in bookings, particularly in Manhattan, with demand spiking during tourist-heavy months (summer and holiday seasons).\n- **Implication:** Private rooms and entire homes see higher bookings during peak seasons, suggesting flexibility in pricing during off-peak periods can help maintain occupancy rates.\n\n## Tableau Features Used\n\n- **Calculated Fields:** For deriving insights based on specific metrics.\n- **Filters:** Allow users to drill down into specific neighborhoods, property types, and pricing strategies.\n- **Visualizations:** Interactive charts and graphs for effective data representation.\n- **Dashboards:** Integrated views for comprehensive analysis.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnagar2nd%2Fairbnb-property-management-optimization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnagar2nd%2Fairbnb-property-management-optimization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnagar2nd%2Fairbnb-property-management-optimization/lists"}