Data Study on Airbnb New York City 2017

Posted on Oct 15, 2017
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Data Study on Airbnb New York City 2017


"Congratulations! You are enrolled at the New York Data Science Academy." I received my Enrollment Letter with a sense of excitement. Immediately, I jumped into action to sort out my maiden trip to the Big Apple.  Instinctively, I logged onto Airbnb, in search of an ideal accommodation. My excitement quickly turned into dismay. My screen was populated with a map full of listings. With a sense of despair I sought answers to these questions: Where is a decent and safe neighborhood to live in? Will the listings be convenient for my commute?


Since its inception in 2008, the rapid rise of Airbnb has dramatically expanded the use of traditional apartments as transient hotel rooms. By February 2017, it was valued at $30 billion, cementing its position as the poster child of the sharing economy.
In the United States, NYC is by far the largest market for Airbnb with over 41,000 listings comprising private rooms, entire homes, and shared rooms. In light of this, a detailed analysis of Airbnb NYC Data is invaluable as Airbnb Hosts jostle for a larger piece of the proverbial pie in this lucrative market. Understanding the Dynamics of Crimes, Subways, and Neighborhoods enables the Supplies (Hosts) to better meet the Demands (Users).

Data Sets

To facilitate my analysis, I incorporated 3 separate datasets, namely:

The resultant findings are showcased in a Shiny App .

Data Consolidation

First, each Airbnb & Crime Listings were grouped under their respective Neighborhoods based on Coordinates. Meanwhile, Crime Density was derived as Crime Counts per Neighborhood Area. They were binned into Crime Levels—Very Low, Low, Moderate, and High—based on Percentiles. Finally, each Airbnb Listing was paired with its nearest Subway Station and Haversine Distance.


The NYC Interactive map was developed to aid the user exploration experience and overall visualization. Some of its features are:

  • Neighborhood Boundaries
  • Airbnb Listings & Subway Station Locations
  • Crime Density Levels denoted by Fill Color
  • Filter by Boroughs or Subway Lines
  • Sliders for Listing Prices and Subway Distances

Data Analysis

Every day, more than two million people use New York City’s mass transit system to get to Manhattan’s core. If New York City is the heart of the economy, its Subways are the main arteries. It is interesting to study the Distribution of Airbnb Listings with regards to Subway Locations.

A Density Plot was produced for each Borough. It showed that 80% of listings fall within a 1000 m distance. An additional 10% of listings fall within the 1000–2000 m range. This shows the importance of Subway Proximity to Airbnb Listings. Between boroughs, Manhattan has the best Subway accessibility with a distance of less than 850 m among all its listings. This is followed by Brooklyn and Bronx (<1500 m) while Queens had a larger distribution spread (<5000 m).  Staten Island is not connected to the Subway grid and hence was excluded.


Data Study on Airbnb New York City 2017

A Bubble Chart was instrumental in depicting the dynamics among the various factors. Manhattan with the largest bubble, had the most listings at 19,053 followed closely by Brooklyn with 17,215. Queens is a distant third with 3,922. Bronx and Staten Island only had 700 and 266 listings respectively. Manhattan, owing to its prime locality proved to be the most popular despite registering the highest crime rates.

The Bronx seems to lag behind presumably from a less than desirable image of crime and poverty. Brooklyn holds its place in the Airbnb market with a good mix of low crime and good subway connectivity. While Queens has one of the lowest crime rates, it suffers marginally from Subway connectivity. Finally, Staten Island stand to lose out largely due to its lack of Subway accessibility.

Data Study on Airbnb New York City 2017


Thus far, the underlying framework of analysis has been developed and discussed. This, I believe, lays the foundation for even more groundbreaking insights to the travel and hospitality juggernaut of Airbnb. For future work, it will be interesting to apply this framework on other major cities like London, Paris, and Hong Kong. Many other factors have been earmarked for data integration including Demographics, Restaurants, and Entertainment Listings.

About Author

Chung Meng Lim

Chung Meng has a Masters in Electronics Engineering. He is forging a path in the exciting field of Data Science.
View all posts by Chung Meng Lim >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI