- Email: [email protected]
Market demand variations, room capacity, and optimal hotel room rates
ARTICLE IN PRESS
Hospitality Management 26 (2007) 748–753 www.elsevier.com/locate/ijhosman
Research note
Market demand variations, room capacity, and optimal hotel room rates Chih-Min Pan Department of Applied Economics, National ChiaYi University, No. 151, Lin-Shen East Rd., Chia-Yi City, 600, Taiwan, ROC
Abstract This short note develops an optimal hotel room rate model and proposes optimal room rate strategies in both high and low seasons. We then examine our model with the data from tourist hotels in Taipei, Taiwan. The empirical results support our model’s major predictions: (1) market demand variations significantly affect the difference between high season and low season optimal room rates; and (2) hotel’s room capacity negatively affect the difference between high season and low season optimal room rates, which also means that fixed costs shall negatively affect the high season optimal room rate. This result contradicts with conventional wisdom. r 2006 Elsevier Ltd. All rights reserved. Keywords: Optimal hotel room rate; Room capacity; Market demand variation
1. Introduction There is no doubt that room rates and occupancy rates are two important factors for determining operating revenue and making profits. In order to generate profits, hotel operators make great efforts in setting their room rate strategies. In the past, hotel rooms are priced mainly based on costs. The $1 per $1000 (invested in construction costs) rule and the Hubbart formula (Hsu and Powers (2002, pp. 256–257) are the most popular pricing strategies adopted in the hotel industry. As Gu (1997) in this journal pointed out that these pricing strategies ignored important market factors and are not able to maximize profits. Therefore, Gu (1997) proposed an optimal room rate model, in which hotel rooms are priced based on both variable costs and two basic features of market demand. Recently, E-mail address: [email protected]. 0278-4319/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhm.2006.04.004
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Steed and Gu (2005) examined four major hotel room pricing methods, including cost based, market based, a combination of cost and market based, and best practice based, and proposed a seven-step approach to set profitable room pricing. However, for any given hotel, room rate strategies are restrained by its room capacity— i.e. the maximum number of rooms for rent. First, hotels’ room capacity restricts the maximum number of rooms available for rent in either a high season or a low season. Second, hotels’ room capacity significantly affects its initial capital investment and following operating costs (mostly fixed costs, which do not vary with changes in the number of rooms rented, including fixed overhead costs, the payments for full-time staff, interests for mortgages, and depreciations, etc.). As a result, the cost structure in the hotel industry is characterized by high fixed costs along with relatively low variable costs (which vary with changes in the number of rooms rented). These critical features result in hotels’ room rate strategies being very sensitive to variations in market demand. Unfortunately, seasonal demand variation is quite common in the hotel industry. In order to earn more profits, hotels might lower room rates in the low season, as long as the room rates could pay for variable costs. Gu (1997) model provided an optimal solution. Both variable costs and market demand determine optimal room rates. Generally, hotels try to raise room rates in the high season. But how high could they charge? What are the factors that determine optimal room rates? Little attention has been given to analyze high season optimal room rates. This note proposes an optimal hotel room rate model that incorporates costs, market demand variations, and hotel’s limited room capacity. Our model develops optimal room rate strategies for hotel operators in both high and low seasons. We then examine our model with the data from tourist hotels in Taipei, Taiwan. The note is organized as follows: Section 2 develops an optimal hotel room rate model. It then examines the formation of average daily rates with the data from tourist hotels in Taipei, Taiwan in Section 3. Section 4 provides concluding remarks. 2. An optimal hotel room rate model ¯ rooms. The hotel’s daily unit variable costs for serving each A monopolistic hotel has Q rented room is a constant r, while its daily fixed cost for rooms operation is F. Therefore, all the costs associated with rooms operation C are defined as follows: ( ¯ Qr þ F if QoQ; C ¯ ¯ Qr þ F if Q ¼ Q; where Q is the number of rooms are rented. Demand for hotel rooms is affected by seasonal factors—i.e. the demand schedules vary between months of the year and days of the week. Let PH and QH denote the rate and quantity of hotel rooms in a high season, while PL and QL denote the rate and quantity of hotel rooms in a low season. Based on Gu (1997) model, we then define market demand schedules as follows: QL ¼ AL PL and QH ¼ AH PH , where AH is the number of rooms demanded when rooms are free (PH ¼ 0) in the high season, i.e. the potential maximum demand in the high season; AL is the number of rooms
