Overview

This dataset provides detailed information about various Airbnb listings from different regions. It includes essential features such as property details, host information, location, pricing, amenities, and guest reviews. This dataset can be utilized for a variety of data analysis and machine learning tasks, including price prediction, sentiment analysis, and geographic trends in short-term rentals.

Dataset Contents

•  id: Unique identifier for each listing
•  name: Name of the Airbnb listing
•  rating: Average rating of the listing
•  reviews: Number of reviews received
•  host_name: Name of the host
•  host_id: Unique identifier for the host
•  address: Location of the listing (city, region, country)
•  features: Summary of features (number of guests, bedrooms, beds, bathrooms)
•  amenities: List of amenities provided
•  price: Price per night in the local currency
•  country: Country where the listing is located
•  bathrooms: Number of bathrooms
•  beds: Number of beds
•  guests: Number of guests the listing can accommodate
•  toilets: Number of toilets
•  bedrooms: Number of bedrooms
•  studios: Number of studio units
•  checkin: Check-in time
•  checkout: Check-out time

Usage Examples

•  Price Prediction: Build machine learning models to predict the price of an Airbnb listing based on its features and location.
•  Sentiment Analysis: Analyze guest reviews to determine the sentiment and identify factors contributing to positive or negative experiences.
•  Geographic Trends: Study the distribution and popularity of Airbnb listings across different regions and countries.

How to Use

1. Exploratory Data Analysis (EDA): Perform EDA to understand the distribution of data, detect missing values, and identify patterns.
2. Feature Engineering: Create new features from the existing columns to enhance the predictive power of machine learning models.
3. Model Building: Use regression models for price prediction or classification models to predict the likelihood of receiving high ratings.

Data Source

The data was collected from publicly available Airbnb listings and includes various property types and locations, providing a comprehensive view of the short-term rental market.

Acknowledgments

This dataset is intended for educational and research purposes. Please ensure to comply with Airbnb’s terms of service and data privacy regulations when using this data.

Airbnb® is an American company operating an online marketplace for lodging, primarily for vacation rentals. The purpose of this study is to perform an exploratory data analysis of the two datasets containing Airbnb® listings and across 10 major cities. We aim to use various data visualizations to gain valuable insight on the effects of pricing, covid, and more!

📊 The Dataset

The data used for this project is sourced from a publicly available Airbnb Listings dataset. The dataset contains over 560,000 records across 10 major cities, including Paris. For this project, the data is filtered to focus solely on Paris listings.

Key Fields:

host_since: Date when the host started listing on Airbnb

neighbourhood: The neighborhood where the listing is located

price: The price per night for the listing

accommodates: Number of people the listing can accommodate

The Dataset Column Descriptions

host_since - Date when the host joined the Airbnb platform. neighbourhood - Name of the neighborhood in Paris where the listing is located. city - City name. This dataset is filtered for Paris listings only. accommodates - The maximum number of guests the listing can accommodate. price - Price per night for the listing in USD. room_type - Type of room offered in the listing (e.g., Entire home/apt, Private room, Shared room). availability_365 - Number of days the listing is available for booking throughout the year. number_of_reviews - Total number of reviews the listing has received. review_scores_rating - Average rating score given by guests for the listing (out of 100). minimum_nights - Minimum number of nights required for booking the listing. host_listings_count - Number of listings managed by the host. latitude - Latitude coordinate of the listing. longitude - Longitude coordinate of the listing.

Dataset

This dataset was created by Ishita Roy

Released under Apache 2.0

Contents

Comprehensive Airbnb dataset repository offering detailed vacation rental analytics worldwide including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

Comprehensive Airbnb Open Data dataset with detailed listings, reviews, and calendar entries from New York City. Ideal for AI/ML models in market analysis, customer behavior study, and property analytics.

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Data Description:

This dataset provides a comprehensive collection of AirBnB listings across various cities and regions in California, offering valuable insights into the state's Airbnb hospitality landscape. Spanning diverse accommodations from cozy studios to luxurious villas, the dataset encapsulates crucial information including listing details, host attributes, pricing dynamics, reviews and geographical coordinates. With a wide array of attributes such as property type, neighborhood, availability, and review scores, analysts and researchers can delve deep into understanding the patterns, trends, and preferences within California's dynamic short-term rental market. Whether investigating the impact of tourism on local economies, exploring the factors influencing rental prices, or identifying emerging hospitality trends, this dataset serves as a rich resource for data-driven exploration and decision-making in the realm of hospitality, tourism, and urban studies.

Data Dictionary

https://docs.google.com/document/d/1STQS6C0Z7p4enVQEInsTUREs9a2DI86d-L1tAzv04qE/edit?usp=sharing

7 Data Usage ideas for this dataset:

1. Geospatial Analysis: Utilize the geographical coordinates to visualize the distribution of AirBnB listings across California. You can create heatmaps, cluster analysis, or spatial interpolation to identify hotspots and trends in different regions.

