Price Determinants of Tourist Accommodation Rental: Airbnb in Barcelona and Madrid

Lladós‐Masllorens, Josep; Artola, Antoni Meseguer

doi:10.1007/978-3-030-30809-4_50

Cited by 4 publications

(9 citation statements)

References 18 publications

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“…(Chattopadhyay & Mitra, 2019;Tang et al, 2019;Wang & Nicolau, 2017), locational attributes, i.e. distances from points of high accessibility or of touristic interest (Chica-Olmo et al, 2020;Onder et al, 2019;Zhang et al, 2017), neighbourhood characteristics (Jiao & Bai, 2020;Llados-Masllorens & Meseguer-Artola, 2020), and rental terms and conditions, e.g. cancelation or smoking policy (Benitez-Aurioles, 2018;Chica-Olmo et al, 2020;Wang & Nicolau, 2017).…”

Section: Price Determinants Of Airbnb Listingsmentioning

confidence: 99%

Trust in peers or in the institution? A decomposition analysis of Airbnb listings’ pricing

Arvanitidis

Economou

Grigoriou

et al. 2020

Current Issues in Tourism

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The present paper examines whether Airbnb listing prices differ significantly between 'casual' hosts and hosts with multiple listings, regarded as professionals. To this effect the Airbnb apartment market in Athens (Greece) is used as a case study. Drawing on a sample of about 3.500 Airbnb apartments listed in May 2017, the study employs, first, typical hedonic price modelling to assess the determinants of listings' price of both groups, and then it uses the Blinder-Oaxaca decomposition methodology to examine whether there is a price gap between casual and professional hosts, the characteristics that cause this price gap and their relative contribution to it. The research finds a statistically significant difference in the Airbnb pricing between casual and professional hosts, where about a half of the price gap is attributed to the 'better' listings that professionals manage (in terms of size, availability of amenities and locational attributes), and a half to the different reaction hosts have towards specific Airbnb characteristics. In particular, professionals value higher (i.e. place more trust on) the Superhost badge issued by Airbnb as an institution, whereas casual hosts place trust on (and price accordingly) the evaluations provided by the guests.

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Section: Price Determinants Of Airbnb Listingsmentioning

confidence: 99%

Trust in peers or in the institution? A decomposition analysis of Airbnb listings’ pricing

Arvanitidis

Economou

Grigoriou

et al. 2020

Current Issues in Tourism

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“…This area has the most blocked state properties, and the number of blocked properties in yellow area are second only to the red area. It can be seen that the properties of the block state are relatively concentrated, and it seems to have a cluster effect [3]. According to the time series distribution of the number of blocked properties per day in April, we found the change of blocked properties has a clear periodicity, and the interval between the periods is approximately one week.…”

Section: Fig 2 the Density Heat Map Of Blocked Properties Locationsmentioning

confidence: 74%

Airbnb Short-term Housing Rental Status Prediction Model Under the Impact of the COVID-19 Pandemic

2021

E3S Web Conf.

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With the vigorous development of the sharing economy, the short-term rental industry has also spawned many emerging industries that belong to the sharing economy. However, due to the impact of the COVID-19 pandemic in 2020, many sharing economy industries, including the short-term housing leasing industry, have been affected. This study takes the rental information of 1,004 short-term rental houses in New York in April 2020 as an example, through machine learning and quantitative analysis, we conducted statistical and visual analysis on the impact of different factors on the housing rental status. This project is based on the machine learning model to predict the changes in the rental status of the house on the time series. The results show that the prediction accuracy of the random forest model has reached more than 94%, and the prediction accuracy of the logistic model has reached more than 74%. At the same time, we have further explored the impact of time span differences and regional differences on the housing rental status.

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“…Different studies (Lladós-Masllorens and Meseguer-Artola, 2019; Wachsmuth and Weisler, 2018) have concluded that, particularly in the most touristified neighborhoods, long-term housing affordability has been directly affected by the appearance of these rent gaps (Gyódi, 2019; Lee, 2016). In their study, Kim et al (2019) showed that this depletion of the long-term rental supply was more concentrated among the lower-priced, affordable, units, raising concerns about the affordability of available housing.…”

Section: Literaturementioning

confidence: 99%

Analyzing the influence of short-term rental platforms on housing affordability in global urban destination neighborhoods

Tamajón

Lladós‐Masllorens

Artola

et al. 2022

Tourism and Hospitality Research

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In the last decade, global urban destinations have witnessed an unprecedented wave of tourism growth based on the development of short-term rental platforms (STAP) and of a particular platform, Airbnb. Literature has demonstrated this growth’s influence in different contexts, highlighting its impact on housing markets, mostly at the city level and through correlation or regression analyses. This study goes a step further, focusing on the neighborhood level for the case of Barcelona (Spain) and using k-means clustering. Results show how the concentration of Airbnb listings in “highly touristified” and “trendy” neighborhoods has been associated with an increase in rental prices in these areas. Moreover, the subsequent increase in the demand for tourism-related services in these neighborhoods has led to a displacement of residents to peripheral neighborhoods, which has also put pressure on these areas’ housing prices. These processes have been accompanied by an increasing reaction from local anti-tourism movements. Based on these findings, we suggest that global urban destinations managers need to manage STAP and tourism-related service development not only in relation to the tourism industry but also regarding its impact on the conditions of residents and should consider the neighborhood as a primary management unit when designing the necessary regulations.

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Price Determinants of Tourist Accommodation Rental: Airbnb in Barcelona and Madrid

Cited by 4 publications

References 18 publications

Trust in peers or in the institution? A decomposition analysis of Airbnb listings’ pricing

Trust in peers or in the institution? A decomposition analysis of Airbnb listings’ pricing

Airbnb Short-term Housing Rental Status Prediction Model Under the Impact of the COVID-19 Pandemic

Analyzing the influence of short-term rental platforms on housing affordability in global urban destination neighborhoods

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