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demanded when rooms are free (PL ¼ 0) in the low season, i.e. the potential maximum demand in the low season; AH 4AL 40. In order to assure tourists’ demand for rooms is positive when the hotel room rate is exactly equal to its daily unit variable costs for serving each rented room (r), we assume that AH 4AL 4r. In the low season, the number of rooms demanded is less than double the room capacity, ¯ In even after the price of hotel rooms is set to its daily unit variable costs (i.e. AL rp2Q). the high season, the number of rooms demanded is more than double the room capacity ¯ when the price of hotel rooms is set to its daily unit variable costs (i.e. AH r42Q). ¯ 2.1. The low season (AL rp2Q) In the low season, following Gu (1997), the daily pre-tax profits of rooms operation are defined as follows: pL ¼ ðAL PL ÞPL ððAL PL Þr þ F Þ. The first term is the daily room revenue, which is the product of the number of rooms rented (AL PL ) and the room rate (PL ); the second term is all the costs related to rooms operation, which are the sum of variable costs and fixed cost. In order to maximize its daily pre-tax profits, the hotel shall set its room rate (PL ) at ðAL þ rÞ=2, and ðAL rÞ=2 rooms are rented. ¯ 2.2. The high season (AH r42Q) ¯ AH PH rooms are In the high season, if the room rate is set so high (AH PH oQ), H H ¯ ðA P Þ rooms are vacant. On the other hand, if the room rate is set so rented and Q ¯ the market demand (AH PH ) shall exceed the hotel’s room capacity low (AH PH 4Q), ¯ but only Q ¯ rooms are rented anyway. Therefore, in the high season, the daily pre-tax (Q), profits of rooms operation are defined as follows: ( H ¯ ðA PH ÞPH ððAH PH Þr þ F Þ ifAH PH oQ; H p ¼ ¯ H H H ¯ ¯ QP ðQr þ F Þ ifA P XQ: ¯ H ) are the daily room revenue, while the second The first terms (ðAH PH ÞPH and QP H H ¯ terms (ðA P Þr þ F and Qr þ F ) are all the costs related to rooms operation. In order to maximize its daily pre-tax profits, the hotel shall then set its room rate (PH ) ¯ and Q ¯ rooms are rented. at AH Q, Following the above analyses, we can see that market demand variations (a low season (AL ) or a high season (AH )) do affect the determination of optimal room rates. In addition, ¯ the daily unit variable costs for serving each rented room (r) and hotel’s room capacity (Q) also affect the formation of the optimal room rate. However, the daily unit variable costs for serving each rented room only affects the formation of the low season optimal room rate. The high season optimal room rate is negatively affected by the hotel’s room capacity instead. As a result, the difference between PH and PL is as follows: ¯ rÞ=240. PH PL ¼ ð2AH AL 2Q
(1)
Eq. (1) states that PH PL is positively related to the potential maximum demand in the high season AH , while PH PL is negatively related to the potential maximum demand in
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¯ and the daily unit variable costs for serving the low season AL , the hotel’s room capacity Q, each rented room r. Based on Eq. (1), the following hypotheses can be formulated. Hypothesis 1. If the potential maximum demand in the low season, the hotel’s room capacity, and the daily unit variable costs for serving each rented room are held constant, an increase in the potential maximum demand in the high season will result in an increase of the difference between PH and PL . The explanation is that a greater high season demand will raise the high season optimal room rate without changing the low season optimal room rate. Hypothesis 2. If the potential maximum demand in the high season, the hotel’s room capacity, and the daily unit variable costs for serving each rented room are held constant, an increase in the potential maximum demand in the low season will result in a decrease of the difference between PH and PL . The reason is that a greater low season demand will raise the low season optimal room rate without changing the high season optimal room rate. Hypothesis 3. Because a hotel with more rooms available shall lower its optimal high season room rate without any impact on the low season optimal room rate, we then expect an increase in the hotel’s room capacity will cause a decrease of the difference between PH and PL when the potential maximum demand in the high season, the potential maximum demand in the low season, and the daily unit variable costs for serving each rented room are held constant. Hypothesis 4. If the daily unit variable costs for serving each rented room increases while the potential maximum demand in the high season, the potential maximum demand in the low season, and hotel’s room capacity are held constant, the difference between PH and PL will decrease. In order to test the above hypotheses, we then have the following regression model: ¯ þ , PH PL ¼ CONSTANT þ b1 AH þ b2 AL þ b3 Q
