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Tourism Economics
Chapter 8: Forecasting Tourism
Demand
TRM 490
Dr. Zongqing Zhou
Chapter 8: Forecasting Tourism
Demand (1)
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The importance of tourism forecasting:
– Competition makes marketing an essential role for competitive
advantage.
– Marketing involves large sums of money
– Marketing is largely based on estimate of future demand and marketing
penetration
– Forecasting provides inputs for decision-making.
The objective of forecasting: predict future events or conditions for decisionmaking
Definition of forecasting: a systematic process involving several steps:
– Data collection (historical data, facts)
– Analysis of changes in past demand trends and differences between
previous forecasts and actual behavior
– examination of factors likely to affect future demand
– Forecasting for some future period
– Monitoring the accuracy and reliability of the forecast
– Revising it as needed.
The most common method of forecasting is based on historical data.
Chapter 8: Forecasting Tourism Demand
• This chapter outlines several quantitative
forecasting methods that make sure of such
historical data. An analysis of the time series
involves the breaking down of past data into four
major components:
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trend
Seasonality
Business cycles
Random variations
• This chapter also gives a brief description of the
accuracy measures that help to evaluate the
suitability of a given model to a known situation
Chapter 8: Forecasting Tourism Demand(2)
Quantitative Forecasting Methods:
Time Series methods (see p. 157)
Trend
– Naïve Method: use the latest historical data point to predict the future
– Simple Moving average (SMA): An un-weighted average of a
consecutive number of data points
– Weighted moving average (WMA): A weighted average o a
consecutive number of data points
– Exponential smoothing Methods: A time-series technique that uses a
weighting factor to develop a forecast. This is comparable to a weighted
moving average technique in which more weight is given to recent data.
Ti give s some influence to all data points, requires minimal data, and
has more flexibility
Causal methods:
Quantitative forecasting methods based upon the mathematical
relationship between the series being examined and those variables
which influence or explain that series.
Chapter 8: Forecasting Tourism Demand (3)
• Forecasting methods (cont.)
– Regression:: A means of fitting an equation to a set of data
– Time series: A set of historical data obtained at regular intervals
– Times Series Regression: Type of regression that develops the
equation from data over a period of time.
– Seasonality: A basic pattern that is often found in an data series.
It indicates movements in a data series during a particular
period in a year (wee, month, or quarter) that repeat year after
year. This effect may be caused by climate/weather, social
customs/holidays, and business policies.
– Business cycles: The up and down movement in business
activities around the long-term trend.
– Trend: A basic pattern found in a data series over a period of
time. The trend may show an increase, a decrease, or a steady
pattern that remains horizontal.
Chapter 8: Forecasting Tourism Demand
• Seasonality: movements in a series during a particular
time of the year/week/month/quarter that recur year after
year.
– It is a result of
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Climate
Social customs
Holidays
Business policy considerations
• Cycles: patterns in the data that occur every few years.
– General economic conditions
– Saving and consumption habits
• Random Variables: variations that can’t be tied to the
trend or the cyclical components
– Unusual events that do not repeat themselves.
Chapter 8: Forecasting Tourism Demand
• Qualitative and Quantitative Forecasting
Methods:
– Qualitative: relies on expert opinion. Can use
technological method or judgmental method
– Quantitative: use statistical tools to examine
data in order to discover underlying patterns
and relationships. Can use the time series
and causal methods.
Chapter 8: Forecasting Tourism Demand
• Data Collection
– Primary data: data collected for the first time for the
purpose of a specific research project
– Secondary data: existing data collected by other
people for their own original purposes and you are
using all or part of it for your own purpose now.
• Population of interest (population for the
research study): The group of people your
research project is interested in and the findings
of the your research may be applied to
Chapter 8: Forecasting Tourism Demand
• Sampling Frame: the list of all the people
of the population of interest
• Sample: a subgroup from the population of
interest that you select for your research
study.
• Sampling: the procedure in which you
select your sample from the population of
interest.
Chapter 8: Forecasting Tourism Demand
• Sampling Methods:
– Simple Random Sampling: random and
representative of the population
– Systematic random sampling, every nth.
