Revenue Per Available Room Research Articles

Determinants of Financial Efficiency and Performance in Big-Cap Chain Hotels

Chain hotels with $1 billion in market capitalization not only contribute to employment creation and indirectly become a driving force in the regeneration of local economies but also play an essential role in recovering local economies with their high revenue per available room (RevPAR) values. Besides, the financial stability of these hotel chains directly influences the popularity of destinations, resulting in an influx of tourists to nearby regions, which in turn generates an economic contribution to the overall development of the tourism industry. Therefore, the aim of this paper is to determine the financial factors that are affecting the RevPAR (RPR), their role in the industry, and the validity of the agency cost theory of nine chain hotels that have at least $1 billion in market capital listed in U.S. stock markets over the period 2013–2022. The results show that, in contrast to the debt service coverage ratio, receivables turnover ratio, and book value per share variables, the day sales outstanding ratio and current ratio have a negative impact on chain hotels' RevPAR.

Role of Artificial Intelligence in Revenue Management and Pricing Strategies in Hotels

Purpose: The general objective of the study was to investigate the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Preliminary empirical review revealed that the integration of artificial intelligence (AI) into revenue management and pricing strategies significantly enhanced the financial performance and operational efficiency of hotels. AI's ability to process large datasets in real-time improved demand forecasting and dynamic pricing, leading to increased revenue per available room (RevPAR) and average daily rate (ADR). Additionally, AI facilitated personalized guest experiences, boosting customer satisfaction and loyalty. Despite these benefits, the study identified challenges such as high implementation costs, data privacy concerns, and the need for robust data infrastructure. Addressing these issues through strategic planning and continuous staff training was deemed essential for maximizing AI's potential in the hotel industry. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations theory, Technology Acceptance Model (TAM) and Resource Based View (RBV) may be used to anchor future studies on the role of AI in revenue management and pricing strategies in hotels. The study concluded that integrating AI into hotel revenue management and pricing strategies significantly enhances performance, contributing to both theoretical and practical advancements. It enriched the Diffusion of Innovations Theory by demonstrating factors influencing AI adoption in hospitality. Practically, it provided actionable insights for hotel managers on leveraging AI for improved key performance indicators and balancing dynamic pricing with customer satisfaction. Policy recommendations included establishing guidelines for AI implementation, enhancing data infrastructure, fostering a culture of innovation, and addressing skills gaps through training and development programs. The study emphasized the need for robust data management systems and regulatory support to facilitate AI adoption.

Examining Patterns of Tourism Recovery in the United Arab Emirates: Towards the Implementation of Effective Marketing Strategies

This study examines tourism recovery patterns in the United Arab Emirates (UAE). The study utilized time series modeling to evaluate the monthly tourism data from the Department of Culture and Tourism (DCT) in Abu Dhabi, covering 2019 to Q1 2023. The LJung box test was used to assess the fit of the dataset with the five tourism parameters: Average Length of Stay (ALOS), Number of Guests (NOG), Revenue Per Available Room (RevPAR), Occupancy Rate (OR), and Tourism Revenue (TR). The presence of nonstationarity was discovered, necessitating the use of data differencing to achieve stationarity.
 
 The results indicate that the model fits the data for the five tourism parameters. The study also reveals a significant decline in UAE tourism visitors, with a modest recovery projected in Q4 2021.The findings demonstrate a substantial decline in hotel occupancy rates (OR) during the COVID-19 pandemic, with a slower recovery rate expected for 2023-2024. The average length of stay (ALOS) in hotels is four nights from Q4 2020 to Q1 2022 and is expected to remain the same in 2023-2024. Revenue per available room (REVPAR) is expected to decline in Q1 2023 and peak in Q3 2023. However, projections for 2023 and 2024 show continued growth in tourism revenue (TR) in the UAE.

