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

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.     

Determining guests’ willingness to pay for hotel room attributes with a discrete choice model

Hotel managers need to understand the marginal utility customers associate with a specific attribute of a hotel in order to effectively set up rate fences and to price their rooms accordingly. This study adopted a stated choice experiment and discrete choice modeling method to obtain hotel guests’ willingness to pay (WTP) for a specific set of room attributes within a single hotel property. The attributes include room views, hotel floor, club access, free mini-bar items, smartphone service, and cancellation policy. The study discovered that leisure travelers versus business travelers, and first-time visitors versus repeat visitors, perceive different WTP values for various attributes. These findings provide valuable information for hotel managers to segment their market and conduct revenue management practices in order to maximize revenue and profit. The results also demonstrate the value of discrete choice modeling in obtaining WTP for hotel room attributes.     

Using machine learning and big data for efficient forecasting of hotel booking cancellations

Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.     

An introduction to helpful forecasting methods for hotel revenue management

Revenue management is a key tool for hotel managers’ decision-making process. Cutting-edge revenue management systems have been developed to support managers’ decisions and all have as an essential component an accurate forecasting module. This paper aims to introduce new time series forecasting models to be considered as a tool for forecasting daily hotel occupancies. These models were developed in a state space modelling framework which is capable of tackling seasonal complexities such as multiple seasonal periods and non-integer seasonality. An empirical study was carried out to illustrate how a practitioner may apply and compare the performance of different models when forecasting a hotel’s daily occupancy. Results showed that the trigonometric model based on the new modelling framework generally outperformed the majority of the other models. These findings are potentially useful to the entire revenue management community facing the challenge of accurately forecasting a hotel’s daily demand.     

On revenue management and the use of occupancy forecasting error measures

This study aims to draw the attention of the revenue management academic community to inherent problems in forecasting accuracy measurement, and to initiate a critical discussion about forecast quality assessment in hotels. An exhaustive, literature-based set of seventeen forecasting accuracy measures was applied to hotel daily occupancy forecasting data of 2043 pairs of computer and human forecast/actuals, across multiple forecasting horizons. The empirical analysis demonstrates endemic inconsistencies across the accuracy measures, and a plethora of theoretical and practical challenges with regard to total hotel, as well as customer segment level forecast accuracy assessment. The analysis illustrates the difficulty of interpreting conflicting results, as well as issues like level of data aggregation and multiple forecasting horizons. The paper concludes by briefly discussing a more comprehensive approach to hotel forecasting quality assessment framework and serves to warn hotel revenue management academics, practitioners and solution providers against the unconsidered use of accuracy measures.     

Revenue management forecasting: The resiliency of advanced booking methods given dynamic booking windows

Forecasting is the initial component of the hospitality revenue management (RM) cycle. The accuracy of the forecast is critical for RM systems to make appropriate recommendations to optimize revenue. Over recent years the industry has cited shifting booking windows due to a variety of macro (e.g., technology and economy) and micro (e.g., promotion) factors. These shifts pose challenges for RM forecasting algorithms particularly in the domain of pick-up based techniques. In this paper, we review the literature on hotel RM forecasting, particularly with respect to popular techniques used in practice. We then introduce a neural network approach to the advance booking environment to address issues related to booking window shifts. The models are estimated and tested for accuracy, and then re-tested years later after the booking window has shifted. The results are synthesized with discussion as to which models are more suitable for forecasting in dynamic booking windows.     

A stochastic cost efficiency analysis of international tourist hotels in Taiwan

The one-stage stochastic frontier approach (SFA) is used in this study to simultaneously estimate cost efficiency scores and factors of cost inefficiency for 66 international tourist hotels in Taiwan during 1997–2006. An SFA model with three outputs and three inputs is defined. The three outputs are room revenue, food and beverage revenue, and other operation revenue while the three inputs are price of labor, price of other operation, and price of food and beverage. This model also takes into account five environmental variables, including dummy variable of the hotels located in non-metropolitan area, dummy variable of chain hotels, the number of tourist guides, the minimum distance from each hotel to Taoyuan international airport and the minimum distance from each hotel to Kaohsiung international airport. Empirical results show that international tourist hotels in Taiwan are on average operating at 91.15% cost efficiency. All nominal variables are transformed into real variables in 1997 prices by GDP deflators. Chain systems, tourist guides, and international transportation can significantly improve the cost efficiency of international tourist hotels in Taiwan.     

