The hotel industry in India is booming due to the growth in tourism and travel. Due to the increase in tourism with rising foreign and domestic tourists, hotel sector is bound to grow. Indians have become extensive travellers. There is an emergence of budget hotels in India to cater to the majority of the population who seek affordable stay. International companies are also increasingly looking at setting up such hotels. Imbalance in increase in tourists both domestic and foreign not been supported with equal number of rooms is a latent source of opportunity for growth. During peak holiday seasons hotels do not have enough capacity to accomadate the large demand and this leads to an increase in prices due to imbalance between demand and suppply.
In the long term there is a need for more hotels. The shortage is especially true within the budget hotels and the mid-market hotels segment. There is an urgent need for budget and mid-market hotels in the country as travelers look for safe and affordable accommodation. Various domestic and international brands have made significant inroads into this space and more are expected to follow as the potential for this segment of hotels becomes more obvious.
Hotels need to strategically innovate to gain and sustain a competitive advantage against rivals.
The primary aim of this research is to explore the level of strategic innovation of hotel firms operating in India.
We analyze the issues concerning the pricing of hotel rooms with respect to hotel pricing in 42 different cities both tourist destinations and non tourist destinations. We show how the room rents change on the characteristics in and around the hotel.
The purpose of this project is to analyze the pricing strategy of hotels in the Indian hotel industry. Many factors drive hotel room prices. The objective of this project is to identify the factors that matter the most. Think about the following problem:
Room Rent = FUNCTION (Date(s); Hotel Features; External Factors).
Our model accounts for both fixed-effects and random effects. First, frequency analysis was performed to reveal the selected characteristics of participating hotel firms including types of hotels. Second, plots and tables were generated to explain and visualise relationships between factors. Third, correlation analysis was conducted to ascertain the relationships between the pricing and selected variables including the star rating, distance from the airport and hotel capacity. Before conducting correlation analysis, scatter diagram is used in order to test linearity among variables.
For this project, our dataset is based on hotels located in 42 cities in India. The hotels are located both in tourist places as well as non tourist places. We collected the data from www.hotels.in that provides the hotel availability, room rent and facilities.
We study how the price of a room at a hotel located in a tourist destination differs from the price at a non tourist destination. We believe that in a tourist destination the demand for hotel rooms is high and so customers are asked to pay a higher price.
H1: The prices of hotel rooms depends on a number of independent variables.
City: It is likely that the city in which a hotel is located in will strongly influence the hotel room prices. We have Considered 7 cities (5- tourist destinations, 2- non-tourist destinations) among the 42 cities which was provided by the dataset. We used a dummy variable City_j, where Mumbai, Delhi , Hyderabad, Udaipur, where j {0,1,4,29},respectively.
Price: We used Price_jk to denote the average price of a room at a hotel. We measured Price_jk, as the average of the most expensive and least expensive room at hotel k in city j.
Rooms: The number of rooms in a hotel denotes the available supply and it is expected that this will keenly influence the price that a hotel will set. An increase in demand over the supply of rooms will naturally increase the price of the room rents.
Distance from the Airport: It is possible that hotels located close to the airport are able to charge a price premium for the greater convenience and easy access. In order to control for this alternate explanation, we recorded the distance between a given hotel and the closest airport. We used the variables Airport_jk to denote the distance of hotel k in city j from the closest airport.
cities <- read.csv(paste("Cities42.csv", sep=""))
library(psych)
describe(cities)[,c(1:5)]## vars n mean sd median
## CityName* 1 13232 18.07 11.72 16
## Population 2 13232 4416836.87 4258386.00 3046163
## CityRank 3 13232 14.83 13.51 9
## IsMetroCity 4 13232 0.28 0.45 0
## IsTouristDestination 5 13232 0.70 0.46 1
## IsWeekend 6 13232 0.62 0.48 1
## IsNewYearEve 7 13232 0.12 0.33 0
## Date* 8 13232 14.30 2.69 14
## HotelName* 9 13232 841.19 488.16 827
## RoomRent 10 13232 5473.99 7333.12 4000
## StarRating 11 13232 3.46 0.76 3
## Airport 12 13232 21.16 22.76 15
## HotelAddress* 13 13232 1202.53 582.17 1261
## HotelPincode 14 13232 397430.26 259837.50 395003
## HotelDescription* 15 13224 581.34 363.26 567
## FreeWifi 16 13232 0.93 0.26 1
## FreeBreakfast 17 13232 0.65 0.48 1
## HotelCapacity 18 13232 62.51 76.66 34
## HasSwimmingPool 19 13232 0.36 0.48 0We first established the effect of tourist destinations on the price of a room in a hotel with the simplest model we could come up with. We regressed the price on the dummy variable for whether a hotel was situated at tourist places.
Then we defined a detailed model accounting for three additional independent variables, which may also influence the variation in hotel prices. Our revised regression model was as follows. \[Price_jk= \alpha_0+\alpha_1*City_jk+\alpha_2*Star_j+\alpha_3*Rooms_jk+\alpha_4*Airport_jk+\epsilon \]
We estimated the Model, described in the above formula using linear least squares. In support of hypotheses H1 we expected that rerunning the regression with the 3 additional independent variables would fit the data better. The benefit of having the three additional regressors outlined in the Model1 was that it helped us rule out some alternate explanations for the variation in hotel prices. For example, it is well-known that five-star hotels are more expensive than four-star hotels. Including the star rating as a regressor, permitted us to investigate the effect of the place(city) on hotel pricing, after controlling for price variation due to the star rating. Similarly, having a dummy variable for each city, permitted us to control for city-wide variation in prices of hotel rooms.
We found empirical support for hhe average room prices at tourist destinations were higher than the prices at non tourist destinations.
The analysis of the model also yielded statistical support for our hypothesis. Recall that the Model extended by including three additional independent variables, as shown in the equation above. We again found that the average room prices at tourist destinations were higher than the prices at non tourist destinations. As expected, we additionally observed a positive relationship between the average hotel room prices and the hotel star ratings,a_2>0, with p < 0.0001. Overall, we find th extended Model to be better in explaining the relationship between hotel pricing in tourist destinations and non tourist places. Also while calculating the correlation between the dependent variables with each of the independent variables, it is clear that the dependent variable (Room Rent) has a positive relationship with each of the other factors ( Star Rating, Airport distance and Hotel Capacity) hence we can conclude that the assumption made regarding the independent variables is valid.
This paper was motivated by the need for research that could improve our understanding of how tourist destinations influence the pricing strategies in the hotel industry. We found that it does not matter if the place is a metro city or not, the final judgment is that the pricing depends on the factors such as Star Rating,Airport Distance and Hotel Capacity.