Airbnb Website Research Articles

Airbnb and urban population change: an empirical analysis of the case of Stockholm, Sweden

ABSTRACT Internet platforms enabling the short-term rental of private homes are an increasingly important provider of tourist accommodation and a challenge for urban policy. Airbnb is frequently blamed for encouraging property owners to remove their homes from the permanent housing market, thus contributing to the gentrification and displacement of local residents. Empirical research on this topic has been mostly represented by qualitative or indirect quantitative studies, which focus on changes in housing availability rather than the actual population. In our study, we fill this gap by presenting a study of the city of Stockholm, using two sources of georeferenced data: information on properties offered for rent on the Airbnb platform and micro-data on individuals and properties derived from the national statistical office. We verify whether the high number of apartment rental offers on the Airbnb website contributes to the high number of apartments that became uninhabited during 2012–2016. Using both aspatial and spatial autoregressive models, we find that the platform expansion does have a significant effect on the number of emptied apartments, and thus conclude that the presence of Airbnb contributes to the process of touristification, understood as the replacement of permanent residents with non-permanent populations. This confirms that when analysing urban population change and the gentrification process, mobile populations should be considered. Context-aware multiscalar and relational approaches are needed to understand the interrelation between human mobility, housing markets and regulations, and transnational internet platforms.

Utilising machine learning to investigate actor engagement in the sharing economy from a cross-cultural perspective

PurposeRecent literature on customer engagement has introduced the concept of “actor engagement,” which serves as the foundation for this study. The study aims to investigate the formation of engagement and engagement's impact on the performance of sharing economy platforms in an international context.Design/methodology/approachThe study analyses unstructured data from 145,434 service providers and 1,703,266 customers on Airbnb across seven countries (USA, Canada, United Kingdom, Australia, South Africa, China and Singapore). Machine learning techniques are used to measure actor engagement, and the research model is tested using structural equation modelling (SEM).FindingsThe findings suggest that actor engagement, encompassing the reciprocal relationship between customer engagement and service provider engagement, has a significant impact on platform performance. The moderator analysis highlights the role of cultural differences in the relationship between customer engagement and service provider engagement and between actor engagement and platform performance. Specifically, the study reveals that actor engagement exhibits a more pronounced impact on platform performance in Western countries (such as the USA, Australia and the UK), compared to Eastern countries (such as China and Singapore).Research limitations/implicationsThe analysis of the conceptual model is based on the utilisation of behavioural data obtained from the Airbnb website. Due to the nature of the available data, proxies are employed as measures for variables such as platform performance.Originality/valueThis research is amongst the first to provide empirical evidence for actor engagement formation and the function's role in platform performance in the sharing economy. The global nature of Airbnb as a platform facilitates the investigation of country-level factors, specifically cultural values, across seven diverse countries and highlight differences from business to customer (B2C) business models.

RESEARCH ON THE DISTRIBUTION OF AIRBNB ACCOMMODATION IN MAJOR EUROPEAN DESTINATIONS

The Airbnb platform, one of the most popular platforms of its kind, is present worldwide, with a variety of accommodations, depending on the type of accommodation it offers (single room, whole apartment, etc.). In this article, we set out to analyse the spatial allocation of accommodation and the density of Airbnb listings in comparison with hotel establishments in Europe. For the spatial analysis of Airbnb accommodations we used data from the Inside Airbnb website and QGIS, an application used for geographic information systems (GIS) that supports visualization, editing, and analysis of geospatial data. In this way, we created a geospatial database that helped us better understand the dynamics of each location. After analysing the data we concluded that a consequence of the concentration of Airbnb accommodation is that life in these areas has become more expensive and more crowded for locals. Airbnb accommodation has caused inconvenience not only to hotel owners but also to locals. In addition, Airbnb is having a strong impact on the rental market by increasing rental prices. Another trend that can be seen from the distribution of Airbnb accommodation is that in cities with a seafront location, accommodation is located close to beaches and most accommodation is of the entire home/apartment type. Differences in the frequency of use and prices of listings highlight the geographical inequality in the benefits and impact of Airbnb’s activity. The Airbnb offer is not a uniform segment of tourist accommodation and its effects on destinations are important and should therefore be analysed in relation to the territorial context.

