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In the accommodation field, reasonable pricing is crucial for hosts to maximize their profits and is also an essential factor influencing tourists’ tendency to choose. The link between price prediction and findings about the causal relationships between key indicators and prices is not well discussed in the literature. This research aims to identify comprehensive pricing determinants for sharing economy-based lodging services and utilize them for lodging price prediction. Utilizing data retrieved from InsideAirbnb, we recognized 50 variables classified into five categories: property functions, host attributes, reputation, location, and indispensable miscellaneous factors. Property descriptions and a featured image posted by hosts were also added as input to indicate price-influencing antecedents. We proposed a price prediction model by incorporating a fully connected neural network, the bidirectional encoder representations from transformers (BERT), and MobileNet with these data sources. The model was validated using 8380 Airbnb listings from Amsterdam, North Holland, Netherlands. Results reveal that our model outperforms other models with simple or fewer inputs, reaching a minimum MAPE (mean absolute percentage error) of 5.5682%. The novelty of this study is the application of multimodal input and multiple neural networks in forecasting sharing economy accommodation prices to boost predictive performance. The findings provide useful guidance on price setting for hosts in the sharing economy that is compliant with rental market regulations, which is particularly important for sustainable hospitality growth.
Tan et al. (Thu,) studied this question.