1. Introduction
Tourism has now become a significant industry in India. It is a sun rise industry, an employment generator, a significant source of foreign exchange for the country. Tourism in India is the third largest foreign exchange earner ofthe country. The booming tourism industry has had a cascading effect on the hospitality sector with an increase in the occupancy ratios and average room rates.As per world travel and tourism Council (WTTC), India is one of the favorite tourist destinations from the year 2009 and will continue to be one of the favorite till 2018. Further, the Travel and Tourism Competitiveness Report by World Economic Forum, has ranked India at the sixth place in tourism and hospitality.The tourism and hospitality sector is among the top 10 sectors in India to attract the highest Foreign Direct Investment (FDI). According to the data released by Department of Industrial Policy and Promotion (DIPP), the hotel and tourism sector attracted around US$ 9.2 billion of FDI between April 2000 and March 2016.The Indian government has also taken several steps to make India a global tourism hub. The government has initiated ‘Project Mausam’ under which it has proposed to establish cross cultural linkages and to revive historic maritime cultural and economic ties with 39 Indian Ocean countries. Further, the government plans to cover 150 countries under e-visa scheme by the end of the year. The government has also introduced e-Tourist Visa (e-TV) for 150 countries as against the earlier coverage of 113 countries (source: Ministry of Tourism). The hotel industry in India thrives largely 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. 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.
2. Literature Review
It is a common phenomena that we experience variation in hotel price not only in India but across the globe. Why this is so? What provoked hotel owners to charge differently and what motivates a tourist to pay more for some hotels or hotel at a particular place. Here in this report we are going to analyze what could be the independent factors that contribute towards this price strategy. We will take the help of the data and some graphs and diagrams and regression analysis and on the basis of these we will try to analyse the data and try to figure out the potential factors affecting the price-behaviour in the hotel industries. A hotel’s price reflects an assessment of the value that tourists see and their willingness-to-pay for the hotel’s rooms and services. It is the price which reflect the fact that whether the hotel is worth for it or not.we will see whether the hotel industry charges tourists a price premium based on location or based on timing of tour visits? We evaluate whether hotels extract the maximum willingness of a tourist to pay for touring any particular location.We are concerned with pricing startegy of hotel industry based on the evidences collected from the 42 different cities dependent on several parameters. Through this we try to establish any sort of influence of TouristDestination on Roomrents of the hotels. The data is collected from 42 cities for 8 different dates. We will do empirical study based on the dataset available with us. The price the hotel industry charges reflects the worth of that place. It represents the true value of the sight. Tourists behaviour bear a direct dependence on this pricing stategy to assess the worth of a particular destination . In this study we have studied the differences in price based on tourist destination vs normal destination along with some other variables like new yeareve vs normal days for different cities to strengthen our analysis. If there is a price for location, we would expect price for tourist destination will be high or price for certain cities will be high as compared to rest.
3. Data Describtion
For this study, we collected data from https://in.hotels.com/.The dataset tracks hotel prices on 8 different dates {Dec 31, Dec 25, Dec 24, Dec 18, Dec 21, Dec 28, Jan 4, Jan 8} at different hotels across 42 different cities. We will focus on the data of hotels of 42 different cities classified in some categories on the basis of their unique features representing their worth or true value. We expected that a comparison of various services offered by hotel and location of the hotel, would explain the extent to which hotels charge Room Rent.consumers are asked to pay a price-premium for the pleasure of watching a particular exotic location from the hotel room. We expect that the hotel rooms with exotic views will be priced higher than the hotel rooms without exotic views, after controlling for other factors.
