Price and RevPAR determinants of Airbnb listings: Convergent and divergent evidence

This paper explores the performance determinants of Airbnb listings, analyzing three research questions. First, the study investigates the different effects generated by the antecedents on price and revenue; second, it ranks different groups of variables; third, it distinguishes between private rooms and entire homes or apartments. These research questions are addressed by analyzing Airbnb listings in Milan, a business city where the sharing economy is growing fast. In particular, the study will use the monthly data of all Airbnb listings in Milan recorded by AirDNA during the period from November 2014 to June 2019, which consists of 323,184 total observations. Some hedonic price models are calculated, adding the Shapley value approach. Empirical findings show some important differences between price and revenue determinants. Furthermore, listing type and size, along with location and seasonality, are by far the most important factors that explain performance differentials among Airbnb properties.     

Employee Relation Programs and Hotel Performance

Abstract

The purpose of this study was to examine the impact of employee relations programs (ERP) on organizational performance in the lodging industry. ERP provides employees with opportunities to participate in planning and improving work-related tasks. Four items were used to measure employee relations: formal complaint-resolution programs, participation programs, attitude surveys, and suggestion systems. Further, three types of organizational performance measures, namely employee turnover, labor productivity, and revenue per available room (RevPAR) were used. Findings indicated that ERP led to higher labor productivity and RevPAR.     

The effects of Airbnb’s price positioning on hotel performance

This study examined the relationship between the price positioning of Airbnb listings, measured in price difference between a hotel property and the nearby Airbnb listings as well as price dispersion among these Airbnb listings, and the performance of nearby hotels. An exploratory analysis using field data points collected from the Airbnb listings and their hotel counterparts in the metropolitan area of Austin, Texas between Quarter 3, 2008 (debut of Airbnb in Austin) and Quarter 2, 2011 reveals intriguing findings. The entry of Airbnb listings was penetrative to local hotels. However, the price positioning of Airbnb, manifested in higher average price as compared to nearby hotels, as well as larger price dispersion among individual listings, significantly mitigated such penetration. Important theoretical contributions and practical implications for hotels are discussed.     

The Debate Regarding Profitability

Abstract

Our research note explores a debate in the hotel industry regarding the relationship between hotel RevPAR and profitability, a debate around which there is a great degree of “noise.” Using a sample of 1,954 actual hotels for which both top line and bottom line indicators were available for the same year, we conclude through our statistical analyses that while hotels with higher revenue, and particularly higher room revenue, have a higher NOI in dollars, they do not necessarily have a more profitable business model in terms of NOI percentage. Also, we present brand level analyses.     

The dimensions of hotel customer ratings that boost RevPAR

Online ratings provide valuable information to operators. However, the use of this information for revenue maximization purposes remains at a moot point. This article proposes a new method to decompose ratings based on their relevance to hotel performance. From a methodological standpoint, we propose a multi-criteria decision analysis approach. The empirical validation includes two independent data sources, online ratings from Booking.com and RevPAR data from STR. By means of pairwise comparisons in PROMETHEE, the findings reveal the different weights of individual ratings, helping operators to understand the weight of each rating attribute in terms of revenue maximization. In particular, apart from the importance of the location, the role of staff and facilities emerge as central in terms of revenue maximization. The proposed model offers new theoretical insights on the relevant dimensions, helping hoteliers to prioritize when making trade-off decisions.     

Organizational Climate of Food and Beverage Employees

Abstract

Seven dimensions of organizational climate and measures of perceived customer satisfaction were gathered from food and beverage employees of 14 hotels. Regression analysis revealed organizational climate to explain 26.9% of the variance in customer satisfaction with food and beverage and only two organizational climate dimensions, professional and organizational esprit, and conflict and ambiguity, displaying a unique relationship to customer satisfaction with food and beverage. Customer satisfaction with food and beverage was found to explain 18.45% of the variation in RevPAR among the hotels. Recommendations are made as to which dimensions of organizational climate should be targeted for intervention programs attempting to increase hotel financial performance.