Boston AirBnb Analysis
By: Petra Mala
Tags: Airbnb, Kaggle, Data

Airbnb began in 2008 when two designers who had space to share hosted three travelers looking for a place to stay. Now, millions of hosts and travelers choose to create a free Airbnb account so they can list their space and book unique accommodations anywhere in the world.

For this analysis, I accessed the Sep 2016-Sep 2017 data set which included over 3,000 property listings and reviews.

Business Questions:

What are the most common listings What type of property has higher listing price? What is the price distribution? What features correlate with price? What’s important to tenants

Data Preparation:

The data required several clean-up steps. First, I wanted to remove columns with majority of missing values and clean up price column by removing a dollar sign and comma.

After that I I removed rows where price, no of bathrooms, no of bedrooms and no of accommodates were zero or less than zero. Because I wanted to use amenities in my correlation matrix, I translated a list of strings into no of items.

The data types for some columns required a change from float to integer so they can be used in calculations. Lastly, I filled NA values with mean values for the column.

Data Exploration:

What are the most common listings

I wanted to understand which areas of Boston and what type of property have the most listings as it could potentially indicate the demand.

The most common listings are for one bedroom apartments with one bathroom. Most properties are in Jamaica Plain and the least in Leather District.

2. What type of property has higher listing price?

I also wanted to understand what type of property and location has the highest average price.

Highest average price lies with townhouse when renting the whole property and interestingly with boats where a private room can be rented. When it comes to the neighbourhood, the most expensive one is South Boston Waterfront followed by Bay Village. However, it has to be noted that most expensive locations are not where majority of listings are. Jamaica Plain with one bedroom, which was the location with the most listings is on lower scale of average prices.

And what is the most expensive street on AirBnb?

Myrtle Street, unit, Boston, MA 02114, United States

3. What is the price distribution?

After exploring the relationship between average prices and properties, I wanted to take a look at price distribution.

We can see from the first graph that majority of listings lies somewhere below $500. After removing price outliers, we get a new view of price distribution in the second graph.

Typical price for a property is around £150.

4. What features correlate with price?

Now when we know the average price and its relationship to locations, let’s explore other features.

It’s probably no surprise that what correlates with the price is the size of the property defined by no of bedroom, bathroom and accommodates.

When exploring average prices using on no of bathrooms and bedrooms there is a high price for a property with 3 bedrooms and 3 bathrooms. It’s also interesting that having a property with 2 or 3 bedrooms and 1 bathroom doesn’t make a big difference in renting price. I also find interesting the price difference between 1 bedroom and 2 bedroom which is higher by 62% but the price difference between 2 bedroom and 3 bedroom has increased only by 11%.

5. What matters to tenants

Lastly, I would like to explore reviews and see what tenants and homeowners talk about the most.

Homeowners:

Tenants:

Among other things, home owners emphasize room types, property types, neighborhood, bed sizes, comfort and distance proximity while tenants comment in their reviews about the host, location and cleanliness.

To see the python notebook for this analysis please check out this GitHub repo.