In 2007, a cash-strapped Brian Chesky came up with a shrewd way to pay his $1,200 San Francisco apartment rent. He would offer “Air bed and breakfast”, which consisted of three airbeds,...

Airbnb has seen some controversy in Los Angeles due to complaints that the influx of short-term rentals was turning apartment buildings into hotels and was using up the city’s limited housing. This caused new restrictions to be put in place on Airbnb and similar services in summer 2019. As a result, short-term rentals can only come from someone's primary residence. In LA, a primary residence is a place someone lives in at least six months out of the year. Additionally, bookings for visitors are limited to *** days a year, however there are exceptions to this. As of the third quarter of 2019, there were ****** active Airbnb rentals in LA. Whether this number will reduce after the restrictions are enforced remains to be seen.

The region with the most nights and experiences booked with Airbnb worldwide in 2025 was Europe, the Middle East, and Africa (or EMEA). That year, the EMEA region reported *** million bookings. Asia Pacific had the lowest number of bookings at ** million. The Asia Pacific region also had the ****** average number of nights per Airbnb booking in 2025.

Exploring Airbnb Market Trends¶ Introduction

New York City, a global hub for tourism, boasts a vibrant and diverse Airbnb market. To meet the high demand for temporary lodging, numerous Airbnb listings offer a range of accommodations, from short-term stays to longer-term rentals. In this project, I delve into the New York Airbnb market.

This notebook analyzes the NYC Airbnb market using data from 2019. The goal is to explore the factors that influence listing prices.

The dataset used contains information on Airbnb listings, including prices, room types, location, and review data. The primary dataset is the NYC Airbnb Open Data.

This analysis will primarily use Python and the Pandas library. Libraries such as Matplotlib and Seaborn will be employed for data visualization.

Introduction:

This dataset, titled 'Airbnb Market Analysis and Real Estate Sales Data (2019),' comprises a comprehensive collection of information pertaining to the Airbnb rental market and property sales in two distinct areas within California: Big Bear and Joshua Tree, along with their associated zip codes (92314, 92315, 92284, and 92252). The dataset provides monthly aggregated data, allowing for an in-depth analysis of rental and real estate market trends in these regions. It includes the following files:

Datasets:

Market Analysis:

This file contains listing-level information from 2019, aggregated on a monthly basis. It encompasses various metrics, such as unique property codes (unified_id), generated revenue, availability (openness), occupancy ratios, nightly rates, lead times, and average length of stay for reservations made each month. Additionally, it provides insights into property amenities.

Amenities:

This file indicates whether a listing has specific amenities, denoting their presence with a value of 1 or their absence with a value of 0. Notably, it identifies the availability of a pool or hot tub in each listing.

Geolocation:

This file contains latitude and longitude coordinates for each listing, enabling precise spatial analysis and visualization.

Sales Properties:

This dataset provides information concerning properties available for sale within the study areas. In the Joshua Tree region (zip codes 92284 and 92252), there are two separate files—one presenting the overall information about sales properties and the other focusing on properties with pools.

This dataset is a valuable resource for researchers and analysts interested in gaining insights into the real estate and Airbnb rental markets in California, particularly within the specified regions."

Potential Applications:

This dataset provides a strong foundation for Power BI reporting, enabling the creation of insightful reports and dashboards. Analysts can utilize joins on unique IDs to extract key factors and KPIs, facilitating data-driven decision-making. Whether it's optimizing Airbnb listings, making informed real estate investments, or shaping policies, this dataset serves as a valuable resource for Power BI users seeking to gain deeper insights and drive data-driven strategies in the California real estate market

Airbnb, a home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes. In 2025, the North America region had the largest share of Airbnb's gross booking value, with **** billion U.S. dollars.

Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this project, we will take a closer look at the New York Airbnb market by combining data from multiple file types like .csv, .tsv, and .xlsx.

