Are Airbnb hotels’ prices in Seattle predictable?
By: Prithvi Raj
Tags: Python, Data Science, Pandas, Dataanalys, Jupyter Notebook

Introduction: For those who are less familiar with what Airbnb is: it is the Uber of hotels.

Infact, now in 2020, Airbnb is one of the largest hotel listing companies in the world and their business has allowed thousands of home owners to rent their unused real estate to earn extra income. At the same time, travelers have got the option of choosing from more stay options than those provided by traditional hotels.

In this post, I wanted to analyze listings and reviews data available from Airbnb for Seattle city in the United States, and get some insights into their business

Data used: The data for the analysis is obtained from Kaggle (https://www.kaggle.com/airbnb/seattle/data) and the following 3 data files are used:

Listings, which includes full descriptions and average review score

Reviews, which includes unique id for each reviewer and detailed comments

Calendar, which includes listing id and the price and availability for that day

Scope of analysis: Using the 3 data files, we will try to answer the following 3 business questions:

How do the prices of Airbnb hotels in Seattle fluctuate over time? How have the total number of bookings increased over time? What factors have the highest correlation with price of Airbnb Seattle hotels?

Now, let’s dive deep into each of them.

How do the prices of Airbnb hotels in Seattle fluctuate over time?

To answer this question, I analyzed the ‘calendar’ data, and plotted the average price of hotel listings in Seattle over the last 1 year, and here is how that trend looks like:

It can be seen that average prices in 2016 have a seasonal behavior, where the prices increase from Jan to July and then start decreasing again in September.

Also, the prices are highest in the months of July and Aug, probably indicating increased travel demand during those months

There also seems to be weekly variation in prices, so I looked at the months of July and Aug more closely, and here is what is found:

It can be seen that prices are highest on Fridays and Saturdays and lowest on Tuesdays and Wednesdays

2. How have the total number of bookings increased over time?

The direct bookings information is not available in the data, but I used the number of reviews submitted by users as a proxy for the number of bookings. It is based on the assumption that there will be more reviews submitted if more users are making bookings.

Here is what I found:

It is clear that number of reviews have increased a lot since the beginning in 2009.

Also, just like observed in the prices, there is seasonality in the number of reviews also. This is further clear in the below zoomed in view for just 2015:

3. What factors have the highest correlation with price of Airbnb Seattle hotels?

To answer this question, I analyzed the listings data set. I checked the correlation of price against each of the other numeric variables, and here is what I found:

The variables with the highest positive correlation (and the corresponding correlation) with price are:

weekly_price: 0.937861

monthly_price: 0.873450

cleaning_fee: 0.656938

accommodates: 0.652218

bedrooms: 0.627720

beds: 0.589525

square_feet: 0.531752

bathrooms: 0.516424

guests_included: 0.392875

security_deposit: 0.373195

And these two variables have a strong negative correlation with price:

number_of_reviews: -0.124695

reviews_per_month: -0.218588

So, we can conclude that prices are higher for listings which have a high cleaning fee, and is can accommodate more guest

And, the prices are lower for listings which have more number of reviews. This indicates that probably a lot of unsatisfied customers post negative reviews, which tends to have a downward pressure on prices in future

You can refer to the complete code used for the analysis here on github: https://github.com/priithvi/Airbnb-Seattle-data-analysis

This is my first Medium post, so let me know your feedback