In season 2 episode 6 of Netflix's Unbreakable Kimmy Schmidt, Kimmy Schmidt (the main character) and her roommate Titus Andromedon rent out their apartment on Airbnb. This action worries their landlord, Lillian Kaushtupper, as Lillian believes that Airbnbs only attract affluent hipsters who will gentrify her beloved lower class New York City neighborhood. While Lillian's immediate correlation of Airbnb to gentrification is an overreaction, it made wonder if Airbnb can lead to an increase in rent prices of surrounding apartments.
Airbnb is an online marketplace and hospitality service for people to lease or rent short-term lodging. Most regular apartment renting is done through leases which last over a long period of time, usually six months to one year. Airbnbs usually only last a few days to a couple of weeks as it is an alternative to hotels and traditional bed and breakfasts place. Because of the shorter length of stay, Airbnbs are more expensive per night than the per night equivalent of a month's rent for a long term apartment lease. So if Airbnbs start cropping up in below middle class neighborhoods and middle class people rent out those Airbnbs, could that influence landlord's to increase rent prices as the landlords see there are people willing to pay more to rent their property?
As indicated by the title, the main question we are trying to answer is does Airbnb cause rent prices in surrounding areas to increase. The way I will be
| Date | Apt. | Airbnb | Difference |
|---|---|---|---|
| 2015 | 100 | 300 | 200 |
| 2016 | 200 | 300 | 100 |
| 2017 | 300 | 350 | 50 |
In order to carry out this experiment, I need historical rent data for apartments (which I will call rent data) in New York City and historical data for Airbnb (which I will call Airbnb data) prices in New York City. The historical rent data will be from Apartment List and historical Airbnb data will be from Inside Airbnb.
I) Data Curation i) Getting the data ii) Cleaning the data iii) Parsing the data II) Exploratory Data Analysis III) Machine Learning IV) Conclusions V) Further Reading