Determinants of revenue per available room: Influential roles of average daily rate, demand, seasonality and yearly trend
Introduction
Economic performance and the interplay of its determinants are the major concerns in the research conducted on the hospitality industry (Assaf et al., 2012). A hotel with better performance generally exhibits a higher occupancy rate, as well as better revenue per available room (RevPar). RevPar is defined in terms of the ratio of total guest room revenue and total number of available rooms. One of the main objectives of the hotel industry is to maximize its RevPar, typically achieved by adopting a suitable revenue management strategy. The average daily rate (ADR) is the ratio of room revenue and room sold and thus, represents the average rate paid for rooms sold. The RevPar for a particular hotel is directly related to the ADR and its occupancy rate (Pan, 2007). To increase its profits, hotel operators make great efforts to fix their room rates. The cost for even a small change in a room rate can aggravate a hotel’s budget, with tremendous financial implications. If there is a mere $1 decrease in an average daily room rate in a hotel with 500 rooms at a 70% occupancy rate, this will lead to a decrease in yearly revenue of $127,750 (Steed and Gu, 2005). However, where the entire hotel industry in a location is concerned, the occupancy rate of one specific hotel is also affected by the overall supply and demand of accommodations in that geographical area. Therefore, a hotel’s revenue is consistently influenced by real demand (i.e.,) and price per hotel room (i.e., room rate) (Vinod and Vinod, 2004). The uncertainty of this demand also affects the capacity investment decisions of the hotel (Abel, 1983; Chen and Lin, 2013).
Taking the hotel industry of Sweden as a case study, this paper examines how ADR and real demand for hotel rooms influence RevPar. Göthesson and Riman (2004) reported that a flexible room rate is necessary to match the hotel demand, and they found that special offers are often used to improve demand in Sweden, although reducing the room rate was not useful in the long run. Sweden is mostly dominated by demand in the domestic and business domains (Andersen, 1997; Göthesson and Riman, 2004). A recent report (pwc, 2017) concluded that tourism demand is increasing in Sweden.
The revenue management and hotel operating performance, based on changing market scenarios (diverse economics, competitions, conditions, events, etc.), involve decision-making with nonlinear characteristics (Madanoglu and Ozdemir, 2016; Choi and Cho, 2000). In the recent literature, few have reported nonlinear relationships between hotel price-growth relationships in Sweden (Falk and Hagsten, 2015), and between hotel price and the number of local competitors (Shi et al., 2016), etc. To deal with nonlinear market dynamics, a sophisticated forecasting tool is necessary. The multivariate adaptive regression spline model (MARS) is a new tool in the econometrician’s toolkit that has been extensively used in diverse domains. It has established its usefulness in forecasting time series (Sephton, 2001). The MARS model is a non-parametric and nonlinear regression that does not make any assumptions of the original relationships between the dependent variable and its determinants. Additionally, it offers more flexibility to discover the nonlinear relationships between the variables, by fitting the data into a number of spline functions. As hotel ADR and real demand might have nonlinear influences on the revenue per available room, the present study used the MARS model to analyze these relationships, then compared its effectiveness with the multivariate regression.
Specifically, our objective is to address the following two research questions:
RQ1: How do room ADR and hotel demand influence RevPar in the hotel industry, and what is their respective variable importance?
RQ2: Can we make a forecast of RevPar that successfully accounts for monthly seasonality, yearly trends, and nonlinear influences of price and demand simultaneously?
The present research stresses forecasting the RevPar as having a great impact on hotel revenue management. As the existing literature has not fully considered the comprehensive relationship of total hotel revenue, ADR, and real demand in the presence of seasonal patterns and time trends, this study is undertaken to make the following contributions:
- 1)
It comprehensively evaluates the relationships of RevPar with two main determinants: —hotel ADR and real demand—using the hotel industry of Sweden as a case study.
- 2)
It uses a different methodology, that of MARS (which has been widely used in other areas but has yet to find an application in the hospitality industry) to capture the nonlinear relationships of the above-mentioned variables.
The paper is organized as follows: Section 2 describes a relevant comprehensive background survey, including the forecasting literature on hotel revenue management. Section 3 introduces the research methodology undertaken in the study: i.e., data description and its stationarity checking, data preparation, model development, and testing of the model. Section 4 presents the empirical results and detail analyses. Contribution and practical implications of the study are presented in Section 5, and finally, Section 6 concludes with future directions.
Section snippets
Background literature survey
Monson (1985) refers to an adage about hotels: “When performance is measured, performance improves. When performance is measured and reported back, the rate of improvement accelerates.” In various research, the most commonly used measures of revenue management are ADR, RevPar, occupancy rate, etc. (Hung et al., 2010). The quality of hotel service and customer satisfaction are influenced by ADR (Hung et al., 2010; Lee, 2015). The occupancy rate and ADR are determinants of hotel performance, but
Research methodology
This section provides the details of data description, variables, and models used in the study.