Novel deep learning approach for forecasting daily hotel demand with agglomeration effect

Increasing competition and adoption of revenue management practices in the hotel industry fuel the need for accurate forecasting to maximize profits and optimize operations. Considering the limitations of relevant research, this study focuses on the daily hotel demand with consideration of agglomeration effect, and proposes a novel deep learning-based model, namely, Deep Learning Model with Spatial and Temporal correlations. This model contributes to relevant research by introducing the agglomeration effect and integrating the attention mechanism and Bayesian optimization algorithm. Historical daily demand data of 210 hotels in Xiamen, China are used to verify the model performance. Results show that the proposed model is significantly better than the benchmarks. This study can help hotel managers improve revenue management through better matching potential demand to available capacity.     

An analysis of asymmetry in dynamic pricing of hospitality industry

The paper analyzes the influence of changing tourist arrival on various hotel performance measures related to prices, such as price charges per room, revenue per available room, occupancy rate, and total revenue using price dynamics of Oslo as a case. It also measured whether there is an asymmetric relation between the above performance measures in response to the increase and decrease of tourist arrival. While the presence of a significant long-run relationship between tourist arrival and revenue per available room, capacity utilization, and total revenue has been confirmed, it was found that the price per room does not have such a relationship, and it suggests that the price adjustments are made only on short-run considerations. Another interesting observation is that the magnitude of asymmetric influence is high for revenue per available room and occupancy rate when tourist arrival declines, compared to the corresponding magnitude when tourist arrival increases. These findings indicate that there lies a scope to improve the dynamic pricing model currently being followed.     

Prediction accuracy for reservation-based forecasting methods applied in Revenue Management

With a few notable exceptions, airlines and hospitality forecasting research has been focused so far on point predictions of customers’ bookings. However, Revenue Management decisions are subject to a much greater risk when based exclusively on point predictions. To overcome this drawback, we propose a stochastic framework that allows the construction of prediction intervals for reservation-based (pickup) forecasting methods, which are widely used in the industry. Moreover, we introduce an extension of the multiplicative pickup technique based on Generalized Linear Models. We test the proposed framework with real reservation data from a medium-sized hotel on Lake Maggiore (Italy) and we obtain more efficient prediction intervals relative to classical time series methods. Our approach can be useful to hotel revenue managers that wish to make more informed decisions, planning alternative pricing and room allocation strategies for a range of possible demand scenarios.     

How do advertising expenditures influence hotels’ performance?

Existing studies have shown a positive relationship between advertising and hotel room revenue. However, it is not clear through which channel advertising affects hotel revenue. We contribute to the literature by showing that advertising has a significant positive impact on hotel room price, but not on room occupancy. Thus, advertising affects room revenues through room price, not quantity demanded.     

Function space revenue management: A product bundling approach to hotel function space utilization

This article extends revenue management research into non-traditional hotel operating departments pertaining to convention hotel function space. This research evaluates utilization patterns of multiple convention hotel function spaces and identifies concurrent high-, medium- and low-utilization groupings of multiple convention spaces using k-means clustering. These high- and low-utilization function space pairings allow us to identify convention spaces to target for additional revenue management oversight. A Bayesian analysis of potential lift in revenue per available foot (RPAF) attributable to product bundling concludes the research linking hot and cold function spaces compatible with value based price packaging opportunities.     

How demand uncertainty and market concentration affect long-term price instability

The object of this paper was to investigate the long-term influences of demand uncertainty and market concentration on price instability in the hotel industry. We applied 1996–2008 price and room revenue data collected by Taiwan's Tourism Bureau to test the following two hypotheses: (1) demand uncertainty is negatively associated with price instability in the hotel industry; (2) the market concentration is negatively associated with hotel price instability. We constructed a two-stage price instability model and the estimate results produced the following two findings: First, the uncertainty in room demand significantly contributed to the price instability. Second, the effects of market structure on price instability were heterogeneous across different levels of price instability distribution. Notably, when the distribution of price instability moved from lower to higher quantiles, the relationship between market concentration and price instability altered from positive to negative.     

Asymmetric price responses to hotel competition caused by heterogeneous customers’ willingness to pay

Heterogeneous customers’ willingness to pay affects hotel competition and results in competitors’ asymmetric price responses. To study this lopsided effect, we construct a game framework featuring an upscale hotel and an upper-midscale hotel under a series of assumptions. We first analyze hotel pricing and competitor price responses and then compare the competitors’ responding adjustments. Primary findings show that (1) the price response expressed as an amount by the upscale hotel is more than that of the upper-midscale hotel, and that (2) the price response expressed as a percentage by the upscale hotel is less than that of the upper-midscale hotel. Finally, we present reasons for our findings and offer suggestions to hotel revenue managers.     

