Capstone Project Report on Hotel pricing in Indian Market,Underguidance of Prof.Sameer Mathur (ph.D),IIM-LUCKNOW
Kathala krishna chaitanya rao
January 31, 2018
----------Report paper on the Analysis of Hotel pricing in Indian Market-----------Pricing for success in the Indian hotel sector
Forecasting is never easy - just ask the weathermen.However, in these uncertain economic times,forecasting pricing decisions will have one of the biggest impacts on hotel profitability. It is more vital than ever that revenue managers in India understand industry best practice and the latest supporting technologies to ensure their hotel rooms and services are priced at the right rate regardless ofperiods of high or low demand.
A successful market pricing strategy will mean maximum sales of rooms and additional services, through the balancing of perceived benefits and price. In developing this strategy, customer responses to price changes, market position and competitors pricing must all be assessed.The new reality for hotel pricing is that revenue management and dynamic pricing, a competency based on demand, length of stay and product mix, are inextricably linked. Dynamic pricing finds an optimal price through a sophisticated pricing strategy, based on demand as a function of price, to maximise revenue.There are a range of factors that need to be taken into consideration when determining how to successfully implement dynamic pricing strategies.
As a starting place, Indian hoteliers need to ensure they have detailed data that is both historical and forward looking. Historical data should include the number of occupied rooms and revenue broken down into market segments by day, for a sufficient period of time to make fact based decisions. If data is then collected every day following, it will allow hoteliers to establish simple booking pace forecasts by segment and day of week, from which they will be able to compare to historical data. If this is done consistently, it will allow hoteliers to quickly adapt to any changes when demand picks up and enable them to tweak their strategies accordingly. Sometimes something as simple as comparing occupancy andaverage daily rates (ADRs) can provide deep insights into pricing and revenue management opportunities, for example by assessing when ADR and occupancy do not move in tandem.
Keep an eye on the competition
The uptake of web and mobile technologies has made it possible for prospective customers to quickly and easily find the cheapest prices available. But hoteliers need to be careful not to get sucked into continually matching competitor prices, and start a price war. This can erode profit margins ever further and damage a hotel’s positioning. The longer a hotel discounts its rate, the more likely this lowered rate will become the reference price in the minds of consumers, making it harder for a hotel to lift its rates back to the original price.
While it may seem logical that lower hotel prices would stimulate demand for the hotel sector in general, demand for hotels has been found to be relatively inelastic. This means that when prices drop, the increase in demand won’t be enough to offset the decreased rate to maintain revenues. Price wars create a lose-lose situation, therefore price matching and deep discounting should be avoided.
But of course competitors’ prices and offerings cannot be ignored either. First and foremost, for the successful optimisation of revenue performance, it is crucial hoteliers take a long term, strategic view, including a rational approach to competitor pricing. In the long-run this will be far more profitable than impulsive pricing decisions, which only have short term benefits.
To do this, it is crucial that hoteliers understand who their true competitors are. Competition comes in many forms. A hotelier may think that the three big brand hotels down the road are their biggest threats, but anything from a hotel in an entirely different country to a family member’s house can be in competition with a hotel’s ability to sell its rooms and services. While it is easy to overlook these indirect competitors, it is just as easy to assume certain hotels are main competitors, when a closer look may reveal in actual fact they are not.
To avoid common pricing and competitor strategy mistakes it is important hoteliers research their competition. Information is a key commodity for the hotel industry and it is imperative hoteliers start collecting information, not just about their own business, but on competitors as well.
Strategies for success
Once hoteliers know where their property sits in relation to competitors and the company has identified its own trends in ADR versus occupancy, management can start putting strategies together to find the optimal set of prices and maximise profit. To minimise the reliance on reducing room rates to attract customers in a competitive environment, hoteliers should look at ways to increase their competitive advantage through increasing their value offering, including during periods of low demand. This should also include targeting specific customers, with price and value to capture a range of markets. The positioning of key products to key market segments is a vital aspect of dynamic pricing.
When demand is on the rise, hoteliers considering increasing their prices accordingly must look at where they sit alongside their closest competition and remember that the only way to operate at higher rates than competitors is to deliver true value that cannot be matched. It is crucial that additional value is emphasised to potential customers, otherwise a hotel risks losing potential guests to the competition.
Any changes in price should be monitored closely for impact on levels of demand. This information can then be used to control demand accordingly, using daily revenue management and pricing strategies.
