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This study aims to examine the impact of a mega-sporting event “2014 FIFA World Cup” on hotel pricing strategies and performance.

The present project examines the host regions’ response to the 2014 FIFA World Cup which was established by the variance in the main hotel key performance indexes: occupancy, average daily rate, revenue per available room (RevPAR) and supply. Using data gathered from STR, this research distinctly shows how the Brazilian host regions reacted to the World Cup.

Results suggest that the key performance indicators of Brazil’s lodging sector reacted differently to the World Cup. Although all hosting cities experienced significant RevPAR growth because of the increase in hotel room rates during the event, the supply and occupancy performed differed from each city.

Research is limited to the case of hotel performance at the country level for mega-events. The study focused on the reaction of revenue managers in the Latin America context. Other contexts may generate different results.

The study helps revenue managers to examine how the FIFA World Cup travel demand affected pricing strategies and revenue management practices in the Brazilian hotel sector in areas undergoing seasonal growths in overnight tourism. This study serves to inform hoteliers and practitioners about revenue management pricing strategies to improve hotel performance during mega-sporting events.

This study reveals that the benefits brought by a mega-event are not always translated into strong hotel revenue performance. This study highlights an important but understudied research area of revenue management pricing strategies and the effect of mega-sporting events in the hotel sector. This study contributes to the literature as one of the few investigations to benefit hotel pricing strategies and overall revenue performance.

This study is one of the few studies about exploring the reaction of revenue managers during the execution of a mega-sporting event. The value of the present study lies in the fact that the authors extend previous studies examining the impact of the most important sporting event in the hotel industry at the country-level perspective. This study serves to inform hoteliers and practitioners about revenue management pricing strategies to improve hotel performance during mega-sporting events.

The purpose of this paper is to explore the intricate relationship between the flagship of the sharing economy, Airbnb and hotel revenue per available room (RevPAR) in South Africa.

To identify the impact of Airbnb on hotel RevPAR, the paper used a triple difference-in-differences framework that compares changes in cities in South Africa where Airbnb started operating relative to areas without Airbnb. A total of 569 hotels were analysed.

While the study finds no evidence of adverse impacts of Airbnb on hotel RevPAR, the findings show that the entry of Airbnb led to a decrease in RevPAR of budget hotels. However, its impact is more pronounced during periods of peak demand, consequently, disrupting the pricing power of hotels.

The research was based on the impact of Airbnb on hotel RevPAR in hotels situated in specific cities in South Africa. Caution is therefore required when generalising the findings of this study to other hotels in other geographic areas. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel RevPAR. However, the findings underscore the notion that innovations are not intrinsically disruptive but only relative to another product. In so doing, the study adds to the limited body of work in the field on disruptive innovation and to the academic discourse on innovation in tourism more broadly.

First, the findings suggest the impact on hotels tends towards Airbnb generally playing a largely complementary role rather than a diversionary one. However, to increase RevPAR, hotels should systematically change their pricing models to account for flexible capacity by rethinking the wisdom of seasonal pricing and reduce prices during peak seasons to avoid inviting more competition from Airbnb.

To the best of the author’s knowledge, this paper is the first to explore the relationship between Airbnb and hotel markets using a triple difference methodology.

This study aims to attempt to examine whether the increase in hotel room capacity in the USA had a significant impact on nationwide aggregated weekly revenue per available room (RevPAR) during the recession of 2007-2009 and forecast average RevPAR, Occupancy and Average Daily Rate (ADR) for 2013 and 2014.

Using Autoregressive Integrated Moving Average with Intervention analysis technique, this study examined the significance of the fluctuations in weekly RevPAR, room capacity and market demand through the recent recession and forecasted hotel performance for 2013 and 2014.

The results of time series analysis suggest that the fast growth of room capacity during the recession was one of the main causes of the decrease in RevPAR. The 9,878 more than expected increase in average weekly number of rooms probably caused at least $0.10 more than expected decrease in average weekly RevPAR. The findings of this study also suggest that the US lodging industry has been facing more severe oversupply since the recession and fully rebound of RevPAR cannot be expected in the very near future.

The findings of this study will help stakeholders make more informed decisions to cope with possible future economic downturns. By quantifying the capacity increase and forecasting future market demand, this study provides hotel investors with empirical evidence on the overdevelopment and insights into expected overall hotel performance in next two years. This study has also discussed the cyclical patterns of hotel development during the past two recessions.

By identifying overdevelopment as one of the main causes of RevPAR decrease during the recession, this study contributes to the literature by adding an alternative explanation of RevPAR fluctuations and deepens the understanding of the adverse effects overdevelopment has on the lodging industry. The findings of this study will help hotel investors develop more informed future expansion plans.