4. Model
Hypothesis: The prices of hotel rooms depend on various internal and external factors In order to test above Hypothesis, we proposed the following model:
RoomRent=??0+??1HasSwimmingPool+??2IsNewYearEve+??3IsMetroCity??4IsTouristDestination+??5StarRating+??5Airport+??6HotelCapacity+??7FreeWifi+??8FreeBreakfast+??
hotel <- read.csv(paste("Cities42.csv", sep=""))
Price <- lm( RoomRent~HasSwimmingPool+IsNewYearEve+IsMetroCity+IsTouristDestination+StarRating+Airport+HotelCapacity+FreeWifi+FreeBreakfast, data=hotel)
summary(Price)##
## Call:
## lm(formula = RoomRent ~ HasSwimmingPool + IsNewYearEve + IsMetroCity +
## IsTouristDestination + StarRating + Airport + HotelCapacity +
## FreeWifi + FreeBreakfast, data = hotel)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11696 -2375 -701 1063 309539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8906.418 405.396 -21.970 < 2e-16 ***
## HasSwimmingPool 2227.069 159.327 13.978 < 2e-16 ***
## IsNewYearEve 844.123 174.046 4.850 1.25e-06 ***
## IsMetroCity -1548.328 138.527 -11.177 < 2e-16 ***
## IsTouristDestination 2113.725 134.336 15.735 < 2e-16 ***
## StarRating 3564.570 110.489 32.262 < 2e-16 ***
## Airport 11.265 2.710 4.157 3.24e-05 ***
## HotelCapacity -10.990 1.026 -10.714 < 2e-16 ***
## FreeWifi 485.597 224.134 2.167 0.0303 *
## FreeBreakfast 182.992 123.296 1.484 0.1378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6607 on 13222 degrees of freedom
## Multiple R-squared: 0.1887, Adjusted R-squared: 0.1881
## F-statistic: 341.7 on 9 and 13222 DF, p-value: < 2.2e-165. Discussion
We established the effect of various internal and external factors on the price of a hotel room with the simplest model. While running regression RoomRent on HasSwimmingPool, IsNewYearEve, IsMetroCity, IsTouristDestination, StarRating, Airport, HotelCapacity, FreeWifi and FreeBreakfast.
From this regression analysis, we find the siginificant factors are : HasSwimmingPool, IsNewYearEve, IsMetroCity, IsTouristDestination, StarRating, Airport and HotelCapacity.
These factors are further verified by T-tests.
6. Conclusion
We observe that price of hotel room increase when hotels provide facilities like swimming pools, we also observe that services like free wifi and free breakfast don’t add too much value.Hotels with higher star rating tend to have higher prices. Location of the hotel affects room rent too as hotels in tourist destination tend to charge higher.The day of booking is another significant factor in deciding the price as we see hotels tend to charge more on new years eve.
7. Reference
https://www.slideshare.net/ramyasn16/pricing-strategies-presentation-871160
https://www.ibef.org/industry/tourism-hospitality-india.aspx
https://blogs.oracle.com/hospitality/how-do-hotels-determine-room-rates
8. Appendices
Read your dataset in R and visualize the length and breadth of your dataset.
hotel <- read.csv(paste("Cities42.csv", sep=""))
View(hotel)
dim(hotel)## [1] 13232 19Create a descriptive statistics (min, max, median etc) of each variable
library(psych)
