Regression Analysis of American Hotels Having Price as Dependent Variable

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ABM PROJECT 2013

1. INTRODUCTION AND SUMMARY OF RESEARCH HYPOTHESIS

It is common knowledge that the prices people have to pay for accommodation in hotels vary enormously. Furthermore, hotel revenue managers probably posses or more or less accurate intuition of what causes room rates to diverge. However, they do not know how Online Travel Agent sites select the leading hotels to be placed on their first search page. In this respect, some determinants are expected to be associated with hotel prices in a more or less linear way. To say it differently, price differences between hotels underscore the presence or not of some variables expected to influence the latter. It is essential for hotels to understand how they can price their rooms and maximize yield while remaining competitive. Therefore, we conducted an extensive analysis to help hotel revenue managers find out what key variables influence price on Orbitz. The data were gathered from Orbitz.com directly. The data is about 1623 hotels that are located in 8 different geographical markets in the United States: Atlanta, Chicago, Los Angeles, Las Vegas, New York, Orlando, Honolulu, and Myrtle Beach.

In section 1, we will present a Causal Relationship Scheme (CRS) that will be used as the framework of our research establishing the different relationships among our selected variables. Section 2 of the report contains the results of our univariate analyses. The distribution, mean and amount of variation of the data will be discussed. The subsequent section then introduces our findings from the bivariate analyses, investigating the presence, nature and strength of the relations between the pairs of variables described in the CRS. The fourth section presents the results of three multivariate analyses consisting first of a 2-factor ANOVA analysis that involves the response variable price and two other factors belonging to the CRS of the report. Secondly, we will formulate a regression model based on the original CSR. Thirdly, we will conduct a second regression model. However this model will not necessarily be based on the CSR of the report and will therefore include new variables thought to generate new insights. Moreover, we will assess the quality of these two models by examining the possible presence of non-linearity, interactions between pairs of explanatory variables and multicollinearity. In the last section of this report we summarize our findings and give recommendations on how to use this information.

1.1 The Research Question
The time when competition between hotel companies took place in guidebooks has almost vanished and the Internet has become the new battlefield for attracting potential customers. Indeed, a fierce battle for top ranked positions on OTA (Online Travel Agent) hotel listings is taking place. The rationale is that top-ranked hotels get the chance to be seen first by customers who search for a hotel and have a substantially higher probability to convert the search to an actual sale, as compared to lower-ranked hotels. This is why hotels will adjust their pricing and marketing strategy in order to win the page ranking battle. However, the main issue for hotel revenue managers is that they simply do not know how OTA websites select the leading hotels to be placed on their first search page. Following SJK consultants request, the main goal of this project is to investigate the determinants of hotel prices on the Orbitz website, given different page rankings. The main research question for this project can then be formulated as follow; “What are the determinants of hotel prices on Orbitz for hotels with different search page locations?”

1.2 Variables
1.2.1 Dependent Variable
The dependent variable for this investigation is price, which represent the price of a 1-night stay at a standard, double occupancy room. This variable will be represented as a quantitative variable.

1.2.2 Independent Variables...
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