Recall that CSV, TSV, and Excel files are three common formats for storing data. Three files containing data on 2019 Airbnb listings are available to you:

data/airbnb_price.csv This is a CSV file containing data on Airbnb listing prices and locations.

listing_id: unique identifier of listing price: nightly listing price in USD nbhood_full: name of borough and neighborhood where listing is located data/airbnb_room_type.xlsx This is an Excel file containing data on Airbnb listing descriptions and room types.

listing_id: unique identifier of listing description: listing description room_type: Airbnb has three types of rooms: shared rooms, private rooms, and entire homes/apartments data/airbnb_last_review.tsv This is a TSV file containing data on Airbnb host names and review dates.

listing_id: unique identifier of listing host_name: name of listing host last_review: date when the listing was last reviewed

Dataset Description: New York City Airbnb Listings (2019)

This dataset provides comprehensive information on Airbnb listings across New York City for the year 2019. The data is sourced from various files, each capturing different aspects of the listings, including pricing, location, room types, host information, and review activity. The dataset is designed to offer insights into the short-term rental market in one of the most-visited cities in the world, making it ideal for analyzing trends, patterns, and factors that influence Airbnb's market dynamics in NYC.

Purpose of the Dataset:

This dataset is intended to facilitate a detailed analysis of the New York City Airbnb market, allowing users to explore various aspects such as pricing trends, room type distribution, neighborhood prevalence, and review activity. By combining these data sources, one can gain a holistic view of the factors influencing Airbnb listings in NYC, making it a valuable resource for researchers, real estate professionals, and data enthusiasts.

Dataset

This dataset was created by Yousef Mohamed

Contents

Airbnb, a home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes. In 2025, ************* earned the largest regional share of Airbnb's revenue at ** percent. Meanwhile, the Europe and the Middle East and Africa (EMEA) region ranked second at ** percent.

The New York City Airbnb 2019 Open Data is a dataset containing varius details about a listed unit, when the goal is to predict the rental price of a unit.

This dataset contains the details for units listed in NYC during 2019, was adapted from the following open kaggle dataset: https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data. This, in turn was downloaded from the Airbnb data repository http://insideairbnb.com/get-the-data.

This dataset is licensed under the CC0 1.0 Universal License (https://creativecommons.org/publicdomain/zero/1.0/).

The typical ML task in this dataset is to build a model that predicts the average rental price of a unit.

************* was the region that brought in the highest amount of Airbnb’s worldwide revenue in 2025, at *********** U.S. dollars. As the company is based in the United States, this is not surprising. However, the Europe, Middle East, and Africa (EMEA) region was not too far behind with *********** U.S. dollars in revenue.************** also reported the highest average number of nights booked by region with Airbnb in 2025.

Overview

This dataset provides a comprehensive snapshot of Airbnb listings in New York City (NYC), scraped from publicly available sources. It includes over 102,000 active listings as of the data collection period (primarily around 2019-2022, based on review dates). The data covers key attributes such as listing details, host information, pricing, location, availability, and house rules, making it ideal for exploratory data analysis (EDA), machine learning models on pricing prediction, geospatial analysis, or studying urban tourism trends.

Key Insights from the Data

Total Listings: 102,599

Geographic Focus: Primarily NYC boroughs (Brooklyn, Manhattan, Queens, Bronx, Staten Island).

Room Types: Dominated by "Entire home/apt" (51.7%), "Private room" (45.2%), and "Shared room" (3.1%).

Pricing: Median nightly price is $106 (after cleaning); service fees add ~20% on average.

Reviews: Average 4.4/5 rating; ~84% of listings have at least one review.

Availability: Listings are available ~70% of the year on average.

Data Quality Notes:

Price and service fee columns include currency symbols (e.g., "$106") and commas for thousands—clean these for numerical analysis. Missing values are common in house_rules (51% null), license (nearly all null), and review-related fields (15% null for last review). Construction year has some future dates (e.g., 2022+), likely data entry errors.

This dataset is great for:

Predicting listing prices based on location and amenities.

Visualizing neighborhood hotspots using lat/long coordinates.

Analyzing host behavior (e.g., instant bookable vs. cancellation policies).

Time-series analysis of reviews and availability.

Source: Aggregated from Airbnb's public API and web scraping (anonymized for privacy).

No personal identifiable information is included beyond host verification status.