Do price promotions drive consumer spending on luxury hotel services? The moderating roles of room price and user-generated content

Price promotion, as price information, and user-generated content (UGC), as non-price information, play an important role in generating luxury hotel revenue. This study empirically investigates how price promotion influences actual consumer spending on luxury hotel services except room price, by considering the contingency role of room price and volume and valence of UGC. Combined data of daily settlements and Tripadvisor customer reviews of a regional luxury hotel chain are used for the analyses. The results indicate that, overall, price promotion negatively influences consumer spending on luxury hotel services and its negative effect is strengthened when the room is higher priced or the valence of UGC is high. Furthermore, a larger volume of intrinsic attribute-related UGC–amenity and location–with price promotion leads to more consumer spending than a larger volume of extrinsic attribute-related UGC–food and staff. The findings provide hotel managers with important insights into pricing and UGC management.     

Identifying critical hotel cancellations using artificial intelligence

Cancellations have a significant impact on the hotel and lodging industry because they directly affect income and are thus considered critical in revenue management. Specifically, cancellations made close to the time of service are the most damaging for hotels because they leave management with no time to react. The use of Personal Name Records (PNR) has led to new approaches in this field, however despite this novel research area there are no investigations focusing on forecasting for individual hotel cancellations made close to the time of service. With the aim of filling this gap, this research is intended to identify those individuals likely to make cancellations in a short-horizon of time using Artificial Intelligence (AI) techniques through PNR data. Promising results have been achieved with 80% accuracy for cancellations made 7 days in advance. By taking this approach, booking management systems, as well as cancellation policies may be optimised.     

酒店市场营销:花得越多越好?

本文使用史密斯旅游研究(Smith Travel Research,STR)提供的酒店数据来探寻市场营销开支对酒店将来财务效益的影响.根据荻取的结论,我们有3大发现:1)市场营销支出对酒店一年后财务影响的边际效应呈递减分布;2)在个体酒店这个层次,总经营利润(gross operating profit)、纯经营利润(net operating income)以及总销售(revenue)有很强的自我预测性;3)对总经营利润、纯经营利润以及总销售的构成作进一步分析可以获得更好的预测效果.     

Optimizing Room Capacity and Profitability for Hong Kong Hotels

This study estimates optimal guestroom capacity for the Hong Kong hotel industry during 2010–2013 using a single-period inventory model. Using Hong Kong hotel operational data for the period 2000–2009, we identify an issue of serious overcapacity of Hong Kong hotels for 2010–2013, the severity of which ranges from 30.6 to 40.7% above the optimal hotel guestroom counts derived from our study. We further estimate an optimal advertising budget as a percentage of total revenue (3.78%) in maximizing profitability. Attention is called for stakeholders to carefully reexamine existing and future hotel development plans and work on pushing up future room demand. The implications of our study are also discussed.     

Quantitative Approaches for Location Decision Strategies of a Hotel Chain Network

Accommodation revenue and demand are affected by hotel location, leading a lot of research to perform theoretical, empirical and operational approaches to analyze and determine the ideal location of hotels. However, operational approaches such as mathematical modeling based optimization have not received sufficient interest in hotel location research while it has been widely used for various kinds of facility locations. Therefore, two mathematical models for opening of new hotels and the closing of existing hotels to a manage hotel chain network are developed using the demand estimation and existing hotel information. In this research, hotel demand is estimated based on the accessibilities to sightseeing, transportation, business points, and market share. Gaining and/or losing of demand and the agglomeration effect are introduced as the objective of two models. Proposed mathematical models are tested to design a hotel chain network with the real data in Seoul, Korea.     

Globalization or localization of consumer preferences: The case of hotel room booking

The purpose of this study is to investigate potential differences in attitudes between Chinese and Western tourists towards various hotel room reservation policies. The study is meant to help the revenue managers of international hotel companies make informed choices on whether to pursue a globalized or a localized approach in the design of hotel room rates and rate restrictions. Three ranges of room prices and four types of rate restrictions are evaluated. The rate restriction policies considered are: a) rule type, b) advance requirement, c) refundability, and d) changes allowed. The results of customer surveys show that the Chinese and Western respondents do not express significantly different preferences concerning room rates, advance requirement, rule type, or refundability. They differ significantly only concerning the changes allowed policy. This result indicates that revenue managers of international hotel companies can take a globalized approach in designing rate restrictions. The identification of these customer preferences provides hotel revenue managers with empirical data on the attitudes of culturally diverse consumers, and this information can enable the design of hotel pricing policies that attract consumers in the global market.