Determinants of revenue per available room: Influential roles of average daily rate, demand, seasonality and yearly trend

https://doi.org/10.1016/j.ijhm.2018.09.001Get rights and content

Highlights

  • Explore the influence of average daily rate and demand on revenue per available room.

  • Also consider seasonality, trend as potential determinants of revenue per available room.

  • Develop model to forecast the revenue per available room of Sweden as case study.

  • Demonstrates that the nonlinear MARS model is better alternative to the OLS model.

Abstract

Determining price per room to be charged to customers is an important decision to be taken by hotel management. Hotels frequently change their room rates based on the demand of room, occupancy rate, seasonal pattern, and strategies undertaken by other hotels on pricing. We formulated four models to analyse how various influencing variables, such as hotel price, demand, yearly trend and monthly seasonality influence hotel revenue per available room (RevPar). To analyse a case, we used monthly accommodation statistics for Sweden taken for Swedish Agency for Economic and Regional Growth and Statistics from January 2008 to July 2017. We carried out data analysis using both multiple regression and Multivariate Adaptive Regression Splines (MARS) model and found that application of MARS can help establishing a nonlinear relationship of RevPar with other determining variables in a superior way. We also proposed the possibility of developing a better forecasting model using MARS.

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.,occupancyrate×roomavailability) 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

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