describe(hotel)## vars n mean sd median trimmed
## CityName* 1 13232 18.07 11.72 16 17.29
## Population 2 13232 4416836.87 4258386.00 3046163 4040816.22
## CityRank 3 13232 14.83 13.51 9 13.30
## IsMetroCity 4 13232 0.28 0.45 0 0.23
## IsTouristDestination 5 13232 0.70 0.46 1 0.75
## IsWeekend 6 13232 0.62 0.48 1 0.65
## IsNewYearEve 7 13232 0.12 0.33 0 0.03
## Date* 8 13232 14.30 2.69 14 14.39
## HotelName* 9 13232 841.19 488.16 827 841.18
## RoomRent 10 13232 5473.99 7333.12 4000 4383.33
## StarRating 11 13232 3.46 0.76 3 3.40
## Airport 12 13232 21.16 22.76 15 16.39
## HotelAddress* 13 13232 1202.53 582.17 1261 1233.25
## HotelPincode 14 13232 397430.26 259837.50 395003 388540.47
## HotelDescription* 15 13224 581.34 363.26 567 575.37
## FreeWifi 16 13232 0.93 0.26 1 1.00
## FreeBreakfast 17 13232 0.65 0.48 1 0.69
## HotelCapacity 18 13232 62.51 76.66 34 46.03
## HasSwimmingPool 19 13232 0.36 0.48 0 0.32
## mad min max range skew
## CityName* 11.86 1.0 42 41.0 0.48
## Population 3846498.95 8096.0 12442373 12434277.0 0.68
## CityRank 11.86 0.0 44 44.0 0.69
## IsMetroCity 0.00 0.0 1 1.0 0.96
## IsTouristDestination 0.00 0.0 1 1.0 -0.86
## IsWeekend 0.00 0.0 1 1.0 -0.51
## IsNewYearEve 0.00 0.0 1 1.0 2.28
## Date* 2.97 1.0 20 19.0 -0.77
## HotelName* 641.97 1.0 1670 1669.0 0.01
## RoomRent 2653.85 299.0 322500 322201.0 16.75
## StarRating 0.74 0.0 5 5.0 0.48
## Airport 11.12 0.2 124 123.8 2.73
## HotelAddress* 668.65 1.0 2108 2107.0 -0.37
## HotelPincode 257975.37 100025.0 7000157 6900132.0 9.99
## HotelDescription* 472.95 1.0 1226 1225.0 0.11
## FreeWifi 0.00 0.0 1 1.0 -3.25
## FreeBreakfast 0.00 0.0 1 1.0 -0.62
## HotelCapacity 28.17 0.0 600 600.0 2.95
## HasSwimmingPool 0.00 0.0 1 1.0 0.60
## kurtosis se
## CityName* -0.88 0.10
## Population -1.08 37019.65
## CityRank -0.76 0.12
## IsMetroCity -1.08 0.00
## IsTouristDestination -1.26 0.00
## IsWeekend -1.74 0.00
## IsNewYearEve 3.18 0.00
## Date* 1.92 0.02
## HotelName* -1.25 4.24
## RoomRent 582.06 63.75
## StarRating 0.25 0.01
## Airport 7.89 0.20
## HotelAddress* -0.88 5.06
## HotelPincode 249.76 2258.86
## HotelDescription* -1.25 3.16
## FreeWifi 8.57 0.00
## FreeBreakfast -1.61 0.00
## HotelCapacity 11.39 0.67
## HasSwimmingPool -1.64 0.00Create one-way contingency tables for the categorical variables in your dataset.
##City
table(hotel$CityName)##
## Agra Ahmedabad Amritsar Bangalore
## 432 424 136 656
## Bhubaneswar Chandigarh Chennai Darjeeling
## 120 336 416 136
## Delhi Gangtok Goa Guwahati
## 2048 128 624 48
## Haridwar Hyderabad Indore Jaipur
## 48 536 160 768
## Jaisalmer Jodhpur Kanpur Kochi
## 264 224 16 608
## Kolkata Lucknow Madurai Manali
## 512 128 112 288
## Mangalore Mumbai Munnar Mysore
## 104 712 328 160
## Nainital Ooty Panchkula Pune
## 144 136 64 600
## Puri Rajkot Rishikesh Shimla
## 56 128 88 280
## Srinagar Surat Thiruvanthipuram Thrissur
## 40 80 392 32
## Udaipur Varanasi
## 456 264##Weekend
table(hotel$IsWeekend)##
## 0 1
## 4991 8241##NewyearEve
table(hotel$IsNewYearEve)##
## 0 1
## 11586 1646##FewwWiFi
table(hotel$FreeWifi)##
## 0 1
## 981 12251##Breakfast
table(hotel$FreeBreakfast)##
## 0 1
## 4643 8589##SwimmingPool
table(hotel$HasSwimmingPool)##
## 0 1
## 8524 4708Create two-way contingency tables for the categorical variables in your dataset.
aggregate(hotel$RoomRent, by=list(weekend = hotel$IsWeekend, newyearseve = hotel$IsNewYearEve), mean)## weekend newyearseve x