– Stratified Sampling: break the population into
meaningful subgroups, reducing the costs
without sacrificing accuracy and ensuring
representation
• Sample size Based on statistical needs
and budget
Chapter 8: Forecasting Tourism Demand
• Sampling error (for more statistical terms, click here):
– In statistics, when analyzing collected data, the samples
observed differ in such things as means and standard deviations
from the population from which the sample is taken. This is
sampling error and is controlled by ensuring that, as much as
possible, the samples taken have no systematic characteristics
and are a true random sample from all possible samples. If the
observations are a true random sample, statistics can make
probability estimates of the sampling error and allow the
researcher to estimate what further experiments are necessary
to minimize it. The larger the sample size and the smaller the
sample error (see Fig. 8-1, p 154). You can calculate sampling
error here
Chapter 8: Forecasting Tourism Demand
• A biased sample:
– is a statistical sample of a population where some members of
the population are less likely to be included than others. An
extreme form of biased sampling occurs when certain members
of the population are totally excluded from the sample (that is,
they have zero probability of being selected). For example, a
survey of visitors to Niagara Falls in July to measure monthly
visitor spending in Niagara Falls will be a biased sample
because it does not include home schooled students or
dropouts. A sample is also biased if certain members are
underrepresented or overrepresented relative to others in the
population. For example, a "man on the street" interview which
selects people who walk by a certain location is going to have an
over-representation of healthy individuals who are more likely to
be out of the home than individuals with a chronic illness.
Chapter 8: Forecasting Tourism Demand
• Linear Trend Smoothing (Linear Regression or time series
regression):
– Plotting a series of past data in a graph, it is often possible to draw a
line that is the closest to the data points. If this is a straight line, it is
called a linear trend (regression)
– Regression is the study of relationships among variables, a principal
purpose of which is to predict, or estimate the value of one variable from
known or assumed values of other variables related to it.
– Variables of Interest: To make predictions or estimates, we must
identify the effective predictors of the variable of interest: which
variables are important indicators? and can be measured at the least
cost? which carry only a little information? and which are redundant?
– Predicting the Future Predicting a change over time or extrapolating
from present conditions to future conditions is not the function of
regression analysis. To make estimates of the future, use time series
analysis.
– Simple Linear Regression: A regression using only one predictor is
called a simple regression.
– Multiple Regressions: Where there are two or more predictors,
multiple regressions analysis is employed.
Chapter 8: Forecasting Tourism Demand
• Capacity Planning For Tourist Facilities
and Services
– Queuing theory
• The term Queuing Theory is often used to describe
the more specialized mathematical theory of
waiting lines.
• The study of the phenomena of standing, waiting,
and serving.
Chapter 8: Forecasting Tourism Demand
• For example, queuing requirements of a
restaurant will depend upon factors like:
– How do customers arrive in the restaurant? Are
customer arrivals more during lunch and dinner time
(a regular restaurant)? Or is the customer traffic more
uniformly distributed (a cafe)?
– How much time do customers spend in the
restaurant? Do customers typically leave the
restaurant in a fixed amount of time? Does the
customer service time vary with the type of customer?
– How many tables does the restaurant have for
servicing customers?
Chapter 8: Forecasting Tourism Demand
• The above three points correspond to the most
important characteristics of a queueing system.
They are explained below:
– Arrival Process
• The probability density distribution that determines the
customer arrivals in the system.
– Service Process
• The probability density distribution that determines the
customer service times in the system.
– Number of Servers
• Number of servers available to service the customers.
Chapter 8: Forecasting Tourism Demand
– Carrying Capacity
• usually refers to the biological carrying capacity of
a population level that can be supported for an
organism, given the quantity of food, habitat, water
and other life infrastructure present.
• Carrying capacity is thus the number of individuals
an environment can support without significant
negative impacts to the given organism and its
environment.