In addition to ensuring the correct prices are allocated to the correct rooms, it is also vital that these rates are distributed and shown to all available channels to maximise bookings. Hoteliers must ensure their hotel iswell represented in all key channels. Establishing win-win relationships with those distribution partners can really help generate incremental business.It is also important forhoteliers to use historical and future channel benchmarking information from third party vendors to understand where they are falling short against their competition and when competitors are moving their rates.
In these interesting times, an effective pricing strategy that makes the most of latest technologies is a way that revenue managers can maximise profits, in the face of waning or soaring demand. Having the right information is also key, as well as continually monitoring impacts caused by changes in strategy. While it is important to take competitors into consideration, when looking to maximise revenue,it is also important that long-term strategies win out over short-term price wars, which make everyone worse off.
About Indian Hotels Company Ltd.
Indian Hotels Company Ltd., incorporated in the year 1902, is a Large Cap company (having a market cap of Rs 16988.56 Crore) operating in Hospitality sector.
Indian Hotels Company Ltd. key Products/Revenue Segments include Income from Rooms, Restaurants & Other Services which contributed Rs 2391.25 Crore to Sales Value (100.00 % of Total Sales)for the year ending 31-Mar-2017
For the quarter ended 30-09-2017, the company has reported a Consolidated sales of Rs 851.67 Crore, down -6.13 % from last quarter Sales of Rs 907.30 Crore and down -3.76 % from last year same quarter Sales of Rs 884.95 Crore Company has reported net profit after tax of Rs -51.10 Crore in latest quarter.
The company’s top management includes Mr.Deepak Parekh, Mr.Gautam Banerjee, Mr.Mehernosh S Kapadia, Mr.N Chandrasekaran, Mr.Nadir Godrej, Mr.Puneet Chhatwal, Mr.Vibha Paul Rishi, Ms.Ireena Vittal. Company has Deloitte Haskins & Sells LLP as its auditoRs As on 31-12-2017, the company has a total of 1,189,258,445 shares outstanding.
Dataset Summary.
This dataset has proved to be extremely useful,also given that it is the of field study which empirically investigates the pricing of hotel rooms located in 42 different cities of India during the time period of December 2016 to January 2016 old there is plenty of information for more in depth analysis and no doubt could provide a deeper insight on the Hotel Industries that may pose a impact to us one day.
source of the data is collected from: https://in.hotels.com/ Be Smart,Book Smart.
Introduction.
India is the most beautiful country which has a lotz of Traditions followed by it’s unique Cultures as will as a very diverse country and split into 29 states and 7 union territories.Tourism is also very common in India. As a result of which a lot of hotels have been built for the convenience of tourists.Ultimately Tourism leads to huge business for Hotel’s.The prices of each hotel are varying dependent on certain factors. The hotels should charge according to the quality of their service and the amenities they provide. There are a few common factors which generally affect the price of hotel rooms in a similar way. But this may not be true always, because few hotels have some distinct feature (for example, the hotel might be a heritage site, so people are willing to pay more to stay in them.
Example 2:if we consider the most loving Spot “GOA” There are the many factors that effect the pricing of the Hotel market because,the customer willing could be the Hotel should be beside the Beachs,location.(Additional factors:Customers will more happy if the hotel itself providing the Vechiles,Bikes,Tourist guide)
This report addresses the factors influencing the “Hotel Room Pricing in The Indian Market.”
1)What are the factors which are the most influential ?
2)when hotel owners decide the pricing of their rooms ?
3)Whether Location of the residence will effect the pricing ?
4)Whether the Internal facilites impacts ?
5)What are the external factors to be considered ?
Overview of the Study
I have done data analysis pertaining to hotel room prices across 42 cities in India. (Cities42.csv) The diversity in India results into a variety of hotels depending on customer’s choices and pocket. There are hotels in metro cities, rural cities, tourist destinations and cities which are not tourist destinations. The dataset has the pricing of the hotels on different dates, including New Year’s,Is the Hotel is near to the Airport,Does the Hotel provides wifi facilites / free breakfast / swimmingpools etc. all will play a Vital role while deciding it’s price.
study of hotel room prices in India
The specific objective of this study is to analyse the pricing strategy used in various hotels located in 42 cities in India. We made comparisons between the prices of various hotel rooms, based on a few factors. Three of the most important factors which affect the pricing of the hotel rooms are:
(THESE FACTORS ARE SELECTED ON THE BASIS OF CORRELATIONS OBTAINED AFTER ANALYSING THE DATASET. IT MAY SEEM ABSURD, BUT I HAVE TESTED EACH VARIABLE USING CORRELATION TEST AND THEN SELECTED 3 FACTORS FOR ROOM RENT.)