Determinants and COVID-19 effects on RevPAR: The case of Europe

Abstract As Europe prepares itself for a new downturn, this paper proposes to examine the determinants of hotel Revenue per Available Room (RevPAR) through literature review, and contribute to improving hotels’ performance by understanding the weight of the occupancy rate and the Average Daily Rate (ADR) on RevPAR, after the pandemic. A quantitative methodology was used, collecting data from STR Share Center and Our World in Data, such as ADR, occupancy rate, RevPAR, and COVID-19 confirmed cases. Results show the overwhelming effect of COVID-19 on hotel performance, conducing to ADR, occupancy rate, and RevPAR decline, and highlighting a co-movement of these indicators during COVID-19. After the lifting of major COVID-19 restrictions, RevPAR had a greater influence from ADR in some European countries, but the occupancy rate should not be disregarded. The findings, however, suggest the absence of the revenge travel phenomenon. The relationship between the number of COVID-19 cases and the decrease in RevPAR is not statistically significant, implying the existence of other factors that probably also had impact. The different measures adopted by governments to contain the virus, and each country's dependency on tourism, led to different impacts on hotel performance. This study helps hoteliers to know how to measure performance and the RevPAR drivers that can improve it, taking into account the situations that differ by country, as well as variables that are not controllable.

Examining the Effects of Online Travel Agents on Room Revenue

Purpose of the study: This study aims to determine the impact of online travel agent distribution channels on the Revenue Per Available Room (RevPAR) at Aloft Bali Seminyak over two years (2021-2022).
 Methodology: This study employs classical assumption tests, simple linear regression tests, t-tests, and coefficient of determination tests. These analyses were performed using IBM SPSS version 25 software.
 Main Findings: The study found that online travel agent distribution channels significantly influence RevPAR at Aloft Bali Seminyak. The impact, quantified at 59%, is considered strong or high, leaving 41% potentially influenced by factors not evaluated in this study.
 Applications of the study: The findings of this study are applicable in the hospitality industry, specifically to hotel operations and revenue management. Additionally, they can assist online travel agencies in understanding their influence on hotel revenues.
 Novelty/Originality of the study: This study presents novel insights into the substantial influence of online travel agent distribution channels on hotel revenue, specifically RevPAR. It advances existing knowledge by quantifying this impact and opens the door for further exploration of the remaining 41% of unexplained influence.

Daily online review sentiment and hotel performance

PurposeThis paper aims to investigate the links between daily review sentiment and the hotel performance measures of occupancy rate (OR), average daily rate (ADR) and revenue per available room (RevPAR).Design/methodology/approachThe authors conducted review sentiment analyses in three moments (−1, −7 and −14 days) before arrival time using a data set of budget hotel performance and online reviews. The aim was to identify the effect of review sentiment in the budget hotel market on the three performance metrics.FindingsDaily sentiment positively affects ADR and negatively affects OR and RevPAR, but only up to a certain threshold, after which the trend reverses. Prices increase with the level of sentiment, and high prices lead to low OR and RevPAR only when the sentiment scores are low. When they are high, they are associated with low rates, which lead to high OR and RevPAR.Research limitations/implicationsDaily review sentiment can be viewed as a valuable “barometer” indicating a hotel’s daily operational effectiveness. Daily sentiment can thus allow hotel managers to adjust their dynamic pricing strategies more accurately.Originality/valueThis study identifies daily sentiment as an alternative predictor of hotel performance. In addition to the roles of valence and volume in the decision-making process, the authors found that daily review sentiment can be an “in-the-moment” factor with a high impact, encouraging consumers to complete their transactions. This study suggests that aggregated measures such as the total number of reviews and overall ratings of the hotel should not be the sole consideration in reputation management.

