Milan Entire Apartment Rentals Data

Columns

• Identification and Host Details: Includes unique identifiers for both the apartment (id) and the host (host_id). Host characteristics such as location (Italian vs. non-Italian), response time (fast/slow), response rate, and Superhost status are available.
• Location: Geographic coordinates (latitude, longitude), postal codes (zipcode), and municipal district classification (neighbourhood_cleansed, referring to the nine municipalities of Milan).
• Apartment Specifications: Variables detailing capacity (accommodates), number of rooms (bathrooms, bedrooms, beds), and the type of bed offered (Real Bed, Pull-out Sofa, Futon, Couch, Airbed). The room_type is consistently "Entire home/apt" throughout.
• Financial Data: Key pricing variables include the daily_price (Price per night), security_deposit, and cleaning_fee, alongside costs for additional guests (extra_people).
• Booking and Availability: Includes minimum night requirements, cancellation policies (flexible or strict), and binary indicators for booking rules (e.g., instant bookable, requiring guest verification). Availability is measured across 30, 60, 90, and 365-day future windows.
• Reviews and Scores: Metrics include the total number_of_reviews and specific review scores for rating, accuracy, cleanliness, check-in process, communication, location, and value, typically ranging from 2 to 100 or 2 to 10.
• Amenities: A series of dummy variables (1 = presence, 0 = absence) detailing services like TV, WiFi, Air_Condition, Kitchen, Breakfast, Elevator, Washer, Iron, Pets_allowed, and 24-hour check-in.

Distribution

Usage

• Market Analysis: Analysing pricing strategies, demand patterns (via availability metrics), and rental trends in Milan's urban landscape.
• Geospatial Research: Mapping property distribution and price variations across the nine municipal districts.
• Predictive Modelling: Developing models to forecast daily prices or predict apartment performance based on amenities, host characteristics, and review scores.
• Hospitality Research: Studying the impact of services (e.g., Superhost status, cancellation policy, available amenities) on guest satisfaction and booking rates.

Coverage

License

Who Can Use It

• Data Scientists and Analysts: For training machine learning models related to pricing prediction or feature importance.
• Tourism and Hospitality Professionals: Gaining insight into supply and demand, competitive pricing, and amenity expectations in the Milan market.
• Urban Planners: Studying the proliferation and geographic density of short-term rentals within the city’s various neighbourhoods.
• Researchers: Conducting academic studies on host behaviour, booking dynamics, and review score correlation.

Dataset Name Suggestions

• Milan Entire Apartment Rentals Data
• Airbnb Milan Market Snapshot
• Milan Short-Term Rental Listings
• Italian City Accommodation Data

Attributes