1.Star Rating of the hotel ?
2.The capacity of the hotel ?
3.Whether or not the hotel has a swimming pool ?
4.Distance from the Airport ?
5.Metro city as well as is that the Tourist Destination ?
6.Is there any special occassion of the Spot EX:newyear ?
Hypothesis.
We study how price of a hotel room varies with star rating of the hotel, the existence of a swimming pool and the capacity of the hotel. Hence, we make the following hypothesis:
1.The price of a hotel room is independent of the star rating of the hotel.
2.The price of a hotel room is independent of the capacity of the hotel.
3.The price of a hotel room is independent of the existence of a swimming pool.
Data set (Cities42.csv) file Description.
A breif summarization of the data The most expensive ones are a heritage hotel, which prove their pricing strategy. This point is also supported by the fact that most of the hotels have a star rating which is average (median star rating is 3). It is expected that hotels which have a swimming pool will have a higher price tag than a hotel which does not have one. Tourists want to relax after a tiring journey, so they tend to book hotels with swimming pool. Hence, demands for such hotel rooms will be higher. Only 4708 hotels out of total have swimming pools. As the data shows, majority of the entries are from hotels without swimming pools, hence swimming pool is a form of luxury. The price of a hotel room is even expected to depend on the number of rooms the hotel has. More the capacity of hotel, indicates that the hotel has huge infrastructure and better amenities. So the cost of room rent increases drastically.
Number of rows:13233 as well as coloumn’s:19 in the following dataset (Cities42.csv)
Room Rent:
The room rent is one of the most important factors when considering which hotel to stay in. Generally, the higher the quality of service, the higher the room rent is. There are a lot of factors which affect the room pricing in the hotel industry.
Star Rating:
In India, hotels are assigned a star rating by the Ministry of Tourism. Hotels are assigned a star rating between 1 and 5. 1 - Poor service and 5-Excellent service. The star rating is assigned according to the services, ambience and quality offered by the hotel to its customers. Since the star rating is an assurance of quality and luxury, the price of the hotel rooms is strongly positively correlated with it. Tourists often choose hotels based on star ratings and testimonials.
Hotel Capacity:
Usually, the more the number of hotels, the bigger is the hotel and the higher the rent of the room is likely to be.This is because most hotels built on large scales tend to have good amenities, a high star rating, good infrastructure so can charge a higher price for their hotel rooms. Most of the hotels with a large number of rooms have a high star rating and our big hotel.brands, so they have a higher price than hotels which low capacities, who cannot afford to maintain a large number of rooms.
Swimming Pools:
It is likely that a room in a hotel with a swimming pool will be more expensive than a room in a hotel without one. Majority of the hotels do not have a swimming pool, due to space constrains, financial constraints and hassles related to management of the pool.This makes the swimming pool a luxury. Also, it is the ideal way for a family, group of friends or an individual to have some fun and relax.
City:
It plays a major role. Tourists especially foreigners choose to stay in well furnished hotels located in elite cities like Mumbai, Delhi, Goa , Hyderabad etc.It is likely that the city in which the hotel is situated in will strongly influence the rent of the rooms of the hotel. Also, a variable, CityRank, was used to uniquely identify each city in the dataset. Each city has different characteristics, which influences the pricing of the hotel rooms in that city. Some cities have a hotel which does not follow the general trend of pricing of the city. This might be because the hotel has something special to offer e.g. it is a heritage hotel.
Tourist Destination:
Cities which are popular tourist destinations will have a higher demand for their hotel rooms, as more people would want to stay in the hotels when they visit the city because tourist spots are the ones with exquisite beauty and nature’s bounty. This allows the hotel owners to charge the tourists a higher price for their rooms, as compared to the hotel owners in cities which are not popular tourist destinations.
Distance from nearest airport:
It is expected that the hotel room prices are correlated to the distance of the hotel from the nearest airport. A hotel which is very far from the airport would not be preferred by most travelers. This is because they would not like to spend a lot of time travelling from the airport to the hotel and back. Instead, if they could find a hotel closer to the airport, it is more likely that they would prefer staying in that hotel.
Regression Models
Model 1: The effect of star rating on room rent was first established in my analysis. The room rent was regressed for the star rating of a hotel as follows: Model 1 was estimated, using linear least squares (linear regression model). If the room rent is higher for hotels with higher star ratings, the coefficient of correlation is positive.
