http://airbnbchallenge.surge.sh
Capital One Software Engineer Winter Summit Challenge. This challenge provided us with public information on Airbnb listings in San Francisco, California. Our goal was to create a web app which vizualizes the data in a creative and interesting way that can help potential hosts make profit.
[x] Visualize the data: Graph some (any 3) interesting metrics, maps, or trends from the dataset. - HeatMap, House Listings, Graphs
[x] Price estimation: Given the geo-location (latitude and longitude) of a new property, estimate the weekly average income the homeowner can make with Airbnb. - Weekly Income Estimation
[x] Bookings optimization: Given the geo-location (latitude and longitude) of a property, what is the ideal price per night that will yield maximum bookings or revenue? - Optimal Price Estimation
[x] Animate: Add an animation to your visualization. - estimation transitions, button slides, and footer appearing/disapearing
[x] Popularity: Can you identify the neighborhood that averages the most positive reviews? - Popularity graph
[x] Investment: If I have $100 million to invest, where in San Francisco should I buy properties so I can maximize my returns with Airbnb? When will I break even? - 12+ years, CSV file of coordinates. See 3.c for more details
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Map
a. Responsive Heatmap giving avergae price per night based on neighborhood
b. Listings of all houses in San Francisco with corresponding information and links to the pages themselves.
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Graphs
a. Popularity graph showing the average review score based on neighborhood
b. Average Price to Average Number of Reviews per month - shows the most profitable neighborhoods on average since reviews/month can be used as a measurement for frequency of tenants.
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Estimations
a. Determining the Weekly Income of a house given coordinates in San Francisco. - determined from averaging the 5 closest houses in proximity.
b. Determining the Optimal Price of a house given coordinates in San Francisco - Using reviews per month as means to determine tenant frequency, determines the most profitable houses in proximity and provides a competing price
c. $100 million investment - median house is $1,236,700 (https://www.zillow.com/san-francisco-ca/home-values/) and the average guest stays 3.5 days (https://blog.atairbnb.com/economic-impact-airbnb/). This means 80 houses can be purchased with $100 million. Using reviews per month (assuming reviews/month is a measure to indicate frquency of guests) and price per night, determine the 80 most profitable houses and their individual incomes per year. This will give a total estimated income of $8,202,694.08 a year. Coordinates for houses along with their yearly income can be found here: http://airbnbchallenge.surge.sh/data/investment.csv
All computations for csv and json data can be found in jupyiter notebook managedata.ipynd.
All other computations made with this data are can be found in estimate.js
- Maxwell Newman - (https://github.com/Maxwhoppa)
This project is licensed under the MIT License