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The effect of time on hotel pricing strategy
Josep Maria Rayaa
a
Departament of Economia i Empresa, Escola Universitaria del Maresme (Universitat Pompeu Fabra),
Barcelona, Spain
First published on: 18 November 2010
To cite this Article Raya, Josep Maria(2010) 'The effect of time on hotel pricing strategy', Applied Economics Letters,, First
published on: 18 November 2010 (iFirst)
To link to this Article: DOI: 10.1080/13504851.2010.532091
URL: http://dx.doi.org/10.1080/13504851.2010.532091
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Applied Economics Letters, 2010, 1–5, iFirst
The effect of time on hotel pricing
strategy
Downloaded By: [Raya Vilchez, Josep Maria][Consorci de Biblioteques Universitaries de Catalunya] At: 17:59 18 November 2010
Josep Maria Raya
Departament of Economia i Empresa, Escola Universitaria del Maresme
(Universitat Pompeu Fabra), C/Ernest Lluch, s/n 08302 Mataro, Barcelona,
08005, Spain
E-mail: [email protected]
Tourist product distribution over the Internet is encouraging companies to
implement dynamic pricing policies. The aim of this article is to present an
empirical model of the dynamics of room prices in tourist resorts on the
Catalan coast. We estimate a discrete time duration model for the
probability of a price change occurring at any particular time and a count
model for the number of price changes occurring over the period. The
results suggest that the largest marginal effects are caused by a change in
the location, the hotel category and the market share.
I. Introduction
The pricing strategy used by a hotel has an immediate
impact on the hotel’s yield. The increase in tourist
product distribution over the Internet is encouraging
companies to implement dynamic pricing policies,
changing their prices over time depending on bookings up until that moment and competitive pressure.
In tourism, most studies in the empirical literature
on hotel price determinants have been conducted
using hedonic price models. This type of model
(Rosen, 1974) shows how heterogeneous products
are composed of various characteristics and the implicit marginal price can be known by estimating a model
(hedonic price model) that accounts for the price of a
product in terms of its characteristics. For the case of
hotel prices, the studies by Coenders et al. (2003),
Espinet et al. (2003), Rigall (2004) and Uriel and
Ferri (2004) are of particular interest. The hedonic
price model was used by Hartman (1989) to identify
pricing strategies for luxury hotels.
In contrast, few empirical studies analyse hotel
prices from the viewpoint of competition (Davies
and Downward, 1998; Davies, 1999). Among them,
Baum and Mudambi (1995) show the short-term
inelasticity of supply in the tourist trade and the
uncertainty of demand. All these papers use economic
theory to build pricing models in which the effect of
time on pricing is not introduced.
There is, however, an ample literature that addresses
the temporal dimension of hotel pricing, albeit indirectly. Dynamic pricing of hotel rooms is a standard
practice of yield management (Kimes, 1989), which
has become widespread with the increase in the use of
the Internet as a means of tourist product distribution.
This practice involves price discrimination between
consumers, the chosen indicator of price sensitivity
being how early the booking is made (Gallego and
van Ryzing, 1994; Zhao and Zheng, 2000; Zhou et al.,
2005). A higher occupancy rate is considered to induce
higher prices, up until a particular time at which, if the
occupancy is not 100%, the price will decrease because
of the perishable nature of the tourist product.
The aim of this article is to present an empirical
model of the dynamics of room prices in a sample
of hotels in different tourist resorts on the Catalan
coast. The article is structured as follows. First we
will present the methodological framework of the
article and then the data used to make the
empirical estimates. This will be followed by a
discussion of the main results, and finally by our
conclusions.
