Should You AirBnB Or Sell Your Property - Big Data Helps Posted on : Oct 17 - 2017

Data-driven decision making is a practice in many commercial industries now. But especially the real estate industry is using data. Companies like Zillow or Redfin provide estimates of a given house in a given neighborhood for renters and buyers alike using historical data. But, there is a third option besides ‘selling’ or ‘subletting’ a house. You could rent it out via AirBnB. There’s no tool that predicts your income from this third option. Let's build one. 

Students of my class at Cornell University created a small neat tool to help you with that decision. Real Estate Advisor lets you enter the address of your spare property (in L.A. only for now) that helps you evaluate the two options on hand – AirBnB and Sell. Check out this video on how data could help you make a decision quickly:

Let's look at what happens in the backend when you hit the “Predict” button. Real Estate Advisor predicts the potential selling price of your house today and compares it to the potential revenue that you could make using prices as seen on AirBnB for similar houses. Like house prices, the potential AirBnB income depends mostly on the number of rooms. Thus, much like a hotel the more rooms there are, the higher the income. Airbnb, however, has a higher rate of seasonality. (Note: The Airbnb prices in the US see a seasonality which we did not include into our discussion here.)

Below, the team of Cornell University students shows how they predict both the property price and the expected perpetual income via Airbnb.

How to calculate the selling price of your property?

Predicting housing data is a typical exercise that many data scientists do. To predict the property price, we need the real estate data. And in this era, there’s no dearth for datasets (for instance, here are many datasets at Kaggle). But data is like vegetables - it perishes easily. Thus, for Real Estate Advisor to work, recent data is needed. Websites like Redfin, Zillow or Trulia can be rather easily scraped to obtain the required data such as the size of the house (in sq.ft), property type, number of beds and baths, lot size (in sq.ft) and, property age. Additionally, we added the quality of the school districts into our model. The proximity to the top schools was calculated by computing the minimum Haversine distance between the schools and the property on sale. The distance to the closest school was then used as a variable. View More