A K-means Group Division and LSTM Based Method for Hotel Demand Forecasting

The accuracy of hotel demand forecasting is affected by factors such as the completeness of historical data and the maturity of models. Most of the existing methods are based on rich data, without considering that single hotels may only obtain sparse data. Therefore, a K-means group division and Long Short-Term Memory (LSTM) based method is proposed in this paper. Guest types are introduced into the forecasting to provide reference for hotel\u27s further decision-making. Using an example of 1493 hotels in Europe, we divide hotel groups and forecast the flow of leisure and business guests. The experimental results show that, compared with the benchmark models, LSTM can improve the forecasting performance of hotel group; compared with single hotels, the forecasting of hotel groups can effectively avoid inaccuracy caused by sparse data. The results can provide necessary reference for hospitality to make decisions based on guest types

Predicting hotel bookings cancellation with a machine learning classification model

Booking cancellations have significant impact on demand-management decisions in the hospitality industry. To mitigate the effect of cancellations, hotels implement rigid cancellation policies and overbooking tactics, which in turn can have a negative impact on revenue and on the hotel reputation. To reduce this impact, a machine learning based system prototype was developed. It makes use of the hotel’s Property Management Systems data and trains a classification model every day to predict which bookings are “likely to cancel” and with that calculate net demand. This prototype, deployed in a production environment in two hotels, by enforcing A/B testing, also enables the measurement of the impact of actions taken to act upon bookings predicted as “likely to cancel”. Results indicate good prototype performance and provide important indications for research progress whilst evidencing that bookings contacted by hotels cancel less than bookings not contacted.info:eu-repo/semantics/acceptedVersio

Big data in hotel revenue management: exploring cancellation drivers to gain insights into booking cancellation behavior

n the hospitality industry, demand forecast accuracy is highly impacted by booking cancellations, which makes demand-management decisions difficult and risky. In attempting to minimize losses, hotels tend to implement restrictive cancellation policies and employ overbooking tactics, which, in turn, reduce the number of bookings and reduce revenue. To tackle the uncertainty arising from booking cancellations, we combined the data from eight hotels’ property management systems with data from several sources (weather, holidays, events, social reputation, and online prices/inventory) and machine learning interpretable algorithms to develop booking cancellation prediction models for the hotels. In a real production environment, improvement of the forecast accuracy due to the use of these models could enable hoteliers to decrease the number of cancellations, thus, increasing confidence in demand-management decisions. Moreover, this work shows that improvement of the demand forecast would allow hoteliers to better understand their net demand, that is, current demand minus predicted cancellations. Simultaneously, by focusing not only on forecast accuracy but also on its explicability, this work illustrates one other advantage of the application of these types of techniques in forecasting: the interpretation of the predictions of the model. By exposing cancellation drivers, models help hoteliers to better understand booking cancellation patterns and enable the adjustment of a hotel’s cancellation policies and overbooking tactics according to the characteristics of its bookings.info:eu-repo/semantics/acceptedVersio

Identification of common city characteristics influencing room occupancy

Purpose National tourism offices worldwide implement marketing strategies to influence tourists’ choices. However, there is more than meets the eye when it comes to choosing a city as a tourism destination. The purpose of this paper is to answer which are the characteristics that play a key role in room occupancy. Design/methodology/approach Diverse characteristics such as the city offer, demographics, natural amenities (e.g. number of beaches) and also politics (e.g. type of government) are combined into a decision tree model to unveil the relevance of each in determining room occupancy. The empirical experiments used data known in 2015 from 43 cities from Europe and the rest of the World to model room occupancy rate in 2016. Findings While the seasonality effect plays the most significant role, other less studied features such as the type of political party prior to current government were found to have an impact in room occupancy. Originality/value This study unveiled that center–right and right governments are generally more sensitive to promote its city as a tourism destination.info:eu-repo/semantics/acceptedVersio

Predictive models for hotel booking cancellation: a semi-automated analysis of the literature

In reservation-based industries, an accurate booking cancellation forecast is of foremost importance to estimate demand. By combining data science tools and capabilities with human judgement and interpretation, this paper aims to demonstrate how the semiautomatic analysis of the literature can contribute to synthesizing research findings and identify research topics about booking cancellation forecasting. Furthermore, this works aims, by detailing the full experimental procedure of the analysis, to encourage other authors to conduct automated literature analysis as a means to understand current research in their working fields. The data used was obtained through a keyword search in Scopus and Web of Science databases. The methodology presented not only diminishes human bias, but also enhances the fact that data visualisation and text mining techniques facilitate abstraction, expedite analysis, and contribute to the improvement of reviews. Results show that despite the importance of bookings’ cancellation forecast in terms of understanding net demand, improving cancellation, and overbooking policies, further research on the subject is still needed.info:eu-repo/semantics/publishedVersio

An automated machine learning based decision support system to predict hotel booking cancellations

Booking cancellations negatively contribute to the production of accurate forecasts, which comprise a critical tool in the hospitality industry. Research has shown that with today’s computational power and advanced machine learning algorithms it is possible to build models to predict bookings cancellation likelihood. However, the effectiveness of these models has never been evaluated in a real environment. To fill this gap and investigate how these models can be implemented in a decision support system and its impact on demand-management decisions, a prototype was built and deployed in two hotels. The prototype, based on an automated machine learning system designed to learn continuously, lead to two important research contributions. First, the development of a training method and weighting mechanism designed to capture changes in cancellations patterns over time and learn from previous days’ predictions hits and errors. Second, the creation of a new measure – Minimum Frequency – to measure the precision of predictions over time. From a business standpoint, the prototype demonstrated its effectiveness, with results exceeding 84% in accuracy, 82% in precision, and 88% in Area Under the Curve (AUC). The system allowed hotels to predict their net demand and thus making better decisions about which bookings to accept and reject, what prices to make, and how many rooms to oversell. The systematic prediction of bookings with high probability of being canceled allowed hotels to reduce cancellations by 37 percentage points by acting to avoid their cancellation.info:eu-repo/semantics/publishedVersio