Applied Economics Letters ISSN 1350–4851 print/ISSN 1466–4291 online # 2010 Taylor & Francis
http://www.informaworld.com
DOI: 10.1080/13504851.2010.532091
1
J. M. Raya
2
Downloaded By: [Raya Vilchez, Josep Maria][Consorci de Biblioteques Universitaries de Catalunya] At: 17:59 18 November 2010
II. Methodological Framework
To model the probability of not continuing in a certain
state (constant pricing) in which the individual has spent
t periods, based on a group of variables, the most suitable method is survival or duration models in discrete
time. In this type of model, time is measured in discrete
intervals. For the case that concerns us, the state of the
price of a hotel room is observed, from time t = 1 to time
t = Ti, whether the hotel keeps the price constant. At Ti,
the event is completed and a sequence of states is thus
obtained for that period. The survival time in a certain
state (constant pricing or dynamic pricing), Ti, is a discrete random variable. The hazard function, which
includes the probability of exiting a state in the period t
conditional on spending t periods in this state ðhit Þ, is
hit ¼ PrðTi ¼ tjTi tÞ
ð1Þ
Once a new binary variable is defined as yit = 1, if the
hotel i reaches the situation of success (completes the
event, i.e. modifies its price) at time t and yit = 0
otherwise, the likelihood function for the whole sample can be written as
log L ¼
ti
n X
X
yij log
i¼1 j¼1
ti n X
X
hij
1 hij
þ
ti
n X
X
logð1 hij Þ
i¼1 j¼1
yij log hij þ ð1 yij Þ logð1 hij Þ
i¼1 j¼1
ð2Þ
The above expression has exactly the same form as the
standard likelihood function for a binary choice
model in which yit is the dependent variable and in
which the data have been reorganized in hotel-period
form (i.e. as many observations per hotel as periods to
success). So it turns out that there is a simple way of
estimating these discrete time probability models following Jenkins (1995). That is, reorganizing the data
in hotel-period form, choosing the functional form hit
and estimating the model using any one of the wellknown binary choice models.
Second, a count model will be estimated for the
number of times the hotel has changed its price over
the period observed. Count models (Poisson and negative binomial) are used when the dependent variable is
discrete and non-negative and has a right-asymmetrical
distribution. The dependent variable usually includes
few values. Estimating this type of model for this type
1
of variable solves the inconsistency problems of
Ordinary Least Squares (OLS) estimation (as the
dependent variable is non-negative and discrete) or
multinomial logit estimation (intended for when the
number of options is not very high). Furthermore, the
Poisson model is a case that is embedded in the negative
binomial one in which the mean and the conditional
variance of the dependent variable are assumed to be
equal.1
Thus, the probability of y taking the value l, conditional on x, where b are the parameters to estimate, is
l
exp½eðxbÞ · ½eðxbÞl
P y¼
¼
x
l!
ð3Þ
So, given a random sample, we can obtain the likelihood function:
LðbÞ ¼
n
X
fyi xi b exi b g
ð4Þ
i¼1
III. Data
The sample consists of 111 hotels in 3 tourist resorts
on the Catalan coast: Calella, Santa Susanna and
Lloret de Mar.2 In each of these hotels, the price of a
double room in the first week of August 2008 was
monitored throughout the period running from the
second week in January to the last week in July.
As can be seen in Fig. 1, 30.67% of the hotels
changed their price in the course of the 30 weeks they
were observed. In addition, 16% changed the price
once, 18.67% twice and 34.67% more than twice (up
to a maximum of six times).
The variables used in the study are as follows.
Dependent variables
Changes: dependent variable of the duration model that
takes the value 1 if the hotel changed the price in a given
week with respect to the previous week, and 0 if it did not.
Number of changes: dependent variable of the count
model, comprising the total number of price changes
made by each hotel over the period analysed.
Explanatory variables
Share: number of hotel rooms with respect to the
total number of rooms in that town.
Town: Lloret de Mar, Santa Susanna or Calella
The assumption of equality between the mean and the variance which gives rise to the Poisson model was rejected, for our data,
at the significance level of 5% by means of the usual test for the alpha parameter (H0: a = 0).
2
The total hotel supply in these 3 towns consists of 127 hotels.
The effect of time on hotel pricing strategy
3
35
30
25
20
Number of changes
15
10
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5
0
1
2
3
Fig. 1.
4
5
6
7
Number of price changes made by hotels over the period
Category: number of stars of the hotel (two-, threeand four-star hotels).
