This project was lead by Tim Gilbert to create target revenue predictions for any house/condo in the United States if it were used as a full-time short-term-rental investment, taking into account property features, offered services, local entertain factors, market demand and penetration, seasonality, local expenses, etc. We extracted ML features from structured fields and descriptions from 6 years of data across 2 million properties, then filtered down to the 600k most relevant houses for predictions and built deep neural network models to predict yearly revenues, daily rates, long-term lease amounts.
We also created a system for finding nearby comparable properties, based not only on distance and number of bedrooms/bathrooms, but also on key features that affect house price and revenue such as pools, ski-in/out access, waterfront access, and much more.
Can you sign up to use the predictor at runrevr.com.
Example prediction results
Market Comparisons