Model 2: The effect of the existence of a swimming pool on room rent was established next in my analysis.If the room rent is higher for hotels having a swimming pool and lesser otherwise, the coefficient of correlation positive.
Model 3: The effect of the capacity of the hotel on room rent was established next in my analysis. If the room rent is higher when the number of rooms is more, the coefficient of correlation is positive.
The following Results/outcomes that occurred through the respective models are described below
Model 1: Empirical support was found for H1. The average room rent increased as the star rating increased. The regression analysis using Ordinary Least Squares yielded >0 and p<0.05, which is the critical value. Hence, the null hypothesis was rejected. The alternative hypothesis i.e. Room Rent and Star Rating are not independent of each other, was accepted.
Model 2: Empirical support was found for H2. The average room rent was higher if the hotel had a swimming pool as compared to the average room rent in hotels which did not have swimming pools. The regression analysis using Ordinary Least Squares yielded >0 and p<0.05, which is the critical value. Hence, the null hypothesis was rejected. The alternative hypothesis i.e. the price ofa hotel room is not independent of the existence of a swimming pool, was accepted.
Model 3: Empirical support was found for H3. The average room rent increased when the capacity of the hotel increased. The regression analysis using Ordinary Least Squares yielded >0 and p<0.05, which is the critical value. Hence, the null hypothesis was rejected. The alternative hypothesis i.e. the price of a hotel room is not independent of the capacity of the hotel, was accepted.
-------------------Some important Data analysis------------------- This explains the number of cities, number of occupied the respective hotels,during the period of dec-jan.
#Read Data using read.csv and view Hotel room pricing.
Cities.df <- read.csv(paste("Cities42.csv", sep=""))
View(Cities.df)#number of the hotels.
with(Cities.df , table(CityName))## 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#Summarizing the data.
View(summary(Cities.df))library(psych)
View(describe(Cities.df))Factors affecting the price of Hotels.
We can say that the factors which effect the hotel price can be broadly classified in 3 catagories
Date and day of room renting
Hotel features
External Factors
Therefore we can say that
Room Rent = F(Date, Hotel Features, External Factors)
We shall analyze all the factors one by one.
Analyzing Effect of dates and day of room renting
##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logit## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit smooth
## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit smooth
## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit smooth
##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha
--------Histograms-------
let us assume that We create a model that shows how the variables effect the Room Rent
-----Our model will be like y = B0 + B1x1 + B2x2 + B3x3.. + E-------y - Room Rent (dependent variable). B0 - intercept. B1, B2, B3 .- Beta coefficients for different variables.And x1, x2, x3. x1, x2, x3 - start rating,Isweekend,IsTouristDestination , Dist to airport, FreeWifi, etc (independent variables).**
Boxplots as per Analysis.
--------T-Tests-------
--------Null Hypothesis - Their is no Difference between the Room Rent on weekdays and on weekends-------t.test(Cities.df$RoomRent ~ Cities.df$IsWeekend)##
## Welch Two Sample t-test
##
## data: Cities.df$RoomRent by Cities.df$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 5500.129As we can see the P-Value = 0.6 (>0.05) , We Fail To reject the Null Hypothesis. It Means Their is No Significant Difference Between the Room rents on Weekdays and Weekends.
#Null Hypothesis - Their is no Difference between the Room Rent on new year's eve and on other days
t.test(Cities.df$RoomRent ~ Cities.df$IsNewYearEve)##
## Welch Two Sample t-test
##
## data: Cities.df$RoomRent by Cities.df$IsNewYearEve
## t = -4.1793, df = 2065, p-value = 3.046e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1256.5297 -453.9099
## sample estimates:
## mean in group 0 mean in group 1
## 5367.606 6222.826P-Value = 3.046e-05 (<0.05) Which is small enough for Rejecting the Null Hupothesis. Hence there is significant difference between the Room Rent on new year’s eve and on other days
#Null Hypothesis - Their is no Difference between the Room Rent of Metro Cities and other cities
t.test(Cities.df$RoomRent ~ Cities.df$IsMetroCity)##
## Welch Two Sample t-test
##
## data: Cities.df$RoomRent by Cities.df$IsMetroCity
## t = 10.721, df = 13224, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 888.0308 1285.4102
## sample estimates:
## mean in group 0 mean in group 1
## 5782.794 4696.073P-Value = 2.2e-16 (<0.05) Which is small enough for Rejecting the Null Hupothesis. Hence there is significant difference between the Room Rent of Metro Cities and other cities
#Null Hypothesis - Their is no Difference between the Room Rent where wifi is free and other rooms.
t.test(Cities.df$RoomRent ~ Cities.df$FreeWifi)##
## Welch Two Sample t-test
##
## data: Cities.df$RoomRent by Cities.df$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 5481.518P-Value = 0.44 (>0.05) , We Fail To reject the Null Hypothesis. It Shows that Their is No Significant Difference Between the Room Rent where wifi is free and other rooms.