СУЧАСНІ МЕТОДИКИ ОЦІНКИ ФІНАНСОВОГО ПОТЕНЦІАЛУ ПІДПРИЄМСТВА ІНДУСТРІЇ ТУРИЗМУ ТА СФЕРИ ГОСТИННОСТІ

The study shows the advantages of forming and maximizing the financial potential of enterprises in the tourism industry and the hospitality sector, which shows the maximum importance of this economic stage of the enterprise. Methods for assessing financial potential in the tourism and hospitality industry include a thorough analysis of financial statements, cash flow projections, return on investment calculations and comparisons with industry standards. In addition, market research, customer feedback and risk assessment contribute to a comprehensive assessment of the company's financial condition and development prospects. The study of the method of assessing the financial potential in the economy of the enterprise involves the analysis of financial statements, the assessment of indicators of liquidity, profitability and solvency, as well as the understanding of the dynamics of cash flows. It also includes an assessment of market conditions, industry trends and potential risks. A holistic approach combiningquantitative and qualitative factors provides a comprehensive understanding of the economic viability and growth potential of the enterprise. Key indicators for assessing financial potential in the tourism and hospitality industry include revenue growth, profit margins, return on investment (ROI), average daily rate (ADR), occupancy rates, and customer satisfaction scores. Additionally, monitoring cash flow, debt levels, and keeping an eye on industry-specific metrics like RevPAR (Revenue per Available Room) can provide valuable insights into the financial health and potential of enterprises in this sector. Assessing the financial potential of a tourism enterprise is crucial for sustainable growth. A robust methodology helps to assess profitability, identify cost-effective strategies and adapt to market trends, ensuring long-term success in the dynamic tourism and hospitality sector.

Are GreenLeaders also performance leaders? An econometric analysis of TripAdvisor hotel certification of GreenLeaders

This paper aims to investigate the impact of the GreenLeaders designation on hotel performance under the joint theoretical framework of environmental corporate social responsibility and social identity theory. We focus on 1,198 individual hotel properties from five major cities in Texas between 2010 and 2019. A two-way fixed-effects model is applied to model monthly revenue per available room (RevPAR) and TripAdvisor ratings for each hotel. Results show that GreenLeaders certification leads to a 6.00% increase in hotels’ monthly RevPAR and a 4.25% increase in hotels’ monthly average TripAdvisor rating. This impact grew over time during the research period. The findings also unveil a set of moderating variables, such as hotel class and chain affiliation. More specifically, the effect of GreenLeaders certification is larger for upscale and independent hotels. The major results are also confirmed through a robustness analysis using propensity-score-based weighting and placebo tests. Lastly, implications are provided for hoteliers and investors.

Energy Use and Its Key Factors in Hotel Chains

Hotel chains are reported as one of the most energy-intensive sectors and a growing number of international studies on this theme have been developed. This research aims to understand energy use and some of its key factors in hotel chains worldwide. Data were collected on variables related to previous research and those present in the Global Reporting Initiative (GRI) framework. The sample was composed by 45 international hotel chains, representing more than 54,000 properties and 7,500,000 rooms. Multiple linear regression was employed to assess how the predictor variables (water use, carbon intensity, RevPAR, and NetRoom) are associated with energy use (dependent variable). It was presented that hotel chains can pass on the price of energy consumption to their guests, increasing their revenue per available room (RevPAR), but the returns in profitability are not being generated. The RevPAR variable maintained a positive relationship, +0.244, with energy use in the first regression model, with R2 adjusted equal to 0.9506, while the net profit per room (NetRoom) presented a negative relationship in both models, −0.0006 and −0.0010, respectively, with R2 adjusted equal to 0.9304 in the second model. Investing in updating their energy systems, hotel chains can contribute to a more sustainable future, build positive marketing, retain guests, and generate a long-run financial return. This research contributes to the scientific literature by confirming relationships and providing evidence among new, and not yet explored, variables. It is expected to create a reference for policies to reduce energy use in hotels and for hotel owners to upgrade their systems.

