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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