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Online short-term rental platforms, such as Airbnb, have been becoming popular, and a better pricing strategy is imperative for hosts of new listings. In this paper, we analyzed the relationship between the description of each listing and its price, and proposed a text-based price recommendation system called TAPE to recommend a reasonable price for newly added listings. We used deep learning techniques (e.g., feedforward network, long short-term memory, and mean shift) to design and implement TAPE. Using two chronologically extracted datasets of the same four cities, we revealed important factors (e.g., indoor equipment and high-density area) that positively or negatively affect each property’s price, and evaluated our preliminary and enhanced models. Our models achieved a Root-Mean-Square Error (RMSE) of 33.73 in Boston, 20.50 in London, 34.68 in Los Angeles, and 26.31 in New York City, which are comparable to an existing model that uses more features.


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Text-Based Price Recommendation System for Online Rental Houses

Show Author's information Lujia Shen( )Qianjun LiuGong ChenShouling Ji
Department of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30318, USA.

Abstract

Online short-term rental platforms, such as Airbnb, have been becoming popular, and a better pricing strategy is imperative for hosts of new listings. In this paper, we analyzed the relationship between the description of each listing and its price, and proposed a text-based price recommendation system called TAPE to recommend a reasonable price for newly added listings. We used deep learning techniques (e.g., feedforward network, long short-term memory, and mean shift) to design and implement TAPE. Using two chronologically extracted datasets of the same four cities, we revealed important factors (e.g., indoor equipment and high-density area) that positively or negatively affect each property’s price, and evaluated our preliminary and enhanced models. Our models achieved a Root-Mean-Square Error (RMSE) of 33.73 in Boston, 20.50 in London, 34.68 in Los Angeles, and 26.31 in New York City, which are comparable to an existing model that uses more features.

Keywords:

price recommendation, natural language processing, sentence embedding, Long Short-Term Memory (LSTM), mean shift
Received: 09 December 2019 Accepted: 10 December 2019 Published: 27 February 2020 Issue date: June 2020
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Received: 09 December 2019
Accepted: 10 December 2019
Published: 27 February 2020
Issue date: June 2020

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