#Null Hypothesis: Their is no difference in the means of room Rent where free Breakfast is available or not
t.test(Cities.df$RoomRent ~ Cities.df$FreeBreakfast)##
## Welch Two Sample t-test
##
## data: Cities.df$RoomRent by Cities.df$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 5420.044The difference between The two means is not different as p-value = 0.32 (>0.05) so we fail to reject the Null hypothesis. It Means The Room rents Are same for all room whether free Breakfast is available or not.
Correlation tests.
cor.test(Cities.df$RoomRent, Cities.df$StarRating)##
## Pearson's product-moment correlation
##
## data: Cities.df$RoomRent and Cities.df$StarRating
## t = 45.719, df = 13230, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3545660 0.3839956
## sample estimates:
## cor
## 0.3693734cor.test(Cities.df$RoomRent, Cities.df$IsMetroCity)##
## Pearson's product-moment correlation
##
## data: Cities.df$RoomRent and Cities.df$IsMetroCity
## t = -7.7053, df = 13230, p-value = 1.399e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08378329 -0.04985761
## sample estimates:
## cor
## -0.06683977cor.test(Cities.df$RoomRent, Cities.df$CityRank)##
## Pearson's product-moment correlation
##
## data: Cities.df$RoomRent and Cities.df$CityRank
## t = 10.858, df = 13230, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07707001 0.11084696
## sample estimates:
## cor
## 0.09398553cor.test(Cities.df$RoomRent, Cities.df$IsNewYearEve)##
## Pearson's product-moment correlation
##
## data: Cities.df$RoomRent and Cities.df$IsNewYearEve
## t = 4.4306, df = 13230, p-value = 9.472e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02146637 0.05549377
## sample estimates:
## cor
## 0.03849123 ---------Chi-Square Tests------------------chisq.test(Cities.df$RoomRent,Cities.df$StarRating)## Warning in chisq.test(Cities.df$RoomRent, Cities.df$StarRating): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: Cities.df$RoomRent and Cities.df$StarRating
## X-squared = 132390, df = 43100, p-value < 2.2e-16chisq.test(Cities.df$IsWeekend,Cities.df$IsTouristDestination)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: Cities.df$IsWeekend and Cities.df$IsTouristDestination
## X-squared = 4.9346, df = 1, p-value = 0.02632chisq.test(Cities.df$HotelCapacity,Cities.df$StarRating)## Warning in chisq.test(Cities.df$HotelCapacity, Cities.df$StarRating): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: Cities.df$HotelCapacity and Cities.df$StarRating
## X-squared = 77180, df = 5060, p-value < 2.2e-16chisq.test(Cities.df$HasSwimmingPool,Cities.df$StarRating,Cities.df$IsMetroCity,Cities.df$IsTouristDestination)## Warning in chisq.test(Cities.df$HasSwimmingPool, Cities.df$StarRating,
## Cities.df$IsMetroCity, : Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: Cities.df$HasSwimmingPool and Cities.df$StarRating
## X-squared = 5810.3, df = 20, p-value < 2.2e-16 ------regressions-----
Linearregression <- lm(RoomRent ~ StarRating + Airport + FreeWifi + FreeBreakfast + HotelCapacity + HasSwimmingPool+IsMetroCity+IsNewYearEve
+IsTouristDestination, data = Cities.df)
summary(Linearregression)##
## Call:
## lm(formula = RoomRent ~ StarRating + Airport + FreeWifi + FreeBreakfast +
## HotelCapacity + HasSwimmingPool + IsMetroCity + IsNewYearEve +
## IsTouristDestination, data = Cities.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11696 -2375 -701 1063 309539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8906.418 405.396 -21.970 < 2e-16 ***
## StarRating 3564.570 110.489 32.262 < 2e-16 ***
## Airport 11.265 2.710 4.157 3.24e-05 ***
## FreeWifi 485.597 224.134 2.167 0.0303 *
## FreeBreakfast 182.992 123.296 1.484 0.1378
## HotelCapacity -10.990 1.026 -10.714 < 2e-16 ***
## HasSwimmingPool 2227.069 159.327 13.978 < 2e-16 ***
## IsMetroCity -1548.328 138.527 -11.177 < 2e-16 ***
## IsNewYearEve 844.123 174.046 4.850 1.25e-06 ***
## IsTouristDestination 2113.725 134.336 15.735 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6607 on 13222 degrees of freedom
## Multiple R-squared: 0.1887, Adjusted R-squared: 0.1881
## F-statistic: 341.7 on 9 and 13222 DF, p-value: < 2.2e-16reg <- lm(Cities.df$RoomRent ~ Cities.df$IsNewYearEve)
summary(reg)##
