Dark Mode
Urban Accommodation Performance Data
Data Science and Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Provides a detailed collection of AirBnB listings data, crucial for understanding the dynamics of the sharing economy and identifying characteristics that define successful properties. This resource includes scores related to the guest experience, host details, and geographical location, aiding in the predictive analysis of highly sought-after accommodations. This dataset allows users to explore factors influencing guest satisfaction and listing performance.
Columns
The dataset contains 33 fields of information on hosts, listings, and reviews:
- listing_id, host_id: Unique identifiers for properties and hosts.
- name: The title or name of the listing.
- city, neighbourhood, district, latitude, longitude: Geographical details of the property location.
- host_since, host_location: Details regarding the host’s tenure on the platform and their origin.
- host_is_superhost, host_response_rate, host_acceptance_rate: Metrics related to host quality and engagement.
- property_type, room_type: Categorisation of the accommodation (e.g., Entire apartment, Private room).
- accommodates, bedrooms, amenities: Physical characteristics of the listing, detailing capacity and available features.
- price: The listing price in the local currency.
- minimum_nights, maximum_nights: Constraints on booking length.
- review_scores_rating: Overall guest rating (out of 100).
- review_scores_accuracy, review_scores_cleanliness, review_scores_checkin, review_scores_communication, review_scores_location, review_scores_value: Granular scores (out of 10) detailing specific aspects of the stay experience.
- instant_bookable: A flag indicating whether the listing can be booked immediately.
Distribution
The data file, provided in CSV format, is approximately 158.5 MB. It is structured across 33 distinct fields and contains roughly 280,000 records. Certain fields, particularly those related to review scores, have a percentage of missing values.
Usage
Ideal for predictive modelling, specifically forecasting the popularity or success metrics of short-term rental listings. Researchers can investigate the socio-economic impacts of the sharing economy on local housing. It is also suitable for hosts analysing key factors like response rates and amenities that influence guest satisfaction and drive positive reviews.
Coverage
Geographical coverage spans multiple international cities, including significant concentrations in major urban areas such as Paris and New York. The temporal scope ranges from the earliest recorded host join date in August 2008 up to February 2021.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Data Scientists: For developing machine learning models to predict listing success (e.g., maximum popularity or revenue).
- Researchers: To study the effects of online accommodation platforms on urban markets and housing affordability.
- AirBnB Hosts/Property Managers: To benchmark their properties against successful listings and optimise their host profile (e.g., response rates and verification status).
Dataset Name Suggestions
- Global Short-Term Rental Listing Metrics
- Urban Accommodation Performance Data
- Predictive Host and Property Analytics
- Worldwide AirBnB Listing Details
Attributes
Original Data Source: Urban Accommodation Performance Data
QUALITYLoading...