Report
Contents Scenario 3 Displaying the data 4 Sampling 6 Questionnaire 7 Correlation 9 Seasonality 11 Forecasting 12 Decision tree 13 References 14
Scenario
Your team have recently been employed in a large hotel chain operating in Europe. Your company own and manage a number of hotels over different classes (Economy, mid-scale, up-scale and upper upscale). You should choose a name for the hotel chain – either use an existing one that does not yet operate in Switzerland or make up your own.
The board has decided to expand into Switzerland and has tasked your team with a number of assignments. Your team’s main objective is to do some research to help them decide what type of hotel and where in Switzerland to open.
In order for them to make an informed, well researched decision, you have obtained data from Bern, Lausanne, Zurich and Geneva. You need to investigate hotels that will be in operating in the same area and possibly the same market as you. The company is wealthy and is doing well across the board but has never operated in Switzerland.
Displaying the data
Lausanne’s hotels are divided into six categories. First, the majority of hotels are Economy Class which has the highest percentage 44%, next is the Upper Midscale Class which has 19% of numbers of hotel in Lausanne, then we go with Midscale Class with 15%, after that is Upscale Class and it has 12%, followed by Luxury Class with 8%, lastly is Upper Upscale Class which has the lowest percentage which is 2%. We can see all the information that is mentioned in this pie chart below.
This chart illustrates the Average numbers of rooms for each class in Lausanne area,
We can see that the Luxury Class has the highest average 145 rooms, then Upscale Class with 92 as an average number of rooms, followed by Economy Clsass with 72 room average, next is Midscale Class with 65 rooms, then Upper Midscale Class with 64 rooms which is only one room as a difference from Midscale class, lastly we have Upper Upscale Class with 50 rooms as an average.
This chart below shows the average room rates, as we can see there is a huge gap between the luxury class and the others class. Luxury class has the highest average room rate 557 CHF, next we have Upper Upscale Class with 351 CHF, then Upscale Class with 242 CHF, followed by Upper Midscale Class with 206 CHF, after that is Midscale Class with 173 CHF, and the lowest average room rate goes with the Economy Class 140CHF.
Sampling
The formula we chose takes into consideration the level of confidence, population size, the variability of the population and the standard error, this helps ensure that everything is taken into account.
In which:
· n= Sample size
· Z= Level of confidence, Z=1.96
· N= Population size, N=
· p= Maximum population variability, p= 0.50
· q= p, q= 0.50
· e= Standard error, e= 0.10
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Sample Size based on the population of 2018 |
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City |
Population |
Population above age 19 (%) |
Sample Size |
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Geneva |
499,480 |
78.9% |
97 people |
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Zurich |
1,520,968 |
80.3% |
97 people |
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Bern |
1,034,977 |
81% |
97 people |
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Lausanne |
145,488 |
80.7 |
96 people |
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(Federal Statistical Office, 2020), (Ville de Lausanne, 2020) |
Questionnaire
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1. Which type of room do you prefer? · Standard · Deluxe · Double Room · Junior Suite · Master Suite |
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2. Where do you prefer your hotel to be closer to? · Train Station · Cathedrale de Lausanne · The Olympic Museum · The Art Museum · Botanic Garden · Other: _________________ |
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3. How much are you willing to spend for a night in a standard room? · 120 – 190 CHF · 191 – 260 CHF · 261 – 330 CHF · 331 – 400 CHF · 401 – 470 CHF |
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4. Which of the following spaces do you prefer your hotel to have? |
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· Restaurant · Business Center · Bar · SPA · Convention Center
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· Banquet Hall · Conference Center · Event Center · Valet Parking |
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5. Which type of restaurant would you prefer for the following meals: |
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Breakfast · Buffet · Á la Carte · Both |
Lunch · Buffet · Á la Carte · Both |
Dinner · Buffet · Á la Carte · Both |
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6. Which of the following would you prefer? · Indoor pool · Outdoor pool · No pool |
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7. Would you like for the hotel to have a babysitting center? · Yes · No |
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Correlation
They have a strong positive relationship, because the correlation coefficient is 0.57, and that implies when the number of rooms increase the room average rate will increase as well.
Seasonality
In this following chart, we can notice the seasonality of the hotel industry in Lausanne, the lower occupancy can be seen in the quarters one and four, this could be caused by the weather, when it gets cold the occupancy goes down, for the higher quarters we have two and three this can also be related to the weather. The occupancy of the last two quarters shows a big reduction, this could be relevant to the global pandemic nowadays.