E-marketing in Accommodation Business Model Innovation at Airbnb in Bali

This study analyzes the implementation of e-marketing in the accommodation business model innovation on Airbnb sites in Bali. Data was collected through observation, in-depth interviews and literature study. Data analysis was descriptive qualitative using Porter's Five Force Models (PFFM). This model is used to analyze competitive forces in marketing accommodation in Bali through the airbnb website. The results showed that the implementation of e-marketing in the innovation of the tourism accommodation business model on the Airbnb site in Bali by applying the PFFM proved to be quite effective. The competitive advantages of the Airbnb site are: multiple listings, two booking process confirmation systems, smaller booking commissions, competitive prices, no disparity rates, ease of registration, and marketing unique accommodation titles. Implementation of Porter's model is used to increase accommodation unit revenue by accepting bookings on the Airbnb situs site.

The Impact on Bed and Breakfast Prices: Evidence from Airbnb in China

As a new type of accommodation and a new way of life, it is of great significance for the spatial optimization and price management of the tourist accommodation market to explore the spatial differentiation characteristics of and influencing factors on bed and breakfast (B&B) house prices. Taking Shandong B&B merchants on the Airbnb website as the research object, this paper discusses the spatial characteristics of and influencing factors on B&B prices in the Shandong province, combining spatial autocorrelation analysis and interpolation analysis to identify the B&B cluster region. Quantile regression was used to reveal the main influencing factors. The results show that: (1) the spatial agglomeration effect of B&B prices in the Shandong province is obvious, and the high value areas form a new pattern between the provincial economic circle and the Jiaodong Economic Circle; (2) the influence of different factors on B&B house prices is very uneven in space. From the regional point of view, there are “sub-regional effects” on the spatial distribution of the influences of various factors on B&B house prices. The results of the study provide references for reasonable pricing, scientific site selection, and spatial optimization of B&Bs.

Mapping the operation of Airbnb in Sri Lankan cities

Airbnb is most famous as a part of the growing “sharing”, “peer-to-peer”, or “digital” economy where the platform transformed the traditional tourist accommodation services through its novel business model. In order to comprehend the influence of Airbnb on the tourism and hospitality landscape, it is critical first to understand the market and how Airbnb operates. Therefore, in this study, we explore the operation of Airbnb in Sri Lanka using descriptive statistics and maps by analysing the scrapped data of the Airbnb website. The study results show that Airbnb has been well established on Sri Lankan soil as an alternative accommodation service provider, and the listings have been scattered across the country. However, most listings tend to concentrate in the city centres of famous touristic districts and tourist resort regions in Sri Lanka. These findings will provide a valuable avenue for further investigation of the operation of Airbnb in Sri Lanka.

FEELING AT HOME WHILE TRAVELING: AN ANALYSIS OF THE EXPERIENCES OF AIRBNB USERS

Purpose – This study analyzes the experiences of Airbnb users in the city of Fortaleza, Ceará, Brazil, through the reviews published on the platform, evealing the relevant issues experienced by users during their stays and the polarity associated with these issues. Design – Collection of 2,338 Airbnb website reviews from users who visited Fortaleza between January 2019 and October 2020, associated with 503 listings. Methodology/Approach – Data was collected between October 24 and October 28, 2020, using Parse Hub and Tableau Public for data extraction and validation, respectively. Content analysis was performed to examine the data, and NVivo was used to support the categorization and coding steps. Findings – The main analysis categories in the online reviews of Airbnb users in Fortaleza were “location”, “physical environment”, “interaction and communication with the host”, “hospitable, friendly host”, “cleanliness and organization”, “near other facilities”, “home appliances” and “view”. In turn, the least referenced categories were “experiencing the local culture”, “interaction with other guests” and “living like a place”. The analysis suggests a positivity bias in comments while negative sentiments are caused mainly by “physical environment” and “home appliances”. Originality of the research – This study identifies the themes pointed out by guests when using Airbnb, that influence the user experience.

