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.     

Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples - Evidence from the Chinese hotel industry

Previous studies on firm failure prediction (FFP) have chiefly addressed predictions based on balanced datasets without considering that the real-world target population consists of imbalanced data. The current study investigates tourism FFP based on the imbalanced data of Chinese listed companies in the hotel industry. The imbalanced dataset was collected and represented in terms of significant financial ratios, and a new up-sampling approach and forecasting method were proposed to correct imbalanced samples. To balance the imbalanced dataset, the up-sampling method generates new minority samples according to random percentage distances from each minority sample to its nearest neighbour (NN). The NNs of unlabelled samples are retrieved from the balanced dataset to produce a knowledge base of nearest-neighbour support vectors, from which base support vector machines (SVMs) are generated and assembled. Empirical results indicate that the proposed sampling approach helped models produce more accurate performance on minority samples, with accuracy rates in excess of 90 per cent. This method of using nearest-neighbour support vectors and correcting imbalanced samples is useful in controlling risk in tourism management.     

Impact of financial/economic crisis on demand for hotel rooms in Hong Kong

The main objectives of this study are (1) to identify the factors that influence the demand for hotel rooms in Hong Kong and (2) to generate quarterly forecasts of that demand to assess the impact of the ongoing financial/economic crisis. The demand for four types of hotel room from the residents of nine major origin countries is considered, and forecasts are generated from the first quarter of 2009 to the fourth quarter of 2015. Econometric approaches are employed to calculate the demand elasticities and their corresponding confidence intervals, which are then used to generate interval demand predictions. The empirical results reveal that the most important factors in determining the demand for hotel rooms in Hong Kong are the economic conditions (measured by income level) in the origin markets, the price of the hotel rooms and the ‘word of mouth’ effect. Demand for High Tariff A and Medium Tariff hotel rooms is estimated to have experienced negative annual growth in 2009 due to the influence of the financial/economic crisis, whereas that for High Tariff B hotel rooms is thought to have grown in 2009 after having decreased in 2008. The demand for tourist guesthouse rooms is expected to be the least affected by the crisis. Overall demand is predicted to recover gradually from 2010 onwards.     

Initially Testing an Improved Extrapolative Hotel Room Occupancy Rate Forecasting Technique

SUMMARY

The existing time series forecasting models either capture the informationof the last few data in the data series or the entire data series is used for projecting future values. In other words, the time series forecasting models are unable to take advantage of the last trend in the data series, which always have a direct influence on the estimated values. This paper proposes an improved extrapolative time series forecasting technique to compute future hotel occupancy rates. The performance of this new technique was tested with officially published room occupancy rates in Hong Kong. Forecasted room occupancy rates were compared with actual room occupancy rates in several accuracy performance dimensions. Empirical results indicate that the new technique is promising with reasonably good forecasting results.     

Forecasting in Short-Term Planning and Management for a Casino Buffet Restaurant

SUMMARY

Demand fluctuation accounts for an important consideration in a restaurant's daily operational decisions. Good short-term planning and management require accurate forecasts of daily demand. The objective of this study is three-fold: (1) to apply, evaluate, and compare different methods of forecasting customer counts for an on-premises buffet restaurant of a local casino in Las Vegas, (2) to describe and propose a combined forecasting approach for this casino buffet restaurant, and (3) to explore the concept of revenue and capacity management for this buffet restaurant. Eight forecasting models were tested and evaluated by two common error measures. The results suggest that a double moving average model was the most accurate model with the smallest MAPE and RMSPE. Extensive discussions on forecasting and planning/management in buffet operations are provided along with recommended future research.     

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.     

Competitive revenue management for fixing quota and price of hotel commodities under uncertainty

This article develops an artificial neural network (ANN) based forecasting model using the past profit records of hotel commodities. Based on forecasting, hotel commodities are categorised into two kinds: ones that push up the revenue, and others, which pull it down. Thereafter, long and short term goals are formulated for fixing quota and proper revenue management under uncertainty. For long term goal, analytical network process (ANP) framework is adopted to establish interrelationships among the factors using DEMATEL methodology. Risk adjusted maximum expected profit is employed for short term goal. Subsequently optimal numbers of commodities are obtained using a fuzzy goal programming approach, and favourable price of the individual commodities is determined keeping the price elasticity as one. Finally, a comparison is made between the respective revenue generated with the new quota and price from the proposed revenue maximization model, and that of the old practised price and quota. The paper demonstrates the superiority of the proposed approach. A case study of a hotel has been taken up to demonstrate the model.     