Rate management for home holiday rentals: A data analytics approach

Home holiday rentals is a growing industry in the city of Lisbon, following the increase of the tourism volume, yet very little explored and with a great lack of studies and information about it. This leads to a need of research, in order to find new patterns in the guest’s and host’s behavior that allow the property’s owners to maximize their profits, by increasing their monthly occupancy rates. This study was made using several Data Mining techniques. This dissertation used the data from the property management company FeelsLikeHome, namely the properties information and reservations’ historical data from January 2017 to May 2019. The relationship between the monthly average rate per night and the monthly occupancy rate was studied, to understand if they affect or explain one another. After this, there was a need to understand which are the variables that better explain and predict the occupancy rate. Finally, with this information, a set of matrices were built based on the most important predictors, displaying the corresponding occupancy rate, with the objective of proposing changes to the rates per nigh currently implemented. A predictive model was obtained for the occupancy rate, through the interpretation of patterns in the properties’ occupancy. With this, properties’ profiles with high and low occupancy were identified and coefficients of rate change were proposed. These models offered useful knowledge for FLH and for the industry professionals, since it allowed them to develop marketing strategies to improve profits.O aluguer de casas de férias é uma indústria em crescimento na cidade de Lisboa, que tem acompanhado o aumento do volume do turismo, no entanto, é ainda muito pouco explorada, verificando-se uma grande falta de estudos e informação sobre esta. Isto leva a uma necessidade de investigação, a fim de encontrar padrões no comportamento dos hóspedes e dos anfitriões que permitam ao dono da propriedade maximizar os seus lucros, aumentando a taxa de ocupação mensal da propriedade. Este estudo foi feito com recurso a diversas técnicas de Data Mining. Esta dissertação utilizou dados da empresa de gestão de propriedades FeelsLikeHome, nomeadamente informação das propriedades e dados históricos das reservas de Janeiro de 2017 a Maio de 2019. A relação entre o preço por noite médio mensal e a ocupação média mensal foi estudada, a fim de entender se eles se afetam ou explicam mutuamente. Depois disto, houve uma necessidade de entender quais as variáveis que melhor explicam e preveem a taxa de ocupação. Finalmente, com esta informação, um conjunto de matrizes foi construído com base nos preditores mais importantes, exibindo a taxa de ocupação correspondente, com o objetivo de propor alterações aos preços por noite praticados atualmente. Foi obtido um modelo preditivo para a taxa de ocupação, através da interpretação de padrões na taxa de ocupação das propriedades. Com isto, foram identificados perfis de propriedades com predições de taxas de ocupação altas e baixas e foram propostos coeficientes de alteração do preço. Estes modelos oferecem conhecimento útil para a FLH e para os profissionais da indústria, uma vez que lhes permite desenvolver estratégias de marketing para aumentar os seus lucros

Does Discounting Work in the Lodging Industry?

The central intent of this econometric case study analysis is to examine the relationship between discounting room rates and hotel financial performance. The study provides a theoretical framework that investigates the fundamentals of discounting and empirically assesses the efficacy of the discounting process in the lodging industry. The study adopts an error correction model to properly account for the dynamics of the industry. The results indicate that the variables may be modeled as an integrated process and which are linked in the long run and also possess a short-term relationship. The research findings suggest that discounting works both in the short term and the long term only if the discount rate exhibits serial correlation or nonstationary tendencies

A Development of a Game-Theoretic Artificially Intelligent Neural Network Revenue Management Forecasting Model

The aim of this dissertation is to create and test a risk induced game-theoretic price forecasting model. The models were tested with datasets from 3 Upper Midscale hotels in 3 locations (urban, interstate and suburb), one hotel from each location. The data was obtained from STR, a leading hospitality marketing company which consolidates all of the daily hotel data from hotels in the United States. Multiple error measures were used to compare the accuracy of models. Three LSTM models were proposed and tested; LSTM model 1 that relied on ADR to forecast ADR, LSTM model 2 that used ADR, supply, demand, and day of the week to generate the forecast, and finally LSTM model 3 that used all the predictors of LSTM model 2 plus ADR of 4 competitors of the same size and scale to predict ADR values. The LSTM models were tested against traditional forecasting methods. The findings showed that LSTM model 2 was the most accurate of all the models tested. Moreover, LSTM model 1 and 3 showed higher accuracy than traditional models in some cases. In particular, all the LSTM models outperformed the traditional methods in the most volatile property (property C). Overall, the results indicated the higher accuracy of LSTM models for times of uncertainty. Finally, estimation of Value at Risk was introduced into the LSTM models, however the accuracy of the models did not change significantly

New Developments in Tourism and Hotel Demand Modeling and Forecasting

Abstract Purpose The purpose of the study is to review recent studies published from 2007-2015 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. Design/Methodology/approach Articles on tourism and hotel demand modeling and forecasting published in both science citation index (SCI) and social science citation index (SSCI) journals were identified and analyzed. Findings This review found 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, while 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. Research limitations/implications 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. Practical implications This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/value 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