THE DETERMINANTS OF HOTEL ROOM PRICE IN PHILIPPINES: A STRUCTURAL EQUATION MODELING ANALYSIS

This study analyzed determinants of hotel room prices in the Philippines using the Structural Equation Modeling (SEM) and assessed how these price determinants affects the economic performance of hotels. Specifically, it aimed to describe hotel characteristics based on STR classification, determine key performance indicators (KPIs) of all Philippine markets, correlate hotel characteristics with hotel pricing and with key performance indicators (KPIs); and key performance indicators (KPIs) with hotel pricing. Lastly, a comprehensive structural equation modeling (SEM) was used to determine hotel industry price determinants. Using an aggregated and quantitative anonymize property level data STR provided, researchers selected 88 hotels active with STR. The study revealed that most hotels in the Philippines are economy in terms of class; chain-owned in terms of hotel operation; located in urban areas; and with 150 – 299 rooms in terms of size. Hotel price was significantly predicted by all hotel characteristics aside from operation. Pricing was also significantly predicted by hotel key performance indicators (KPIs)—average daily rate (ADR), revenue per available room (RevPAR), and occupancy. The structural equation modeling (SEM) revealed that key performance indicators (KPIs) such as average daily rate (ADR), revenue per available room (RevPAR), and occupancy were main determinants of hotel pricing. Further, data showed that hotel characteristics such as operation, class, location, and size code mediated the influence of key performance indicators (KPIs) on hotel pricing. Results of this study contribute to widening existing studies of hotel room pricing which hospitality executives may use as reference to come up with appropriate pricing strategies

Small Hotel Location Selection Problem: The Case of Cappadocia

Identifying an appropriate location is one of the most significant factors in determining the success of a facility. This paper focuses on the selection of the most appropriate small hotel location in inner Cappadocia, based on seven destination-specific criteria. The Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) and Additive Ratio Assessment (ARAS) methodology is utilized to obtain the relative criteria weights and the final ranking of six alternative small hotel locations. The results indicate that the most important factors in small hotel location selection in Cappadocia are revenue per available room (REVPAR) potential, investment amount and view of hot air balloon flights. Goreme and Uchisar are identified as the most appropriate locations for small hotel investment in the region.

Inclusion of Condominium Units in Luxury Hotels as a Diversification Strategy: Property Performance Perspective

There is a new wave of mixed development where luxury hotels come with condominium units, though this type of diversification has gained scant attention. Prior hospitality literature on diversification strategies has mostly taken the firm-level approach and documented its impact on performance from various angles such as brand diversification, segment diversification, and geographic diversification, therefore leaving a void. In this work, we use the multilevel mixed effect model to examine 15,340 property-level data points from 2010 to 2019 for U.S. luxury hotels with and without condominium units. Our objective is to compare, at a property level, the performances of luxury hotels with condominium units with the performances of those not having condominium units and to determine whether the difference varies by hotel location. Our findings suggest that the Average Daily Rate (ADR), Revenue per Available Room (RevPAR), and Total Revenue per Available Rooms (TRevPAR) of luxury hotels with condominium units were significantly higher than those of hotels without condominium units. Significant moderating effect of location was found for Occupancy, ADR, Food and Beverage Revenue per Available Rooms (F&B RevPAR), and TRevPAR while no such effect was found for RevPAR and GOPPAR.

Non-financial factors affecting the operational performance of hospitality companies: Evidence from Vietnam

Non-financial factors including quality of services, the flexibility of a company, utilization of resources, and market orientation are regarded as significant determinants that enhance the profitability-based performance of a service company or a hotel. The study investigated the interaction between these factors and hotel operating performance measured by the hospitality performance and results indicators. Data on 346 executives of Vietnam’s hospitality companies were collected. A structural equation modeling (SEM) method was utilized to examine the positive-direct and moderating effects of non-financial factors on hotel performance in terms of occupancy rate (OCR), average daily rate (ADR), and the revenue per available room (RevPAR). The findings showed that service quality ( = 0.118, p < 0.05), flexibility ( = 0.173, p < 0.05) and resource utilization (= 0.172, p < 0.05) positively affected the performance of Vietnam’s hospitality companies. Meanwhile, innovation showed no direct influence (p = 0.068) but an indirect impact on the performance through service quality ( = 0.311, p < 0.05). Market orientation did not impact the performance (p = 0.076) but it positively affected both innovation ( = 0.322, p < 0.05) and service quality ( = 0.146, p < 0.05). The study contributed to a theoretical enhancement of the current level of knowledge on the factors that affect the performance and developed a reliable scale for measuring the performance of hotels in Vietnam.