Week: week in which the price is under observation.
Starting price: the price set in the second week of
January for occupancy of a double room in the
first week of August.
IV. Results
Table 1 presents the descriptive statistics of the variables of the study. It shows that the price was changed
in 3.41%3 of the observations. In addition, the mean
number of price changes made by one hotel was 2.01.
Regarding the explanatory variables, the average market share of each hotel was 3.64%, whereas the average price observed in the first week (i.e. the second
week of January) was E130.25. Santa Susanna
accounted for 26.66% of the hotels in the sample,
Lloret de Mar 37.33% and Calella the remaining
36%. Finally, 6.75% of the hotels had two stars,
66.26% three stars and 26.98% four stars.
Table 2 shows the results of estimating a duration
model for the probability of a price change occurring,
without any change having taken place previously.
Note that a larger market share and a higher starting
price increase the probability of a price change in any
given week. With regard to market share, this effect
seems to indicate that hotels with a larger market
share are more professionalized and probably make
more use of tools such as yield management. As far as
the starting price of the room is concerned, a higher
starting price also may be indicative of a more aggressive price discrimination policy (expecting to gain the
maximum possible revenue from guests who book
late) that therefore requires a dynamic price policy to
also meet targets in terms of occupancy rate. Revenue
management is executed more closely on average by
hotels that price above their competitive set than by
those that price below their competitive set.
Table 1. Descriptive statistics of the variables of the study
Table 2. Duration model (dependent variable: probability of a
price change occurring (transition))
Variable
Transition
Number of changes
Share
Town
Lloret de Mar
Santa Susanna
Category
Two stars
Three stars
Four stars
Starting price
3
Mean
SD
0.034
2.01
0.036
0.181
1.906
0.030
0.266
0.373
0.442
0.483
0.067
0.662
0.269
130.25
0.251
0.472
0.443
30.77
Coefficient
Category
Three stars
Four stars
Town
Lloret de Mar
Santa Susanna
Share
Starting price
Week
Constant
log L
Z-Statistic
0.733
1.630
1.27
2.59
0.937
1.031
11.007
0.017
0.029
-6.234
-198.79
2.10
2.15
1.66
4.43
1.71
-6.34
In fact, there was a price change in 7.95% of the weeks, because, as mentioned earlier, 34.67% of the hotels made more than
one price change.
J. M. Raya
Downloaded By: [Raya Vilchez, Josep Maria][Consorci de Biblioteques Universitaries de Catalunya] At: 17:59 18 November 2010
4
Furthermore, as the weeks pass without any price
change having occurred, a price change becomes more
probable. This is a consequence of the perishable nature
of the tourism product, given that as the weeks go by
and revenues have already been maximized by charging
higher prices to guests who book ahead, the objective
becomes the maximization of the occupancy rate.
With regard to category, four-star hotels are observed
to have a lower probability than two- and three-star
hotels of making a price change at any given time, without a change having already taken place. The fact that
the supply of accommodation in the area is very specialized in two- and three-star hotels may account for this
effect, as four-star hotels have a customer segment with a
more inelastic demand, and hence require a less dynamic
price policy. Lastly, in comparison with Santa Susanna,
hotels located in both Calella and Lloret de Mar show a
higher probability of making a price change in any given
week. Again, the explanation can be found in the characteristics of the area, as Santa Susanna is more specialized in family holidays than the other two destinations,
and as such enjoys a more stable and inelastic demand in
relation to price.
Qualitatively very similar conclusions can be
reached by observing Table 3, in which we present
the results of an explanatory model of the number of
price changes made by a hotel over the period analysed. Thus, a higher starting price or being located in
Lloret de Mar or Calella as opposed to Santa Susanna
increases the number of price changes made over the
period, whereas four-star hotels (in relation to two- or
three-star hotels) register a lower number of price
changes over the period. The only difference is that
now market share is not a significant variable.
Probably the greater professionalization and stronger
market power of these hotels enables them to rapidly
capture customers with inelastic demand and, with all
the available information, reach the optimal price and
so minimize the number of price changes necessary to
capture the rest of the clientele.
