Multi-level differentiation of short-term rental properties: A deep learning-based analysis of aesthetic design

Zhang, Huihui; Zach, Florian J.; Xiang, Zheng

doi:10.1016/j.tourman.2023.104832

Tourism Management

2024

DOI: 10.1016/j.tourman.2023.104832

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Multi-level differentiation of short-term rental properties: A deep learning-based analysis of aesthetic design

Huihui Zhang,

Florian J. Zach,

Zheng Xiang

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2024

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Cited by 3 publications

References 85 publications

(117 reference statements)

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Machine learning applied to tourism: A systematic review

Núñez,

Gómez‐Pulido,

Ramírez

2024

WIREs Data Min & Knowl

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The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry

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Machine learning applied to tourism: A systematic review

Núñez,

Gómez‐Pulido,

Ramírez

2024

WIREs Data Min & Knowl

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Optimal distinctiveness of short-term rental property design

Zhang,

Zach,

Xiang

2024

International Journal of Hospitality Management

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A Sustainable Rental Price Prediction Model Based on Multimodal Input and Deep Learning—Evidence from Airbnb

Tan,

Su,

et al. 2024

Sustainability

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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.

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Multi-level differentiation of short-term rental properties: A deep learning-based analysis of aesthetic design

Cited by 3 publications

References 85 publications

Machine learning applied to tourism: A systematic review

Machine learning applied to tourism: A systematic review

Optimal distinctiveness of short-term rental property design

A Sustainable Rental Price Prediction Model Based on Multimodal Input and Deep Learning—Evidence from Airbnb

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