2. Price Prediction: Build machine learning models to predict the price of AirBnB listings based on various features such as property type, location, amenities, and host attributes. Regression techniques like linear regression, decision trees, or ensemble methods can be employed for this purpose.

3. Sentiment Analysis: Analyze reviews to extract sentiments and understand the satisfaction level of guests. Natural Language Processing (NLP) techniques such as sentiment analysis, topic modeling, and keyword extraction can be applied to gain insights into guest experiences.

4. Time Series Analysis: Explore temporal patterns and seasonality in booking trends and prices. This can help in understanding peak seasons, demand fluctuations, and pricing strategies over time.

5. Segmentation & Cluster Analysis: Group similar listings based on their attributes such as location, amenities, and pricing using clustering algorithms like k-means or hierarchical clustering. This can help in identifying market segments and targeting specific customer groups.

6. Anomaly Detection: Identify unusual patterns or outliers in the data that may indicate fraudulent activities, unusual pricing behavior, or exceptional guest experiences.

7. Recommendation Systems: Develop recommendation systems to suggest personalized listings to users based on their preferences, past bookings, and browsing history. Collaborative filtering, content-based filtering, or hybrid approaches can be employed for this purpose.

Unlock the full potential of the short-term rental market with our comprehensive Airbnb Listing Data. This dataset provides a granular, 360-degree view of listing performance, property characteristics, and market dynamics across key global geographies. Designed for Real Estate Investors, Property Managers, Hedge Funds, and Travel Analysts, our data serves as the backbone for data-driven decision-making in the hospitality sector.

Whether you are looking to optimize pricing strategies, identify high-yield investment neighborhoods, or analyze amenity trends, this dataset delivers the raw intelligence required to stay ahead of the competition. We capture high-fidelity signals on listings, availability, pricing, and reviews, allowing you to model supply and demand with precision.

Key Questions This Data Answers Our data is structured to answer the most pressing commercial questions in the short-term rental industry. By leveraging our granular fields, analysts can immediately address:

Market Composition: What is the exact distribution of property types (Entire Home vs. Private Room vs. Shared) in a specific market? Understand supply saturation instantly.

Amenity ROI: Which amenities are most common in top-performing listings? Correlate features (e.g., Pools, Hot Tubs, Wi-Fi speeds) with Occupancy Rates and ADR (Average Daily Rate) to determine the ROI of renovations.

Pricing Intelligence: How does nightly price vary by neighborhood, seasonality, and property type? Visualize price elasticity and identify arbitrage opportunities between sub-markets.

Geospatial Density: What is the density of listings in different geographical areas? Pinpoint "hot zones" for tourism and identify underserved areas ripe for new inventory.

Performance Benchmarking: How do my listings compare to the top 10% of competitors in the same zip code?

Comprehensive Use Cases 1. Market Analysis & Competitive Positioning Gain a competitive edge by understanding the landscape of any target city.

Competitor Mapping: Track the growth of listing supply in real-time. Identify which property managers control the market share.

Saturation Analysis: Avoid over-supplied markets. Use density metrics to find neighborhoods with high demand but low inventory.

Trend Forecasting: Analyze historical data to predict future supply shifts and market saturation points before they occur.

1. Pricing Strategy & Revenue Management Move beyond static pricing. Our data enables dynamic pricing models based on real-world market conditions.

Attribute-Based Pricing: Quantify exactly how much a "Sea View" or "King Bed" adds to the nightly rate.

Seasonality Adjustments: Optimize calendars by analyzing historical price surges during holidays, events, and peak seasons.

RevPAR Optimization: Balance Occupancy and ADR to maximize Revenue Per Available Room (RevPAR).

1. Real Estate Investment & Valuation For investors and funds, this data acts as a fundamental layer for asset valuation.

Cap Rate Calculation: Combine our revenue data with property values to estimate potential yields and Cap Rates for prospective acquisitions.

Investment Scouting: Filter entire regions by "High Occupancy / Low Price" to find undervalued assets.

Due Diligence: Validate seller claims regarding income potential with independent, third-party data history.

1. Property Type & Amenity Distribution Analysis Understand what guests actually want.

Amenity Gap Analysis: Identify amenities that are in high demand (high search volume) but low supply in specific neighborhoods.

Renovation Planning: Data-driven insights on whether installing A/C or allowing pets will significantly increase booking conversion.

Data Dictionary & Key Attributes Our schema is designed for financial modeling and granular analysis. We provide over 50 distinct fields per listing, including calculated financial metrics for Trailing Twelve Months (TTM) and Last 90 Days (L90D).

Listing Identity & Characteristics:

listing_id: Unique identifier for the listing

listing_name & cover_photo_url: Title and main visual

listing_type & room_type: Property classification (e.g., villa, entire home)

amenities: Comprehensive list of offered features

min_nights & cancellation_policy: Booking rules and restrictions

instant_book & professional_management: Operational indicators

Property Specs & Capacity:

guests, bedrooms, beds, baths: Full capacity details

latitude, longitude, city, state, country: Precise geospatial coordinates

photos_count: Quantity of listing images

Host Intelligence:

host_id & host_name: Primary operator details

cohost_ids & cohost_names: Extended management team details

superhost: Quality badge status

Financial Performance (TTM - Trailing 12 Months):

ttm_revenue & ttm_revenue_native: Total gross revenue generated

ttm_avg_rate (ADR): Average Daily Rate achieved

ttm_occupancy & ttm_adjusted_occupancy: Raw vs. Adjusted (excluding owner blocks) occupancy

ttm_revpar & ttm_adjusted_revpar: Revenue Per ...

