NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > Data Visualization > Data Analysis on the Airbnb NYC Market

Data Analysis on the Airbnb NYC Market

Daniel Ellenbogen
Posted on Feb 26, 2021
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Shiny app | Github Repo | LinkedIn

What is this project about?

Airbnb has allowed for much innovation in the hospitality industry, in some ways, disrupting traditional models and allowing smaller players to more easily enter the market by simplifying some of the business aspects. The data that Airbnb has made available can shed light on many aspects of the hospitality market that would be useful  for potential investors and entrepreneurs to make informed investment decisions. Some of the data available and used in this project is listings, coordinate location, neighbourhood, borough, price per night, number of reviews, and availability days per year.

The purpose of this project is to gather insights from this data and allow for easy exploration and customization through the use of an interactive visual dashboard using a Shiny app. However, this is more a proof of concept for the tools that can be used, and requires further analysis of the impact of COVID-19.

Data Analysis on the Airbnb NYC Market

The data is sourced from insideairbnb.com, an independent non-commercial project that gathers publicly available Airbnb information.

Some of the questions we'll be looking to answer are:

  1. How is the market composed in terms of size, price, demand, supply and other characteristics?
  2. How has the market evolved over time?
  3. As this is a novel business model, how have characteristics in the offering changed?
  4. What has happened to the supply and demand side of the equation in this market?
  5. Are there any underlying factors impacting those changes?
  6. What has been the impact of COVID-19 in the market?
  7. Who has benefited, and where is the impact more pronounced?
  8. Where are the most interesting investment opportunities with regards to the largest mismatch between supply and demand that could lead to higher occupancy rates and price per night increases?

How has the market evolved over time?

Our first objective will be to understand how has the market changed in the last five years, since we have data available.

The first variable we will analyze is market size, which we have calculated as the product of the listings by their average availability days per year (listings days) by their price. This metric gives us the supply side of the market by telling us how much inventory is being offered for any period.

Data Analysis on the Airbnb NYC Market

We can see an initial reduction followed by small increases over time before a moderate fall in 2020.

In order to have a better understanding of what is going on let's take a look at the underlying variables.

Data Analysis on the Airbnb NYC Market

We can see that the initial drop in market size, from 2015 to 2016, was caused by a large drop on the average available days per listing. This probably has to do with a behavioral change by the owners of the apartments after being able to test the business model.

We can also see that the drop in 2020 was caused by a reduction in both listings and average price, which was mitigated by an increase in average availability. 

I believe that this decrease in 2020 was caused mainly by a large reduction in demand for the service. This can be observed by our next metric, number of reviews, which we use as a proxy for demand.

Data on the Reviews

So, while we see a decrease of 25.4% in the market size from 2019 to 2020, we see a much larger decrease (67.1% )  in the number of reviews. This could be, in part , the result of changes in the use habit by customers, such as longer stays on average, which produce fewer reviews but not less revenue necessarily.  But we can also suspect that there has been a large decrease in demand.

Ratio of Reviews

have calculated another metric to allow us to easily compare the relative size of the market to reviews. This is the ratio of reviews divided by listings days (listings multiplied by average available days per year). This ratio increases when there is a larger relative increase in reviews (demand) than market size (supply). The following graph allows us to visually examine the changes this ratio has had over the years.

Here we can easily see that there was a trend towards a larger ratio, meaning larger relative increases in demand than supply. Until 2020 where the impact of COVID-19 caused a major decrease in the ratio for all neighbourhoods. 

Price

We can suspect that this ratio has had an effect on the prices since the supply curve has changed so dramatically. The next graph allows us to see the changes in prices per night by neighbourhood.

We can see that although there was not much change for most neighbourhoods, there was a large decrease in Manhattan, which is the largest neighbourhood by far. There was a reduction of 13.1 % in the average price per night for Manhattan. We can also assume, due to the large decrease in the reviews per listing days ratio, that the occupancy rate must have gone down.

Distribution

Finally, we can also compare the distribution of the market by room type.

We can see some minor changes over time but it has mostly stayed the same. What we can also learn is that the distribution is composed, in a large proportion, by the type entire home/apartment followed by private room. The distribution for 2020 is the following:

  • Entire home or apartment: 69.7%
  • Private room: 26.1%
  • Hotel room: 2.4%
  • Shared room: 1.8%

In the next section we will take a look at some tools that will allow us to more closely understand and examine the market each year.

Data on Yearly Snapshot

The next section of the app allows for more exploration of the yearly data. There are several types of graphs that allow us to get a quick understanding of the characteristics of the market at that point in time. 

