曼谷四星级酒店平均每日房价(ADR)预测模型

IF 0.8 Q3 Economics, Econometrics and Finance
Kongkoon Tochaiwat, Warakorn Likitanupak
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引用次数: 1

摘要

摘要本研究旨在提出曼谷四星级酒店平均每日房价(ADR)的预测模型。该模型是使用享乐价格模型开发的,该模型是从曼谷的158家四星级酒店中计算出来的。模型中的变量来源于文献综述,并由专家小组提出建议。采用因子分析合并高相关变量。最佳拟合模型是一个对数线性形式,调整后的R2值为0.576。最敏感的前五个变量是(1)Agoda评价中的员工绩效得分,(2)Agoda评价中的位置得分,(3)Agoda评价中的房间标准得分,(4)健身可用性(是否存在),(5)酒店网点数量。通过对30家标准相似的酒店进行配对样本t检验,对模型进行了验证。从分析来看,观察到的adr与预测的adr在0.05的显著性水平上无显著差异(p值= 0.849)。此外,Theil的U统计量为0.578,这表明该模型具有较高的准确性。综上所述,该模型可以为投资者或开发商在酒店投资、酒店装修、酒店客房定价、经营酒店adr复核等决策过程中提供有用的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bangkok four-star hotels’ Average Daily Rate (ADR) prediction model
Abstract This research aims to propose a prediction model for the Average Daily Rate (ADR) for Bangkok four-star hotels. The model was developed using the hedonic price model, which was calculated from 158 four-star hotels in Bangkok. The variables in the model were derived from a literature review and suggested by an expert panel. Factor Analysis was adopted to merge the highly correlated variables. The best-fit model is a log-linear form with a .576 Adjusted R2 value. The top five most sensitive variables are (1) staff performance score from Agoda’s review, (2) location score from Agoda’s review, (3) room standard score from Agoda’s review, (4) fitness availability (presence or absence), and (5) the number of hotel outlets. The model was verified by a paired-sample t-test from 30 hotels with similar criteria. From the analysis, the observed ADRs were not significantly different from the predicted ADRs at the .05 significance level (p-value = .849). Furthermore, Theil’s U statistic was .578, which could suggest that the model has high accuracy. In summary, this model can give useful information to investors or developers in the decision-making process for hotel investment, hotel renovation, hotel room pricing, and rechecking the ADRs of operating hotels.
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CiteScore
1.10
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发文量
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