Akbar Ali Qazi, Ash Sandhu, and Mark Kopani

As our university careers came to an end, we were given the opportunity to work together one final time on what was essentially our capstone project. Our task was to choose a topic of interest which would allow us to best apply everything we learned in our Time Series and Forecasting course. This repository includes the data acquisition and cleaning (Python), the data exploration and feature selection (R), the full R analysis script, and the project report itself.

Airbnb is a technology company that is part of the fast growing ‘sharing economy’, where it acts as an ebroker, allowing anyone to rent out their property or a spare room in their house to guests, using their web or phone apps. They collect a small commision from each booking and they operate globally with over $2.6 billion in annual revenue. The goal of our term project was to analyze Airbnb listing data to better understand the trends in pricing. The data is available for many cities, however we chose to focus on the pricing of properties in Metro Vancouver. A better understanding of the trends is of great importance to users who have to decide how to price their listing, thus understanding the weekly, monthly and seasonal trends would allow users to determine their earning potential during certain periods. It would also allow guests to book at a time that is priced ideally for them.

Due to the large amount of data available for a given listing, such as the amenities present, the location etc, we explored any leading indicators that impact the price of the listing. We will also look at how the price of similar of listings change overtime, to see if there is any underlying seasonality and what the general pricing trends are. Finally, with a thorough understanding of the underlying trends, we provided a forecast for what the future prices will be for the current year and up to 2021.