The spatial penetration of Airbnb in the main tourist cities of Spain: Extent and Determinants

Uneven spatial distribution of rented accommodation offered on Airbnb has potential to create spatial inequalities in host cities. In this regard, the claim of the proponents of Airbnb that ‘it increases spatial equality in the cities by creating opportunities for the house-owners in peripheral areas’ requires scientific analysis and empirical evidence. Using the data obtained from the Inside Airbnb website, 2011 census, the municipal register of Spain and OpenStreetMap, this article first measures the degree of penetration and spatial clustering (with LISA statistics) of the Airbnb listings in seven main tourist cities of Spain (Barcelona, Madrid, Valencia, Seville, Mallorca and Malaga). Second, it analyses (with multiple linear regression models) the spatial, socio-economic, demographic and cultural determinants of the spatial clustering of accommodation offered on Airbnb in the selected cities; and finally, it compares the growth of Airbnb and its impact on spatial inequalities in the selected cities.

Impact of the COVID-19 pandemic: Insights from vacation rentals in twelve mega cities

Coronavirus disease 2019 (COVID-19) is a challenging global problem. COVID-19 has caused shocks to various urban systems, and the tourism industry is no exception. We analyzed the impact on vacation rentals by conducting diachronic data mining on nearly 10 GB of rental data (calendar, listings, and reviews) in twelve highly internationalized megacities distributed across Asia, Europe, America, and Oceania based on the data set from the Inside Airbnb website. All twelve cities were adversely affected. The specific time of the impact is related to the pandemic's outbreak and enforced lockdowns policies. Affected by the epidemic, reservation rates decreased, tourists preferred renting in suburbs instead of city centers, the proportion of foreign tourists in all destinations dropped sharply, tourist sentiment scores fluctuated dramatically especially among foreigners, and people focused less on tourism related activities. This study reveals the changing illustrations of vacation rentals in highly internationalized megacities under the pandemic's influence. It offers a methodological assessment framework to monitor the hospitality sector over time and aims to serve as a reference for preparedness in similar cities worldwide.

City Production Strategies in the Neoliberalization Context - Salvador as Example

Abstract The aim of the current article is to think over economic development and urban infrastructure propositions by Salvador 360 Strategic Plan from 2017, which was elaborated by the municipal government. The plan allows analyzing the neoliberal city production approach which, despite its modernizing profile, is just an update of old models that have great potential to worsen structural socio-spatial segregation issues. Neoliberalism, urban spoliation, land value and gentrification are key concepts in this article. Neil Brenner, Nick Theodore, Jamie Peck, David Harvey, Lúcio Kowarick, Carlos Vainer and Inaiá de Carvalho are its main theoretical references. The methodology addresses documental analysis, as well as the analysis of secondary data from official sources (DIEESE and IBGE) and digital platforms analysis (AirBnB website). Based on the results, Salvador 360 Strategic Plan deepens eviction processes experienced by the poor population in the city’s central zones and increases the appropriation of private urban added value and service provision concentration, within a political setback context. This process can aggravate urban environment precariousness and quality of life in the city.

City Production Strategies in the Neoliberalization Context - Salvador as Example

Abstract The aim of the current article is to think over economic development and urban infrastructure propositions by Salvador 360 Strategic Plan from 2017, which was elaborated by the municipal government. The plan allows analyzing the neoliberal city production approach which, despite its modernizing profile, is just an update of old models that have great potential to worsen structural socio-spatial segregation issues. Neoliberalism, urban spoliation, land value and gentrification are key concepts in this article. Neil Brenner, Nick Theodore, Jamie Peck, David Harvey, Lúcio Kowarick, Carlos Vainer and Inaiá de Carvalho are its main theoretical references. The methodology addresses documental analysis, as well as the analysis of secondary data from official sources (DIEESE and IBGE) and digital platforms analysis (AirBnB website). Based on the results, Salvador 360 Strategic Plan deepens eviction processes experienced by the poor population in the city’s central zones and increases the appropriation of private urban added value and service provision concentration, within a political setback context. This process can aggravate urban environment precariousness and quality of life in the city.

