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Airbnb Property Attributes Data
Data Science and Analytics
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About
This data resource offers a detailed view of short-term rental properties worldwide. It captures vital operational and characteristic data for 12,800 listings. The information is designed to support the understanding of global market dynamics and consumer satisfaction, encompassing everything from physical attributes (like the number of bedrooms and bathrooms) to performance indicators (such as average rating and review count).
Columns
This file contains 23 distinct fields providing granular detail on each listing:
- 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 key property attributes (e.g., number of guests, bedrooms, beds, bathrooms).
- amenities: List of facilities 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.
- safety_rules: Safety rules for the Airbnb property.
- hourse_rules: Specific house rules set by the host.
- img_links: Image link associated with the property.
Distribution
The data is structured as a single flat file, typically distributed in CSV format. It contains 12,800 records of Airbnb properties. The file size is 16.18 MB. The data is highly usable, with minimal missing values across the 23 columns.
Usage
This data is perfectly suited for several analytical applications:
- Price Prediction: Building machine learning models to forecast the rental price of an Airbnb listing based on its features and location.
- Sentiment Analysis: Evaluating guest reviews to determine sentiment and identify factors that contribute to positive or negative experiences.
- Geographic Trends: Studying the distribution and popularity of listings across different regions and countries.
- Exploratory Data Analysis (EDA): Performing EDA to understand data distribution and identify underlying patterns.
- Model Building: Utilizing regression models for value prediction or classification models for rating outcomes.
Coverage
The data provides global coverage of listings. It spans 148 unique countries around the world. Listings from India represent 22% of the records, making it the most frequently represented country, followed by Italy at 9%.
License
CC0: Public Domain
Who Can Use It
This resource is valuable for various types of users:
- Data Scientists and Machine Learning Engineers: Those aiming to build and train prediction models for pricing, demand forecasting, or rating likelihood.
- Market Researchers: Individuals studying geographic trends and the competitive landscape of the short-term rental sector.
- Academic Researchers: Users focused on urban planning, tourism economies, or property valuation studies.
Dataset Name Suggestions
- Global Short-Term Rental Listing Analytics
- 12.8K Worldwide Accommodation Metrics
- Airbnb Property Attributes Data
Attributes
Original Data Source: Airbnb Property Attributes Data
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