Financing behaviour of Portuguese SMEs in hotel industry

In this study, using panel data models, we analyze whether the capital structure decisions of SMEs in the hotel sector follow the predictions of Pecking Order and Trade-Off theories. The results suggest that these theories are not mutually exclusive in explaining the capital structure decisions of SME hotels. The results obtained indicate that these firms follow a hierarchical order in their selection of financing sources, corroborating the assumptions of theory. The results also show that SME hotels adjust the level of actual debt towards optimal debt ratio as well as size, asset tangibility, growth opportunities, non-debt tax shields, and risk influence debt. These results suggest that the financing behaviour of SME hotels is in agreement with the predictions of theory. Therefore, Pecking Order and Trade-Off theories contribute to explaining the financing behaviour of SMEs in the hotel sector.     

Hotel pricing at tourist destinations – A comparison across emerging and developed markets

The application of price hedonic theory in the hotel pricing domain has relatively ignored the destination and country-level differences. We compare hotel rents across tourist and non-tourist destinations for an emerging (India) and a developed (USA) market. Utilizing multiple regression on a combined dataset of 21,904 data points spread over 2458 unique hotels, we show that the nature of destination and market moderates the association between hotel attributes and rents. Further, the results show that hotels at tourist destinations enjoy a rent premium across markets. However, this rent premium is positively associated with star rating, only in emerging markets, and is stable in developed markets. We contribute to price hedonic theory by proposing destination and market variables as moderators. Globalization and industry concentration make location decisions recurring and flexible. The study aims to help hotel managers by providing a contextual framework for making these strategic investment decisions.     

Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach

After more than ten years of exponential development, the growth rate of cruise tourist in China is slowing down. There is increasingly financial risk of investing in homeports, cruise ships and promotional activities. Therefore, forecasting Chinese cruise tourism demand is a prerequisite for investment decision-making and planning. In order to enhance forecasting performance, a least squares support vector regression model with gravitational search algorithm (LSSVR-GSA) is proposed for forecasting cruise tourism demand with big data, which are search query data (SQD) from Baidu and economic indexes. In the proposed model, hyper-parameters of the LSSVR model are optimized with GSA. By comparing these models with various settings, we find that LSSVR-GSA with selected mobile keywords and economic indexes can achieve the highest forecasting performance. The results indicate the proposed framework of the methodology is effective and big data can be helpful predictors for forecasting Chinese cruise tourism demand.     

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 exploratory analysis of hotel selection factors: A comprehensive survey of Tehran hotels

The selection of residence location in different countries is of high priority and significance for tourists. The selection of the most appropriate hotel entails a rather complicated decision-making process. A comprehensive hotel selection model can empower the hotel managers, the tourists, and the tourism industry to make decisions based on more effective indicators of high quality services for a higher rate of satisfaction. The purpose of this research is to deeply explore the broad literature and to identify the most significant hotel selection indicators and factors in Tehran hotels and to present a comprehensive model through an exploratory factor analysis of the extracted indicators so as to provide the managers and tourists with a firm ground for making better decisions regarding the indicators of hotel selection. Promenade and comfort, security and protection, network services, pleasure, staff and their services, news and recreational information, cleanliness and room comfort, expenditure, room facilities and car parking were identified as the main hotel selection factors of Tehran hotels. Afterwards, another factor analysis has been done in order to extract the next hidden set of factors within the aforementioned factors which return two main factors of “Hotel Comfort Factors” and “Hotel Compensatory Factors”. Following the creation of the final model and based on the intrinsic vagueness of decision making in the process of selection, a set of fuzzy membership functions for the extracted factors has been provided. The intention has been to provide the expert system and decision support system developers and users with a set of practical indicators in order to help them design and implement realistic systems based on the deeply studied indicators and factors of hotel selection. Such supportive systems can be directly presented to the tourists requesting a mechanism for selecting the most appropriate hotel but lacking enough information about the important indicators and factors and also to the managers of hotels who are trying to make strategic decisions regarding the most optimized investments on the indicators of selecting a hotel. Considering the priorities of tourists, hotel managers, entrepreneurs and investors in the hotel industry require deep investigations and studies for which this paper provides a firm basis.     

