A Hybrid Neural Network for Airbnb Demand
· 4 min read
Google Colab notebook: Open the hybrid Airbnb demand notebook · Raw Notebook: Download .ipynb
I wanted to find out whether combining text, location context and ordinary listing details could predict Airbnb demand more effectively than structured data alone. I tested this using the Airbnb NYC 2019 dataset, with 48,884 listings remaining after invalid rows were removed. For this experiment, I defined “high demand” as at least 1.58 reviews per month, the upper quartile of the dataset. This is a proxy for demand rather than a measure of confirmed bookings or occupancy, so the results should be interpreted as predictions of review activity.