(2)
where e is the disturbance term; AH is the number of rooms demanded when rooms are free in the high season; AL is the number of rooms demanded when rooms are free in the low ¯ is the hotel’s maximum number of rooms for rent. On account of the season; and Q availability of data, we remove the daily unit variable costs for serving each rented room (r) from our regression model and will not test Hypothesis IV. This minor adjustment shall not affect our major empirical results. There are four regression coefficients to be estimated and tested in the model, namely, CONSTANT, b1, b2, and b3. Among these coefficients, CONSTANT captures the mean of PH PL as well as the effects of omitted variables; b1 measures the average effect on PH PL when only AH is changed, i.e. DðPH PL Þ=DAH ¼ b1 , and is expected to be positive; b2 measures the average effect on PH PL when only AL is changed, i.e. DðPH PL Þ=DAL ¼ b2 , and is expected to be negative; b3 measures the effect on PH ¯ is changed, i.e. DðPH PL Þ=DQ ¯ ¼ b3 , and is expected to be negative. PL when only Q
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3. Data and empirical results In this section, we estimate the regression model (Eq. (2)) and examine how the variation of market demand and room capacity will affect the difference between the high season optimal room rate (PH ) and the low season optimal room rate (PL ) with the data from tourist hotels in Taipei, Taiwan. ¯ of various tourist hotels is obtained from the Tourism Bureau, The room capacity (Q) Ministry of Transportation and Communication, Taiwan. However, data on PH (the high season optimal room rate), PL (the low season optimal room rate), AH (the potential maximum demand in the high season) and AL (the potential maximum demand in the low season) is not available in printed form. We have to find proxies for these variables. We obtained monthly data (from 2001 to 2004) on the number of rooms rented and average daily room rates of various tourist hotels from the Tourism Bureau, Ministry of Transportation and Communication, Taiwan. Due to the limitation of data, the true values of the potential maximum demand in the high season (AH) and the potential maximum demand in the low season (AL ) are not available. Therefore, based on the available monthly data, we take the maximum number of rooms rented in a given year as a proxy for the potential maximum demand in the high season (AH) and the average daily room rate of the corresponding month as a proxy for the high season optimal room rate (PH ). Also, we take the minimum number of rooms rented in a given year as a proxy for the potential maximum demand in the low season (AL) and the average daily room rate of the corresponding month as a proxy for the low season optimal room rate (PL ). We have made two adjustments over the average room rates before estimating Eq. (2). The average room rates are quoted in terms of US dollars and are deflated. The data of monthly average exchange rates between the NT dollar and the US dollar is available from the Central Bank of China website. The data of consumer price index in Taipei is available from Department of Budget, Accounting and Statistics, Taipei City Government, website. The empirical results for Eq. (2) are reported in Table 1. Table 1 indicates that the empirical results are as expected. (1) The coefficient of AH is significantly positive. A greater high season demand will raise the high season optimal room rate and the difference between high season and low season optimal room rates. (2) The coefficient of AL is significantly negative. A greater low season demand will raise the low season optimal room rate and undercut the difference between high season and low ¯ is significantly negative. Hotels with season optimal room rates. (3) The coefficient of Q Table 1 Market demand variations, room capacity, and optimal hotel room rate differences Variables
Coefficient
Std. error
t-Statistic
CONSTANT AH AL ¯ Q
3.561 0.004 0.001 0.034 0.243 0.220
2.176 0.001 0.001 0.019 F-statistic Prob (F-statistic)
1.636 4.436a 1.810b 1.845b 10.577 0.000
R2 ¯2 R a
Denotes significance at the 0.01 level in the one-tailed test. Denotes significance at the 0.05 level in the one-tailed test.