License: CC0: Public Domain (feel free to use, modify, and share).

Tags: airbnb, nyc, real-estate, housing, tourism, geospatial, machine-learning, pricing-analysis

Data Cleaning Recommendations

Price/Service Fee: Remove "$" and commas, then convert to float. Median price: ~$106; handle outliers (e.g., >$10,000).

Dates: Parse last review to datetime for recency analysis.

Geospatial: Use lat/long for mapping (e.g., with Folium or Plotly).

Text Fields: house_rules is rich for NLP (e.g., sentiment analysis on rules like "no smoking").

Duplicates: No exact duplicates found, but check by id.

Inspiration

EDA Notebook Ideas:

Map listings by neighborhood density.

Price vs. room type scatter plot.

Host superstars: Filter by calculated host listings count > 10 and review rate number > 4.5.

ML Projects:

Regression: Predict price using features like room type, neighbourhood, availability 365.

Clustering: Group listings by location and amenities.

Visuals: Heatmap of reviews per neighborhood; bar chart of cancellation policies.

If you fork or use this data, share your notebooks in the discussions tab!

Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this project, we will take a closer look at the New York Airbnb market by combining data from multiple file types like .tsv.

This files containing data on 2019 Airbnb listings are available to you:

data/airbnb_last_review.tsv This is a TSV file containing data on Airbnb host names and review dates.

listing_id: unique identifier of listing host_name: name of listing host last_review: date when the listing was last reviewed

Content

Detailed listings data, review data and calendar data of Airbnb listings in Munich. The dataset was generated on November 25th, 2019 by http://insideairbnb.com/

Acknowledgements

The original data was created by Murray Cox - link to get all the data. http://data.insideairbnb.com/germany/bv/munich/2019-11-25/data/calendar.csv.gz http://data.insideairbnb.com/germany/bv/munich/2019-11-25/data/listings.csv.gz http://data.insideairbnb.com/germany/bv/munich/2019-11-25/data/reviews.csv.gz

Context

Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in NYC, NY for 2019.

Content

This data file includes all needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions.

Acknowledgements

This public dataset is part of Airbnb, and the original source can be found on this website.

Inspiration

• What can we learn about different hosts and areas?
• What can we learn from predictions? (ex: locations, prices, reviews, etc)
• Which hosts are the busiest and why?
• Is there any noticeable difference of traffic among different areas and what could be the reason for it?

London Airbnb Dataset (2009 - Nov 2019)

This dataset comprises Airbnb listings in London, UK from 2009 to 5th November 2019. The original dataset comes from InsideAirbnb but I have done some cleaning of the data to make it easier for analysis and visualization.

Cleaning Process

I have:

• Removed what I felt were irrelevant columns or columns with majority NA values
• Changed "t" and "f" values to boolean form
• Replaced "none" and "N/A" with NA values
• Changed Dollar and Percentage values from character format to numeric
• Changed Dates to R readable format
• Changed numerical variables to numerical form
• Added a numerical factor form for neighbourhood_cleansed and room_type
• Removed punctuations for amenities

Discover the booming housing rental platform market! This in-depth analysis reveals market size, growth trends (2019-2033), key players (Airbnb, Booking.com, etc.), regional insights, and future forecasts. Learn about the impact of short-term rentals, long-term leases, and emerging technologies. Invest wisely in this rapidly expanding sector.

Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this project, we will take a closer look at the New York Airbnb market by combining data from multiple file types like .csv.

This files containing data on 2019 Airbnb listings are available to you:

data/airbnb_price.csv This is a CSV file containing data on Airbnb listing prices and locations.

listing_id: unique identifier of listing price: nightly listing price in USD nbhood_full: name of borough and neighborhood where listing is located

This dataset contains Airbnb listings of 23 small towns (less than 20k inhabitants mainly) from different regions in Brazil. It was collected in June 2019 through web-scrapping directly from the Airbnb site. The data includes the following information related to each listing: city, name of the accommodation, neighborhood or area, type of accommodation, guests capacity, number of bedrooms, host name, URL, price, number of reviews, date of first review and geographical coordinates.