Dark Mode
NYC Short-Term Rental Metrics
Data Science and Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This collection offers detailed information on Airbnb listings operating within New York City. The data provides crucial insights into the local short-term rental market, helping analysts understand host characteristics, geographic distribution, pricing strategies, and guest preferences. It features a rich blend of categorical and numerical values across sixteen attributes, suitable for robust market analysis and trend identification.
Columns
- id: Unique identifier assigned to each individual listing.
- name: The title or designation given to the listing.
- host_id: Unique identification number for the host associated with the listing.
- host_name: The name of the host.
- neighbourhood_group: Broad geographical grouping of the listing (e.g., Manhattan, Brooklyn). Manhattan and Brooklyn account for the majority of observations.
- neighbourhood: The specific location or neighbourhood of the listing.
- latitude: The geographic latitude coordinate of the property.
- longitude: The geographic longitude coordinate of the property.
- room_type: Specifies the type of accommodation offered (e.g., Entire home/apt, Private room).
- price: The nightly rental price for the listing, denominated in US dollars (ranging from 0 to 10,000).
- minimum_nights: The shortest stay duration required for a booking.
- number_of_reviews: The accumulated total count of reviews the listing has received.
- last_review: The date of the most recent review submitted for the listing.
- reviews_per_month: The average number of reviews received monthly.
- calculated_host_listings_count: The total number of properties the specific host has listed on Airbnb.
- availability_365: The total number of days the listing is available for booking during a 365-day period.
Distribution
The dataset includes 48,895 distinct observations, featuring sixteen features. The data file itself is approximately 7.08 MB. It is structured with both numerical metrics and categorical labels. While usually delivered in CSV format, it is important to note that the data is static and has an expected update frequency of "Never."
Usage
This data is excellent for market research, allowing users to map pricing against geographical location and room type. It can be used to build predictive models for listing success based on reviews and host activity. Analysts can track trends in minimum stay requirements and annual availability. The geographical data is useful for urban planning studies and spatial analysis of short-term rental impacts.
Coverage
The dataset focuses solely on Airbnb listings within the New York City metropolitan area. The geographic scope heavily features listings in Manhattan (44%) and Brooklyn (41%). Temporally, the data spans a period from March 2011 to July 2019, based on review dates. Note that approximately 21% of records lack information for the
last_review and reviews_per_month fields.License
CC0: Public Domain
Who Can Use It
- Data Scientists: For training machine learning models to forecast rental prices or predict listing occupancy rates.
- Real Estate Investors: To assess market saturation, identify high-yield neighbourhood groups, and benchmark rental performance.
- Urban Planners and Government Analysts: For studying the spatial distribution and economic impact of short-term rentals in dense urban environments.
- Market Researchers: To analyse consumer booking behaviour and room type preferences in a major global city.
Dataset Name Suggestions
- New York City Airbnb Market Trends
- NYC Short-Term Rental Metrics
- Airbnb Listing Details for NYC
- New York Accommodation Price and Review Data
Attributes
Original Data Source: NYC Short-Term Rental Metrics
QUALITYLoading...