Airbnb Listings Research Articles

Enhancing our understanding of short-term rental activity: A daily scrape-based approach for Airbnb listings.

The growth of the online short-term rental market, facilitated by platforms such as Airbnb, has added to pressure on cities' housing supply. Without detailed data on activity levels, it is difficult to design and evaluate appropriate policy interventions. Up until now, the data sources and methods used to derive activity measures have not provided the detail and rigour needed to robustly carry out these tasks. This paper demonstrates an approach based on daily scrapes of the calendars of Airbnb listings. We provide a systematic interpretation of types of calendar activity derived from these scrapes and define a set of indicators of listing activity levels. We exploit a unique period in short-term rental markets during the UK's first COVID-19 lockdown to demonstrate the value of this approach.

The effect of short‐term rentals on local consumption amenities: Evidence from Madrid

AbstractThis paper examines the impact of the arrival of Airbnb on local consumption amenities in Madrid. We exploit the exogenous variation created by the timing and uneven distribution of Airbnb listings in the city to determine the impact on food and beverage establishments. Using an instrumental variable strategy, we find positive local effects on both the number of restaurants and their employees: an increase of 14 Airbnb rooms in a given census tract leads to almost one more restaurant, and the same increase in a given neighborhood generates 11 new tourist‐related employees. The results are robust to the specification and sample composition. This paper contributes to the literature on the economic impact of the platform economy on urban areas by providing evidence of market expansion externalities from short‐term rentals.

Virtue Signaling in the Sharing Economy: The Effect of Airbnb Entrepreneurs’ Virtue Language on Airbnb Price Premiums

Drawing from costless signaling research, we examine the role of virtue language in Airbnb listings. We propose that virtue language espoused by entrepreneurs is beneficial for Airbnb price premiums—but only to a certain extent. Specifically, we argue that virtue language espoused by entrepreneurs has a curvilinear relationship with the price premium of their listings, suggesting that excessive use is seen as dishonest and becomes detrimental to their performance. We also suggest that this relationship is more pronounced when the entrepreneur is perceived to be more credible and when the entrepreneur is venturing into more conservative markets.

Predicting US Airbnb Listing Prices by Machine Learning Models

This paper addresses the prediction of Airbnb property prices using 2023 open data through the application of machine learning methodologies. In the context of the flourishing sharing economy, accurate price prediction within the short-term rental market holds great significance for hosts and users alike. Drawing on the 2023 Airbnb open dataset, the study employs three distinct models – Linear Regression, Random Forest, and XGBoost. Rigorous training, testing, and evaluation of these models reveal insights into their predictive capabilities. The focus centers on assessing model fit using essential evaluation metrics including R-squared, Mean Squared Error, and Root Mean Squared Error. Results demonstrate that the XGBoost model outperforms both Linear Regression and Random Forest. After parameter tuning, the best parameter for XGBoost regressor exhibits the lowest prediction error and highest R-squared value, showcasing its ability to capture intricate patterns within the data. This outcome underscores the potency of advanced ensemble learning techniques for precise property price predictions. The study's implications are substantial, offering hosts and potential guests improved decision-making insights.

The Impact of Spatial and Locational Characteristics on Airbnb Prices in London

This research delves into the intricate relationship between spatial and locational attributes and Airbnb pricing in London. Utilizing data encompassing Airbnb listings in London collected over one year concluding on December 10, 2022, and employing advanced geospatial statistical techniques, including a geographically weighted regression (GWR) model equipped with ten select explanatory variables, this study reveals multifaceted spatial patterns underlying Airbnb pricing. The study underscores the paramount significance of dissecting the multifaceted determinants of Airbnb pricing, encompassing property characteristics, location-specific variables, host-related attributes, and customer feedback. Through empirical analyses, this research illuminates pronounced spatial heterogeneity within Airbnb pricing, with notable variations discerned across different room types. Interpretation of model coefficients reveals the multifaceted influence of factors, such as proximity to subway stations, volume of customer reviews, and specific scores, on pricing dynamics. Additionally, the GWR model exposes significant spatial variations in the impact of location and neighborhood-related variables on pricing, with particularly marked effects in the realms of entire homes and private rooms. This study aims to illustrate the intricate interplay between spatial and locational characteristics and Airbnb pricing dynamics, offering invaluable insights for researchers and industry practitioners.

