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The purpose of this paper is to use demand behavior estimation to find the sources of price variability among resort hotels at different Spanish destinations.

This paper estimates online demand functions during high season for seven four-star resort hotels located at different Spanish destinations. Different price elasticity values are compared, and different factors affecting price elasticity are analyzed.

The main findings indicate that most of the high season periods display elastic demands, but factors such as a central location at a resort, recent refurbishments, the availability of additional facilities/services and a hotel targeted at the couples and/or half-board segments make the demand more inelastic; the Tenerife hotels had the most price-elastic demand; during the closest booking periods to the date of stay, the demand is usually more elastic; and a higher number of local competitors pushes down hotel prices.

This paper highlights the managerial implications of focusing on more profitable demand segments for hoteliers. This is especially useful for the development of revenue management software aimed at improving forecasts.

该论文评估了位于西班牙不同目的地的七家四星级度假酒店在旺季期间的在线需求函数。比较不同的价格弹性值, 分析影响价格弹性的不同因素。

本文的目的是使用需求行为估计来找出西班牙不同目的地度假酒店价格变化的来源。

主要调查结果表明：（1）大多数旺季期间的需求弹性, 但诸如度假村的中心位置、最近的翻新、额外设施/服务的可用性以及针对夫妻和/的酒店等因素或半食宿使需求更加缺乏弹性; (2) 特内里费酒店的价格弹性需求最大; (3) 在离入住日期最近的预订期间, 需求通常更具弹性; (4) 更多的本地竞争者压低了酒店价格。

本论文强调了酒店经营者应关注更有利可图的需求细分市场的管理意义。研究发现对于开发旨在改进预测的收益管理软件特别有价值。

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

This study aims to examine the impact of a mega-sporting event “2014 FIFA World Cup” on hotel pricing strategies and performance.

The present project examines the host regions’ response to the 2014 FIFA World Cup which was established by the variance in the main hotel key performance indexes: occupancy, average daily rate, revenue per available room (RevPAR) and supply. Using data gathered from STR, this research distinctly shows how the Brazilian host regions reacted to the World Cup.

Results suggest that the key performance indicators of Brazil’s lodging sector reacted differently to the World Cup. Although all hosting cities experienced significant RevPAR growth because of the increase in hotel room rates during the event, the supply and occupancy performed differed from each city.

Research is limited to the case of hotel performance at the country level for mega-events. The study focused on the reaction of revenue managers in the Latin America context. Other contexts may generate different results.

The study helps revenue managers to examine how the FIFA World Cup travel demand affected pricing strategies and revenue management practices in the Brazilian hotel sector in areas undergoing seasonal growths in overnight tourism. This study serves to inform hoteliers and practitioners about revenue management pricing strategies to improve hotel performance during mega-sporting events.

This study reveals that the benefits brought by a mega-event are not always translated into strong hotel revenue performance. This study highlights an important but understudied research area of revenue management pricing strategies and the effect of mega-sporting events in the hotel sector. This study contributes to the literature as one of the few investigations to benefit hotel pricing strategies and overall revenue performance.

This study is one of the few studies about exploring the reaction of revenue managers during the execution of a mega-sporting event. The value of the present study lies in the fact that the authors extend previous studies examining the impact of the most important sporting event in the hotel industry at the country-level perspective. This study serves to inform hoteliers and practitioners about revenue management pricing strategies to improve hotel performance during mega-sporting events.

The objectives of this study are to investigate the relationship between hotel room demand and room rates, and to find a viable solution for the optimal room rate that maximizes the total profit.

There are various studies in the literature on how room rates affect profitability, and how the optimal room rate that maximizes the total revenue can be determined. Most of these studies assume an algebraic relationship between room rates and room demand, and obtain the optimal solution by applying calculus to the revenue or profit function. This study adopts the alternative approach of using a model with a demand function that has been shown to be a superior causal forecasting model in some markets, and develops a new method to optimize the total profit.

The traditional method of applying calculus to the profit function based on a causal forecasting model leads to unrealistic solutions. This gives rise to the paradox that, on the one hand, there is a superior causal forecasting model based on room rates, but on the other hand, the traditional method does not yield a realistic solution for room rate optimization. This study analyzes the underlying causes of this paradox and proposes a method to resolve it.

The findings can be used by hotels to fine‐tune the room rates determined by conventional methods to arrive at a realistic and definitive value for the optimal room rate.

This study highlights the problems that arise with the traditional method of applying calculus to revenue and profit optimization and proposes a new method to resolve it.

The revenue management (RM) discipline is built on the principle of demand-based pricing. This study aims to examine how and to what extent RM can be implemented in the hotel industry during low-demand periods, particularly during the coronavirus disease 2019 (COVID-19) crisis.

This paper used semi-structured interviews to gather information from hotel RM executives, RM consultants and RM system providers. Participants were asked to think about the impact of COVID-19 on their RM practices. This paper interviewed 26 revenue executives between January and March 2020.

Core RM processes are still relevant during the COVID-19 crisis; however, not all components are equally important. Business analysis, pricing strategy and demand modeling and forecasting are the most critical RM processes. Inventory and price optimization and setting booking controls are not as important at this time; along with distribution channel management, these processes will become more relevant as demand picks up.

Future research in this area should focus on each core RM process separately and in-depth to understand how implementation changes during the crisis and recovery periods. Future studies should also investigate how these processes operate during the recovery period. The full breadth of consequences of the COVID-19 crisis in hotel RM will likely manifest gradually. Therefore, the core RM processes should also be examined when the crisis is over.