Multi criteria based personalized recommendation service using analytical hierarchy process for airbnb

There are many accommodation rental services like Hotels.com, Hotels Combined, Trivago, Airbnb, and so on. Airbnb, in particular, uses the service known as P2P (peer to peer) technology. When the guest searches for rooms or a house to rent, he or she will have to consider a lot of information that Airbnb provides to the guest such as photos of the room/house, the host, rating of reviews, number of reviews, number of guests who can stay, number of bedrooms and bathrooms, description of the room, price. When there is a lot of information, it needs to be displayed effectively. Otherwise, it can complicate the guest’s choice. This research aims to make a personalized recommendation model and to analyze the guest preferences for accommodation using Airbnb. For this process, the study constructs criteria from accommodation information in Airbnb and it calculates and analyzes the guest’s preference by AHP (Analytic Hierarchy Process). The result shows the optimal room choices from the Airbnb website according to the guest’s preferences.

What can we learn from Airbnb data on tourist flows? A case study on Iceland

Tourist flows are increasingly important – at least up to the covid-19 crisis - and the Arctic regions are no exception. In particular, Iceland is experiencing an unprecedented tourism boom that began a decade ago. This goes hand in hand with the development of online short-term rental platforms such as Airbnb. This article questions how public information on the Airbnb website can be used to study tourist flows, with Iceland as a case study. Using a program we developed, we collected information about the accommodation available on the platform, as well as any comments left by visitors.Each member’s profile also includes the place of residence declared by the user, which makes it possible to know the country of origin. We then compared this collected information with official statistical data. Airbnb, which is growing steadily, seems to be gaining ground on the classic hotel business. The Airbnb data reflects the officially recorded flows by country of origin. This makes the case for using this data for the Arctic region, for which statistical data is hard to find at a fine-scale.

A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends

Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods.

Innovations in Tourism Marketing: Sharing Economy Platform

This research summarizes the arguments and counterarguments within the scientific discussion on the issue of potential tax evasions and increased rental rates that one of innovative home-sharing platforms might causes. The study aims to find possible tax evasion that each property and each host can cause by considering creative activities in tourism marketing and a sharing economy platform, Airbnb in Prague. Besides, the authors identified the potential problems in the rental accommodation market. Systematization literary sources and approaches for solving the problem of sharing economy platforms indicate that although these platforms benefit for some economic, social and environmental issues, they pose some troubles in various markets. Regarding methodological tools of the research method, this paper used a web scraping technique to gain data from the Airbnb website. The authors analysed 13918 accommodations that were rented by 6768 Airbnb users between April 2016 and March 2017. In the study, the authors used the Microsoft Excel 2016 program and a model created by researchers to make calculations. The paper presented the results of an empirical analysis showing that in case the sensitive regulation of the number of nights will apply the potential tax incomes will lower only on 0.98% when considering accommodations and on 6.40% when considering users. In this case, 16832 rental units will appear in the long-term housing market. Moreover, tax evasion becomes more when considering each dwelling instead of each host because some users rented more than one rental accommodation. The research also empirically confirms and theoretically proves that that extended limits for overnight stays yield benefits for costs, supply and demand of rental housing. Those findings can be useful for governments, academicians that are interested in tourism marketing, firms in the accommodation industry that look for new marketing innovations and short or long-term housing market participants such as lessor and lessees. Keywords: accommodation, tourism, marketing, Airbnb, innovation, home-sharing, Prague, sharing economy, taxation.

The Impact of Sharing Economy: Evidence from Airbnb and Taiwanese Hotel Industry

We investigate the impact of Airbnb on incumbent hotels' revenue in Taiwan. Combining listing information scraped from Airbnb website with a panel of hotel revenues, we propose a novel set of instrumental variables to identify the casual impact. Relative to OLS estimate, IV-2SLS estimates indicate larger negative effects from Airbnb. Furthermore, smaller and lower quality hotels are heavily affected by Airbnb listings.