Co-creating cultural heterotopic spaces in Airbnb

This article considers the concept of heterotopia in shared tourism accommodation. Based on an auto-ethnographic account and interviews with 27 hosts and guests of Airbnb shared spaces and their Airbnb listings, the article explains how a home with mixed statuses of accommodation and homeyness can provide transitional experiences that link the ordinary and extra-ordinary, order and disorder and conflicting cultural meanings that impact social interaction. We find that Airbnb spaces contain layers of cultural complexity that shape how hosts and guests co-create their exchanges and transition through the space. Applying the concept of heterotopia to a site that disrupts taken-for-granted notions about the ordering of space offers a dynamic view of how the peer-to-peer accommodation experience is co-created. We also develop implications for consumption and service provision within the sharing economy.

Neighborhood Trip. How do guests’ neighborhood preferences shape the touristification process?

Urban tourists experience neighborhoods in a Peer-to-Peer context. However, literature overlooked the neighborhood concept in guests' accommodation location decisions, emphasizing the centrality (for a given quality) as the primary driver of urban touristification. This paper assesses how guests' preferences for neighborhoods could disrupt tourists' preferences for centrality. We discuss the exogeneity of the neighborhood tourism concept, verify its significance, and explore the relationship between neighborhoods and centrality in the urban touristification process. By extracting latent dimensions of 2,186 neighborhoods in 31 French cities and using a two-step hedonic approach (GW/2SLS regressions) on 45,600 Airbnb listings, we identify significant guests' preferences for neighborhood dimensions independently of embedded accommodation characteristics. Findings indicate that guests’ preference for touristy neighborhoods reinforces touristification but non-linearly (concavity), suggesting a touristification maximum threshold. With threshold effects, the other neighborhood dimensions (youthful-hub, popular, well-off) shape the touristification overflow, suggesting new regulations of the accommodations gathering.

The Host Canceled My Reservation! Impact of Host Cancelations on Occupancy Rate in the P2P Context: A Signaling Theory Perspective

The business of hosts in peer-to-peer (P2P) accommodation sharing has become an important source of revenue for individuals in many economies. However, there is a dearth of studies on hosts, specifically on the factors that affect host performance (i.e., occupancy rates). Drawing on the signaling theory and the source credibility theory and using a dataset of 41 610 reviews of 7004 Airbnb listings, we investigated the impact of cancelation rate—that conspicuously signals how many times a host has canceled a pre-existing reservation—on the host occupancy rate. Furthermore, we investigate the role of source credibility signals in reducing the impact of host cancelations. The results show that host signals of reputation, responsiveness, and expertise minimize the negative effect of cancelations on the occupancy rate. Theoretically, we advance the academic literature on credibility signals in P2P platforms and their moderating role on host performance. Managerially, the study helps P2P hosts in understanding the role of signals on occupancy rate.

Airbab에서 숙소 인기도에 영향 미치는 요인

This study aims to understand the factors that influence the popularity of listings on Airbnb. For this purpose, data on the attributes of hosts (who are service providers), the attributes of listings (which are the core facilities of the Airbnb service), and the content of reviews (which represent the existing customers' perceptions of their experience) were obtained from Inside Airbnb, a non-for-profit data site related to Airbnb. To develop the research hypotheses, the effects of the five sub-dimensions of the SERVQUAL model, perceived value, and review sentiment on the popularity of a listing were examined. The popularity of a listing was measured by the number of new reviews, and since it is a count variable and has a skewed distribution, it was analyzed through Poisson regression. The results showed that all five dimensions of the service quality model have a positive impact on the popularity of Airbnb listings, and perceived value, which is important in the Airbnb context, also has a significant positive impact. However, the positive and negative sentiments in reviews did not yield the expected results. Our findings have academic contributions in the form of a new theoretical framework, a new dependent variable, and the application of a longitudinal research method. Additionally, they also have practical implications for the management of Airbnb listings.

