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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 article seeks to (1) identify forecasting techniques used to estimate taxable sales in California counties; (2) analyze which of these produces the most accurate estimate; (3) document what prevented officials from using the most accurate forecasting technique in California counties; and (4) determine what forecasting approach would work best for individual counties. This research generally confirms previous research findings that judgmental approaches are the most commonly used method of revenue forecasting in smaller localities. In terms of accuracy, econometric models outperform other quantitative methods, particularly compared to trend line fitting and extrapolation-by-average approaches. The “not now but later” perception in the use of econometric models can be ascribed to California county forecasters’ discomfort and lack of preparation for using this sophisticated technique. Once the critical prerequisites for the use of econometric models are provided -- such as statewide training, timely inter-governmental data sharing, easy access to economic data, and user-friendly forecasting formats with automated procedures -- econometric models can serve the needs of California counties.

The marriage of new scanner‐type data sources and new computing and analysis methods is allowing a new approach to the development and use of models for decision support and product line management. Data‐driven modeling describes a process of model‐building wherein models are created that fit the dynamics of the data rather than assuming a priori relationships among brands and their marketing mix elements. Based on a combination of time‐series and econometric modeling methods, these models can significantly improve a modeler’s ability to capture marketplace structure and dynamics. Although more complex than their predecessors, the capabilities of these new data‐driven decision support models make them potentially very powerful tools, improving intuition and managerial understanding while suggesting improved decision alternatives. Develops such a model using detailed multiproduct retail data and demonstrates its capabilities.

The purpose of this paper is to investigate the usefulness of search query volume data in forecasting demand for hotel rooms and identify the best econometric forecasting model.

The authors used search volume data on five related queries to predict demand for hotel rooms in a specific tourist city and employed three ARMA family models and their ARMAX counterparts to evaluate the usefulness of these data. The authors also evaluated three widely used causal econometric models – ADL, TVP, and VAR – for comparison.

All three ARMAX models consistently outperformed their ARMA counterparts, validating the value of search volume data in facilitating the accurate prediction of demand for hotel rooms. When the three causal econometric models were included for forecasting competition, the ARX model produced the most accurate forecasts, suggesting its usefulness in forecasting demand for hotel rooms.

To demonstrate the usefulness of this data type, the authors focused on one tourist city with five specific tourist‐related queries. Future studies could focus on other aspects of tourist consumption and on more destinations, using a larger number of queries to increase accuracy.

Search volume data are an early indicator of travelers' interest and could be used to predict various types of tourist consumption and activities, such as hotel occupancy, spending, and event attendance.

The paper's findings validate the value of search query volume data in predicting hotel room demand, and the paper is the first of its kind in the field of tourism and hospitality research.

The purpose of this paper is to investigate the behavior peculiarities of the labor force participation in Eastern European countries.

The authors provide the analysis of nonlinearity in dynamics of economic active population and perform the econometric analysis using logistic smooth transition autoregressive models that are flexible and capture various kinds of behavior for different modes. The paper investigates labor markets of six Eastern European countries, Hungary, Bulgaria, Poland, Slovakia, Romania and Croatia that are characterized by lower level of labor force participation rate (LFPR) than average level in EU.

The results of modeling quantitatively characterize smooth changes in the behavior modes of labor force activity for each country and indicate how population economic activity depends on previous labor market states. The estimated slope parameters that determine the smoothness of transition between regimes show that, in all countries, the labor force participation quite quickly reacts to changes that occurred on the labor market in the past. During recession periods, households of European countries that joint EU last decade in order to prevent the depletion of their total income increased labor supply and showed increased activity in job search.

This paper indicates the nonlinearity and asymmetry in LFPR in transition economies, discovers variety of its dynamics in the different regimes and determines the indicators that cause the change of the population economic activity behavior in each country.

A recent survey by Social Systems, Inc., indicates that since 1970 over 1000 corporations in the United States and Canada have begun developing and using corporate simulation models. However, the percentage of corporations that have experience with corporate models remains relatively small. Why aren't more corporations using these models today? Why are the corporate model users restricted to the larger corporations? Are the primary constraints impeding the use of these models technical problems, computer hardware problems, software problems, or political problems?

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 Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The purpose of this paper is to explore the forecasting effectiveness of Black-Scholes (BS) focussing parity analysis of time series econometric and implied volatility (IV) numerical techniques.

To analyze the comparative competitiveness of econometric time series and IV models this paper consolidated the study with their inter-relations leading toward multilayered moneyness-maturity correlation of model and market option prices, thoroughly determined the moneyness-maturity combinations of error metrics of Nifty index options.

Out of six models tested and critically examined here, the paper procures only a single model, IV, which best caters to the requirements of option traders and as a result the paper ended up that only IV supports to multifarious moneyness-maturity dimension of option pricing of Nifty index options. The analysis also confirms that the standard VIX is not a reliable tool for determining the base price of Nifty index options (via BS). As the IV landmarks during the most dynamic phase of Indian capital market which is a touchstone to justify the quality of any model, the paper can deduce that IV could continue to perform in hardships of financial contraction par smoothly and effectively.

The final outcome of this research which ended successfully in exploring a dominant model, guided successfully through the most volatile period of Indian economy can be used to safe guard investor's faith and to figure a design which could compete on the canvass of option pricing.

As equity market is always subject to highly unpredictable conditions and may keep on experiencing it through all times to come, the unified objective of research is to find out the most impeccable volatility model to meet out the requirements of option practitioners, specifically contributing upto the satisfaction and expected results during tumultuous period.

This study aims to examine whether and when real-time updated online search engine data such as the daily Baidu Index can be useful for improving the accuracy of tourism demand nowcasting once monthly official statistical data, including historical visitor arrival data and macroeconomic variables, become available.

This study is the first attempt to use the LASSO-MIDAS model proposed by Marsilli (2014) to field of the tourism demand forecasting to deal with the inconsistency in the frequency of data and the curse problem caused by the high dimensionality of search engine data.

The empirical results in the context of visitor arrivals in Hong Kong show that the application of a combination of daily Baidu Index data and monthly official statistical data produces more accurate nowcasting results when MIDAS-type models are used. The effectiveness of the LASSO-MIDAS model for tourism demand nowcasting indicates that such penalty-based MIDAS model is a useful option when using high-dimensional mixed-frequency data.

This study represents the first attempt to progressively compare whether there are any differences between using daily search engine data, monthly official statistical data and a combination of the aforementioned two types of data with different frequencies to nowcast tourism demand. This study also contributes to the tourism forecasting literature by presenting the first attempt to evaluate the applicability and effectiveness of the LASSO-MIDAS model in tourism demand nowcasting.