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European Accommodation Pricing Analysis
Data Science and Analytics
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About
This dataset contains valuable Airbnb rental data for various European cities, focusing on characteristics and their influence on rental prices. It features details such as the listing's total price, room type, host status, available amenities, and precise location information. The data enables analysis of key factors affecting Airbnb prices, assisting travellers in finding suitable accommodation within their budget. Furthermore, it offers business owners crucial insights for setting competitive prices and optimising listings to increase bookings. Property investors can also leverage this dataset to understand pricing trends across different European cities and make informed real estate investment decisions.
Columns
- realSum: The total price of the Airbnb listing. (Numeric)
- room_type: The type of room offered (e.g., private room, shared room, entire home/apt). (Categorical)
- room_shared: Indicates whether the room is shared or not. (Boolean)
- room_private: Indicates whether the room is private or not. (Boolean)
- person_capacity: The maximum number of people that can be accommodated in a single listing. (Numeric)
- host_is_superhost: Indicates whether a particular host is identified as a superhost on Airbnb. (Boolean)
- multi: Indicates whether multiple rooms are provided in one individual listing or not. (Boolean)
- biz: Indicates whether a particular listing offers business facilities like meeting areas/conference rooms in addition to accommodation options. (Boolean)
- cleanliness_rating: The rating associated with how clean an individual property was after guests stayed at it. (Numeric)
- guest_satisfaction_overall: The overall rating showing how satisfied guests are with their stay after visiting an Airbnb property. (Numeric)
- bedrooms: The total quantity of bedrooms available among all properties against a single hosting ID. (Numeric)
- dist: Distance from the city centre associated with every rental property. (Measurement may vary depending upon scale, e.g., kilometres/miles etc.)
- metro_dist: Distance from the nearest metro station associated with every rental property. (Measurement may vary depending upon scale, e.g., kilometres/miles etc.)
- lng: Longitude measurement corresponding to each rental unit. (Numeric)
- lat: Latitude measurement corresponding to each rental unit. (Numeric)
- attr_index: An attribute index for the rental property. (Numeric)
- attr_index_norm: Normalised attribute index for the rental property. (Numeric)
- rest_index: A restaurant index for the rental property. (Numeric)
- rest_index_norm: Normalised restaurant index for the rental property. (Numeric)
Distribution
The dataset is typically provided in CSV format. It includes files for multiple European cities, such as
vienna_weekdays.csv, vienna_weekends.csv, and amsterdam_weekdays.csv. The amsterdam_weekdays.csv file, for example, contains approximately 1103 records across 20 columns and is around 226.65 kB in size. Specific numbers for rows/records for all files are not explicitly available, but the sample indicates a consistent structure. The dataset does not provide dates, and its expected update frequency is never.Usage
This dataset is ideal for:
- Price forecasting: Analysing historical data on Airbnb listings, including pricing, room types, and amenities, to predict future rental prices.
- Identifying business or tourist rental hotspots: Examining each listing’s location in relation to business and tourism hubs and correlating this with pricing to determine the most profitable areas for Airbnb rentals.
- Customer sentiment analysis: Evaluating customer comments and satisfaction ratings to measure the effectiveness of specific listings, helping hosts to optimise their services and enhance user satisfaction.
- Understanding price determinants: Gaining insights into factors influencing Airbnb rental prices across Europe.
- Property investment analysis: Informing investment decisions by understanding pricing trends in various cities.
Coverage
The dataset covers Airbnb rental properties in multiple European cities, including specific data for Vienna and Amsterdam. It focuses on the characteristics of these listings and their effects on price. The geographical scope is broad across European cities, with location details provided through longitude and latitude. The dataset does not include dates, meaning there is no specific time range for the data points, and it is not expected to be updated. Demographic scope is inferred through host status, guest capacity, and satisfaction ratings.
License
CC0 1.0 Universal (Public Domain Dedication)
Who Can Use It
- Travellers: To find accommodation that meets their needs without overspending.
- Business Owners/Hosts: To set competitive prices, optimise listings for increased bookings, and improve customer satisfaction.
- Property Investors: To understand pricing trends in different European cities and make informed real estate investment decisions.
- Researchers/Data Analysts: To study market dynamics, conduct price forecasting, and identify key determinants of rental success.
Dataset Name Suggestions
- European Airbnb Price Determinants
- Airbnb Rental Data Europe
- Europe City Airbnb Listings
- European Accommodation Pricing Analysis
- Airbnb Characteristics and Price Effects
Attributes
Original Data Source: European Accommodation Pricing Analysis
QUALITY