/airbnb-optimization

Price & bookings optimization tool for Airbnb.

Primary LanguageCSS

Airbnb Optimization

Price & bookings optimization tool for Airbnb. Submission for Capital One Software Engineering Summit.

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Contents

Stack outline

Front End

  • Deployed on Github Pages
  • jQuery + AJAX
  • Google Maps API
  • Chart.js

Back End

Strategy

1. Visualize the data:

  • Nightly Prices vs. Location Review Score
  • Nightly Price Heat Map
  • Review/Month Heat Map
    • If I was a host looking to buy a new house (to rent out), I would want to buy a house in high demand (assuming more reviews/month correlates to more customers).

2. Price estimation:

Estimate the weekly average income a homeowner could make w/ Airbnb.

Inputs: latitude, longitude, property type, room type

Output: weekly income

Strategy:

  1. Get inputs from user.
  2. Filter listings down to those of the same property and room type.
  3. Find 5 nearest houses based on latitude & longitude (considered neighborhood name as well but might not be as good of a predictor).
  4. Calculate nightly averages of those houses.
  5. Multiply the nightly price x 7 to get the weekly income.

Assumptions:

  • Income meant gross income (revenue), not net income (profit).
  • Predicted weekly income is assuming that the listing is booked every day of the week. I attempted to figure out the average nights/week that a listing is booked based on the location and reviews per month (another possible assumption: more reviews correlates to more bookings). Possible improvement in future.

3. Booking Optimization:

Optimize the nightly price for maximum # of bookings.

Inputs: latitude, longitude, property type, room type

Outputs: optimum nightly price

Strategy: Same as #2. However this time, instead of multiplying by 7 to get the the weekly income, I subtracted $15 from the the average cost/night. This should undercut the other hosts and is a simple way to optimize bookings.

Assumptions:

  • Lower price will lead to more bookings.

Bonuses

Animate: Add an animation to your visualization.

  • First data visualization graph expands from the bottom up.
  • Other data visualization maps are interactive.

Popularity: Can you identify the neighborhood that averages the most positive reviews?

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?