Data Analysis on Airbnb in NYC
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Airbnb serves as an online marketplace for lodging. Hosts can list housing through the website and Airbnb acts as the broker, connecting Airbnb hosts with those looking for accommodations. Airbnb has undergone tremendous growth in New York City since first listing in 2008. Data shows there are currently roughly 50,000 units in the city, stretching through all 5 boroughs.
The platform has quickly become a viable alternative to hotel stays. When searching for short term stays in a city, Airbnb is a first choice for many patrons. However, when visiting a new city in which one has little familiarity, it can be difficult to decide where to stay. A user is inundated with pages and pages of listings, which in turn have pages and pages of reviews to diligently scour through. This can quickly become overwhelming. With so many choices, and little standard for comparison, how can a wary traveler make a more informed decision on where to stay?
This project aimed to look at 3 questions:
- What is the pricing landscape of Airbnb listings in New York City?
- As a potential customer, where can a user get the best pricing and how can they quickly compare neighborhoods in terms of price and ratings.
- What impact do subways have on Airbnb pricing?
The data was sourced from the inside Airbnb website. The website has detailed listings data in the city as of September 12, 2019. All information was scraped from the Airbnb website. The listings level detail included over 50 features, including but not limited to zip code, listing description, house type, borough, geo location, price per night, bed types, and various rating scores. Each listing was then paired with its nearest two subway line information.
New York City subway station entrance data was sourced from the NYC open data website. This data included individual station longitude, latitude, and the subway lines serviced at that station.
Listing prices range from $10 per night for a room up to $10,000 for a house. Manhattan and Brooklyn dominate the listings market. Roughly 85% of all listings are in those 2 boroughs. Listings are split between Entire Home/apartments (52%), private rooms (46%), and shared rooms (2%). Half of all listings fell under $105 a night.
The graph below shows the pricing density for each room type in the city on Airbnb: entire home/apt, private room, shared room. Based on the graph, there is psychological economic based pricing evident in each room type. Psychological pricing is the idea that certain prices have a psychological impact on a consumers decision making. T
he idea being that slightly lower prices at an ‘odd price’ (i.e. pricing right below a round number, $9.99 instead of $10) elicit the consumer to perceive the ‘odd price; as lower. We can see ‘odd price’ humps, where the density plot jumps right before a round number, for example $199 per night as opposed to $200.
For this project I used Shiny App to build an app of the listing data.
I built an app that would allow a traveler to quickly get on and compare different neighborhoods so that they could compare cost and location. Using this app, users interact with a heat map of the city, which allows them to see the relative average cost or average rating score for each neighborhood.
To get more granularity, the user can view individual listing information.
Finally, they can also see each neighborhood ranked by either price or rating score based on standard deviations from the borough average.
Subway Data Value Add
New York City has a large subway system. Mass transit runs 24 hours a day and allows visitors access to virtually any part of the city on a single swipe of a MetroCard. Given this fact, I hypothesized that there was a relationship between distance from the subway station and the price of an Airbnb. To explore this hunch, I matched each Airbnb listing in NYC to its nearest two subway stations based on distance in feet. As part of the app you can go to the Subway view layer to see which subway stations serve the most Airbnb’s. Each subway station is represented by a circle, the larger the circle, the more listings it serves.
As a next step, I wanted to build a simplistic model to explore the correlation between price and distance in feet to a subway line.
To do this I ran a linear regression, regressing distance in feet on Airbnb listing price per night. As a result, the distance in feet was a statistically significant variable. It turns out that every foot in distance reduces the price of an Airbnb listing by $0.01. This may sound like an insignificant amount, however a quarter mile is 1,320 feet, or roughly 5 NYC blocks. Thus, an Airbnb listing that is 5 blocks from the subway is priced at about $10 less per night than a listing that is a block from the subway! This difference can add up over the year.
You can view the app in full here - Airbnb shiny app.