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.
Highlights
The sharing economy has changed users’ consumption behaviors in the past decade
We briefly talk about four sentence embedding methods (i.e., Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), selfattention, and Smooth Inverse Frequency (SIF)), and a location clustering method
As a special architecture of the Recurrent Neural Network (RNN), LSTM[11] is well suited for classifying, processing, and making predictions based on time series data
Summary
The sharing economy has changed users’ consumption behaviors in the past decade. Notably, the emergence of short-term online rentals redefines the lodging business. Airbnb, which is available in over 34 000 cities with 1.5 million hosts and 50 million guests[1,2], is one of the most well-known online platforms in this industry for people to discover and book unique accommodations around the world[3]. Each layer has two sublayers, including a multi-head self-attention mechanism and a simple
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