The objective of this exploratory study is to investigate the “flow-through” or relationship between top-line measures of hotel operating performance (occupancy, average daily rate and revenue per available room) and bottom-line measures of profitability (gross operating profit and net operating income), before and during the recent great recession.

This study uses data provided by PKF Hospitality Research for the period from 2007-2009. A total of 714 hotels were analyzed and various top-line and bottom-line profitability changes were computed using both absolute levels and percentages. Multiple regression analysis was used to examine the relationship between top and bottom line measures, and to derive flow-through ratios.

The results show that average daily rate (ADR) and occupancy are significantly and positively related to gross operating profit per available room (GOPPAR) and net operating income per available room (NOIPAR). The evidence indicates that ADR, rather than occupancy, appears to be the stronger predictor and better measure of RevPAR growth and bottom-line profitability. The correlations and explained variances are also higher than those reported in prior research. Flow-through ratios range between 1.83 and 1.91 for NOIPAR, and between 1.55 and 1.65 for GOPPAR, across all chain-scales.

Limitations of this study include the limited number of years in the study period, limited number of hotels in a competitive set, and self-selection of hotels by the researchers.

While ADR and occupancy work in combination to drive profitability, the authors' study shows that ADR is the stronger predictor of profitability. Hotel managers can use flow-through ratios to make financial forecasts, or use them as inputs in valuation models, to forecast future profitability.

This paper extends prior research on the relationship between top-line measures and bottom-line profitability and serves to inform lodging owners, operators and asset managers about flow-through ratios, and how these ratios impact hotel profitability.

This study explores the short- and medium-term effects generated by the Milan Expo 2015, adopting a microeconomic approach. The focus is on the hospitality sector. The study embraces nine years, identifying three intervals: pre- (2011–2014), during- (2015) and post-Expo (2016–2019). The time span does not include the Covid-19 pandemic period, which started in 2020. The dataset is composed of daily data. Three research questions are explored. First, an overall evaluation of the short- and medium-term effects is performed. Second, the seasonal effects are measured. Finally, the impacts for different classes of hotels are considered. The findings are supportive for the legacy generated by the Milan Expo. The results confirm the ability of the Milan Expo to strengthen the leisure segment. Positive results have been observed for all classes of hotels, relevantly augmenting the real revenue per available room (RevPAR). Luxury hotels achieved the highest increase of RevPAR, while economy class hotels registered the highest percentage of increase of RevPAR.

This chapter examines the pricing, demand (occupancy), and revenue per available room (RevPAR) dynamics of European hotels for the period 2006–2007. The importance of understanding the pricing behavior of direct competitors is critical to effective strategy formulation and meaningful industry analysis. Nevertheless, existing demand studies miss a critical link to local market dynamics. This study offers an alternative approach to examining competitive set pricing behavior that yields insights into the inelasticity of lodging demand. The results of this study of over 3,000 European hotel observations reveal that hotels that offered average daily rates (ADRs) above those of their direct competitors had lower comparative occupancies but higher relative RevPARs. The observed pattern of demand and revenue behavior was consistent for hotels in all market segments from luxury to economy. Country-specific analyses reveal a similar pattern, with more volatility in the results for hotels in Spain and Italy. Overall, the results suggest that the best way for a hotel to have higher revenue performance than its competitive group is to maintain higher rates. The results of this study support the position that hotel operators who resist pressures to undercut competitor's prices may be better served with higher revenues.

The purpose of this paper is to test the relationships between a hotel’s design quality and the property’s business performance.

Hotel guests’ assessments of the design quality of hotels that they recently visited are tested using the design quality indicator (DQI). Business performance is measured using indexed values for each property’s occupancy, average daily rate (ADR) and revenue per available room (RevPAR). The data are analyzed using exploratory factor analysis and a variation of a hedonic pricing model.

Factor analysis reduced the DQI instrument to 19 measurement items. Factors that measure navigability and signage positively impact occupancy index. Factors that measure flexible space usage negatively impact the RevPAR and ADR indices. Factors that reflect aesthetic constructs, including Urban & Social Integration and Character & Form, positively impact the RevPAR and ADR indices.

The study examines a nationwide sample of guests from two full-service brands of a single multi-brand hotel company. The study provides a parsimonious, validated design measurement instrument and a revised hedonic pricing analysis.

Hoteliers can use this technique to assist with resource allocation decisions. Aesthetic elements, including the building’s Urban & Social Integration with its surroundings and its Character & Form, lead to higher ADR and RevPAR performance. Managers should ensure a coherent layout and good signage program to drive occupancy.

This study offers a technique to measure design quality and a new method of performing a hedonic pricing analysis.