Table 3. Count model (dependent variable: number of price
changes)
Coefficient
Category
Three stars
Four stars
Town
Lloret de Mar
Santa Susanna
Starting price
Share
Constant
log L
Z-Statistic
0.389
1.347
1.19
3.35
0.838
0.920
0.164
4.285
-1.719
-129.31
2.08
1.82
3.39
0.84
-2.21
Table 4. Marginal effects
Transition
Duration model
(Table 2)
Category
Three
-0.0205
stars
Four
-0.0456
stars
Town
Lloret de 0.0341
Mar
Santa
0.0341
Susanna
Share
0.0030
Starting
0.0004
price
Week
0.0008
Count model
(Table 3)
-0.6693
-2.3160
1.8005
1.8329
0.0736
0.0283
–
With the aim of obtaining quantitative effects, the
marginal effects of the two models are presented in
Table 4. The first column shows the effect of each
explanatory variable on the probability of a price
change occurring at a particular time, without one
having taken place previously (0.0288 on average).
Thus, the market share of the hotel being 1% larger
increases the probability of a price change occurring
by 0.0031, whereas the starting price of the room being
E1 higher increases this probability by 0.0005. One
week elapsing increases the probability of a price
change occurring at any given time by 0.0008. The
hotel being located in Calella or Lloret de Mar has a
greater effect on the probability of a price change
occurring (0.0341), as this effect more than doubles
the average probability of a price change occurring.
Lastly, we obtain the greatest effect for accommodation in a four-star hotel (as opposed to in a two- or
three-star hotel), for which the probability of a price
change occurring decreases by 0.0457.
The second column of Table 4 presents the marginal effects obtained through the estimates of the
count model. With an average prediction of the number of price changes of 1.72, similar results are
obtained. Thus, the increase in the number of price
changes is 1.80 (accommodation in Calella as
opposed to Santa Susanna), 1.83 (accommodation
in Lloret de Mar as opposed to Santa Susanna),
0.028 (E1 increase in the starting price) and -2.32
(accommodation in a four-star hotel as opposed to
a two- or three-star hotel).
V. Conclusion
The aim of this article was to present an empirical
model of the dynamics of room prices in a sample
Downloaded By: [Raya Vilchez, Josep Maria][Consorci de Biblioteques Universitaries de Catalunya] At: 17:59 18 November 2010
The effect of time on hotel pricing strategy
of hotels in different tourist resorts on the Catalan
coast, with a view to obtaining the impacts of their
explanatory factors. To this end, I use survival or
duration models in discrete time when the dependent variable is the time at which a price change
occurs and a count model (negative binomial) for
the number of times the hotel has changed the price
over the period observed.
The results highlight that a higher starting price
(indicative of a more aggressive price discrimination policy) increases the number of price changes
made over the period (and the probability of a
price change occurring at any given time).
However, although a larger market share increases
the probability of a price change occurring in a
particular period (reflecting a greater use of tools
such as yield management), this variable does not
affect the number of price changes made. Lastly, as
the weeks pass without any price change, the probability of a price change being made becomes
greater, thus bearing witness to the perishable nature of the tourist good.
To summarize, the results obtained provide
empirical evidence of how hotels apply techniques
with the aim of offering their guests the right product and selling it at the right price at the right
time. This is supported by price changes and by the
importance of the starting price and the time elapsing before these changes take place. Furthermore,
this behaviour varies from hotel to hotel, depending on its category and location, more dynamic
pricing being found when demand is more elastic,
either because of the destination being undifferentiated and therefore faced with keen competition or
because the hotel belongs to a category in which
price competition is very high.
Lastly, the fact that the data are for 2008, a year
marked by a serious economic crisis and a reduction
in demand for tourist services worldwide, prevents us
from discriminating what part of the results are
caused by the use of yield management and what
part corresponds to supply responding to falling
demand (what is known as ‘the dilemma of the
empty room’).
5
Acknowledgements
This research has benefited from research grant
ECO2008-06395-C05-01 from the Spanish Ministry
of Educational Science.
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