b
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greater room capacity would have lower high season optimal room rates than hotels with less capacity. Therefore, for hotels with greater room capacity, the difference between high season and low season optimal room rates would be smaller than that of hotels with less room capacity. 4. Concluding remarks Room rate strategy is one of the most important strategies taken by hotel operators. This note develops an optimal hotel room rate model and proposes optimal room rate strategies in both high and low season. Our model indicates that optimal room rates are determined by costs, market demand variations, and room capacity. The empirical results support our model’s major predictions and contribute to the literature on optimal hotel room rates. First, Gu (1997) showed that two basic features of market demand affected optimal hotel room rates. This note, in addition, shows that market demand ‘variations’ significantly affect the optimal hotel room rates. The result could explain the variations of hotel room rates in reality. Based on our analysis on the data of tourist hotels in Taipei, Taiwan, this short note also provides a quite different role fixed costs play in the formation of optimal hotel room rates. While both the $1 per $1000 rule and the Hubbart formula proposed that the higher the fixed costs are, the higher the optimal room rate shall be, Gu (1997) indicated that the optimal room rate shall not be affected by fixed costs. As Lawson (1995) indicated that hotel’s room capacity determines major parts of fixed costs (and daily fixed costs) including fixed overhead costs, payrolls for full-time staff, interest for mortgages, and depreciations, etc. The larger the hotel’s room capacity, the higher the fixed costs. This note shows that hotel’s room capacity negatively affect the high season optimal room rate, which, in turn, means that fixed costs negatively affect the high season optimal room rate. Our result contradicts with conventional wisdom from cost-based approaches and Gu (1997). References Gu, Z., 1997. Proposing a room pricing for optimizing profitability. International Journal of Hospitality Management 16 (3), 273–277. Hsu, C.H.C., Power, T., 2002. Marketing Hospitality. Wiley, New York. Lawson, F.R., 1995. Hotels and Resorts: Planning, Design and Refurbishment. Architectural Press, Oxford. Steed, E., Gu, Z., 2005. An examination of hotel room pricing methods: practised and proposed. Journal of Revenue and Pricing Management 3 (4), 369–379.
Hospitality Management 26 (2007) 748–753 www.elsevier.com/locate/ijhosman
Research note
Market demand variations, room capacity, and optimal hotel room rates Chih-Min Pan Department of Applied Economics, National ChiaYi University, No. 151, Lin-Shen East Rd., Chia-Yi City, 600, Taiwan, ROC
Abstract This short note develops an optimal hotel room rate model and proposes optimal room rate strategies in both high and low seasons. We then examine our model with the data from tourist hotels in Taipei, Taiwan. The empirical results support our model’s major predictions: (1) market demand variations significantly affect the difference between high season and low season optimal room rates; and (2) hotel’s room capacity negatively affect the difference between high season and low season optimal room rates, which also means that fixed costs shall negatively affect the high season optimal room rate. This result contradicts with conventional wisdom. r 2006 Elsevier Ltd. All rights reserved. Keywords: Optimal hotel room rate; Room capacity; Market demand variation
1. Introduction There is no doubt that room rates and occupancy rates are two important factors for determining operating revenue and making profits. In order to generate profits, hotel operators make great efforts in setting their room rate strategies. In the past, hotel rooms are priced mainly based on costs. The $1 per $1000 (invested in construction costs) rule and the Hubbart formula (Hsu and Powers (2002, pp. 256–257) are the most popular pricing strategies adopted in the hotel industry. As Gu (1997) in this journal pointed out that these pricing strategies ignored important market factors and are not able to maximize profits. Therefore, Gu (1997) proposed an optimal room rate model, in which hotel rooms are priced based on both variable costs and two basic features of market demand. Recently, E-mail address: [email protected]. 0278-4319/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhm.2006.04.004
ARTICLE IN PRESS C.-M. Pan / Hospitality Management 26 (2007) 748–753
749