Empirical results and analysis
The results of the multiple regression model estimation for Eq. (3) using OLS are given below.
The results show that both price and demand have positive and significant relations to explain the dependent variable revenue per available room. Monthly seasonality is also strong, as most of the monthly dummies are significant. However, the coefficient of Y is negative, showing a negative time trend.
The result of the MARS model (Eq. 5) includes an intercept, seven monthly dummy variables, and 10
Discussion and implications
In this study, we investigated the influence of determinants of revenue per available room using linear multiple regression and a nonparametric MARS model in the accommodation industry data of Sweden. In summary, the analyses have explored the answers of the two research questions presented earlier in Introduction section as follows: (1) The study explored the answer of the first question after developing four models based on Eqs. (2), (3), (4), (5). The comparative performance of the models
Conclusion and limitation
The key objective of any hotel manager is to maximize profitability using resources available to them. This can be achieved only by the maximizing revenue per available room. Only instrument available to hotel manager is the average daily rate. When there is sufficient demand, increase in demand generally increase revenue per available room but when demand is lacking, increase in average daily rate would decrease occupancy rate and thus, revenue per available room would fall. Therefore, to
References (71)
- et al.
When quality signals talk: evidence from the Turin hotel industry
Tour. Manag.
(2011) - et al.
“Where you do it” matters: the impact of hotels’ revenue-management implementation strategies on performance
Int. J. Hosp. Manag.
(2017) - et al.
Does triple Bottom line reporting improve hotel performance?
Int. J. Hosp. Manag.
(2012) Measuring efficiency in the hotel sector
Ann. Tourism Res.
(2005)- et al.
Being better vs. Being different: differentiation, competition, and pricing strategies in the Spanish hotel industry
Tour. Manag.
(2013) - et al.
Multivariate exponential smoothing: a Bayesian forecast approach based on simulation
Math. Comput. Simul.
(2009) - et al.
A new approach to modelling and forecasting monthly guest nights in hotels
Int. J. Forecast.
(2002) - et al.
The influence of uncertain demand on hotel capacity
Int. J. Hosp. Manag.
(2013) - et al.
Towards a knowledge discovery framework for yield management in the Hong Kong hotel industry
Hosp. Manag.
(2000) Forecasting hotel-industry performance
Tour. Manag.
(1985)
Modelling growth and revenue for Swedish hotel establishments
Int. J. Hosp. Manag.
Technology revenue management system for customer groups in hotels
J. Bus. Res.
Booking horizon forecasting with dynamic updating: a case study of hotel reservation data
Int. J. Forecast.
Hotel roomrates under the influence of a large event: the Oktoberfest in Munich 2012
Int. J. Hosp. Manag.
Pricing determinants in the hotel industry: quantile regression analysis
Int. J. Hosp. Manag.
Using RevPAR to analyze lodging-segment variability
Cornell Hotel Restaur. Adm. Q.
Star rating and corporate affiliation: their influence on room price and performance of hotels in Israel
Int. J. Hosp. Manag.
Determinants affecting comprehensive property-level hotel performance: the moderating role of hotel type
Int. J. Hosp. Manag.
Quality differentiation and conditional spatial price competition among hotels
Tour. Manag.
Forecasting h(m)otel guest nights in New Zealand
Int. J. Hosp. Manag.
Effects of user-provided photos on hotel review helpfulness: an analytical approach with deep leaning
Int. J. Hosp. Manag.
Is more better? The relationship between meeting space capacity and hotel operating performance
Tour. Manag.
System reliability analysis of soil slopes with general slip surfaces using multivariate adaptive regression splines
Comput. Geotech.
Market demand variations, room capacity, and optimal hotel room rates
Int. J. Hosp. Manag.
Firm growth patterns: examining the associations with firm size and internationalization
Int. J. Hosp. Manag.
An introduction to helpful forecasting methods for hotel revenue management
Int. J. Hosp. Manag.
A simultaneous equations model of the hotel room supply and demand in Hong Kong
Int. J. Hosp. Manag.
Forecasting uncertain hotel room demand
Inf. Sci.
The impacts of international tourism demand on economic growth of small economies dependent on tourism
Tour. Manag.
Reported RevPAR: unreliable measures, flawed interpretations and the remedy
Int. J. Hosp. Manag.
Impact of financial/economic crisis on demand for hotel rooms in Hong Kong
Tour. Manag.
A comparison of forecasting methods for hotel revenue management
Int. J. Forecast.
Forecasting for hotel revenue management: testing aggregation against disaggregation
Cornell Hotel Restaur. Adm. Q.
Hotel location evaluation: a combination of machine learning tools and web GIS
Int. J. Hosp. Manag.
An integrated forecasting approach to hotel demand
Math. Comput.Model.
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