Airbnb Data Dictionary
Explore our comprehensive Airbnb dataset with detailed short-term rental statistics, pricing analytics, and market insights for investors, researchers, and property managers.
Listings Data
Comprehensive details of all Airbnb listings providing essential insights into property distribution, amenities, pricing strategies, and competitive positioning across markets.
Common Use Cases
- Market analysis and competitive positioning
- Property type and amenity distribution analysis
- Pricing strategy development based on property attributes
- Geographical analysis of listing density and characteristics
Sample Visualizations
- Heat maps showing property density by neighborhood
- Price distribution charts by property type
- Amenity correlation matrices
- Property type distribution pie charts
Schema
38 fields| Field Name | Description |
|---|---|
| listing_id | Unique identifier for the listing |
| host_id | Unique identifier for the host |
| host_name | Name of the host |
| listing_name | Title of the listing |
| latitude | Geographical latitude coordinate |
| longitude | Geographical longitude coordinate |
| guests | Maximum number of guests allowed |
| bedrooms | Number of bedrooms available |
| listing_type | Type of property (e.g., apartment, house, villa) |
| room_type | Type of room (e.g., entire home, private room) |
| beds | Number of beds available |
| baths | Number of bathrooms available |
| min_nights | Minimum number of nights required to book |
| num_reviews | Total number of reviews received |
| star_rating | Overall star rating of the property |
| cover_photo_url | URL of the main listing photo |
| photos_count | Number of photos available for the listing |
| superhost | Whether the host is a superhost |
| cohost | Whether the property has a co-host |
| registration | Regulatory registration or license number required for operating the listing |
| amenities | List of amenities offered |
| cancellation_policy | Type of cancellation policy offered |
| tier | Quality classification (Basic, Plus, or Luxury tier) of the listing |
| rating_overall | Overall rating score |
| rating_accuracy | Rating score for listing accuracy |
| rating_communication | Rating score for host communication |
| rating_cleanliness | Rating score for cleanliness |
| rating_location | Rating score for location |
| rating_checkin | Rating score for check-in experience |
| rating_value | Rating score for value |
| currency | Currency used for pricing |
| ttm_vacant_days | Number of vacant days in trailing twelve months |
| ttm_booked_days | Number of booked days in trailing twelve months |
| ttm_occupancy | Occupancy rate in trailing twelve months |
| ttm_revenue | Total revenue in trailing twelve months |
| ttm_native_revenue | Total revenue in native currency in trailing twelve months |
| ttm_avg_rate | Average daily rate in trailing twelve months |
| ttm_avg_native_rate | Average daily rate in native currency in trailing twelve months |
Calendar Rates
Availability and pricing information crucial for understanding occupancy patterns, pricing strategies, seasonal variations, and special event impacts.
Common Use Cases
- Seasonal pricing pattern analysis
- Occupancy rate calculations and forecasting
- Special event pricing impact studies
- Dynamic pricing strategy development
Sample Visualizations
- Occupancy rate calendars by market
- Price fluctuation charts throughout the year
- Special event pricing premium analysis
- Booking window visualization by season
Schema
13 fields| Field Name | Description |
|---|---|
| listing_id | Unique identifier for the listing |
| date | First day of the month for aggregated monthly data |
| vacant_days | Number of days the property was vacant |
| booked_days | Number of days the property was booked |
| occupancy | Occupancy rate |
| revenue | Total revenue generated during the month |
| rate_avg | Average daily rate |
| booked_rate_avg | Average rate when booked |
| booking_lead_time_avg | Average booking lead time in days |
| min_nights_avg | Average minimum nights requirement |
| native_booked_rate_avg | Average rate when booked in native currency |
| native_rate_avg | Average daily rate in native currency |
| native_revenue | Revenue generated in native currency |
Reviews Data
Guest reviews and ratings with sentiment analysis providing invaluable insights into guest satisfaction, property performance, and host-guest interactions.
Common Use Cases
- Guest satisfaction analysis by property type or location
- Sentiment trend analysis over time
- Common complaint and praise identification
- Correlation between amenities and positive reviews
Sample Visualizations
- Sentiment score heat maps by neighborhood
- Word clouds of most common positive/negative terms
- Rating trends over time by property category
- Review volume seasonality charts
Schema
4 fields| Field Name | Description |
|---|---|
| listing_id | Unique identifier for the listing |
| date | First day of the month when reviews were aggregated |
| num_reviews | Number of reviews for the listing |
| reviewers | List of reviewer IDs |
Host Data✨ Coming Soon
Detailed host information revealing behaviors, performance metrics, and profile characteristics to understand host professionalism, experience levels, and management practices.
Common Use Cases
- Professional vs. amateur host analysis
- Superhost performance metrics and characteristics
- Multi-property host portfolio analysis
- Host listing growth patterns over time
Sample Visualizations
- Distribution of hosts by property count
- Superhost percentage by neighborhood
- Host performance comparison by experience level
- Host experience timeline analysis
Schema
11 fields| Field Name | Description |
|---|---|
| host_id | Unique identifier for the host |
| host_name | Name of the host |
| is_host | Whether the user is a host |
| is_superhost | Whether the host is a superhost |
| ratings | Host rating score |
| reviews_count | Number of reviews received by the host |
| listing_count | Number of properties managed by host |
| member_since | Date the host joined the platform |
| languages | Languages spoken by the host |
| profile_picture | URL to host profile image |
| about | Host self-description and biography |
Data Quality Commitment
We are committed to providing the highest quality data for your research and business needs. Our rigorous data collection and processing methodology ensures:
Comprehensive Coverage
Our data collection process captures over 95% of all active listings in each market, ensuring you have the complete picture.
Regular Updates
All datasets are updated monthly, with timestamps indicating the exact collection date for transparency.
Data Cleaning
Our automated and manual cleaning processes remove duplicates, correct errors, and standardize formats for consistency.