Steed and Gu (2005) examined four major hotel room pricing methods, including cost based, market based, a combination of cost and market based, and best practice based, and proposed a seven-step approach to set profitable room pricing. However, for any given hotel, room rate strategies are restrained by its room capacity— i.e. the maximum number of rooms for rent. First, hotels’ room capacity restricts the maximum number of rooms available for rent in either a high season or a low season. Second, hotels’ room capacity significantly affects its initial capital investment and following operating costs (mostly fixed costs, which do not vary with changes in the number of rooms rented, including fixed overhead costs, the payments for full-time staff, interests for mortgages, and depreciations, etc.). As a result, the cost structure in the hotel industry is characterized by high fixed costs along with relatively low variable costs (which vary with changes in the number of rooms rented). These critical features result in hotels’ room rate strategies being very sensitive to variations in market demand. Unfortunately, seasonal demand variation is quite common in the hotel industry. In order to earn more profits, hotels might lower room rates in the low season, as long as the room rates could pay for variable costs. Gu (1997) model provided an optimal solution. Both variable costs and market demand determine optimal room rates. Generally, hotels try to raise room rates in the high season. But how high could they charge? What are the factors that determine optimal room rates? Little attention has been given to analyze high season optimal room rates. This note proposes an optimal hotel room rate model that incorporates costs, market demand variations, and hotel’s limited room capacity. Our model develops optimal room rate strategies for hotel operators in both high and low seasons. We then examine our model with the data from tourist hotels in Taipei, Taiwan. The note is organized as follows: Section 2 develops an optimal hotel room rate model. It then examines the formation of average daily rates with the data from tourist hotels in Taipei, Taiwan in Section 3. Section 4 provides concluding remarks. 2. An optimal hotel room rate model ¯ rooms. The hotel’s daily unit variable costs for serving each A monopolistic hotel has Q rented room is a constant r, while its daily fixed cost for rooms operation is F. Therefore, all the costs associated with rooms operation C are defined as follows: ( ¯ Qr þ F if QoQ; C ¯ ¯ Qr þ F if Q ¼ Q; where Q is the number of rooms are rented. Demand for hotel rooms is affected by seasonal factors—i.e. the demand schedules vary between months of the year and days of the week. Let PH and QH denote the rate and quantity of hotel rooms in a high season, while PL and QL denote the rate and quantity of hotel rooms in a low season. Based on Gu (1997) model, we then define market demand schedules as follows: QL ¼ AL PL and QH ¼ AH PH , where AH is the number of rooms demanded when rooms are free (PH ¼ 0) in the high season, i.e. the potential maximum demand in the high season; AL is the number of rooms
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demanded when rooms are free (PL ¼ 0) in the low season, i.e. the potential maximum demand in the low season; AH 4AL 40. In order to assure tourists’ demand for rooms is positive when the hotel room rate is exactly equal to its daily unit variable costs for serving each rented room (r), we assume that AH 4AL 4r. In the low season, the number of rooms demanded is less than double the room capacity, ¯ In even after the price of hotel rooms is set to its daily unit variable costs (i.e. AL rp2Q). the high season, the number of rooms demanded is more than double the room capacity ¯ when the price of hotel rooms is set to its daily unit variable costs (i.e. AH r42Q). ¯ 2.1. The low season (AL rp2Q) In the low season, following Gu (1997), the daily pre-tax profits of rooms operation are defined as follows: pL ¼ ðAL PL ÞPL ððAL PL Þr þ F Þ. The first term is the daily room revenue, which is the product of the number of rooms rented (AL PL ) and the room rate (PL ); the second term is all the costs related to rooms operation, which are the sum of variable costs and fixed cost. In order to maximize its daily pre-tax profits, the hotel shall set its room rate (PL ) at ðAL þ rÞ=2, and ðAL rÞ=2 rooms are rented. ¯ 2.2. The high season (AH r42Q) ¯ AH PH rooms are In the high season, if the room rate is set so high (AH PH oQ), H H ¯ ðA P Þ rooms are vacant. On the other hand, if the room rate is set so rented and Q ¯ the market demand (AH PH ) shall exceed the hotel’s room capacity low (AH PH 4Q), ¯ but only Q ¯ rooms are rented anyway. Therefore, in the high season, the daily pre-tax (Q), profits of rooms operation are defined as follows: ( H ¯ ðA PH ÞPH ððAH PH Þr þ F Þ ifAH PH oQ; H p ¼ ¯ H H H ¯ ¯ QP ðQr þ F Þ ifA P XQ: ¯ H ) are the daily room revenue, while the second The first terms (ðAH PH ÞPH and QP H H ¯ terms (ðA P Þr þ F and Qr þ F ) are all the costs related to rooms operation. In order to maximize its daily