ASSESSING AIRBNB LISTING CHARACTERISTICS AND PRICING PATTERNS IN THE SHORT-TERM RENTAL MARKET
Deprecated: htmlspecialchars(): Passing null to parameter #1 ($string) of type string is deprecated in /var/www/html/lib/pkp/lib/vendor/smarty/smarty/libs/plugins/modifier.escape.php on line 36
DOI:
https://doi.org/10.53555/th.v1i2.2595Keywords:
Airbnb; short-term rentals, listing characteristics, pricing patterns, room type, spatial analysisAbstract
Airbnb has become a major component of the urban short-term rental market, yet listing prices vary considerably according to accommodation type, location, and host-related characteristics. We analyzed 48,884 observations of Airbnb listings that excluded zero-priced observations. Using descriptive statistics, comparative tests, Spearman correlation, spatial analysis and multiple regression, the effects of room type, borough, minimum night requirements, review activity, host listing concentration, and annual availability on nightly prices were evaluated. It was found that there was a significant difference in the prices for various categories of accommodation and geographic regions. The private and shared rooms were significantly less expensive than an entire room or apartment, and Manhattan had the highest median nightly rate of the five boroughs. The minimum nights, the number of reviews, the number of hosts and the availability were weakly associated with Price. The regression model accounted for around 47.3% of the variance in the log-transformed prices, with room type and borough being the most important factors. Whole rooms or apartments were found to be much more costly than a private room, while shared rooms were found to be less costly. There was also a premium in the Manhattan listings over similar listings in Brooklyn. In sum, the study shows that the main factors that influence Airbnb prices are accommodation type and spatial location, whereas the impact of the review and operational characteristics is comparatively minor.
References
Abrate, G., Sainaghi, R., & Mauri, A. G. (2022). Dynamic pricing in Airbnb: Individual versus professional hosts. Journal of Business Research, 141, 191-199.
Adamiak, C. (2022). Current state and development of Airbnb accommodation offer in 167 countries. Current Issues in Tourism, 25(19), 3131-3149.
Alharbi, Z. H. (2023). A sustainable price prediction model for airbnb listings using machine learning and sentiment analysis. Sustainability, 15(17), 13159.
Arvanitidis, P., Economou, A., Grigoriou, G., & Kollias, C. (2022). Trust in peers or in the institution? A decomposition analysis of Airbnb listings’ pricing. Current Issues in Tourism, 25(21), 3500-3517.
Benítez-Aurioles, B., & Tussyadiah, I. (2020). What Airbnb does to the housing market. Annals of Tourism Research.
Boto-García, D., Mayor, M., & De la Vega, P. (2021). Spatial price mimicking on Airbnb: Multi-host vs single-host. Tourism Management, 87, 104365.
Bremser, K., & Wüst, K. (2021). Money or love-Why do people share properties on Airbnb?. Journal of Hospitality and Tourism Management, 48, 23-31.
Celata, F., & Romano, A. (2022). Overtourism and online short-term rental platforms in Italian cities. Journal of Sustainable Tourism, 30(5), 1020-1039.
Chattopadhyay, M., & Mitra, S. K. (2024). Do lists of amenities influence quality management in the P2P platform of Airbnb?-A study from 15 cities of the US and Canada. Journal of Quality Assurance in Hospitality & Tourism, 25(4), 908-928.
Chen, W., Wei, Z., & Xie, K. (2023). Regulating professional players in peer-to-peer markets: Evidence from Airbnb. Management Science, 69(5), 2893-2918.
Chen, Y., Huang, Y., & Tan, C. H. (2021). Short-term rental and its regulations on the home-sharing platform. Information & Management, 58(3), 103322.
Chica-Olmo, J., González-Morales, J. G., & Zafra-Gómez, J. L. (2020). Effects of location on Airbnb apartment pricing in Málaga. Tourism Management, 77, 103981.
Cocola-Gant, A., Jover, J., Carvalho, L., & Chamusca, P. (2021). Corporate hosts: The rise of professional management in the short-term rental industry. Tourism Management Perspectives, 40, 100879.
Congiu, R., Pino, F., & Rondi, L. (2025). The uneven effect of Airbnb on the housing market: Evidence across and within Italian cities. Journal of Regional Science, 65(2), 339-377.
Contu, G., Frigau, L., & Conversano, C. (2023). Price indicators for Airbnb accommodations: G. Contu et al. Quality & Quantity, 57(5), 4779-4802.
Dgomonov. (2019). New York City Airbnb Open Data [Data set]. Kaggle. https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data
Dolnicar, S., & Zare, S. (2020). COVID19 and Airbnb–Disrupting the disruptor. Annals of tourism research, 83, 102961.
Engin, A., & Vetschera, R. (2022). In whose bed shall I sleep tonight? The impact of transaction-specific versus partner-specific information on pricing on a sharing platform. Information & Management, 59(6), 103666.
Faye, B. (2021). Methodological discussion of Airbnb's hedonic study: A review of the problems and some proposals tested on Bordeaux City data. Annals of Tourism Research, 86, 103079.
Franco, S. F., & Santos, C. D. (2021). The impact of Airbnb on residential property values and rents: Evidence from Portugal. Regional Science and Urban Economics, 88, 103667.
Gárate Alvarez, S., & Pennington-Cross, A. (2023). Short-term property rental platforms and the housing market: House prices and liquidity. Journal of Housing Research, 32(1), 1-20.
Ghosh, I., Jana, R. K., & Abedin, M. Z. (2023). An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features. International Journal of Contemporary Hospitality Management, 35(10), 3592-3611.
Lee, S., & Kim, H. (2023). Four shades of Airbnb and its impact on locals: A spatiotemporal analysis of Airbnb, rent, housing prices, and gentrification. Tourism Management Perspectives, 49, 101192.
Lektorov, A., Abdelfattah, E., & Joshi, S. (2023, March). Airbnb rental price prediction using machine learning models. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0339-0344). IEEE.
Li, Z., Liang, S., & Law, R. (2026). Effect of professionalism on hosts’ pricing strategy in Peer-to-Peer rental platforms: Evidence from Airbnb. Journal of Smart Tourism, 6(1), 58-74.
Morales-Alonso, G., & Núñez, Y. M. (2022). Dragging on multilisting: the reason why home-sharing platforms make long-term rental prices increase and how to fix it. Technological Forecasting and Social Change, 174, 121297.
Moreno-Izquierdo, L., Rubia-Serrano, A., Perles-Ribes, J. F., Ramón-Rodríguez, A. B., & Such-Devesa, M. J. (2020). Determining factors in the choice of prices of tourist rental accommodation. New evidence using the quantile regression approach. Tourism Management Perspectives, 33, 100632.
Sainaghi, R., Abrate, G., & Mauri, A. (2021). Price and RevPAR determinants of Airbnb listings: Convergent and divergent evidence. International Journal of Hospitality Management, 92, 102709.