## 1 0 0 5429.473
## 2 1 0 5320.820
## 3 0 1 8829.500
## 4 1 1 6219.655aggregate(hotel$RoomRent, by=list(Metrocity = hotel$IsMetroCity, TouristPlace = hotel$IsTouristDestination), mean)## Metrocity TouristPlace x
## 1 0 0 4006.435
## 2 1 0 4646.136
## 3 0 1 6755.728
## 4 1 1 4706.608aggregate(hotel$RoomRent, by=list(freewifi = hotel$FreeWifi, freeBreakfast = hotel$FreeBreakfast, swimmingPool = hotel$HasSwimmingPool), mean)## freewifi freeBreakfast swimmingPool x
## 1 0 0 0 3538.085
## 2 1 0 0 3148.628
## 3 0 1 0 5636.617
## 4 1 1 0 3984.457
## 5 0 0 1 7378.590
## 6 1 0 1 9530.906
## 7 0 1 1 5207.000
## 8 1 1 1 8246.284Draw a boxplot of the variables that belong to your study.
boxplot(hotel$RoomRent ~ hotel$IsWeekend, xlab = "Weekend" , ylab = "Room Rent" , main = "Comparison of Room rent over weekend or weekdays", ylim = c(0,30000), col = "skyblue")
Draw Histograms for your suitable data fields.
hist(hotel$StarRating, main = "star Rating Distribution", xlab = "Stars")
hist(hotel$Airport, main = "Distrubtion of distance to the nearest major airport", xlab = "Dist to the nearest major Airport in km")
hist(hotel$HotelCapacity, main = "distribution of capacity of hotels", xlab = "capacity of hotels")
Draw suitable plot for your data fields.
Analyzing Effect of dates and day of room renting
library(car)
scatterplotMatrix(formula = ~ RoomRent + IsWeekend + IsNewYearEve, data = hotel, pch = 16)
##Create a correlation matrix. ##Correlation matrix of Room Rent with IsWeekend and IsnewyearEve
cor(hotel$RoomRent, hotel[,c("IsWeekend","IsNewYearEve")])## IsWeekend IsNewYearEve
## [1,] 0.004580134 0.03849123Visualize your correlation matrix using corrgram
library(corrgram)
corrgram(hotel[c("RoomRent","IsWeekend","IsNewYearEve", "IsTouristDestination", "HasSwimmingPool")], upper.panel = panel.pie)
T-tests
T-test between RoomRent and HasSwimmingPool
Ho: There is no significant difference between the Room Rent of Hotels with swimming pool and hotels without swimming pool H1: The Room Rent of Hotel without swimming pool is less then Room Rent of hotels with swimming pool.
t.test(RoomRent~HasSwimmingPool,data=hotel,alternative = "less")##
## Welch Two Sample t-test
##
## data: RoomRent by HasSwimmingPool
## t = -29.013, df = 5011.3, p-value < 2.2e-16
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf -4502.814
## sample estimates:
## mean in group 0 mean in group 1
## 3775.566 8549.052Inference: Since p-value<0.05, we accept H1,hence, there is a significant difference between the Room Rent of Hotels with swimming pool and hotels without swimming pool and the rates of hotel with swimming pool are higher.
T-test between RoomRent and FreeBreakfast
Ho: There is no significant difference between the Room Rent of Hotels with free breakfast and hotels without free breakfast. H1: There is a significant difference between the Room Rent of Hotels with free breakfast and hotels without free breakfast
t.test(RoomRent~FreeBreakfast,data=hotel)##
## Welch Two Sample t-test
##
## data: RoomRent by FreeBreakfast
## t = 0.98095, df = 6212.3, p-value = 0.3267
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -153.5017 460.9935
## sample estimates:
## mean in group 0 mean in group 1
## 5573.790 5420.044nference: Since p-value>0.05, we accept H0,hence, There is no significant difference between the Room Rent of Hotels with free breakfast and hotels without free breakfast.
