A Cross-Cultural Study of Value Priorities between U.S. and Chinese Airbnb Guests: An Analysis of Social and Economic Benefits

Tosun, Cevat; Eck, Thomas; An, So-Young

doi:10.3390/su15010223

Cited by 2 publications

(1 citation statement)

References 59 publications

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“…Cities are tasked with formulating and implementing policies that not only harness the positive impacts of Airbnb but also mitigate its drawbacks. This entails fostering dialogue among stakeholders, refining regulatory frameworks, and fortifying enforcement mechanisms, all aimed at ensuring the equitable distribution of benefits and maintaining the essence of neighborhoods [68].…”

Section: Balancing Benefits and Drawbacksmentioning

confidence: 99%

The Airbnb Phenomenon: A Qualitative Analysis of Its Consequences on Urban Housing Markets

Ekeroma

2023

Preprint

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The phenomenon of Airbnb has significantly permeated the urban housing markets of major U.S. cities. This qualitative analysis meticulously examines its wide-ranging impacts, exploring Airbnb’s operational model, regulatory challenges, and its influence on housing availability, prices, and neighborhood dynamics. The research aggregates insights from various sources, including academic articles, web-based resources, and case studies, providing a nuanced understanding of the intricate balance cities are endeavoring to achieve between the economic opportunities Airbnb presents and the potential strains it places on local housing markets. The paper also scrutinizes the evolving policy frameworks and their effectiveness in mitigating the impacts of short-term rental platforms. Furthermore, it deliberates on future trends in the short-term rental market and the wider implications for the sharing economy. Concluding, the paper acknowledges the limitations of the qualitative method and suggests areas for future in-depth research, aiming to offer insightful contributions to policymakers, researchers, and urban planners engaged in urban housing discourse in the United States.

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Section: Balancing Benefits and Drawbacksmentioning

confidence: 99%

The Airbnb Phenomenon: A Qualitative Analysis of Its Consequences on Urban Housing Markets

Ekeroma

2023

Preprint

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A Sustainable Price Prediction Model for Airbnb Listings Using Machine Learning and Sentiment Analysis

Alharbi

2023

Sustainability

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Since 2008, the company Airbnb has brought significant changes to the hospitality industry worldwide. Experiencing remarkable growth, it currently offers over six million listings in 191 countries across one hundred thousand cities. Airbnb has gained immense popularity among travellers seeking accommodations globally. Consequently, Airbnb generates extensive datasets from its listings that contain rich features that have captured the attention of researchers. These datasets offer potentially valuable information that can be extracted to greatly assist individuals and governments in making more informed decisions. Pricing rental properties on Airbnb still presents a challenge for owners, as it directly impacts customer demand. This research aimed to conquer the challenge by developing a sustainable price prediction model for Airbnb listings by incorporating property specifications, owner information and customer reviews. By utilising this model, owners can estimate the expected value of their Airbnb listings. We trained and fine-tuned several machine learning models using an Airbnb listing dataset from Barcelona. Performance evaluation metrics, such as mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE) and R2 score were then used to compare the models. To enhance the performance of the predictive models, sentiment analysis was used to extract relevant features from customer reviews. Feature importance analysis was also conducted to determine which attributes were the most influential on listing price predictions. The results show that the Lasso and Ridge models outperformed the others considered in the study, with an average R2 score of 99%. We found that amenities-related features had a negligible impact on all models’ performance. The most significant features found were polarity (positive/negative sentiment), the number of bedrooms, the accommodation’s maximum capacity, the number of beds and the quantity of reviews received by the listing in the past 12 months, respectively. We found that certain room types (categorized as entire home/apartment, private room or shared room) are associated with lower predicted prices.

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A Cross-Cultural Study of Value Priorities between U.S. and Chinese Airbnb Guests: An Analysis of Social and Economic Benefits

Cited by 2 publications

References 59 publications

The Airbnb Phenomenon: A Qualitative Analysis of Its Consequences on Urban Housing Markets

The Airbnb Phenomenon: A Qualitative Analysis of Its Consequences on Urban Housing Markets

A Sustainable Price Prediction Model for Airbnb Listings Using Machine Learning and Sentiment Analysis

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