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

TL;DR

This paper develops machine learning models to accurately predict Airbnb rental prices in Austin, Texas, considering demand, seasonality, and sentiment analysis, to help hosts optimize revenue and inform travelers.

Contribution

It introduces a methodical machine learning approach with sentiment analysis for Airbnb price prediction, expanding prior research with a focus on key price drivers and regional variations.

Findings

High prediction accuracy achieved with ML models.

Sentiment analysis improves feature relevance.

Key factors influencing prices vary by location and property type.

Abstract

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.