The purpose of this study is to examine how the lodging market and the state economy affected by Hurricane Sandy have recovered from the damages sustained. Specifically, this study examines and predicts the influence of revenue management key performance indicators (KPIs) on recovery and lodging revenue in the affected states and the states’ economies. These KPIs include average daily rate (ADR), occupancy and revenue per available room (RevPAR).

Secondary financial data were collected for the states most damaged by Hurricane Sandy. Subsequently, pooled Ordinary Least Square (OLS) regression was conducted combining time and non-time dependent variables based on the states and radius from the landfall.

The results indicate that although the lodging market and the state economies have recovered since the onslaught of Hurricane Sandy, certain KPIs still need to improve.

Managerial implications are suggested in terms of dynamic pricing, market-based recovery, the KPIs, federal aid and facility management.

Despite its importance, research on the effects of climate change in the hospitality context has not actively progressed after Hurricane Katrina. Time and non-time dependent variables are combined in this analysis to gain a richer understanding of the impacts and recovery of KPIs on the revenue in the lodging market and the revenue on states’ economies. Additional analysis based on the radius from the landfall of the hurricane was performed to examine the impact and recovery based on geographical proximity.

The sharing economy has caught great attention from researchers and policymakers. However, due to the dearth of available data, not much empirical evidence has been provided. This paper aims to empirically assess the impacts of Airbnb on hotel performances in South Africa.

Using South Africa as a case study, the study measures the impacts of Airbnb on hotel performances on three key metrics, namely, room prices, occupancy and Revenue per available room (RevPAR). A difference-in-difference model is estimated using a population-based data set of 809 hotels from 2016 to 2018.

The results reveal that despite Airbnb significantly and negatively impacting on hotel occupancies it has a non-significant effect on hotel prices and RevPAR. Although from the theoretical perspective a disruptive innovation business model such as Airbnb can possibly have a negligible effect on hotel performances because it may attract a different group of customers and create a new market, the empirical findings of this study fail to support this theoretical hypothesis. Consequently, the findings diverge with newly developed knowledge in other markets and point to nuanced and contextual complementary effects.

Although some interesting findings are revealed into his study, some caveats remain. For instance, the study relied on data from hotels not from Airbnb. If the data of Airbnb can become available, it would be interesting to further examine whether the aggregated RevPAR of Airbnb can compensate for the aggregated loss of hotel RevPAR. This type of analysis could provide a broader evaluation scope regarding the overall effect of Airbnb on hotel performances. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel performances.

While hotels have launched a campaign to portray Airbnb as being commercial operators looking to compete illegally with hotels for the same segment of customers, this study shows that the rhetoric has been exaggerated. Airbnb, and more broadly, vacation rentals do not represent a war with hotels. They represent an answer to a different need. Indeed, the study reveals that Airbnb’s offer is a mere supplement to the market contrary to media rhetoric that it is meant to substitute hotels. The study has several implications for practitioners. First, these results are important because they serve as evidence against news articles that claim Airbnb is driving hotels out of business. They also show that if current trends continue, employees in the hotel industry in South Africa do not need to be concerned about losing their jobs because of Airbnb’s emergence. It is also important information for investors who may be concerned that Airbnb is hurting the hotel industry’s bottom line. Second, as the share of Airbnb listings on the accommodation market varies dramatically between cities, it is likely that eventual regulations/restrictions should be introduced in the provincial levels, while most of the cities continue benefiting from the increasing number of Airbnb visitors.

To the best of the author’s knowledge, this study is the first in South Africa to provide empirical evidence that Airbnb is significantly changing consumption patterns in the hotel industry, as opposed to generating purely incremental economic activity.

This study aims to validate the value-added hypothesis in hotel franchising using data from 2,120 properties across the United States with a total of 12,720 observations over a six-year period of 2008-2013.

A series of annual cross-sectional regressions for each of the sample years and aggregated panel regressions for all sample hotel years were conducted. Newey–West errors were computed to address potential issues of autocorrelation and heteroscedasticity, and sensitivity tests were also performed.

The paper concludes that franchise royalty fee adds value to hotel franchisees as it significantly and positively affects revenue per available room (RevPAR) for all sample years after controlling for the major determining dimensions of RevPAR. A series of sensitivity tests also show robustness of results.

This study offers a rational and empirical explanation for the positive and significant effect of franchise royalty fees on hotel performance and the value-added hypothesis. Hoteliers need to ensure that there is a proper match between hotel specific attributes and the potential franchise when making a franchise selection. Individual entrepreneurs can partner with franchisors to reap the benefits of franchising, while experienced hoteliers can also use the findings of this study to make strategic decisions.

This study is the first using actual performance data from a large hotel property sample over multiple years to validate the value-added theory, where a higher royalty fee does command a higher RevPAR.