Worked on real life business problem where due to Covid-19, Airbnb has seen a major decline in revenue. To prepare for the next best steps that Airbnb needs to take as a business, analysis has been done on a dataset consisting of various Airbnb listings in New York. This analysis served as the basis for the presentation created for the Lead Data Analyst and Data Analysis Managers

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