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Property Rental Characteristics
Data Science and Analytics
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About
captures rents and specific characteristics of various properties available for rental. It allows users to examine details about each rented property, including location, size attributes like bathrooms and bedrooms, and the price charged per night. It serves as a valuable resource for understanding factors that influence rental pricing in the market.
Columns
The dataset contains 9 key fields:
- id: A unique numerical identification number assigned to each specific property listing.
- latitude: The numerical latitude coordinate corresponding to the property's location.
- longitude: The numerical longitude coordinate corresponding to the property's location.
- property_type: Describes the type of dwelling, such as "Apartment," "House," or other categories. Apartments represent the largest single group.
- room_type: Identifies the nature of the rental offering, with "Entire home/apt" being the most common category, followed by "Private room."
- bathrooms: A numerical count of the bathrooms available within the property.
- bedrooms: A numerical count of the bedrooms available within the property.
- minimum_nights: The smallest number of nights required for a booking.
- price: The cost in dollars charged for one night’s stay, represented as a character field (e.g., "$150.00").
Distribution
The data is structured in a tabular format and is available as a CSV file named
property_rentals.csv. The file size is approximately 162.55 kB. It consists of 2,222 records across 9 distinct columns. The data is static, as the expected update frequency is 'Never'. Data validity is high, with no observed mismatched or missing values across all records.Usage
This resource is useful for business analysis and research projects focused on real estate and the housing market. Ideal applications include creating predictive models for nightly rental rates based on property features and location, conducting geographical analyses of rental availability, and examining trends related to minimum stay requirements. It is suitable for users across beginner, intermediate, and advanced skill levels.
Coverage
Geographical coverage is defined by the latitude and longitude fields. The location data primarily focuses on a specific urban region, with latitudes ranging from approximately 37.7 to 37.8 and longitudes clustered between -123 and -122. No specific temporal range or demographic segmentation notes are included.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For developing machine learning models to predict rental costs based on inputs like size (bedrooms/bathrooms) and location.
- Property Developers: To assess market demand and prevailing prices for different property types (e.g., Apartments vs Houses) before making investment decisions.
- Tourism and Hospitality Researchers: To study preferred rental types ("Entire home/apt" versus "Private room") and common booking patterns (minimum nights).
Dataset Name Suggestions
- Property Rental Characteristics
- Nightly Accommodation Prices
- Rental Market Property Details
- Real Estate Listing Snapshot
Attributes
Original Data Source: Property Rental Characteristics
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