How to Analyze Airbnb Performance Data in the Right Way
Understand Airbnb Performance Using Tableau Data Analysis
Goal of the Project
- Understand the popularity of Airbnb based on locations
- Analyze the reason about variation based on locations by prices (which area is expensive), reviews (which area is the best), and type of rooms
- Realize the situation of the busyness of the host
- Predict the future 2020 Airbnb performance in New York City
Introduction to Airbnb
Founded in August of 2008 and based in San Francisco, California, Airbnb is a trusted community marketplace for people to list, discover, and book unique accommodations around the world — online or from a mobile phone or tablet. Whether a spare bedroom, an apartment, a villa or a private island, Airbnb connects travelers to a more diverse and authentic range of experiences in over 34,000 cities and 190 countries.
Description of Data
The dataset includes three main tables:
listings- Detailed listings data about hosts, Airbnb houses and price. The attributes used in the analysis areid(listing ID),name(name of the listing),host_id(host ID),host_name(name of the host),room_type(listing space type), andprice(price in dollars).reviews- Detailed reviews given by the guests. Key attributes includenumber_of_reviews(number of reviews),last_review(latest review), andreviews_per_month(number of reviews per month).location- Provides details about Airbnb location in New York city. Four attributes includingneighbourhood_group(main area),neighbourhood(area),longitude, andlatitude.
Popularity of Airbnb by Locations in NYC
Based on the neighbourhood locations, the chart shows that 44.3% of Airbnb housings are located in Manhattan, and 41.12% of Airbnb housings are located in Brooklyn. Almost 90% of Airbnb housings are located near by the most famous city hotspots, such as Times Square, Empire State, Wall Street, Central Park, and DUMBO, and tend to have convenient transportation(subway station).
Location! Variation by prices, reviews and type of rooms
Airbnb users rate their stay on the basis of location, price, cleanliness, living experience and a host of other parameters. Here I used the data to get the chart about variation of average price and numbers of reviews by locations. It is really interesting because average price and numbers of reviews didn’t have a large positive correlation. Highly expensive neighbourhoods will not tend to have more numbers of reviews because travelers will decide more cheaper Airbnb to live in and take the transportation to famous city hotspots.
Which area is expensive and which area is the best?
Which area is expensive?
According to the map showing the variation of average price by location, the average price is largely in line with the locations. It is obvious that the highly rated locations would also tend to be costly this is because in the case of constant supply, the higher the demand, and the higher the price is. Also, Manhattan receives highest average price $196.9 because of its highly demand. And then, it is interesting to observe that the highly average price areas correspond with the transportation. The same is true for Brooklyn and Staten Island.
Which area is the best?
Based on the map showing the variation of numbers of review by location, Manhattan receives the highest numbers of reviews because it closes to the most famous city hotspots (Times Square, Empire State, Wall Street, and Central Park) and has the more convenient transportation. In Brooklyn, the locations close to the Highland Park have the highest numbers of review. Brooklyn neighbourhoods close to Manhattan tend to have higher location ratings.
Taking a look for the last review by year, it shows that the number of reviews per year has been constantly increasing because the total visitors have been increased from 2011(50.9 millions) to 2018 (65.2 millions). Also, more users want to write down the reviews based on their experience so that they can give potential users a chance to choose the best place to live in. As a result, New York City have already become one of the most famous city to visit!
Different types of rooms in New York City
Now let’s explore the types of rooms that are there in New York City. Below is a chart that shows distribution of types of rooms by different locations (Bronx, Brooklyn, Manhattan, Queens, and Staten Island).
According to the chart, it shows that Brooklyn and Manhattan provide the most houses and rooms, representing a high demand there. Also, because of the high housing prices, New York City in Airbnb gives users more choices for private rooms to reduce the burden on life.
The busyness of the host
Based on the charts we have above. Let’s explore the busyness of the host that are there in NYC. In line with different charts about numbers of Airbnb by locations in NYC, variation of average price by location, and variation of numbers of review by location, we understand Brooklyn and Manhattan provide the most houses and rooms. Therefore, we can imagine that top 10 busy host are from the area of Brooklyn and Manhattan.
Future 2020 Airbnb performance in New York City
In 2020, I think Airbnb will definitely increase in New York City. Let’s take a look between demand and pricing.
As mentioned earlier, I take number of total visitors from 2011 to 2018 and last review by year for realizing the demand for Airbnb rentals in 2020. Moreover, I assume that the number of reviews correspond to the demand for the Airbnb rentals that 60% of guests should review their stay based on their experience. For number of total visitors from 2011 to 2018, we can know that the total visitors have been increased from 2011(50.9 millions) to 2018 (65.2 millions). Also, chart of last review by year shows that the number of reviews per year has been constantly increasing. Therefore, the more visitors come, the higher the demand for housings, and the higher the price for Airbnb.
To sum up, Airbnb has seen a significant increase in New York City. The above analysis represents that Airbnb still has a strong market in New York City.
About me
Thank you so much for reading my article! Hi, I’m Shirley, currently studying for a Master Degree in MS-Business Analytics at ASU. Special thanks to my teammates Divya, Raghu, and Andrew for their contribution. If you have questions, please don’t hesitate to contact me!
Email me at kchen122@asu.edu and feel free to connect me on LinkedIn!