First there is a dashboard with a summary of the most important variables:

One can select the year from the drop down menu and get a quick view of the most important variables from this dashboard. This yearly selection can be applied to any of the following graphs as well.

Heatmap Data

Next there are several heatmaps which overlay the listings, price per night, and reviews on a map of NYC. The listings heatmap also has dynamic clusters that show how many units are available for every area encompassed.

One can zoom in and out to get a quick visual of the data for different geographical areas. From this map we can quickly see how Manhattan and Brooklyn are the most densely populated areas.

Treemap Data

We have also created several treemaps which display the different boroughs and their neighbourhoods. These areas are based on either the listings or market value. They also show a heatmap which can either display the price per night or the reviews per listing days ratio.

We can see how the number of listings in Manhattan and Brooklyn look similar. However, once the area is based on market value, it is clear that Manhattan makes up over 50% of the market.

In the second graph we can also get an idea of which neighbourhoods are more attractive for investment due to having a higher reviews per listing days ratio. Also, if we compare it to the same graph for 2019 we see a large difference in the heatmap due to the general decrease in this ratio.

The next graph is designed to also help us understand which neighbourhoods are more attractive for investment based on the reviews per listing days ratio. 

Neighborhood Data

It shows the top neighbourhoods with a market value above a certain amount, so we can see which could potentially be the best opportunities. The graph also allows us to modify both how many results to display and which is the minimum market value for the neighbourhood.

The graph also correlates the market size with the dot size so we can quickly get an idea of which markets are larger. For example, East Elmhurst is the most attractive neighbourhood based on this ratio for the neighbourhoods with a market value above USD $ 1 million annually.

Data Table and Download

Lastly, we have a section that allows us to get a look at the neighbourhood data for each year and also download it in CSV format. This allows us to take a closer look at a specific neighbourhood that might be of interest or download it for further manipulation if desired.

Final Thoughts and Further Opportunities

Thanks to the visualization tools, we have been able to quickly understand how the major features of the Airbnb market in NYC have evolved over time. It seemed to have been progressing toward a market with greater demand than supply over time, as the reviews increased at a faster pace than the supply of days available for rent. 

This, however, abruptly changed due to the impact of COVID-19, which wiped out demand t in NYC. Once the pandemic struck the city, and lockdowns were imposed, far fewer people were travelling into the city due to tourist attractions and restaurants  being closed. Even those with business interests were not likely to come in as remote work and communication was adopted for greater safety. The result was a much lower relative demand for Airbnb rentals. Still, with real estate prices falling, there could still be adequate investment opportunities despite this fact. More analysis on this variable would allow us to have a more complete picture in order to make an educated investment decisiรณn. 

We also were able to quickly identify the largest markets and which are the most attractive today. We also gain an idea of the different characteristics of the neighbourhoods and their geographical locations.

Future Research

Nevertheless, some further research that could bring addition insights are:

  1. Study of real estate prices to determine if an adequate ROI can be achieved for investments in properties that will be used as Airbnb rentals or similar.
  2. Analysis of individual reviews to understand the factors that impact the value perceived by the customers in order to tailor offerings that generate the most value.
  3. Analysis of the elasticity of the price in the market to determine if there are a subset of assets where value could be generated through acquisition and optimization of the pricing strategy.
  4. Combining this data with available real estate data on land value to determine if this data can help predict increases in value in the real estate market.
  5. Comparing this data with hotel information to gather insights into how the traditional hospitality industry could better compete by tailoring its offer to the needs of Airbnb customers.

Finally, I hope this presentation and data visualization tool can bring value to you and your organization. I'm passionate about data and its potential impacts, so please feel free to connect with me in Linkedin to discuss any topic related or in case you want to stay in touch for future discussions.

About Author

Daniel Ellenbogen

Daniel Ellenbogen is an experienced Data Science professional that has worked in finance and co-founded a health and nutrition start-up. He holds a B.A in Economics with a minor in Business from the University of Texas at Austin....
View all posts by Daniel Ellenbogen >

Related Articles

Student Works
Airbnb vs Long-Term Rentals: Understanding NYC Real Estate
Student Works
Data Analysis on Airbnb in NYC
Data Science News and Sharing
โ€˜Airbnb com vs Hotels.comโ€™ - A Webscraping Project
Featured
Data Visualization of Panda Go
Python
Data Scraping Airbnb: Manhattan Listings

Leave a Comment

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

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 ChatGPT 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 football 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 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

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application