Ready for recovery: Hoteliers’ insights into the impact of COVID-19 on the Indian hotel industry

ABSTRACT This research studies the hotel manager perspective of COVID-19 (the novel coronavirus) on the Indian hotel industry by qualitatively analysing inputs from the human resources department, general management and top management of five-star hotels across India. In doing so, it advances knowledge on the impact of the COVID-19 situation in the domains of human resources (HR), strategy and business operations in the hospitality industry. The study analyses qualitative data collected through online interviews with 17 top-level managers of five-star hotels spread across India. Content analysis is done and the key findings with practical implications have been highlighted. This initial study on the hotel industry in India uncovers how the hoteliers are currently dealing with the pandemic across the country. Findings show that there is a massive negative impact on the Indian hotel industry, in terms of the revenue per available room (RevPAR) and occupancy rates. However, the study also highlights how hoteliers look forward to a phased re-opening of hotels with an increased focus on safety and hygiene after the COVID-19 crisis. The study provides a significant contribution to academic practitioners, hoteliers, and policymakers by examining the future plans in areas of hotels’ HR, strategy and business plans.

Hotel Analytics: The Case for Reverse Competitive Sets

Hotel industry practitioners and scholars expressed concerns regarding the reliability of the commonly used comparative performance indices, given the subjective nature of the competitive (comp) sets. This study explores the potential of two alternative comp sets: reverse and name-back. It is demonstrated that they have a higher level of integrity as they moderate the values of a traditional comp set–based RevPAR (revenue per available room) index, are simple to implement, and are more dynamic. The study shows analytically and empirically that the two are likely to mitigate biases and that the reverse comp set appears to be more effective than the name-back. Multiple practical implications are outlined and discussed.

Penerapan Metode Yield/Revenue dengan RevPar: Studi Pada Hotel and Convention X

This study aims to determine the application of yield/revenue with RevPAR (Revenue Per Available Room). This type of research is quantitative research. The data analysis method in this study uses the trend analysis method. Data sources were obtained from Hotel X & Conventions in the form of daily hotel reports, revenue management forecast reports and pattern reports from January to December 2018. The results show that Yield/Revenue Management has been implemented well at Hotel X & Conventions Kelapa Gading in 2018 by making a RevPAR estimate that is close to the actual, with the difference between the estimated data and the actual data being only 5.34% to 17%. The highest RevPAR was in August 2018 which was Rp. 585,690,- and the lowest was in February 2018 which was Rp. 474,114. The conclusion of this study is that Hotel X & Conventions Kelapa Gading has implemented yield/revenue management well in 2018 by carrying out RevPAR estimates which are almost in accordance with the actual, with the difference between the estimated data and the actual data. Keywords: Yield/Revenue Management, RevPar ( Revenue Per Available Room )

Airbnb’s Success: Does It Depend on Who Is Measuring?

Because individual listing data for Airbnb typically are not publicly available, private companies have emerged to estimate the performance of Airbnb listings. The implicit assumption of a growing number of academics, policymakers, and consultants is that Airdna’s performance measures are directly comparable with those of STR. We argue that Airdna’s measures of Occupancy, Average Daily Rate (ADR), and Revenue per Available Room (RevPAR) do not conform to industry standards and exhibit significant bias. We expand available evidence by explicitly quantifying the sources and magnitude of the biases for Airdna’s performance measures for Airbnb listings. Using Airdna’s individual listing data for Virginia between the first quarter of 2015 and the 4th quarter of 2019, we find, on average, Airdna’s performance measures for Occupancy, ADR, and RevPAR were biased upward by 60 percent, 78 percent, and 179 percent, respectively.

