# Airbnb Price Prediction Using Machine Learning and Sentiment Analysis

arXiv: https://arxiv.org/abs/1907.12665 · HTML: https://mlsanity.com/paper/1907.12665/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Integrating Random Effects in Deep Neural Networks](https://mlsanity.com/paper/2206.03314/) (2022)
- [Predicting Airbnb Rental Prices Using Multiple Feature Modalities](https://mlsanity.com/paper/2112.06430/) (2021)
- [Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data](https://mlsanity.com/paper/2412.16899/) (2024)
- [Improving Group Lasso for high-dimensional categorical data](https://mlsanity.com/paper/2210.14021/) (2022)
- [A Multi-Source Information Learning Framework for Airbnb Price Prediction](https://mlsanity.com/paper/2301.01222/) (2023)
- [Predicting Listing Prices In Dynamic Short Term Rental Markets Using Machine Learning Models](https://mlsanity.com/paper/2308.06929/) (2023)

## Abstract (arXiv)

Pricing a rental property on Airbnb is a challenging task for the owner as it determines the number of customers for the place. On the other hand, customers have to evaluate an offered price with minimal knowledge of an optimal value for the property. This paper aims to develop a reliable price prediction model using machine learning, deep learning, and natural language processing techniques to aid both the property owners and the customers with price evaluation given minimal available information about the property. Features of the rentals, owner characteristics, and the customer reviews will comprise the predictors, and a range of methods from linear regression to tree-based models, support-vector regression (SVR), K-means Clustering (KMC), and neural networks (NNs) will be used for creating the prediction model.