Impact of Airbnb on the hotel industry in Japan

The main aim of this paper is to find out whether the occupancy rate of the hotels would increase or decrease with the number of listings in Airbnb in Japan. It employs a panel data gathered from annual Airbnb's listings and hotel occupancy rates, covering all 47 prefectures in Japan and 5 accommodation types for 7 years. The findings indicate that the number of Airbnb listings did not have a statistically significant impact on the overall hotel occupancy rates. Based on the type of accommodations, both Ryokan and Common Lodging House show significant results and are positively influenced by Airbnb listings. The study concludes that Airbnb does not disrupt the hotel industry and suggests easing some of the current restrictions on accommodation sharing in Japan.

Images’ features and Airbnb listing price: the mediation effect of visual aesthetic perception

ObjetivoEste estudio explora los determinantes visuales del precio basados en las imágenes (características de las imágenes y percepción estética visual) y cómo afectan las características de las imágenes al precio de los anuncios de Airbnb en una plataforma de alojamiento compartido.Diseño/metodología/enfoqueEl estudio emplea un modelo SOR y un modelo de precios hedónicos para examinar las conexiones entre las características de los rasgos de la imagen, la percepción estética visual y los precios de Airbnb. A continuación, se examina el modelo mediante un modelo econométrico utilizando datos de Insideairbnb.com.ResultadosLos resultados empíricos revelan que 1) las características de la imagen tienen un efecto positivo significativo sobre la percepción estética visual, 2) la percepción estética visual tiene un efecto positivo significativo sobre el precio de los anuncios de Airbnb, y 3) la percepción estética visual tiene un efecto mediador significativo entre las características de la imagen y el precio de los anuncios de Airbnb.Originalidad/valorEste estudio contribuye al mecanismo de relación y efecto entre las características de la imagen, la percepción estética visual y el precio del anuncio de Airbnb, y tiene algunas implicaciones tanto para los operadores inmobiliarios como para la plataforma de alojamiento compartido.

Four shades of Airbnb and its impact on locals: A spatiotemporal analysis of Airbnb, rent, housing prices, and gentrification

Despite the growing amount of tourism literature examining short-term rentals, few studies have examined their economic impact on locals. This study is unique in that it elucidates Airbnb listings' heterogeneity and reflects spatiotemporal effects in the analysis of their impact on locals. The study revealed that Airbnb has different impacts on local housing markets by their characteristics. Furthermore, this study provides quantitative evidence delineating the relationship between Airbnb and gentrification. Overall, entire home listings by multi-unit hosts were found to exhibit the greatest impact on rent, housing value, and gentrification. The study findings provide practical implications for destinations to implement concrete strategies for balancing the positive and negative impacts of Airbnb.

Tourism gentrification and P2P accommodation: The case of Airbnb in Bandung City

In the accommodation sector, the sharing economy has caused a change of lifestyle that has led to gentrification. This paper chose Bandung City as a case study as the city is one of Indonesia's creative centres and tourism destinations. This study identifies the increasing number of Airbnb listings and how it relates to gentrification. Two sources of data are used: Airbnb and the economic and demographic data in Bandung City. We construct the gentrification index and present the non-parametric tests on the impact of Airbnb on gentrification. The analysis demonstrates the gentrification process in the fringe area near tourist attractions and residential areas, which differs from cities in developed countries. Our findings suggest that Airbnb listings may lead to tourism gentrification in peripheral and residential areas. This study proposes the importance of identification and policies on gentrification development to hinder over-tourism in peripheral and second-tier cities in developing countries.

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

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.

Forecasting Airbnb prices through machine learning

AbstractAchieving accurate pricing is critical for both peer‐to‐peer (P2P) accommodation platforms and hosts. An understanding of the determinants of prices on P2P platforms, such as Airbnb, can improve service quality and help make pricing more rational. In this study, machine learning (ML) was applied to P2P accommodation pricing prediction. Data from Airbnb listings in Sydney, Australia, was collected, and 10 ML algorithms were used to predict prices. Host data were divided into training and testing sets. A total of 35 variables, including price and 34 independent variables, were identified. The 10 algorithms were evaluated using the Student's t test, the root mean squared error, and the R2 value. The CatBoostRegressor algorithm had the best performance. According to the relative weights in the optimized CatBoostRegressor algorithm, the key factors affecting pricing are the maximum number of guests, the number of bedrooms, and whether the room is private. Platforms can use these results to share accurate rental pricing information with hosts. Registered hosts can obtain timely information regarding the house rental market to set reasonable prices.