Apart from a few studies that touched on RM-related strategies during economic downturns, to the knowledge, this is the first study to systematically examine the extent to which RM can be implemented during a crisis.

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region.

For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units.

The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period).

From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region.

The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

This study aims to provide a comprehensive review of hotel demand forecasting to identify its key fundamentals and evolution and future research directions and trends to advance the field.

Articles on hotel demand modeling and forecasting were identified and rigorously selected using transparent inclusion and exclusion criteria. A final sample of 85 empirical studies was obtained for comprehensive analysis through content analysis.

Synthesis of the literature highlights that hotel forecasting based on historical demand data dominates the research, and reservation/cancellation data and combined data gradually attracted research attention in recent years. In terms of model evolution, time series and AI-based models are the most popular models for hotel demand forecasting. Review results show that numerous studies focused on hybrid models and AI-based models.

To the best of the authors’ knowledge, this study is the first systematic review of the literature on hotel demand forecasting from the perspective of data source and methodological development and indicates future research directions.

本研究旨在对酒店需求预测进行全面回顾, 以确定其关键基础和演变以及未来的研究方向和趋势, 以推动该领域的发展。

使用严格和透明的纳入和排除的标准对酒店需求建模和预测的文章进行识别和选择。通过内容分析, 最终有 85个实证研究作为综合分析的样本。

综合文献发现, 基于历史需求数据的酒店预测在研究中占主导地位, 近年来预订/取消数据和组合数据逐渐引起研究关注。在模型演化方面, 时间序列和基于人工智能的模型是最受欢迎的酒店需求预测模型。审查结果表明, 许多研究都集中在混合模型和基于 AI 的模型上。

本研究是第一次从数据源和方法发展的角度对酒店需求预测文献进行系统回顾, 并指出未来的研究方向。

Este estudio tiene como objetivo proporcionar una revisión amplia de la previsión sobre la demanda hotelera a la hora de identificar sus fundamentos clave, la evolución y las direcciones y tendencias de investigación futuras para avanzar en el campo de estudio.

Se identificaron y seleccionaron de forma rigurosa artículos sobre modelado y previsión de la demanda hotelera utilizando criterios transparentes de inclusión y exclusión. Se obtuvo una muestra final de 85 estudios empíricos para su análisis integral a través del análisis de contenido.

La síntesis de la literatura destaca que la previsión hotelera basada en datos históricos de demanda ha dominado la investigación, y los datos de reserva/cancelación, así como los datos combinados han atraído gradualmente en los últimos años la atención de la investigación. En términos de evolución del modelo, las series temporales y los modelos basados en IA son los modelos más populares para la previsión de la demanda hotelera. Los resultados de la revisión muestran que numerosos estudios se han centrado en modelos híbridos y basados en IA.

Este estudio es la primera revisión sistemática de la literatura sobre la previsión de la demanda hotelera desde la perspectiva de la fuente de datos y el desarrollo metodológico e indica futuras líneas de investigación.

The 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.

This 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.

Random-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.

This 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.

This 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.

This chapter examines the pricing, demand (occupancy), and revenue per available room (RevPAR) dynamics of European hotels for the period 2006–2007. The importance of understanding the pricing behavior of direct competitors is critical to effective strategy formulation and meaningful industry analysis. Nevertheless, existing demand studies miss a critical link to local market dynamics. This study offers an alternative approach to examining competitive set pricing behavior that yields insights into the inelasticity of lodging demand. The results of this study of over 3,000 European hotel observations reveal that hotels that offered average daily rates (ADRs) above those of their direct competitors had lower comparative occupancies but higher relative RevPARs. The observed pattern of demand and revenue behavior was consistent for hotels in all market segments from luxury to economy. Country-specific analyses reveal a similar pattern, with more volatility in the results for hotels in Spain and Italy. Overall, the results suggest that the best way for a hotel to have higher revenue performance than its competitive group is to maintain higher rates. The results of this study support the position that hotel operators who resist pressures to undercut competitor's prices may be better served with higher revenues.

The purpose of this paper is to examine the hotel growth model including hotel brand, culture and life cycle phases of the Myrtle Beach, South Carolina, the fastest growing tourism destination in the United States.

Culture reflecting consuming behaviour of low-context innovators and high-context imitators is measured by the price elasticity of demand (PED). Hotel brand reflecting guests’ hotel class is measured by the income elasticity of demand. Autoregressive distributed lag has been conducted on the Smith Travel Research data in 33 years (1989–2022) to determine the relationship among hotel brand, culture and life cycles.

Skilled labour is the key to make hotels grow. Therefore, increase room rates when hotels possess skilled professionals and decrease room rates when hotels have no skilled professionals. During the rejuvenation in Myrtle Beach (1999–2003), hoteliers increased room rates for innovators due to skilled professionals to increase revenue. Otherwise, a decrease in room rates due to lack of skilled professionals would lead to increase revenue.

(1) Although Myrtle Beach is one of the fastest growing tourism destinations in the US, it has a relatively small geographic area relative to the country. (2) Data cover over one tourist life cycle, so the time span is relatively short. Hoteliers can forecast the number of guests in different culture by changing room rates.

To optimize revenue, hoteliers can select skilled labour in professional design hotel brands which could make an increase in demand for leisure transient guests no matter what room rates increase after COVID-19 pandemic.

The study has considered the applied ethical processes regarding revenue management that would maximize both revenue and customer satisfaction when it set up an increase in room rates to compensate for professional hotel room design or it decreases room rates for low-income imitators in exploration and development.

This research highlights that (1) skilled design in the luxury hotel brand is the key for the hotel growth and (2) there is a steady state of the growth model in the destination life cycle.