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London is the city with the most Airbnbs listings in the world at 156,511.

Global short-term rental intelligence sourced from leading Online Travel Agencies (OTAs). The OTA Real Estate Dataset provides a comprehensive view of the global vacation rental market by combining verified OTA property listings with valuation, tax, and physical asset data. Each record includes unique property identifiers, geolocation details, pricing indicators, and occupancy metrics—enabling robust market analysis and investment-grade insights.

Continuously sourced from major OTA platforms and refined through proprietary data-cleaning models, this dataset ensures accuracy, consistency, and comparability across regions. Available globally with flexible delivery via API or flat files, it serves as a foundational dataset for those analyzing market performance, forecasting development potential, or conducting housing research.

Key Highlights: Granular Real Estate Intelligence: Combines OTA listing data with valuation, tax, and property attributes for a holistic view of market activity.

Global and Standardized: Harmonized schema and coverage across countries, cities, and neighborhoods for cross-market comparability.

High-Fidelity Data: Proprietary normalization removes duplicates and outliers to ensure analytical precision.

Flexible Access: Delivered through API or CSV, updated regularly for timely decision-making.

Ideal For: Real Estate Investors: Identify high-performing short-term rental markets and assess yield potential.

Developers & Urban Planners: Evaluate spatial demand patterns and inform development feasibility studies.

Financial Institutions: Integrate standardized OTA data into underwriting, risk, and valuation models.

Tourism Economists & Market Researchers: Quantify the impact of vacation rentals on local housing and tourism dynamics.

Use It To: Benchmark short-term rental performance by region or property type.

Analyze shifts in rental demand and pricing over time.

Support market-entry, site-selection, and feasibility studies with real OTA-backed data.

Enhance research and policy analysis with consistent, globally comparable property-level insights.

See the average Airbnb revenue & other vacation rental data in Bali in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

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Airbnb In NYC

Room Prices, Reviews, and Availability

By Huggingface Hub [source]

About this dataset

This dataset offers a unique and comprehensive look into the expansive Airbnb industry in New York City. We capture 20,000+ Airbnbs with its associated data such as descriptions, rates, reviews and availability. Professionals researching this industry will find it an invaluable resource in providing insight to the ever popular Airbnb market that can be used for their advantage.

This dataset showcases some of the most important attributes for each listing: host name, neighborhood group, location (latitude/longitude coordinates), room type, price per night, minimum nights required to book a stay at this listing , total number of reviews and ratings received by guests over time (including reviews per month and last review date), calculated host listing count (indicates how many listings are offered by each host) along with 365 days worth of availability score. With all these parameters one can understand dynamics of demand & supply & further utilize them accordingly to maximize returns or occupancy greeting never before seen transparency into NYC’s Airbnb scene

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How to use the dataset

This dataset can be used to gain a comprehensive understanding of the Airbnb market in New York City. The data offers descriptions, rates, reviews and availability for over 20,000 Airbnbs in NYC.

Here are few tips on how to use this dataset: - Use the latitude and longitude coordinates to visualize the variety of Airbnbs located across all five boroughs of New York City using mapping programs like Google Maps or ArcGIS. - Determine the versatile price ranges offered by Airbnb listings by looking at the “price” column available for each listing . - Analyze reviews scored by guests who have used an Airbnb in order to better understand customer experience with different services through columns such as “number_of_reviews” and “last_review.
4 Understand how often properties are made available for booking based on their popularity through columns like “availability_365 and reviews_per_month. . 5 Investigate listing host data by looking into their description (host name) as well as number of listings they have booked (calculated host listing count)

Research Ideas

• Determining the listings with the highest satisfaction ratings for potential customers to book.
• Analyzing neighborhood trends in prices, availability, and reviews to identify hot areas of competition within the Airbnb market.
• Predicting future prices throughput examining properties such as review scores and availability rate to provide forecast information to AirBnB owners

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:-----------------------------------|:------------------------------------------------------------------------------------| | name | The name of the Airbnb listing. (String) | | host_name | The name of the host of the Airbnb listing. (String) | | neighbourhood_group | The neighbourhood group the Airbnb listing is located in. (String) | | latitude | The latitude coordinate of the Airbnb listing. (Float) | | longitude | The longitude coordinate of the Airbnb listing. (Float) | | room_type | The type of room offered by the Airbnb listing. (String) | | price | The price per night of the Airbnb listing. (Integer) | | minimum_nights | The minimum number of nights required for booking the Airbnb listing. (Integer) | | number_of_reviews | T...

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