Determinants of the continuance intention of Airbnb users: consumption values, co-creation, information overload and satisfaction

x00F3PropósitoEste estudio examina las relaciones entre las dimensiones de los valores de consumo (funcional, social y emocional), co-creación, sobrecarga de información, satisfacción e intención de continuidad, derivada del uso de la plataforma Airbnb.Diseño/metodología/enfoqueSe realizó una encuesta “en línea”, entre italianos, y se obtuvo para el análisis de datos, una muestra válida de 259 personas.titleResultadosLos resultados de la encuesta indicaron, que solo el valor funcional y el valor social, son fuertes predictores de la satisfacción en el uso del sitio web de Airbnb a la hora de hacer reservas de alojamiento. La creación conjunta y la ausencia de sobrecarga de información, también contribuyen a la satisfacción de usar el sitio web de Airbnb, para reservar alojamiento, lo que a su vez afecta la intención de continuidad.Limitaciones/implicaciones de la investigaciónLos resultados de este estudio son específicos para un destino, dado que la recolección de los los datos solo se efectúo sobre residentes italianos. La muestra final, se obtuvo con una técnica de muestreo de bolas de nieve; por lo tanto, los resultados del estudio podrían tergiversarse debido al sesgo de selección de muestreo. Además, el estudio se limitó al uso de tres dimensiones de valores de consumo, co-creación, sobrecarga de información y satisfacción para predecir la intención de continuidad. Además, el presente estudio adoptó un cuestionario de encuesta basado en la web.Implicaciones prácticasLas implicaciones gerenciales incluyen recomendaciones para que los anfitriones resalten sus precios, al enumerar alojamientos de Airbnb y centrarse en la interacción activa con posibles huéspedes. La información en el sitio web también debe organizarse para evitar la sobrecarga de información.Originalidad/valorEste estudio permite una comprensión más profunda sobre la intención de continuidad de los usuarios con respecto al sitio web de Airbnb, al explorar los posibles determinantes que la afectan.

Current state and development of Airbnb accommodation offer in 167 countries

ABSTRACT The paper examines the size, structure, distribution, dynamics, and use of Airbnb accommodation offer in 167 countries. Web-scrapping Airbnb website in fall 2018 and 2019 resulted in a datasets on 5.7 million listings, including 3.6 million active listings which have been rented out (reviewed) during the last year. Listings are divided into four groups based on types of properties and numbers of offers hosted by one platform user. The results show that the platform is most commonly used to rent out entire apartments by multi-hosts. The numbers of Airbnb listings in countries depend on the level of economic development and size of inbound tourism. One third of Airbnb supply is located in big cities, another one third near seacoasts. Airbnb offer grows most quickly in its relatively new markets, while in primary urban destinations of some European countries it is stable or decreases. The offer of professional hosts is growing more quickly than of peer-to-peer hosts. Differences in the frequency of use and prices of listings exaggerate the geographical unevenness in benefits and impacts of Airbnb activity. Airbnb supply is not a uniform segment of tourist accommodation and its effects on destinations should be considered in relation to territorial context.

Department location: Factor for Airbnb user’s valuation perception

The objective is to analyze the impact of the location in the quality perception of customers. Data was obtained from the stars-based valuation of Airbnb website, considering a standardized option of accommodation just apartments of one room up to two guest maximum, with an average cost of USD 50 per night and located in four cities: New York and Miami in USA, and Mexico City and Cancun in Mexico, using a chi-square analysis to identify if there is a difference in quality perception considering if the destination place have beach or not. The results showed than departments located in New York had the most significant difference in valuation of quality of hosts.

Analysis on Customer Satisfaction Dimensions in P2P Accommodation using LDA: A Case Study of Airbnb

Customer satisfaction becomes a key influencer for people's habits or daily activities. One of the examples is in the decision-making process about whether they will use specific products or services. People often need other's review or rating about what they are going to use or consume. In this research, by using customer's online review that available from Airbnb website, we try to extract what are the most talked factors about peer-to-peer accommodation, and how customer sentiment about them. We use Latent Dirichlet Allocation (LDA) to extract that factors and conduct sentiment analysis by utilizing semantic analyzer from Google Cloud NLP. We analyze which factors that has more effect on customer satisfaction, not only in general but more specific based on customer gender and tourism destination object. The result shows that factors related to social benefit and service quality have impact on customer satisfaction, moreover different customer gender and different tourism object destination bring different sentiment among customer. We also find several factors that can be improved by the owner of the accommodation to improve customer satisfaction toward their services.