Seattle Airbnb Data Analysis

Overview

This project explores Airbnb listings in Seattle using Kaggle data from 2016. The dataset consists of three sheets: Listings, Reviews, and Calendar. Using Tableau, I performed several joins and visualized key insights such as price trends, location-based pricing, and the impact of property size on rental prices. The goal is to determine optimal strategies for hosting Airbnb properties in Seattle, focusing on price and availability.

Project Details

1. Dataset

• Listings Sheet: Contains details about the properties, including location, type of property, number of beds/baths, and prices (daily, weekly, monthly).
• Reviews Sheet: Includes review ID, listing ID, and review comments.
• Calendar Sheet: Tracks the availability of listings (occupied or vacant) for each date, using listing_id for identification.

2. Data Preparation in Tableau

• Imported the listings.csv file into Tableau.
• Performed joins on the listing_id field between the Listings, Calendar, and Reviews tables.
• Due to the dataset’s size (23 million rows), applied filters to focus on data from 2016 and excluded the Reviews table to reduce the dataset to around 10 million rows.

3. Key Visualizations

Price by Zipcode (Sheet 1)

• Visualized the average price for Airbnb listings by Seattle zip codes.
• Purpose: Identify the most expensive areas to guide real estate investment for Airbnb properties.
• Visualization Type: Bar chart labeled as "Price by Zipcode."

Price per Zipcode Map (Sheet 2)

• Created a map using zip codes, with colors representing average prices in each area.
• Purpose: Display the geographic distribution of rental prices across Seattle.
• Visualization Type: Map, labeled "Price per Zipcode."

Revenue per Year (Sheet 3)

• Analyzed weekly revenue trends across 2016, filtered by calendar dates.
• Purpose: Determine the best time to list properties for maximum revenue.
• Visualization Type: Line chart, labeled "Revenue per Year."

Average Price per Bedroom (Sheet 4)

• Explored how the number of bedrooms impacts the average price of listings.
• Purpose: Assess which property sizes are most profitable for Airbnb rentals.
• Visualization Type: Bar chart, labeled "Average Price per Bedroom."

Distinct Count of Bedroom Listings (Sheet 5)

• Displayed the number of listings based on the number of bedrooms, with null and zero-bedroom listings filtered out.
• Purpose: Understand the availability of different property sizes.
• Visualization Type: Bar chart, labeled "Distinct Count of Bedroom Listings."

4. Final Dashboard

• Combined all visualizations into a comprehensive dashboard named Airbnb Full Project.
• Purpose: Provide a full view of the pricing and availability trends for Airbnb listings in Seattle.

Tools Used

• Tableau Public: Used for data preparation, joining, and visualizing.
• Excel (CSV): Data source containing the 2016 Seattle Airbnb listings, reviews, and calendar information.

How to Use

1. Open Tableau Workbook: Access the workbook via Tableau Public to explore the visualizations and interact with the dashboard.
2. Explore Visualizations: Use filters to adjust the data, and drill down into specific zip codes, property types, or price ranges.
3. Understand the Insights: Use the dashboard to identify the most profitable areas, optimal pricing strategies, and the best times to list properties.

Key Insights

• Zip code-based price trends can help potential Airbnb hosts decide where to buy properties.
• One-bedroom properties in Seattle are performing well based on average price per day.
• Revenue trends suggest there are certain times in the year that are more profitable for listing properties on Airbnb.

Future Enhancements

• Include data from 2021 for a broader analysis of recent trends.
• Integrate additional metrics like occupancy rates and guest reviews for a deeper analysis.