Airbnb pricing and term structure: A temporal analysis of omitted variable bias and repeat sales method as remedies

Many tourism studies leverage the hedonic price model to gauge tourists’ willingness to pay for diverse attributes of short-term rental properties. However, when this estimation is applied to the temporal analysis, it can be biased if variables varying with time, such as term structure effects in short-term rentals, are omitted. This paper introduces a repeat sales Airbnb ADR (average daily rate) index to track the change in quality-adjusted rentals of Airbnb properties over time in Auckland, New Zealand, while factoring in the term structure. The findings confirm that using repeat sales data from Airbnb listings can significantly mitigate the bias linked to time-varying attributes. Results demonstrate that when the term structure is not considered, the ADR calculated by the hedonic method may be overestimated by 0.2% per day of the tenancy term. The inventive Airbnb ADR repeat sales index enables the assessment of Airbnb rental trends, taking into account changes in the term structure of leases. This new index can potentially enhance Airbnb listings by incorporating the effects of lease term structures.

Negative signals on Peer-to-Peer platforms: The impact of cancellations on host performance across different property types

Scholars investigated the factors enhancing Airbnb hosts’ performance; however, less research focused on negative signals, such as host cancellation messages. Cancellations are a negative signal that conspicuously reveals the number of times a host has canceled a pre-existing reservation. Drawing upon signaling theory and product involvement, we argue that cancellation signals have a negative impact on host occupancy, but this impact is moderated by the level of involvement associated with the accommodation type (i.e., private room, shared room, entire apartment). The study used a dataset of 31,778 reviews of 6384 Airbnb listings. The results show that accommodation type moderates the relationship, that is, the impact of cancellations is stronger for higher involvement accommodations (entire apartment) versus low involvement ones (shared rooms). This study advances the literature on negative signals and helps P2P managers understand the impact of cancellations on their revenues.

Inside Airbnb titles: The location choices of listings from Chinese hosts' advertising expressions

This study explores the location choices of Airbnb from the hosts' perspective. Drawing on accommodation location models, cognitive maps and advertising place references, hosts' location choices and spatial advertising hotspots are discovered. 136,000 pieces of Airbnb listing data and Airbnb titles in 18 Chinese cities are used to explain the relationship between Airbnb and hosts' place references. Toponymy mining methods and GIS mapping results confirm the existence of a common expression logic in Airbnb locations. Airbnb titles frequently provide the general characteristics of the location and the relevance to spatial attractions, which are oriented to recreation and travel, subjected to effects from multiple groups, and informed by four trends for urban functional zones. The study extends the Tourist-Historic City model into sharing accommodation research through multiple cases and utilizes Airbnb titles data to uncover host-oriented location choices in the context of Airbnb, it reveals the pragmatism features of Airbnb.

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.

Examining the sources of pricing power on Airbnb

ABSTRACT Pricing power, a firm’s ability to set prices above the competition without tempering demand, is a critical concern for any business. However, it has received little attention in the Airbnb pricing literature, although revenue maximization through effective pricing is a key priority for many Airbnb hosts. The Airbnb pricing literature has mainly examined price differences among diverse Airbnb properties, which fails to capture pricing power, as the properties being compared do not compete for the same guest segments. To address this gap, the present study examines pricing power and its sources on Airbnb. Drawing on the pricing power literature, the study develops a measure of pricing power based on a property’s ability to set prices higher than the average price set by similar properties on Airbnb. Using data from thousands of Airbnb listings in Denver City from 2019 (before the COVID outbreak), the study estimates a model in which a property’s pricing power is regressed on a range of property-, host-, and guest-related factors. The findings show that the most significant drivers of a property’s pricing power are guest capacity (–), competition (–), distance (+), and booking price (+). The study discusses the implications of these findings.