Next graph shows the rooms' demand for hotels in Geneva. It illustrates similar seasonality as the previous, high quarters two and three and low one and four, also with a big fall in the last two quarters cause of the pandemic.
Forecasting
In the next chart, we can observe the occupancy forecast for the hotels in Lausanne until December 2020. The forecast implies that if we have low confidence the number will be negative with a number of -3.9, if the same situation continues the occupancy will be 14.9%, and if we take a look at the upper confidence level the occupancy rises to 33.87%.
This chart, we can see the Rooms demand forecast for the hotels in Lausanne until December 2020. The forecast indicates that if we have low confidence the number will be with a value of 54.08, if the same situation continues the Rooms demand will be 95.99, and if we take a look at the upper confidence level the Room demand rises to 138.9.
Decision tree
In this decision tree below we can see which solution is the best, which is Buy an existing hotel. The net expected outcome is CHF 1.7 M, which is 410,000 CHF more than the next best option.
References
Praga, E. Formula used for random sampling calculation. Research Gate. [Online]. Available at: < https://www.researchgate.net/figure/Formula-used-for-Simple-Random-Sampling-Calculation-of-the-sample-size-n-for-finite_fig5_320365299 >
[Accessed 03 September 2020]
Ville de Lausanne, 2020. 01 – Population. [online] Site officiel de la Ville de Lausanne. Available at: < https://www.lausanne.ch/en/officiel/statistique/themes/01-population.html >
[Accessed 3 September 2020].
Average room rates Economy class Midscale Cl ass Upper Midscale Class Upscale Class Upper Upscale Class Luxury Class 139.55208333333331 172.52083333333334 205.8666666666667 242.3125 351 556.79166666666674
Class
Room rates
relationship between number of rooms and average room rates
Average room rates 12 14 14 19 22 22 24 28 30 39 40 42 47 49 54 55 60 79 84 98 116 147 215 75 140 168 196 26 33 34 60 62 66 113 127 21 22 26 31 44 51 75 109 113 143 50 11 33 114 116 154 337 141 137 50 111 50 145.5 96 109.5 113 84 118.5 127.5 167 155.5 153 131 123 85 170.83333333333334 240 85.5 188.33333333333334 181.16666666666666 375 634.33333333333337 941.33333333333337 276.5 127 168 200.33333333333334 264.33333333333331 167.5 142.5 169 141.5 209.83333333333334 209 185 167.33333333333334 240.16666666666666 184.5 258.5 260.33333333333331 166 178 351 258 286 235.5 199.5 201 733.33333333333337
number of Rooms
Average room rate
Actual VS Recentered moving average Occupancy Lausanne
Occupancy 62.043253278209534 67.813256457279067 74.329410194000289 63.73660535477709 57.596112667569365 71.265859444803894 72.712049788951518 56.5857008523761 68.431089617809633 68.929178766711132 58.743553262975702 57.398672570554197 72.550077352390076 74.166167456326662 61.822894290445639 60.806019063407433 67.323340752465796 74.511580517802898 58.930533065678539 56.90647927766414 61.974570426707828 67.702703227749168 52.445290781443134 50.643934198460236 64.229298494033671 65.313638509078558 54.729670929392135 53.518076251845137 63.563699352478729 70.567668923403701 57.974624614658467 53.919725549804561 64.583084428514539 71.703384736787754 58.989750652683767 54.071932510480195 69.836700001474966 75.089605734767034 55.635440382864907 39.390217586085903 12.059462128576749 Re-centered avergae 65.867107646536198 66.212064835930903 66.19519179622452 65.40637274896018 65.115295965482048 64.404469856025173 64.166998082050071 64.995016155165303 65.985182651697755 66.391241377323666 66.491576626190863 66.21868048429323 65.489103949983331 64.751900307808825 63.567553518663694 62.158651663301796 60.602061977098607 59.520690940463155 58.715558189539152 58.458526840626668 58.772538437500991 58.904029583820268 59.513214175951774 60.182434614165615 60.697124255165363 61.317125409764529 61.679731901804878 61.902974165892665 62.110626283900217 62.582917807965558 63.195865906068093 63.53572423493037 62.948462445975792 58.42177231665503 56.396697347256207 52.765836192185922 45.543681458073642
Quarters
Occupancy
Actual VS Recentered moving average Occupancy Lausanne
Demand 62170 71849 79458 68155.333333333328 60188.333333333336 75480 77775.333333333328 64210.333333333336 59722.333333333336 73971.333333333328 75154.666666666672 64000.333333333336 61028 78202.666666666672 80740.333333333328 67335.666666666672 64585.666666666664 72460 80932 64051.666666666664 61508.666666666664 69363.666666666672 76553.333333333328 61229 58907.333333333336 75695.666666666672 77741 65152.666666666664 62465.333333333336 75020.666666666672 84030.333333333328 69093 62885 76146.333333333328 85414 70320 63048 82361.666666666672 89501 66810.333333333328 46977 Re-centered avergae 70136.583333333328 69982.666666666657 69507.520833333328 68973.75 68673.416666666657 68483.8125 68652.958333333328 69335 70238.625 71283 71704 71787.875 71458.041666666672 70713.541666666657 70134.4375 69269.6875 68433.770833333328 67627.583333333328 67410.666666666657 67541.645833333328 68094.270833333328 69031.854166666672 69531.500000000015 70350 71169.541666666672 71792.9375 72528.875 72958.208333333328 73217.958333333328 73461.666666666657 74023.479166666672 74754.25 75188.979166666672 74609.083333333343 74383.472222222234 73421.375 71412.5
