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.
Key Questions This Data Answers
- What is the distribution of property types in a market?
- Which amenities are most common in top-performing listings?
- How does nightly price vary by neighborhood and property type?
- What is the density of listings in different geographical areas?
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
64 fields| Field Name | Description |
|---|---|
| listing_id | Unique identifier for the listing |
| listing_name | Title of the listing |
| listing_type | Type of property (e.g., apartment, house, villa) |
| room_type | Type of room (e.g., entire home, private room) |
| cover_photo_url | URL of the main listing photo |
| photos_count | Number of photos available for the listing |
| host_id | Unique identifier for the host |
| host_name | Name of the host |
| cohost_ids | Ids of co-hosts associated with the listing |
| cohost_names | Names of co-hosts associated with the listing |
| superhost | Whether the host is a superhost |
| country | Country where the listing is located |
| state | State or province where the listing is located |
| city | City where the listing is located |
| latitude | Geographical latitude coordinate |
| longitude | Geographical longitude coordinate |
| guests | Maximum number of guests allowed |
| bedrooms | Number of bedrooms available |
| beds | Number of beds available |
| baths | Number of bathrooms available |
| registration | Indicates if the listing has a registration number |
| amenities | List of amenities offered |
| instant_book | Whether the listing can be booked instantly |
| min_nights | Minimum number of nights required to book |
| cancellation_policy | Type of cancellation policy offered |
| currency | Currency used for pricing |
| cleaning_fee | The cleaning fee for the listing |
| extra_guest_fee | Fee for each extra guest |
| num_reviews | Total number of reviews received |
| rating_overall | Overall rating score |
| rating_accuracy | Rating score for listing accuracy |
| rating_checkin | Rating score for check-in experience |
| rating_cleanliness | Rating score for cleanliness |
| rating_communication | Rating score for host communication |
| rating_location | Rating score for location |
| rating_value | Rating score for value |
| ttm_revenue | Total revenue in trailing twelve months |
| ttm_revenue_native | Total revenue in native currency in trailing twelve months |
| ttm_avg_rate | Average daily rate in trailing twelve months |
| ttm_avg_rate_native | Average daily rate in native currency in trailing twelve months |
| ttm_occupancy | Occupancy rate in trailing twelve months |
| ttm_adjusted_occupancy | Adjusted occupancy rate in trailing twelve months, excluding owner-blocked days |
| ttm_revpar | Revenue per available room (RevPAR) in trailing twelve months |
| ttm_revpar_native | Revenue per available room (RevPAR) in native currency in trailing twelve months |
| ttm_adjusted_revpar | Adjusted RevPAR in trailing twelve months |
| ttm_adjusted_revpar_native | Adjusted RevPAR in native currency in trailing twelve months |
| ttm_reserved_days | Number of booked/reserved days in trailing twelve months |
| ttm_blocked_days | Number of host-blocked days in trailing twelve months |
| ttm_available_days | Number of available days in trailing twelve months |
| ttm_total_days | Total number of days in trailing twelve months (reserved + available) |
| l90d_revenue | Revenue in the last 90 days |
| l90d_revenue_native | Revenue in native currency in the last 90 days |
| l90d_avg_rate | Average daily rate in the last 90 days |
| l90d_avg_rate_native | Average daily rate in native currency in the last 90 days |
| l90d_occupancy | Occupancy rate in the last 90 days |
| l90d_adjusted_occupancy | Adjusted occupancy rate in the last 90 days |
| l90d_revpar | RevPAR in the last 90 days |
| l90d_revpar_native | RevPAR in native currency in the last 90 days |
| l90d_adjusted_revpar | Adjusted RevPAR in the last 90 days |
| l90d_adjusted_revpar_native | Adjusted RevPAR in native currency in the last 90 days |
| l90d_reserved_days | Number of reserved days in the last 90 days |
| l90d_blocked_days | Number of blocked days in the last 90 days |
| l90d_available_days | Number of available days in the last 90 days |
| l90d_total_days | Total number of days considered in the last 90 days period |
Calendar Rates
Availability and pricing information crucial for understanding occupancy patterns, pricing strategies, seasonal variations, and special event impacts.
Key Questions This Data Answers
- How does seasonality affect market occupancy?
- How far in advance are properties typically booked?
- What is the impact of holidays and local events on nightly rates?
- How do hosts adjust their pricing strategies over the next year?
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
14 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 |
| reserved_days | Number of days the property was reserved |
| 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 |
| length_of_stay_avg | Average length of stay 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.
Key Questions This Data Answers
- What are the common themes in positive and negative guest reviews?
- How do guest satisfaction scores correlate with property location?
- Are there trends in guest feedback over time?
- Which hosts or properties consistently receive the best ratings?
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.
Key Questions This Data Answers
- What is the ratio of professional property managers to individual hosts?
- How many properties does a typical host manage?
- What are the characteristics of Superhosts in a given market?
- How does host experience level correlate with property performance?
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.