Tourism Economics

TRM 490

Dr. Zongqing Zhou

Chapter 8: Forecasting Tourism Demand

Chapter 8: Forecasting Tourism Demand (1)

• 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 decision-making

• 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:– 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

• 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 decision-making on management action options.

Chapter 8: Forecasting Tourism Demand

• Examples of the level of capacity for the physical-ecological 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 socio-psychological 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 socio-demographic 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.