Quarters
Rooms demand
Occupancy Forecasting - Lausanne
Occupancy 62.043253278209534 67.813256457279067 74.329410194000289 63.73660535477709 57.596112667569365 71.265859444803894 72.712049788951518 56.5857008523761 68.431089617809633 68.929178766711132 58.743553262975702 57.398672570554197 72.550077352390076 74.166167456326662 61.822894290445639 60.806019063407433 67.323340752465796 74.511580517802898 58.930533065678539 56.90647927766414 61.974570426707828 67.702703227749168 52.445290781443134 50.643934198460236 64.229298494033671 65.313638509078558 54.729670929392135 53.518076251845137 63.563699352478729 70.567668923403701 57.974624614658467 53.919725549804561 64.583084428514539 71.703384736787754 58.989750652683767 54.071932510480195 69.836700001474966 75.089605734767034 55.635440382864907 39.390217586085903 12.059462128576749 15.496670772756499 Forecasting 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 15.496670772756499 14.992904973777843 14.489139174799201 Lower Confidence Level 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 15.496670772756499 -3.8936176996353389 -4.9832320709368947 Upper Confidence Level 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 15.496670772756499 33.879427647191022 33.961510420535298
Quarters
Occupancy
Rooms demand forecasting
Rev PAR 141.75333333333333 163.54666666666665 180.00333333333333 153.85666666666665 140.86000000000001 182.24666666666667 167.11333333333334 130.26666666666668 122.13 157.94000000000003 155.94 122.15333333333332 115.60000000000001 153.81000000000003 158.78666666666666 117.91666666666667 129.6 150.71333333333334 167.5 122.01666666666667 118.91666666666667 128.94333333333336 136.26666666666665 102.06 103.58 131.08666666666667 135.02000000000001 106.08999999999999 103.27333333333333 127.71666666666665 145.56 114.39999999999999 105.44666666666666 128.10333333333332 148.30666666666667 112 .67999999999999 102.57333333333334 145.24 157.59 104.23666666666666 75.913333333333327 21.676666666666666 Forecasting 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 21.676666666666666 97.331491164106112 95.992540600788971 Lower Confidence Level 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 21.676666666666666 55.754997521482579 54.082094518514893 Upper Confidence Level 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 21.676666666666666 138.90798480672964 137.90298668306303
Quarters
Rooms demand
NuMBER OF HotelS by class IN LAUSANNE
Hotel by class(count)
Economy class Midscale Class Upper Midscale Class Upscale Class Upper Upscale Class Luxury Class 23 8 10 6 1 4
Average numbers of rooms Economy class Midsc ale Class Upper Midscale Class Upscale Class Upper Upscale Class Luxury Class 73.9375 65.125 63.5 91.8125 50 144.75
Class
Rooms number
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Contents
Scenario
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Displaying the data
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Sampling
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Questionnaire
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Correlation
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Seasonality
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Forecasting
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Decision tree
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References
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1
Contents
Scenario......................................................................................................................... 3
Displaying the data ...................................................................................................... 4
Sampling ....................................................................................................................... 6
Questionnaire ............................................................................................................... 7
Correlation.................................................................................................................... 9
Seasonality .................................................................................................................. 11
Forecasting.................................................................................................................. 12
Decision tree................................................................................................................ 13
References ................................................................................................................... 14