Seattle is a popular tourist destination in addition to the surge of people migrating there to live due to its expanding economy and job possibilities. One of the factors that will make an investment in an Airbnb rental home in Seattle beneficial is the city’s robust tourism sector.
Seattle, sometimes known as “The Emerald City,” is a popular vacation spot for outdoor enthusiasts since it is permanently surrounded by vegetation. In addition to stunning outdoor spaces and a variety of entertainment alternatives for children, couples, and lone travelers, the city is rich in culture and the arts. Seattle, in general, offers a wide range of tourist attractions and experiences, making it an excellent place to invest in short-term rentals.
People all over the world have gauged into the world of Airbnb since the year 2008. It is a more unique and personalized way to travel. The company has brought in a transformation to the world of hotels. It gives the travellers a more grounded and localized experience of the place they are travelling to. The selected dataset describes the overall listing activity of homestays in Seattle, WA.
The data set is retrieved from Kaggle.com and gives information on activity of Airbnb in Seattle for the year of 2016. It gives an overview on a number of perspectives. It gives an idea of each Seattle neighborhood’s atmosphere with the help of the descriptions given with every listing. What would be the busiest times of the year to travel and what are the price fluctuations, how are the number of new listings and overall guests in seattle affected at different times of the year.
The questions that my report focuses on are as follows;
- Where is it profitable to buy a home for Airbnb investment to start renting it out?
- Which zipcode is the most expensive ?
- How many bedroom property is the most expensive one?
- How much is the price of the properties at different times of the year?
- What time of the year shows maximum tourism?
- Distinct count of bedroom listings.
The above is a dashboard created in Tableau. It shows the set of different visualisations that give an idea of the activity related to airbnb in Seattle.The visualisation so created was created by linking two datasets of listings with calendars. First visualisation(extreme bottom left) is created by placing zipcodes on the columns and then creating a visualisation for maps. This spreads the zipcodes to longitudes and latitudes by placing a filter for average price against it shows the average price in every respective county. The second visualisation(smiddle one in the bottom) gives an idea highest to lowest average pricing of each zip code. It is created by placing zip codes against average price. The first and second visualisations are interconnected and show the average price in each zip code. The map shows the geographical representation of all the zipcodes and the legend along with it shows the colour for every respective zip code. The first and second visualisations are interconnected and show the average price in each zip code. The map shows the geographical representation of all the zipcodes and the legend along with it shows the colour coding for each zip code. Using the map one can make a sound decision to invest in which zipcode according to the investment that they hold. Similar information is shown in the bargraph alongside, which shows all the zipcodes arranged from highest to lowest according to the average prices.
The next visualisation shows the revenue for the entire year. It is created by placing the year as per the week of every month against the sum of the price. We see that starting from January through February the prices are very low and as we move forward in the year we see a steep rise in the number of prices. This is a clear indication that a lot of people travel near the end of the year.
The top left visualization looks at the price of the airbnb’s with respect to the number of rooms. As the number of rooms increases the price of the airbnb also increases. Larger the house, more the number of rooms and greater the price.
The top right visualization shows how many listings are available for each number of bedrooms. There is only one listing with seven bedrooms and highest number of listings are present for one bedroom listings. It is created by placing bedrooms in the rows and placing ID as a filter with distinct count as a measure.
In retrospect the collected data and visualization so derived give a basic idea of the nature of airbnb’s and their use in Seattle. The future study could introspect the prices that are charger weekly or if someone is interested in renting for months.A study could also be conducted to analyse the reviews of people on listings with different number of bedrooms.
The selected dataset is very dynamic and can give a lot of information creating different permutations and combinations. It would help someone thinking of investing in Airbnb’s in Seattle.