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Hotel profitability: a multilayer neural network approach

Rubén Lado-Sestayo (Department of Business, University of Corunna, A Coruna, Spain)
Milagros Vivel-Búa (Department of Financial Economics and Accounting, Universidade de Santiago de Compostela, Santiago de Compostela, Spain)

Journal of Hospitality and Tourism Technology

ISSN: 1757-9880

Article publication date: 6 June 2019

Issue publication date: 20 May 2020




The purpose of this paper is to design an algorithm to predict hotel profitability by means of deep learning techniques.


The methodology consists of a multi-layered neural network that includes a lag of profitability as the input. Furthermore, other input variables are related to hotel and tourist destinations; the raw data for hotel and tourist destinations were collected from multiple public access data sources.


The results show that the proposed model has a high predictive capacity of hotel profitability in all the years studied (2005-2011), according to the performance metrics evaluated within the sample. Thus, the authors can conclude that deep learning algorithms can be a useful tool to evaluate hotel performance.

Practical implications

The algorithm designed in this research could be of interest to improve decision-making processes related to profitability, for example, in evaluating the creation of new hotels. Moreover, the model provides a quick and efficient analyses that could be of interest to investors and lenders. In particular, they could compare investment alternatives in the hotel sector. Also, according to the results, the location variables are important determinants of hotel profitability, and consequently, hotel managers should collaborate with the tourist destination managers to improve profitability. From an internal perspective, hotel managers should focus on the management of human resources.


This paper is the first empirical study that predicts hotel profitability using deep learning techniques. In addition, this methodology is applied to analyse hotel profitability, for the first time, in the Spanish market. This market is an ideal analytical framework because of its heterogeneity with respect to hotel supply in terms of seasonality and coastal characteristics, among others.




本论文采用多层神经网络、以盈利时间区间作为单位, 来进行计算。此外, 其他关于酒店和旅游目的地的数据因子也包含在研究范围内;研究样本包括酒店和旅游目的地的原数据, 这些数据通过多方公开数据渠道获得。


研究结果表明提出的数算制度模型对于所有年份(2005-2011)的酒店盈利性研究有显著预测功效。此判定是由数据样本的效益矩阵得出。因此, 我们能够形成结论:深度学习的数算制度可以作为衡量酒店效益的有力工具。


本论文设计的数算制度对于提高盈利性的决策过程有很大意义, 比如评估新酒店建成后效益等。此外, 本论文设计的模型对于投资者和贷款方做出快速和有效分析有着显著意义。特别是这个模型能够使得他们在酒店业中对多个投资方案做横向比较。此外, 根据结果表明地理位置因素对于酒店盈利性占据重要位置, 因此, 酒店经理应该与旅游目的地经理协作来提高酒店盈利性。从酒店内部管理角度出发, 酒店经理应该着重人力资源的管理。


本论文是首个实证研究通过深度学习技术来预测酒店盈利性。此外, 这种研究方法也是首次在西班牙市场实证分析酒店盈利性。西班牙市场是理想的分析样本, 因为其由于季节性和海岸特点的酒店市场的多样性。



This paper forms part of special section “Big data in tourism and hospitality”, guest edited by Marianna Sigala and Roya Rahimi.


Lado-Sestayo, R. and Vivel-Búa, M. (2020), "Hotel profitability: a multilayer neural network approach", Journal of Hospitality and Tourism Technology, Vol. 11 No. 1, pp. 35-48.



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