Chapter 8: Forecasting Tourism Demand
• Carrying capacity considerations revolve around three
basic components or dimensions:
– physical-ecological
– socio-demographic
– political-economic
• These dimensions also reflect the range of issues
considered in practice. Obviously, when considering
carrying capacity the three components should be
considered with different weights (of importance) in
different destinations. These differences stem from the
type (characteristics/particularities) of the place, the
type(s) of tourism present (coastal, protected, rural,
mountain, historical) and the tourism/environment
interface. However, the three components are
interrelated to some extent
Chapter 8: Forecasting Tourism Demand
• A. Physical-ecological component
– The physical-ecological set comprises all fixed and flexible
components of the natural and cultural environment as well as
infrastructure. The fixed components refers to the capacity of
natural systems. Occasionally, it is expressed as ecological
capacity, assimilative capacity, etc. The components cannot be
manipulated easily by human interference. The limits can be
estimated, they should be carefully observed and respected as
such. The flexible components refer primarily to infrastructure
systems like water supply, sewerage, electricity, transportation,
social amenities such as postal and telecommunication services,
health services, law and order services, banks, shops and other
services. The capacity limits of the infrastructure components
can rise through investments in infrastructure, taxes,
Organizational -regulatory measures, etc. For this reason their
values cannot be used as a basis for determining carrying
capacity but rather as a framework for orientation and decisionmaking on management action options.
Chapter 8: Forecasting Tourism Demand
• Examples of the level of capacity for the physicalecological component (EC, 2002)
– Acceptable level of congestion or density in key areas/spatial
units such as parks,museums, city streets, etc.;
– Maximum acceptable loss of natural resources (i.e. water or
land) without significant degradation of ecosystem functions or
biodiversity or the loss of species;
– Acceptable level of air, water and noise pollution on the basis of
tolerance or the assimilative capacity of local ecosystems;
– Intensity of use of transport infrastructure, facilities and services;
– Use and congestion of utility facilities and services of water
supply, electric power, waste management of sewage and solid
waste collection, treatment and disposal and
telecommunications;
– Adequate availability of other community facilities and services
such as those related to public health and safety, housing,
community services, etc.
Chapter 8: Forecasting Tourism Demand
• B. Socio-demographic component
– The socio-demographic set refers to those social aspects which
are important to local communities. They relate to the presence
and growth of tourism. Social and demographic issues, such as
available manpower or trained personnel, etc. Also including
socio-cultural issues such as the sense of identity of the local
community or the tourist experience etc. Some of these can be
expressed in quantitative terms but most require suitable sociopsychological research. Social capacity thresholds are perhaps
the most difficult to evaluate as opposed to physical-ecological
and economic ones since they depend to a great extent on value
judgments. Political and economic decisions may affect some of
the socio-demographic parameters such as, for example
migration policies. Social carrying capacity is used as a generic
term to include both the levels of tolerance of the host population
as well as the quality of the experience of visitors of the area.
Chapter 8: Forecasting Tourism Demand
• Examples of the level of capacity for the sociodemographic component
– Number of tourists and tourist/recreation activity types which can
be absorbed without affecting the sense of identity, life style,
social patterns and activities of host communities;
– Level and type of tourism which does not significantly alter local
culture in direct or indirect ways in terms of arts, crafts, religion,
ceremonies, customs and traditions;
– Level of tourism that will not be resented by a local population or
pre-empt their use of services and amenities;
– Level of tourism (number of visitors and compatibility of types of
activities) in an area without unacceptable decline of experience
of visitor.
Chapter 8: Forecasting Tourism Demand
• C. Political-economic component
– The political-economic set refers to the impacts of
tourism on the local economic structure, activities, etc.
, including competition to other sectors. Institutional
issues are also included to the extent that they
involve local capacities to manage the presence of
tourism. Considerations of political-economic
parameters may also be necessary to express
divergence in values and attitudes within the local
community with regard to tourism.
Chapter 8: Forecasting Tourism Demand
• Examples of the level of capacity for
the political-economic component (EC,
2002)
– Level of specialization in tourism;
– Loss of human labour in other sectors due to
tourism attraction;
– Revenue from tourism distribution issues at
local level;
– Level of tourism employment in relation to
local human resources.