pre-tax profits, the hotel shall then set its room rate (PH ) ¯ and Q ¯ rooms are rented. at AH Q, Following the above analyses, we can see that market demand variations (a low season (AL ) or a high season (AH )) do affect the determination of optimal room rates. In addition, ¯ the daily unit variable costs for serving each rented room (r) and hotel’s room capacity (Q) also affect the formation of the optimal room rate. However, the daily unit variable costs for serving each rented room only affects the formation of the low season optimal room rate. The high season optimal room rate is negatively affected by the hotel’s room capacity instead. As a result, the difference between PH and PL is as follows: ¯ rÞ=240. PH PL ¼ ð2AH AL 2Q
(1)
Eq. (1) states that PH PL is positively related to the potential maximum demand in the high season AH , while PH PL is negatively related to the potential maximum demand in
ARTICLE IN PRESS C.-M. Pan / Hospitality Management 26 (2007) 748–753
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¯ and the daily unit variable costs for serving the low season AL , the hotel’s room capacity Q, each rented room r. Based on Eq. (1), the following hypotheses can be formulated. Hypothesis 1. If the potential maximum demand in the low season, the hotel’s room capacity, and the daily unit variable costs for serving each rented room are held constant, an increase in the potential maximum demand in the high season will result in an increase of the difference between PH and PL . The explanation is that a greater high season demand will raise the high season optimal room rate without changing the low season optimal room rate. Hypothesis 2. If the potential maximum demand in the high season, the hotel’s room capacity, and the daily unit variable costs for serving each rented room are held constant, an increase in the potential maximum demand in the low season will result in a decrease of the difference between PH and PL . The reason is that a greater low season demand will raise the low season optimal room rate without changing the high season optimal room rate. Hypothesis 3. Because a hotel with more rooms available shall lower its optimal high season room rate without any impact on the low season optimal room rate, we then expect an increase in the hotel’s room capacity will cause a decrease of the difference between PH and PL when the potential maximum demand in the high season, the potential maximum demand in the low season, and the daily unit variable costs for serving each rented room are held constant. Hypothesis 4. If the daily unit variable costs for serving each rented room increases while the potential maximum demand in the high season, the potential maximum demand in the low season, and hotel’s room capacity are held constant, the difference between PH and PL will decrease. In order to test the above hypotheses, we then have the following regression model: ¯ þ , PH PL ¼ CONSTANT þ b1 AH þ b2 AL þ b3 Q
(2)
where e is the disturbance term; AH is the number of rooms demanded when rooms are free in the high season; AL is the number of rooms demanded when rooms are free in the low ¯ is the hotel’s maximum number of rooms for rent. On account of the season; and Q availability of data, we remove the daily unit variable costs for serving each rented room (r) from our regression model and will not test Hypothesis IV. This minor adjustment shall not affect our major empirical results. There are four regression coefficients to be estimated and tested in the model, namely, CONSTANT, b1, b2, and b3. Among these coefficients, CONSTANT captures the mean of PH PL as well as the effects of omitted variables; b1 measures the average effect on PH PL when only AH is changed, i.e. DðPH PL Þ=DAH ¼ b1 , and is expected to be positive; b2 measures the average effect on PH PL when only AL is changed, i.e. DðPH PL Þ=DAL ¼ b2 , and is expected to be negative; b3 measures the effect on PH ¯ is changed, i.e. DðPH PL Þ=DQ ¯ ¼ b3 , and is expected to be negative. PL when only Q
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3. Data and empirical results In this section, we estimate the regression model (Eq. (2)) and examine how the variation of market demand and room capacity will affect the difference between the high season optimal room rate (PH ) and the low season optimal room rate (PL ) with the data from tourist hotels in Taipei, Taiwan. ¯ of various tourist hotels is obtained from the Tourism Bureau, The room capacity (Q) Ministry of Transportation and Communication, Taiwan. However, data on PH (the high season optimal room rate), PL (the low season optimal room rate), AH (the potential maximum demand in the high season) and AL (the potential maximum demand in the low season) is not available in printed form. We have to find proxies for these variables. We obtained monthly data (from 2001 