This repository contains an in-depth analysis of Airbnb property listings in New York. The data was scraped from the Airbnb website and analyzed using Python to uncover insights about pricing trends, neighborhood characteristics, host behaviors, and more.
The dataset includes details such as property features, host information, geographical coordinates, pricing, and guest reviews. The findings are presented in a detailed report within the AIRBNB DATA ANALYSIS.pptx.
Dataset: View Here
Here's a brief overview of the dataset's features:
- id: Unique identifier for each listing.
- name: Name of the property.
- host_id: Unique identifier for the host.
- host_identity_verified: Whether the host's identity has bbeen verified by Airbnb.
- host_name: Host's name.
- neighbourhood_group: The broader neighborhood group (e.g., Manhattan, Brooklyn).
- neighbourhood: Specific neighborhood within the group.
- lat, long: Geographical coordinates of the property.
- country, country_code: Country information.
- instant_bookable: Whether the property can be booked immediately.
- cancellation_policy: The cancellation policy for the listing.
- room_type: Type of room (e.g., Private Room, Entire Home).
- construction_year: Year the property was built.
- price: Price per night in USD.
- service_fee: Service fee charged by Airbnb.
- minimum_nights: Minimum number of nights required for a booking.
- number_of_reviews: Total number of reviews received by the most.
- last_review: Date of the most recent review.
- reviews_per_month: Average number of reviews per month.
- review_rate_number: Overall rating based on guest reviews.
- calculated_host_listings_count: Number of listings the host has on Airbnb.
- availability_365: Number of days the property was available in the past 365 days.
- house_rules: Rules set by the host for guests.
The findings of this analysis are presented in the AIRBNB DATA ANALYSIS.pptx file. Key insights include:
- Price trends across neighborhoods and time.
- Host behavior patterns (e.g., cancellation policies, response rates).
- Relationships between property features and pricing.
- Review patterns and guest preferences.
This project provides a comprehensive analysis of Airbnb listings in New York, offering valuable insights into pricing dynamics, neighborhood characteristics, and host behaviors. The findings can help hosts optimize their listings and provide actionable recommendations for improving profitability and guest satisfaction.