Airbnb Rental Price Prediction Using Machine Learning Models

Alexander Lektorov · Eman Abdelfattah · Shreehar Joshi

6 citations0/0 files storedIEEE Annual Computing and Communication Workshop and Conference (CCWC)

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Sharing Economy and Platforms Housing Market and Economics Data Mining and Machine Learning Applications

Abstract

Founded in 2008 Airbnb has become an attractive alternative within the hospitality industry as an online booking service based in San Francisco, California. It has grown since then and spread throughout the world. Today it is present in over 220 countries and 81,000 cities, has listings in millions, and its revenue in billions. The focus of this paper is to find the best price prediction model using machine learning techniques such as Decision Tree, K-Nearest Neighbors, Extra Trees, Support Vector Machines, Random Forests, and XGBoost, using Airbnb data collected in New York City five boroughs. Experiments are conducted using two datasets. Decision tree-based models; Random Forests, Extra Trees and XGB regressors demonstrated comparable performance with respect to the measured performance metrics.

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2014Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data 2020Housing Price Prediction via Improved Machine Learning Techniques 2018House Price Prediction Using Machine Learning and Neural Networks 2018Identifying Real Estate Opportunities Using Machine Learning 2019Housing Prices Prediction with a Deep Learning and Random Forest Ensemble 2021Airbnb Price Prediction Using Machine Learning and Sentiment Analysis 2021A Client-Centric Evaluation System to Evaluate Guest’s Satisfaction on Airbnb Using Machine Learning and NLP 2020Machine Learning Prediction of New York Airbnb Prices

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