Unravel interesting facts of Boston Airbnb trend
Exploratory data analysis on Airbnb data
If travelling is a hobby, one must arrange for staying where he/she can maximize the travel convenience by booking online. When it comes to booking online, Airbnb comes as the front runner because of its widespread rent availability and utmost satisfaction. This article focusses on the traveling hotspot of US northeast city of Boston.
The data for Boston Airbnb was obtained from Kaggle Airbnb resource and systematically analyzed to decipher some of the trend that the megacity is inherently marching. We are interested to answer the following questions to observe the big picture and most importantly strategize the business.
Question 1 → What are the factors of price variability?
Question 2 → Is there any correlation between the availability of rents and the price?
Question 3 → Can the company impact the monthly variation of rents after analyzing the year long trend?
The database has rent information based on several rent types like Apartment, condo, full house, villa and even boat. The ratings varies little across the board. Most of the renters of entire floor, guestroom and villa are very satisfied with the location as well as service. Boat and dorm are residing on the other side of the spectrum but do not vary wildly. People are enjoying much. Huh !!!
The data revealed that most of the rental types are apartments which outweighs all other rentals. This is a data for a whole year but this is definitely a clear indication that people tend to rent apartments more whereas houses or condos are less likely to be listed on the web.
What are the factors?
To answer our first question, we need to deep dive on the price variability for different factors such as property type, policy and other accommodations. Here we will factorize the rental price across property type.
Since the apartment type is widely available, variation in price is also expected. But there is a catch. The real picture may be disclosed when those deviant dots (exclusive and deluxe rentals) are removed. Here is the picture underneath.
The data reveals that the price varies widely; showing ups and downs across the rental types. The median price of boat is far higher than any other types when in fact high-end apartments and villas have higher prices (as high as 400 bucks per stay).
Next the policy was investigated and it came out that the rental with the most strict policy, usually have the highest rent. This data does not show whether those rental are available or not but when it comes to the total number of rentals per type, less rentals have the most strict policy.
Finally the bed type plays a pivotal role of the price estimation of the rental. Those having real bed are listed with higher prices compared to other bed types including sofa, futon or couch.
Is price independent of availability?
Next we move forward to answer the second question. To find out the relation between availability of rentals and price, we needed to crunch the data with timestamps. We analyzed the data from August 2016 to August 2017 and tried to dig out that relationship. Some interesting incidents were observed when the availability and prices are shown together. Roughly towards the end of 2016 and in February 2017, there were two sharp drops in rental price when in fact the availability did not vary drastically. The trend seemed to stay flat for the rest of the year except a deep dive at some point in April 2017. The price actually mirrored the availability at that time which tells the fundamental rule of demand and supply. The cyclic trend of rental availability is mainly attributed to the fact that weekends offer less number of properties available or listed on the site.
Is there a seasonal trend?
Lastly the monthly variation of rentals are studied to obtain the trend over a year. This is specially crucial for company revenue to be focused on certain months rather than highly depending on peak seasons. Data shows that September and October have the highest rental price (possible off-peak season) due to less availability. The company may interpret this data to dig out the underlying cause and provide to stakeholders.
Putting it altogether
The observation on the niche parts of Boston Airbnb data divulges the variation of price mainly over property type, bed type and policy. Although the apartments are the most common rentals, high-end apartments are good contenders with villas or full house in terms of price. Other than that, apartments are the most widely listed rentals. Some interesting factor was observed when timestamp data was explored. Some high rises and deep dives are not uncommon for a whole year data. End of the summer season signifies the highest price of the rentals during the year.
