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Forecasting Tourism Demands Report

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Updated: Jan 14th, 2022


Globally, tourism demand depends on critical factors including:

  1. Political risks. Tourism being a hospitality industry is always associated with peace and stable politics in a tourism destination. Lack of stability in the geographical area affects negatively tourism demand as tourists shy away to vices linked to political instability.
  2. Services offered to tourists. Tourists would prefer destinations that offer a wide variety of experiences and services. These services nevertheless need to be carried out most conveniently, as tourists would engage in areas where the highest grade of hospitality is done.
  3. Currency strength of a tourist country in respect to the destination country being toured. Tourists do prefer destinations that seem cheap in terms of spending money. Tourists would invest in areas where their currency is relatively strong in comparison to the local economy to realize maximum returns in form of services offered.
  4. Infrastructure status of the destination country. Good infrastructure conditions would favor high tourism demand as these services will provide avenues for free movement and accommodation enhancing the stay of tourists.

Models Used in Tourism Demand Forecasting

To prepare adequately in achieving the full potential of the tourism sector, professionals do rely on mathematical computations in predicting future tourism demand trends based on linear and non-linear mathematical models.

  1. These models base their predictions on future tourism demands from past variables. Variables that are important in modeling will range from past tourism demand trends to factors that favor/do not favor tourism demand to a particular destination.
  2. Comparative analyses of three conventional models used in predicting tourism demand in Australia (2006-2015) with the most preferred model are discussed (Linear, Exponential and Modified exponential).

Forecasts’ using the above model yield results from the table shown below.

Linear model Exponential Modified exponential
New Zealand Japan UK New Zealand Japan UK New Zealand Japan UK
2006 1,123 684 753 1,180 708 794 9,775 574 742
2007 1,168 671 792 1,248 698 857 -11 0 0
2008 1,214 659 830 1,321 688 926 -14 0 0
2009 1,259 646 869 1,397 679 1,000 -17 0 0
2010 1,304 633 907 1,478 669 1,080 -22 0 0
2011 1,349 620 946 1,564 660 1,166 -27 0 0
2012 1,394 607 984 1,654 651 1,259 -34 0 0
2013 1,439 594 1,023 1,750 642 1,360 -43 0 0
2014 1,484 581 1,061 1,852 633 1,468 -53 0 0
2015 1,529 568 1,100 1,959 624 1,586 -66 0 0

Linear Model

The linear model is the most suitable for predicting tourism demands from the three countries. Statistically, it incorporates the past year’s average number of visitors to predict future forecasts resulting in small standard errors of results (2006-2015). Taking New Zealand as a case study, the linear model has an average tourist value of 1326±43.17, the Exponential model has 1540±82.84 tourists and the Modified exponential has 949±980.7 tourists. Taking the standard errors as a percentage of average value we have 0.033%, 0.054%, and 103.34% for linear, exponential, and modified exponential respectively. Therefore, linear model has the least standard error and therefore most accurate and appropriate for use for this purpose. The linear model integrates both sides to give a comprehensible analysis.

Exponential and Modified Exponential Model

The exponential model assumes only positive external changes whereas the modified exponential model takes into concern only the negative effects to tourists’ demands. The modified exponential model has a shortcoming in the sense that predicted values do not follow any degree of linearity, giving values that are difficult to interpret for future purposes.

Implications of Tourism Demand Forecasts

The importance of tourism demand forecasting statistics enables adequate planning in the tourism sector with the emphasis being attributed to a specific country.

New Zealand

It is the most significant country with a lot of visitors’ potential to Australia. On an average annual basis, 133 more tourists are expected every year from the country. This translates to annual growth of +8.15%. More tourism services should be introduced to this segment to achieve full growth potential.

United Kingdom

93 more tourists are expected to tour Australia from the UK translating to annual average growth of +5.69%. Based on its currency strength in comparison to Australia’s, UK tourists have a cheap avenue for tourism encouraging them to spend more on the local economy.


Japan offers the least tourism demand potential; tourists are expected to decline in number at an annual average of 63 tourists a year compared to a previous year. Japan paints a bleak picture in the annual average growth rate of tourism demand potential at -3.85%. The most probable reason for low tourism demands from Japan is the low value of the country currency in comparison to Australian currency which deters potential tourists as an expensive destination. The situation could be addressed by offering tourism services at standard international acceptable currency with discounts to Japanese tourists.


Several models are used in the tourism industry in predicting forecasts for demand. The most appropriate model depends on the availability of enough past statistical data to make conclusive predictions with a high degree of accuracy. The linear model as discussed in this paper seems the most accurate in comparison to exponential and modified exponential models.

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1. IvyPanda. "Forecasting Tourism Demands." January 14, 2022. https://ivypanda.com/essays/forecasting-tourism-demands/.


IvyPanda. "Forecasting Tourism Demands." January 14, 2022. https://ivypanda.com/essays/forecasting-tourism-demands/.


IvyPanda. 2022. "Forecasting Tourism Demands." January 14, 2022. https://ivypanda.com/essays/forecasting-tourism-demands/.


IvyPanda. (2022) 'Forecasting Tourism Demands'. 14 January.

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