AirBnB Analysis of 5 Major U.S. Cities
Creators: name: Johnson, Tristin
Publisher: name: Zenodo
Year: 2020

description: I. Introduction Today, I will be doing a complete analysis on AirBnB listings across five major U.S. cities, including Washington D.C., Boston, New York City, Los Angeles, and San Francisco. Throughout this presentation, I will be covering various analytics regarding several factors in these AirBnB listings, such as the total number of listings, listings compared to population, categorizing the listings into price categories, the different types of rental rooms available, analyzing the most popular neighborhoods, looking at the neighborhoods by review score, the amenities that come with each listing, and finally an interactive experience that filters down AirBnB listings for a user based on his/her parameters. AirBnB is taking over the vacation-rental online marketplace as they make it so easy to seach and find rentals, whether that's for a weekend with your freinds in a new city or a week-long business trip. There is an estimated 660,000 listings on AirBnB across the whole United States, and just over 7 million listings world-wide, meaning there are thousands of different options when it comes to renting with AirBnB in just about every city across the world, making accessible and affordable rentals easier than ever before. I decided to do this analysis on AirBnB because I personally use AirBnB for renting places in just about every city I visit, and I wanted to learn more about how these listings correlate not only within single cities, but how they correlate and differ across multiple cities. II. Gathering/Cleaning the Data When it comes to gathering my data, I got all of my AirBnB data from insideAirBnB.com, which is a public website that contains all AirBnB data across major cities around the world. For each city, there contains multiple .csv files, some of which are summaries of the data, others of which are detailed and exact data for each listing. Since I am doing 5 major cities in the U.S., I only needed one file from each city. One thing to mention is that all of this data was web-scraped from AirBnB.com in October of 2020, so it is quite relavent and recent data, along with a trustworthy source. I then got the population of each city from WorldPopulationReview.com, which has the most up to date population figures by city. The links for all of this information is listed below: AirBnB Listing Data: http://insideairbnb.com/get-the-data.html U.S. City Population Data: https://worldpopulationreview.com/us-cities When it came to cleaning the data, the .csv files consisted of 74 different columns of data regarding each AirBnB listing in each city. The main part I had to do was filter down the data into only the columns that I needed, such as the price, the neighborhood, the review score, and so on. There was also some columns with NaN's, columns with unnecessary characters that needed to be stripped, and some bad formatting within these columns that was all adjusted throughout the presentation as needed for each analysis. III. Total Listings on AirBnB by City First, I wanted to take a look at the total number of AirBnB listings by each city. This gives a good idea of how many rows of data there exists to deal with, along with which city will provide more accurate data as there is simply just more of it. Next, I wanted to take a look at the total number of listings versus the population of each city, to see if there was any proportionality regarding the number of listings and the population. IV. AirBnB Price Analysis by Price Categories I then wanted to take a the number of listings within some relatively standard price categories. Each city's AirBnB listings were categorized into price categories so that we could see where a majority of the listings fall into in terms of how expensive or cheap they are. V. Different Room Types on AirBnB I then wanted to take a look at the different types of rooms that are offered on AirBnB, and see which city has the more dominating room type. The user has the choice of selecting a single or multiple cities to analyze these different room types. AirBnB categorizes these rooms into 4 different types, the entire home/apartment, a private room within a house or apartment, a shared room (similiar to a hostel, or room with lots of bunk beds), and finally hotel rooms. When it comes to hotel rooms, turns our hotels can put their rooms on AirBnB's website. They have some standards when it comes to listing a room as a hotel, and usually it is private hotel business, and not many of the corporate hotel business such as The Mariott and The Hyatt. VI. Analyzing Neighborhoods by City Next, I wanted to take a look at the most popular neighborhoods of each city. By most popular, I mean the most number of AirBnB listings by neighborhood, which allow us to see where a majority of vacation-rentals are located in each city, along with looking at the average price of these most popular neighborhoods. This is also interactive in the sense that the user can enter any of the 5 cities and analyze these neighborhoods. Then, I wanted to take a look at the AirBnB review scores by each neighborhood in which a user can also enter any of the city's and analyze the information. Since there are over 200 neighborhoods in some of these cities, we can clearly analyze the top 10 neighborhoods (where most of the people who have stayed there rated these places the highest) and the bottom 10 neighborhoods (more than likely the places that either didn't have the best host's or weren't the nicest places to stay). VII. Popular Amenities Among AirBnB Listings Next, I wanted to look at the amenities within each AirBnB listing. The user also has the option to enter a city and can view multiple cities or just one. The purpose of this was to see which amenities are in almost every city, along with looking for the different popular amenities between cities. VIII. Interactive User AirBnB Finder Lastly, I wanted to create an interactive user program in which the program asks the user some questions to try and find the best matches for his/her AirBnB rental. The user just has to follow and answer the given questions with multiple options, and a list of multiple AirBnB rentals will be displayed at the bottom. IX. Future Predictions In terms of Futre Predictions, it is safe to say that the cities with the hightest population (New York and Los Angeles) will stay having the most AirBnB listings, as with more population usually means it is a popular destination for tourists to visit. Categorizing the listings into price ranges was quite interesting also, as we can notice that the more expensive a city is to live in (rent and housing prices), the more expensive the AirBnB prices will be as we saw with San Francisco. Regarding the room types, it is no surprise that a majority of the room types of each city consists of having the entire home or apartment, as that is more private and better if you're with a group of freinds or family. When it comes to the neighborhoods within each city, it makes sense that a majority of the listings are in areas that are surrounding by highly-touristed attractions, as these listings are mainly for people who are traveling and visiting for a weekend. I don't see that changing any time soon as people want to be as close as possible to certain attractions and populated areas. When it comes to the amenities, there wasn't too much to differentiate between the cities as the most basic items (Air conditioning, laptop friendly, workspace) are standard in just about every home and are expected when renting a place. X. Conclusion Overall, I had a lot of fun doing this analysis as I got to visually see some differences between the cities regarding AirBnB details. There are many conclusions and inferences that can be made when it comes to looking at the total number of listings, the listings in different price categories, looking at the different room types that are offered by these cities, and then analyzing the most popular and higly rated neighborhoods within each city. Furthermore, this data was pulled from AirBnB in October of 2020, about 7 months into the COVID pandemic. I am sure a lot of these AirBnB listings were greatly effected as travel has become very limited and not many tourists are visiting these cities. It would be interesting to compare the listings that we currently analyzed, the listings before COVID, and the listings right when COVID hit the United States, along with the capacity at which these listings are being reserved. One thing that surprised me the most was the review scores of each neighborhood. A majority of the ratings across several neighborhoods mainly stayed between 80 and 100, which is good as it shows that most of these AirBnB listings can be trusted and guests have a great experience renting these places out for the most part. There were only a handful of bad reviews from guests, as is expected in any business, but this goes to show that AirBnB does a good job when it comes to allowing certain hosts and places to go on their website as they want to maintain a good reputation. XI. Learning Processes Throughout this analysis, I most definitely learned a lot. This was probably the biggest data set that I have worked with, and had a lot of fun applying technical skills to a real-world application, such as AirBnB. My python knowledge has expanded vastly, as I used several different packages that I have never worked with before and got quite comfortable using them with practice. I also learned a lot with the different types of plots within Plotly, and how to better customize the graphs, and creating functions to plot each city data set the same. Finally, creating the interactive program the finds AirBnB listings was fun as there are a lot of aspects that needed to be correlated into the program, such as error handling and flexibility within a search.