Airbnb Listing Data Reports Dashboard
PowerBI Assignment Project
Data analysis & visualizations in PowerBI
For my next case study project, I have created an interactive dashboard for Airbnb listing data. This dashboard is designed for the marketing team with the goal of exploring accommodation listing data in the United States, generating reports, and devising quick and accurate strategies.
The interactive dashboard consists of two pages: a dedicated listing data page and a reports page. It also includes a filter pane on the left-hand side, which consists of slicers for room type, neighborhood, price, and number of reviews.
On the listing data page, the dashboard displays information such as host name, ID, listing name, number of reviews, price, reviews per month, minimum nights, last review, and listing availability throughout the year.
The reports page contains key metrics, including the total number of hosts, total number of listings, average number of reviews, average price, and other comparative metrics.
PROCESS
First, the datasets used can be accessed/downloaded here. There are 4 .csv files that will be used in this dashboard. Import all of them into Power BI. These datasets have already been cleaned, so there is no need to perform any further cleaning.
You can check if the data types in the dataset are correct by clicking on the “Transform Data” button, which will take you directly to Power Query. For these datasets, I recommend changing the data type of the “id” column in each table to whole number data type (if the imported dataset does not already have it as a whole number). Data transformations can also be done by clicking on the “Transform Data” option in the Home ribbon in Power BI after importing your dataset into Power BI.
Once we have ensured that the datasets are securely imported, proceed to connect the primary key of each table with the corresponding foreign key in other tables to create the data model.
ANALYZE
On the first page of this dashboard, the listing data page, I display a visualization of a table that contains columns such as ID, host name, listing name, number of reviews, price, reviews per month, minimum nights, last review, and availability 365. This table provides a detailed overview of the listing data report and its supporting components.
On the second page, which is the reports page, it contains key metrics summarizing the data from the listings for the marketing team to review the performance of the listings.
I present several key metrics in the form of cards to display the total number of hosts, total number of listings, average number of reviews, and average price. These KPIs provide a quick overview of the listing performance.
I use a stacked bar chart to showcase the total listings by neighborhood and room type, where Williamsburg neighborhood has the highest number of listings, with over 4000 listings, surpassing all others. In this chart, you can also see the breakdown of room types for each listing based on their respective neighborhoods.
I utilize a scatter plot chart to explore the correlation between variables such as the number of reviews and the price for each listing. This chart demonstrates that as the price of the listing increases, there are fewer reviews recorded.
Lastly, on both pages of this dashboard, to facilitate navigation between the pages, I have added navigation buttons for the “Listing Data” and “Reports” pages on the top section. Additionally, a filter pane is included on the left-hand side to facilitate the exploration of desired data, such as room type, neighborhood, price, and number of reviews.
INSIGHTS
- The total number of listings on Airbnb is 48,900.
- Airbnb has a total of 37,460 hosts.
- The average price of the listings is $153.
- The average number of reviews is 23.27.
- Williamsburg is the neighborhood with the highest number of listings based on neighborhood and room type.
- The room type “Entire home/apt” contributes the most, with a total of 40,309 listings, followed by “Private room” with 25,299 listings, and “Shared room” with 1,257 listings.
- The price of the listing affects the number of reviews it receives. If the listing price is high, the number of reviews tends to be lower. This suggests that listings with higher prices have a limited number of potential guests compared to listings with lower prices.
RECOMMENDATIONS
- Williamsburg Neighborhood: Focus on Williamsburg as it has the highest number of listings. Allocate marketing efforts and resources to attract more guests and increase bookings in this neighborhood.
- Room Type Distribution: Pay attention to the room type distribution. “Entire home/apt” is the most popular room type, followed by “Private room” and “Shared room.” Consider targeting promotions and marketing strategies towards these popular room types to maximize bookings.
- Pricing Strategy: Take into account the relationship between listing price and the number of reviews. Higher-priced listings tend to receive fewer reviews. Consider adjusting the pricing strategy to attract more guests and encourage higher review rates. Analyze the market competition and ensure the prices are competitive and appealing to potential guests.
- Utilize Reviews: Leverage the power of reviews. Encourage guests to leave reviews by providing exceptional experiences and maintaining high-quality listings. Positive reviews can significantly impact the reputation and trustworthiness of the listings, attracting more guests and boosting bookings.
- Explore Other Neighborhoods: While Williamsburg may have the highest number of listings, don’t overlook other neighborhoods. Analyze data from other neighborhoods to identify potential growth opportunities and develop targeted marketing campaigns to increase bookings in those areas.
Check out the repository here : https://github.com/rionovriandra/Airbnb-listing-data-dashboard
Visit my website : https://rionovriandra.github.io/
BI Portfolio : https://www.novypro.com/profile_projects/rionov
Check out my other blogs : https://medium.com/@riogeorge.n
LinkedIn : https://www.linkedin.com/in/rionovriandra/