## Call:
## lm(formula = Cities.df$RoomRent ~ Cities.df$IsNewYearEve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5874 -3031 -1444 804 317132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5367.61 68.08 78.843 < 2e-16 ***
## Cities.df$IsNewYearEve 855.22 193.03 4.431 9.47e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7328 on 13230 degrees of freedom
## Multiple R-squared: 0.001482, Adjusted R-squared: 0.001406
## F-statistic: 19.63 on 1 and 13230 DF, p-value: 9.472e-06reg1 <- lm(Cities.df$RoomRent ~ Cities.df$IsWeekend)
summary(reg1)##
## Call:
## lm(formula = Cities.df$RoomRent ~ Cities.df$IsWeekend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5201 -3032 -1476 800 317069
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5430.84 103.80 52.319 <2e-16 ***
## Cities.df$IsWeekend 69.29 131.53 0.527 0.598
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7333 on 13230 degrees of freedom
## Multiple R-squared: 2.098e-05, Adjusted R-squared: -5.461e-05
## F-statistic: 0.2775 on 1 and 13230 DF, p-value: 0.5983reg3 <- lm(Cities.df$RoomRent ~ Cities.df$IsTouristDestination)
summary(reg3)##
## Call:
## lm(formula = Cities.df$RoomRent ~ Cities.df$IsTouristDestination)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5767 -2966 -1281 934 316434
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4111.0 115.0 35.76 <2e-16 ***
## Cities.df$IsTouristDestination 1955.0 137.7 14.20 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7278 on 13230 degrees of freedom
## Multiple R-squared: 0.01501, Adjusted R-squared: 0.01493
## F-statistic: 201.6 on 1 and 13230 DF, p-value: < 2.2e-16Managerial relavance:
This analysis was necessary so that we can understand the reasons for variation in room rents of hotels in India and adopt strategies to maximize profit.Being a manager he should understand on what factors the impact rate is High The contribution of the analysis was that the room rents of hotels of 42 major Indian cities were incorporated and analyzed. The observation made was that hotels with a higher star rating charge customers more than hotels with a lower star rating. Plus, the hotels with swimming pools tend to be more expensive than hotels without swimming pools. Another observation made was that the room rents were also dependent on the number of rooms in the hotel. These are the factors which affect the Hotel room pricing in Indian Market. Hotel pricing is mainly dependent on factors like new year events, whether the hotel is in metro city or not, whether the hotel is in tourist place or not, Star rating of hotel, Hotel capacity, whether it has swimming pool or not, And distance between Hotel and Airport.
we can summarize it as:
Room Rent = f(NewYearseve, IsMetroCity, IsTouristDestination, StarRating, Distance from the airport, HotelCapacity and HasSwimmingPool).
References.
https://economictimes.indiatimes.com/indian-hotels-company-ltd/stocks/companyid-13586.cms ndian Hotels Company LtdVs peers CompaniesLTPIntraday1W %1M %1 QTR %1 YR %3 YR %5 Yr % Trend - D|M|Y Indian Hotels137.75-3.57-12.0416.9921.7927.6614.17123.26
India Tourism472.80-3.83-9.23-8.68-9.0771.71264.11-76.42
Hotel Leela21.80-1.80-2.465.069.0030.93-3.75-19.85
Speciality Rest159.80-2.56-8.13-6.8229.18105.80-11.66-7.34
1Mahindra Holiday331.10-4.61-10.90-1.21-11.9618.8090.1062.36
http://www.lkpsec.com/company/the-indian-hotels-company-ltd/100850
https://www.expedia.co.in/Hotels
https://www.voyagersworld.in/content/pricing-success-indian-hotel-sector
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