Pengaruh Average Daily Rate dan Nilai Tukar Mata Uang terhadap Revenue Per Available Room di Hotel XYZ

Revenue Per Available Room (RevPAR) at Hotel XYZ has decreased since 2017 to 2019. Hotel XYZ applied Best Average Daily Rate (ADR) to increase the income without reducing the guest satisfaction. This ADR used USD nominal, so the currency exchange rate (dollar to rupiah) will have an effect on RevPAR that should be reported by using rupiah nominal. There was a phenomenon that the increase of ADR and the currency exchange rates were not followed by the increase of RevPAR at Hotel XYZ in 2017 until 2019. The purpose of this research was to analyze the effect of Average Daily Rate (ADR) and the currency exchange rate on Revenue Per Available Room (RevPAR) at Hotel XYZ. This research was a quantitative research and used multiple linier regression analysis techniques, t-test, F test and coefficient of determination test by using SPSS version 23.0 program. In determining the sample of this study, non-probability sampling method was used. The sample used in this study is data from the last 3 years, 2017, 2018, and 2019. The result of the study showed that Average Daily Rate (X1) and the currency exchange rate (X2) had a significant effect on Revenue Per Available Room (Y) at Hotel XYZ, both partially or simultaneously.

Resource Similarity, Market Commonality, and Spatial Distribution of Hotel Competitive Sets

Index scores and competitive sets (compsets) play a critical role in the performance and evaluation of hotels. The reliance on these metrics has drawn skepticism in recent years as competitive sets may be opportunistically chosen, creating bias in performance evaluation. Drawing from the principal–agent theory and the theory of incentives, we explore whether the distance of the competitors chosen for a hotel’s compset influences revenue per available room (RevPAR) index scores. Based on the concepts of resource similarity and market commonality, we develop a novel mathematical model through which we empirically analyze a large dataset of 10,000 compsets. We find evidence that competitor distance influences index performance and that this relationship is bidirectional. Results show that hotels that outperform the competition may use distance to inflate RevPAR indices, while those that underperform may use distance to further reduce scores. These conflicting results may be reflected from the reverse motivations of the stakeholders.

Economic policy uncertainty and hotel occupancy: the mediating effect of consumer sentiment

PurposeThe purpose of this paper is twofold. First, the study examines the prolonged effect of policy-related economic uncertainty on hotel operating performance, particularly the room demand (occupancy). Second, the study attempts to explain why occupancy drops when the perceived economic uncertainty is high by studying the mediating effect of consumer sentiment in the relationship between economic policy uncertainty and hotel demand.Design/methodology/approachThis quantitative study uses secondary data – US economic policy uncertainty (EPU) index, University of Michigan's index of consumer sentiment (ICS), and property-level hotel operating data from three states of the US – California, Florida and New York. Data were analyzed using random effect regression and structural equation modeling. Robustness tests were conducted to enhance the reliability of the research findings.FindingsRandom-effects regression analysis reveals that policy-related economic uncertainty has a negative and lead-lag effect on hotel occupancy, average daily rate and revenue per available room (RevPAR). Structural equation modeling results show that the relationship between economic policy uncertainty and hotel occupancy is significantly mediated by consumer sentiment. Robustness test results support the findings from the main analysis.Practical implicationsThis study offers valuable implications for the hotel professionals in regard to anticipating the economic impact of policy-related uncertainty on hotel industry and understanding how consumer sentiment affects demand at such crises times. Moreover, the study suggests potential course of actions to deal with declining room demand at times of uncertainty.Originality/valueThis empirical study explores how economic policy uncertainty affects hotel performance at the property level and explains the mediating effect of consumer sentiment on hotel room demand. The study provides a first-hand evidence of how consumer sentiment relates to the perception of economic uncertainty and leads to decline in consumer demand. In that regard, findings of the study have valuable implications for hospitality industry practitioners and relevant policymakers.