to 2004) on the number of rooms rented and average daily room rates of various tourist hotels from the Tourism Bureau, Ministry of Transportation and Communication, Taiwan. Due to the limitation of data, the true values of the potential maximum demand in the high season (AH) and the potential maximum demand in the low season (AL ) are not available. Therefore, based on the available monthly data, we take the maximum number of rooms rented in a given year as a proxy for the potential maximum demand in the high season (AH) and the average daily room rate of the corresponding month as a proxy for the high season optimal room rate (PH ). Also, we take the minimum number of rooms rented in a given year as a proxy for the potential maximum demand in the low season (AL) and the average daily room rate of the corresponding month as a proxy for the low season optimal room rate (PL ). We have made two adjustments over the average room rates before estimating Eq. (2). The average room rates are quoted in terms of US dollars and are deflated. The data of monthly average exchange rates between the NT dollar and the US dollar is available from the Central Bank of China website. The data of consumer price index in Taipei is available from Department of Budget, Accounting and Statistics, Taipei City Government, website. The empirical results for Eq. (2) are reported in Table 1. Table 1 indicates that the empirical results are as expected. (1) The coefficient of AH is significantly positive. A greater high season demand will raise the high season optimal room rate and the difference between high season and low season optimal room rates. (2) The coefficient of AL is significantly negative. A greater low season demand will raise the low season optimal room rate and undercut the difference between high season and low ¯ is significantly negative. Hotels with season optimal room rates. (3) The coefficient of Q Table 1 Market demand variations, room capacity, and optimal hotel room rate differences Variables
Coefficient
Std. error
t-Statistic
CONSTANT AH AL ¯ Q
3.561 0.004 0.001 0.034 0.243 0.220
2.176 0.001 0.001 0.019 F-statistic Prob (F-statistic)
1.636 4.436a 1.810b 1.845b 10.577 0.000
R2 ¯2 R a
Denotes significance at the 0.01 level in the one-tailed test. Denotes significance at the 0.05 level in the one-tailed test.
b
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greater room capacity would have lower high season optimal room rates than hotels with less capacity. Therefore, for hotels with greater room capacity, the difference between high season and low season optimal room rates would be smaller than that of hotels with less room capacity. 4. Concluding remarks Room rate strategy is one of the most important strategies taken by hotel operators. This note develops an optimal hotel room rate model and proposes optimal room rate strategies in both high and low season. Our model indicates that optimal room rates are determined by costs, market demand variations, and room capacity. The empirical results support our model’s major predictions and contribute to the literature on optimal hotel room rates. First, Gu (1997) showed that two basic features of market demand affected optimal hotel room rates. This note, in addition, shows that market demand ‘variations’ significantly affect the optimal hotel room rates. The result could explain the variations of hotel room rates in reality. Based on our analysis on the data of tourist hotels in Taipei, Taiwan, this short note also provides a quite different role fixed costs play in the formation of optimal hotel room rates. While both the $1 per $1000 rule and the Hubbart formula proposed that the higher the fixed costs are, the higher the optimal room rate shall be, Gu (1997) indicated that the optimal room rate shall not be affected by fixed costs. As Lawson (1995) indicated that hotel’s room capacity determines major parts of fixed costs (and daily fixed costs) including fixed overhead costs, payrolls for full-time staff, interest for mortgages, and depreciations, etc. The larger the hotel’s room capacity, the higher the fixed costs. This note shows that hotel’s room capacity negatively affect the high season optimal room rate, which, in turn, means that fixed costs negatively affect the high season optimal room rate. Our result contradicts with conventional wisdom from cost-based approaches and Gu (1997). References Gu, Z., 1997. Proposing a room pricing for optimizing profitability. International Journal of Hospitality Management 16 (3), 273–277. Hsu, C.H.C., Power, T., 2002. Marketing Hospitality. Wiley, New York. Lawson, F.R., 1995. Hotels and Resorts: Planning, Design and Refurbishment. Architectural Press, Oxford. Steed, E., Gu, Z., 2005. An examination of hotel room pricing methods: practised and proposed. Journal of Revenue and Pricing Management 3 (4), 369–379.