T-test between RoomRent and FreeWifi
Ho:-There is no significant difference between the Room Rent of Hotels providing free wifi and those which do not H1:-There is a significant difference between the Room Rent of Hotels providing free wifi and those which do not.
t.test(RoomRent~FreeWifi,data=hotel)##
## Welch Two Sample t-test
##
## data: RoomRent by FreeWifi
## t = -0.76847, df = 1804.7, p-value = 0.4423
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -360.5977 157.5701
## sample estimates:
## mean in group 0 mean in group 1
## 5380.004 5481.518nference:-Since p-vale>0.05, we accept Ho,hence there is no significant difference between the Room Rent of Hotels providing free wifi and those which do not.
T-test between RoomRent and IsWeekend
Ho:-There is no significant difference between the Room Rent of Hotels on weekdays and weekends. H1:-There is a significant difference between the Room Rent of Hotels on weekdays and weekends.
t.test(RoomRent~IsWeekend,data=hotel)##
## Welch Two Sample t-test
##
## data: RoomRent by IsWeekend
## t = -0.51853, df = 9999.4, p-value = 0.6041
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -331.2427 192.6559
## sample estimates:
## mean in group 0 mean in group 1
## 5430.835 5500.129nference:-Since p-vale>0.05, we accept Ho,hence there is no significant difference between the Room Rent of Hotels on weekdays and weekends.
T-test between RoomRent and IsNewYearEve
Ho: There is no significant difference between the Room Rent of Hotels on normal Eve and New Year’s Eve. H1: The Room Rents of Hotels on normal Eve are cheaper than that on New Year’s Eve
t.test(RoomRent~IsNewYearEve,data=hotel,alternative = "less")##
## Welch Two Sample t-test
##
## data: RoomRent by IsNewYearEve
## t = -4.1793, df = 2065, p-value = 1.523e-05
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf -518.4763
## sample estimates:
## mean in group 0 mean in group 1
## 5367.606 6222.826Inference: Since p-value<0.05, we accept H1,hence,the Room Rents of Hotels on normal Eve are cheaper than that on New Year’s Eve.
T-test between RoomRent and IsWeekend
Ho: There is no significant difference between the Room Rent of Hotels on normal weekdays and Weekends. H1: The Room Rents of Hotels on normal weekdays are cheaper than that on weekends
t.test(RoomRent~IsWeekend,data=hotel,alternative = "less")##
## Welch Two Sample t-test
##
## data: RoomRent by IsWeekend
## t = -0.51853, df = 9999.4, p-value = 0.302
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf 150.5351
## sample estimates:
## mean in group 0 mean in group 1
## 5430.835 5500.129Inference: Since p-value>0.05, we reject H1,hence,the Room Rents of Hotels on normal weekdays are cheaper than that on Weekends.
T-test between RoomRent and IsMetroCity
Ho: There is no significant difference between the Room Rent of Hotels in non-metro cities and metro cities. H1: Hotels in non-metro cities are more expensive than that in metro cities.
t.test(RoomRent~IsMetroCity,data=hotel,alternative = "greater")##
## Welch Two Sample t-test
##
## data: RoomRent by IsMetroCity
## t = 10.721, df = 13224, p-value < 2.2e-16
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## 919.9785 Inf
## sample estimates:
## mean in group 0 mean in group 1
## 5782.794 4696.073Inference: Since p-value<0.05, we accept H1,hence,the Room Rents of Hotels in non-metro cities is more than that of metro cities.
T-test between RoomRent and IsTouristDestination
Ho: There is no significant difference between the Room Rent of Hotels in Tourist destinations and non tourist destinations. H1: The Room Rents of Hotels in Tourist destinations are greater than that in non tourist destinations
t.test(RoomRent~IsTouristDestination,data = hotel,alternative = "less")##
## Welch Two Sample t-test
##
## data: RoomRent by IsTouristDestination
## t = -19.449, df = 12888, p-value < 2.2e-16
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf -1789.665
## sample estimates:
## mean in group 0 mean in group 1
## 4111.003 6066.024Inference: Since p-value<0.05, we accept H1,hence,the Room Rents of Hotels in Tourist destinations are greater than that in non tourist destinations
The following variable are significant when considering Room Rent: 1. HasSwimmingPool 2. IsNewYearEve 3. IsMetroCity 4. IsTouristDestination