Exploring Seattle Airbnb Data
Tags: Python, Linear Regression, Seattle Airbnb, Pandas, Airbnb

Let’s start with some exploratory analysis and get some sort of insights out of this. I will be using Python and various packages to perform this analysis.

Importing Packages is a key element of python data analysis

Then we have to import their CSV files and store them into a data frame.

Store them into its respective names. For this analysis purpose.

For this analysis purpose, we will mostly focus on the listings data frame which has most of the data ranging from price to listings details, reviews, comments etc.

This is the listings data frame, with only the first 5 rows and 92 columns which couldn’t be shown entirely on this screenshot. But we can work from here!

Let’s remove outliers for listings that are over $500 USD per night and look at how much they cost on average based on how many bedrooms they have.

This is interesting:

- Listings without bedrooms(Studio) is more expensive than one bedroom. Would this be because of its renovations? Features? What could this be?

- Listings with 6 bedrooms are the most expensive, even costing on average $100 per night higher than listings with 7 bedrooms.

Now， let's look at the most popular listings in Seattle.

Most of them are really just around the $100 to $150 mark. Expensive listings are not that popular according to the data set.

What are some of the correlations between these variables?

We see that the higher the coefficient, the higher the correlation strength of those variables. From here, we see that the correlations that reach at least 0.6 and above are:

1. The more nights available, the more nights you can reserve

2. The number of beds are correlated with how many guests it can handle

3. Review scores arent too correlated with the hosts’ communication with the traveler

Let’s slice and dice the data frame into the smaller segments to analyze its correlations.

Seems like review scores value has little to do with its listing size(square feet) as we see scores of 8 to 10 are well distributed throughout 0 ft to 3000 ft.

We can also see that the number of bathrooms has a little correlation to its review score. The more bathrooms, the higher the review score whereas having only 1 bathroom actually has more of a lower review score at only 5 or 6.

Maybe this is common sense but having a bigger unit size(square feet) is correlated to how many people it can accommodate.

Most of the airbnb units in Seattle are entire “Apartment”.

On average, the most expensive listings have real beds instead of transformable/portable couches or sofa beds.

We can also look to see if it affects the ratings on Airbnb. But judging from the chart below, it doesn’t seem like there is any correlation between what bed it is.

Is the host a super host an important factor for its price?

Not necessarily, the prices are more or less the same.

Based on these insights, we finally have a better understanding of what’s crucial to achieve a higher rating in Seattle Airbnb listings.

Here is a conclusion of what’s highly usable and needs to be focused on in order to achieve better ratings.

Studio apartments are not only cheaper to purchase to list on airbnb, but they also generate a higher interest, more popularity amongst travelers and also generate more bookings.

Furnitures within the listing is also quite an important factor as we see “real-bed” is what most travellers are looking for and that they will generate more revenue per night.

Listing size isn't too big of a deal when it comes to Airbnb bookings, most travelers would love to save money on accommodations where possible.

Also, as we see from the above, listings with 2–3 bedrooms seem to be the optimal bedrooms to generate income and positive reviews.