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Our research group wanted to take on the difficult task of predicting pricesin a dynamic market. And short term rentals such as Airbnb listings seemed tobe the perfect proving ground to do such a thing. Airbnb has revolutionized thetravel industry by providing a platform for homeowners to rent out theirproperties to travelers. The pricing of Airbnb rentals is prone to highfluctuations, with prices changing frequently based on demand, seasonality, andother factors. Accurate prediction of Airbnb rental prices is crucial for hoststo 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 machinelearning modeling approach.  Our project expands on earlier research in the area of analyzing Airbnbrental prices by taking a methodical machine learning approach as well asincorporating sentiment analysis into our feature engineering. We intend togain a deeper understanding on periodic changes of Airbnb rental prices. Theprimary objective of this study is to construct an accurate machine learningmodel for predicting Airbnb rental prices specifically in Austin, Texas. Ourproject's secondary objective is to identify the key factors that drive Airbnbrental prices and to investigate how these factors vary across differentlocations and property types.

Predicting Listing Prices In Dynamic Short Term Rental Markets Using Machine Learning Models