Deep Neural Network based Data Analysis and Price Prediction framework for Rio de Janeiro Airbnb

Airbnb is an online platform that has a remarkable performance of helping rent a local home to tourists for a short period of time. While the ever-changing nature of the tourism industry necessitates an interminable upgradation of the state-of-the-art algorithms such as XGBoost, Support Vector Regression and Regression Tree used to predict rental pricing for Airbnb listings, a need to change and upgrade persists. The homeowners are still in the dilemma of the factors that help in pricing their rental home to attract consumers. Even the customers face a challenge to properly access a rental property’s worth. With the highly correlated, complex, and incomplete data that the past researchers have worked on, understanding the limitations and overcoming them has been a difficult endeavor. The objective of this paper is to conquer the challenges by extensively understanding the factors that affect the nightly charges while presenting a solution to price property with a three-layered deep neural network with two outcomes that has an accuracy of 74.43%. These outputs represent the minimum and the maximum amount that a renter could charge based on the information he provided about his property. The advantage of such a system is the ability of the user to dynamically change price based on various considerations like increase in demand, peak season, and improvement of locality and the house itself. This research also presents the top factors which one must include in their renting place to increase their profits.