A novel hotel recommendation method based on personalized preferences and implicit relationships

On tourism websites, hotel recommendations have drawn growing attention from researchers, as they can help customers select a satisfactory hotel from many options with massive information. However, some inherent challenges exist in conventional hotel recommendations, specifically the extent to which there is considerable room for improvement in user preference models and neighbour recognition. Therefore, we propose a two-stage hotel recommendation approach that employs hotel feature information to support preference analysis. First, in the filling stage, association rules between features are considered to accurately capture users’ personalized preferences, which can be incorporated with public preferences to estimate potential ratings of users for unvisited hotels. Then, in the recommendation stage, we combine rating similarities between users with their closeness relationships to identify more reliable neighbours. Finally, a hotel recommendation case on Ctrip.com is performed to evaluate the model. Experimental results confirm that our method outperforms the other five benchmark methods.     

Nature based solutions and customer retention strategy: Eliciting customer well-being experiences and self-rated mental health

This research attempted to explore the specific role of a hotel’s green physical environment as nature-based solution (NBS) in the customer retention process. Our results showed that the green spaces within a hotel and existing outdoor natural environment as NBS significantly increase guests’ perceptions of well-being and self-rated mental health. In addition, our very significant discovery is that among the examined variables, environmental values have a regulatory role. Well-being perception, self-rated mental health, satisfaction, and affective commitment were important mediators. The proposed theoretical framework encompassing NBS factors and these mediators included a strong prediction power for retention. Keeping in line with emerging NBS in environmental behavior and public health, the present study provides a critical guiding framework helping hotel researchers and operators maximize NBS in guest retention process. We discussed the theoretical/practical implications based on the results in detail in the discussion section.     

Profiling satisfied and dissatisfied hotel visitors using publicly available data from a booking platform

We develop a set of models for predicting hotel visitor satisfaction and the probability of complaints about various service aspects. Our empirical analysis is based on 3630 reviews from one of the Dubai hotels. We identify profiles of visitors who are likely to be dissatisfied with the hotel service and need special attention, as well as of visitors, who are likely to be satisfied with the service and, therefore, do not require extra attention. The predictions are based on observable characteristics of visitors, thus making it possible for hotel managers to apply the models in their everyday work. Using content analysis we also reveal specific problems that different groups of visitors encountered and infer which of the problems has the highest impact on the overall satisfaction with the hotel.     

A management orientated approach to combination forecasting of tourism demand

Volatility, exponential smoothing, regression and Naïve 2 models are considered singly and in combination in terms of forecasting demand for international tourism. These models generate accurate predictions of tourism flows, but their prime utility is when combined with other models. Usually, models are combined by means of purely statistical criteria. We show that goal programming (GP) offers an alternative, flexible approach to model combination. GP offers planners a practical solution to tourism forecasting problems, since the method is more adaptable than conventional minimisation of prediction error, by permitting practitioners to prioritise a series of management related goals. Forecasters can focus on longer- and short-term goals, minimising forecast under- and over-estimation and/or concentrate on prediction errors in tourism flows at various times of the year.     

A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data

Recently, studies have used search query volume (SQV) data to forecast a given process of interest. However, Google Trends SQV data comes from a periodic sample of queries. As a result, Google Trends data is different every week. We propose a Dynamic Linear Model that treats SQV data as a representation of an unobservable process. We apply our model to forecast the number of hotel nonresident registrations in Puerto Rico using SQV data downloaded in 11 different occasions. The model provides better inference on the association between the number of hotel nonresident registrations and Google Trends SQV than using Google Trends data retrieved only on one occasion. Furthermore, our model results in more realistic prediction intervals of forecasts. However, compared to simpler models we only find evidence of better performance for our model when making forecasts on a horizon of over 6 months.     

Forecasting Travel Patterns Using Palmore's Cohort Analysis

Summary

The purpose of this study was to introduce Palmore's (1978) method of cohort analysis and illustrate its potential application to tourism forecasting. Results suggested that (a) older cohorts participate less frequently in international travel than younger cohorts, (b) decrease in participation continues as one ages, and (c) changes in travel behavior are due primarily to period effects. With respect to the impact these findings may have on the tourism industry, the results suggest that marketers should monitor the aggregate changes taking place within targeted cohorts, and strategic planning should not be based on an assessment of differences between cohorts at one point in time.     

The impact of information technology on hotel operations,service management and transaction costs: A conceptual framework for full-service hotel firms

This paper develops a conceptual framework that describes the impact of information technology (IT) on service management and transaction costs in full service hotel firms. It details how IT would help such firms to lower operations-related transaction costs. Further, the underpinnings of how IT would impact service management in full service hotel firms is discussed more specifically from a customer satisfaction point of view while focusing on two aspects, i.e. managing customer delight and the customer's role as a co-producer. Propositions are developed and a discussion on the impact of IT on firm profitability from a transaction cost perspective ensues while concluding with managerial implications.     

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.