Timing matters: crisis severity and occupancy rate forecasts in social unrest periods

Qiu, Richard T.R.; Liu, Anyu; Stienmetz, Jason L.; Yu, Yang

doi:10.1108/ijchm-06-2020-0629

Cited by 16 publications

(8 citation statements)

References 46 publications

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“…The rapid development of tourism has contributed to the prosperity of the hotel industry (Huang and Zheng, 2021), but its fragile nature has also been exposed (Zheng et al , 2020). Economic crises (Webb et al , 2020), unexpected events (Qiu et al , 2021) and fierce competition within the industry (Tang et al , 2015) would have an incalculable impact on the development of the hospitality industry. For example, the spread of COVID-19 has created considerable uncertainty for the hotel industry (Hao et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Daily hotel demand forecasting with spatiotemporal features

Huang

Zheng

2022

IJCHM

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Purpose 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. Design/methodology/approach 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. Findings 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). Practical implications 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. Originality/value 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.

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Section: Introductionmentioning

confidence: 99%

Daily hotel demand forecasting with spatiotemporal features

Huang

Zheng

2022

IJCHM

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“…However, other industries such as energy (Luo et al, 2020), medicine (Tyrer et al, 2004) and geography (Kasiyanchuk et al, 2015) have long used riskinduced forecasting models. Moreover, previous studies focus on demand forecasting, with little attention to average daily rates (ADR) forecasting (Binesh et al, 2021;Qiu et al, 2021), even though demand, duration of stay and occupancy are affected by room rates (Zheng et al, 2020). Additionally, competitors' prices are largely ignored, a common limitation of forecasting studies (Moncarz and Kron, 2010), even though they are used in the real world (Mohamed, 2020).…”

Section: Global Crises and Tourism (In Billions)mentioning

confidence: 99%

Forecasting hotel room prices when entering turbulent times: a game-theoretic artificial neural network model

Binesh

Belarmino

Rest

et al. 2023

IJCHM

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Purpose This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network. Design/methodology/approach Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models. Findings The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels. Research limitations/implications This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR. Practical implications This study produced a reliable, accurate forecasting model considering risk and competitor behavior. Theoretical implications This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times. Originality/value This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

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“…The data on occupancy rate of Andalusia hotels was collected from IECA organizations, which contains annual and monthly aggregate occupancy rates of hotel classes from 1 January to 31 December 2022. Of particular interest to the hotel industry is assessing the impact of accommodation supply on key hotel performance indicators [see 26,69,86,87,88,89,90]. Supply and demand imbalances in hotel markets cause short-term growth or declines in rate and are inefficiently self-correcting over the long term [90].…”

Section: Demand Indicatorsmentioning

confidence: 99%

The Impact of Tourism Promotion at Andalusian Eight Provinces

Florido-Benítez¹

2023

Preprint

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The aim of this study is to analyse tourism promotion of eight provinces of Andalusia from 2010 to 2022, and to examine the ways that tourism promotion campaigns influence and stimulate the improving of tourism supply and demand at Andalusian provinces. The methodology used in this study collected data from Andalusian public organizations (secondary data), and information related to this topic from researchers and organizations (primary data) for the purpose of this research project. This research provides a new contribution to tourism industry and promotion strategies context. Findings of the current study prove that DMOs of Andalusian provinces increased the tourism promotion budgets and campaigns to revitalize the tourism demand and supply due to the pandemic crisis and Russia’s invasion of Ukraine. Indeed, results of this research suggest that Andalusia’s tourism promotion budgets and campaigns have helped to improve the number of passenger arrivals, number of tourists, hotel occupancy rate, overnight stays at hotels by international and Spanish, and total overnight stay indicators in the period analysed, especially in the years 2021 and 2022. The contribution of this study has a great relevance to tourist destinations, DMOs, hotels, airports, and stakeholders where application of tourism promotion budgets, joint promotion campaigns, and new tourism products and services should be implemented to improve the tourism supply and demand, guarantee the regional and local economy and well-being of population.

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Timing matters: crisis severity and occupancy rate forecasts in social unrest periods

Cited by 16 publications

References 46 publications

Daily hotel demand forecasting with spatiotemporal features

Daily hotel demand forecasting with spatiotemporal features

Forecasting hotel room prices when entering turbulent times: a game-theoretic artificial neural network model

The Impact of Tourism Promotion at Andalusian Eight Provinces

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