Predicting Listing Prices In Dynamic Short Term Rental Markets Using
  Machine Learning Models
Sam Chapman Seifey Mohammad Kimberly Villegas
Our research group wanted to take on the difficult task of predicting prices
in a dynamic market. And short term rentals such as Airbnb listings seemed to
be the perfect proving ground to do such a thing. Airbnb has revolutionized the
travel industry by providing a platform for homeowners to rent out their
properties to travelers. The pricing of Airbnb rentals is prone to high
fluctuations, with prices changing frequently based on demand, seasonality, and
other factors. Accurate prediction of Airbnb rental prices is crucial for hosts
to optimize their revenue and for travelers to make informed booking decisions.
In this project, we aim to predict the prices of Airbnb rentals using a machine
learning modeling approach.
  Our project expands on earlier research in the area of analyzing Airbnb
rental prices by taking a methodical machine learning approach as well as
incorporating sentiment analysis into our feature engineering. We intend to
gain a deeper understanding on periodic changes of Airbnb rental prices. The
primary objective of this study is to construct an accurate machine learning
model for predicting Airbnb rental prices specifically in Austin, Texas. Our
project's secondary objective is to identify the key factors that drive Airbnb
rental prices and to investigate how these factors vary across different
locations and property types.