Airbnb analysis

Data from :

Files used : 2 csv files, listing and listing detailed



FeatureCleaning actionMethod
neighbourhood_groupFill in the names corresponding to the Zip codesMapping with a dictionary and replace method
licensedrop the columnpd.drop
Price Analysis of the price feature who didn't include fees and cleaningCreation of a cleaning fee proportional to number of bedrooms in the Airbnb
Last_reviewParse datespd.to_datetime

Distribution :

We can already notice that despite Airbnb being originally created to share rooms, in Austin more than 80% of the listings are actually about entire homes or apartments or even hotel rooms, while private and shared rooms make less than 20%. This is already an alarming piece of information.


We already knew that Airbnb was used as an investment but the proportions are staggering. 

% distribution of listings :

South central and Central Austin are really the sparkling area of Austin, close to UT and very practical for tourists.

East Austin is obviously an areas where investors try to buy because it’s relatively cheap compared to central.

Travis county represent the part of the population with large lots or home and using this to make profit.

Listing per users :

Here we focus our data analysis on how much of real estate is concentrated for investment rather than resident housing.

The results are here, in Downtown Austin almost every host has on average 2 listings, making actual apartment  for resident harder to find.

Price distribution :

Since they are still slightly skewed, I normalize prices with a logaritmic transformation.

Let’s see if price has different distribution depending on the language. To do this, we will do a graphical evaluation and then we will apply a statistical test (T-test for independent samples)

Log transformation is mainly used if you want to pursue a predictive model, they tend to work better with those distribution.

3 thoughts on “Airbnb analysis”

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