The case study examines Airbnb's strategies to adapt to post-COVID changes by analyzing customer preferences and property offerings in NYC. Key insights reveal that entire homes/apartments and private rooms dominate preferences, with pricing dynamics influenced by location and minimum stay requirements. Recommendations include focusing on popular property types and leveraging data-driven insights to enhance listings and attract customers.

1. STORYTELLING CASE STUDY: AIRBNB
-Devanshi Sinha

2. AGENDA
o Objective
o Background
o Insights
o Inference
o Appendix:
• Data Assumptions
• Data Methodology

3. OBJECTIVE
o Improve business strategies and estimate customer
preferences to revive the business in the post-COVID period.
o Understand critical pre-COVID period insights from the
Airbnb NYC business.
o Make recommendations to various departments on how to
prepare for post-pandemic changes.

4. BACKGROUND
o Airbnb's revenue has been significantly reduced in recent
months as a result of COVID-19.
o People have begun to travel more now that the restrictions
are lifted.
o Airbnb wants to make sure that it is fully prepared for this
change.

5. INSIGHTS
o Entire home/apt account for 72.07% of
total price share.
o Private room and entire homes/apt are
preferred over shared rooms offered for
rent by Airbnb hosts.
o Entire home/apt and Private room account
for the majority of listed properties in NYC
(approx. 97.6%).
o Shared rooms account for only 2.4% of all
listed properties.
45.66% 51.97%
2.37%
Customer Preferences and Availability of the three property types
o Manhattan has the most entire homes/apts
available, whereas Brooklyn has the most
private and shared rooms.
o Overall entire home/apt has most availability
than any other room type.
o There are more private rooms available across
every neighbourhood other than Manhattan.
o Shared rooms have limited listings but high
availability and affordable prices.

6. INSIGHTS
o The number of listings crosses 12k for
min nights to stay below 5 nights and
drops until a spike at 30 min nights.
o Lower-priced properties have more
reviews, which means more bookings for
such properties.
o Low reviews for properties with longer
minimum stays and higher prices.
Pricing in Preferred Locations
o Private rooms are more popular in NYC, with
over 21 reviews per listing.
o Manhatten’s entire home/apt have 35%
fewer reviews per listing than the overall
entire home/apt average of 27.7.
o Except Manhatten, all neighbourhood groups
performed poorly in shared rooms with an
average of 7.3 reviews per listing.
Customer Preferences for Neighbourhoods, Min Night Stays and Property Prices

7. INSIGHTS
o Manhattan and Brooklyn properties are the most expensive across all room types,
accounting for the majority of entire house/apt or private room type contributions.
o There is only one location from the Bronx, Brooklyn, and Queens among the top 15
neighbourhood locations based on average pricing in the area.
o The first two properties are from Staten Island, demonstrating that the average
price of properties in that location is very high.
Pricing in Preferred Locations

8. INSIGHTS
o Top 5 most reviewed property hosts in NYC with Maya from Queens having
the highest number of total reviews.
o No hosts from Bronx and Staten Island are to be seen in the top five.
Hosts with most Reviewed Properties

9. INFERENCE
o Shared rooms have fewer listings but more availability and lower prices, so
they can be maximized.
o The number of reviews is higher at lower-priced properties than at higher-
priced properties as people are less likely to book expensive rooms.
o Most of the listed properties are private rooms and complete homes/apt,
which also account for the majority of the total price share.
o Expensive prime locations like Manhattan and Brooklyn can be targeted for non
premium properties and Bronx for premium properties.
o The minimum number of nights to stay decreases with an increase in price.
o Property host Maya from Queens has the highest number of total reviews.
o Most popular listings have a minimum number of nights stay requirement
ranging from 1 to 5 nights or 30 nights.
o Acquire private rooms and entire home/apartments since they are more
popular room type having more number of reviews per listing.

10. APPENDIX: DATA ASSUMPTIONS
o Assumed that pre-pandemic data was generating the desired revenue.
o Assumed that the company does not wish to expand into new markets in NYC.
o To learn about customer preferences, used the number of reviews per listing as
a popularity metric.
o Assumed number of reviews provided to be positive to use as a base measure to
find customer preferences.
o Null values are assumed to have no effect on the analysis.

11. APPENDIX: DATA METHODOLOGY
o Used Tableau to visualize data from the NYC Airbnb dataset in order to obtain
accurate insights.
o Checked the dataset for Null values. Some columns, such as names, host_name,
last_review, and review_per_month, had null values.
o Checked the dataset for outliers.
o Exploratory data analysis was used to identify customer preferences based on
various parameters such as area preferences, property prices, and listing
preferences.

12. THANK YOU