Seasonality in tourism: A review of seasonality of hotel accomodation in New Zealand E-Book

Stephan Weidner

• Herausgeber: GRIN Verlag
• Sprache: Englisch

Veröffentlichungsjahr: 2009

Beschreibung

Seminar paper from the year 2006 in the subject Tourism - Miscellaneous, grade: 1,7, Stralsund University of Applied Sciences (FB Wirtschaft), course: Revenue Managment, language: English, abstract: This paper outlines the main facts about the topic “seasonality” in general with special consideration given to the hotel industry of New Zealand. The aim of this article is to recognise and understand the patterns of seasonality in this industry. For a better understanding the term “seasonality” and the different ways of measuring it are briefly described. Particular attention is paid to the definition of the relevant data sets and the capacity effect. The findings are based on reliable statistical data taken from a specialised institution in New Zealand.

Leseprobe

Table of Content

Particularities of the New Zealand tourism market

Statistical Data

Raw Data.

Definition of data sets

The calendar effect.

The capacity effect

Definition of seasons.

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Stephan Weidner

Student of Leisure and Tourism Management, University of Applied Sciences Stralsund, Germany

Abstract

This paper outlines the main facts about the topic “seasonality” in general with special consideration given to the hotel industry of New Zealand. The aim of this article is to recognise and understand the patterns of seasonality in this industry. For a better understanding the term “seasonality” and the different ways of measuring it are briefly described. Particular attention is paid to the definition of the relevant data sets and the capacity effect. The findings are based on reliable statistical data taken from a specialised institution in New Zealand.

Key words: seasonality, capacity, calendar effect, capacity effect

Introduction

Seasonality is an important factor in the tourism industry. Economically, ecologically and socially it can have many negative influences on a destination, but at the same time it may provide a time for both the environment and the host communities to recover from the stress of the peak season(s). This paper aims to show the pattern of demand regarding the hotel industry of New Zealand. Data of visitor arrivals, guest nights and room nights is analysed in order to clearly present the patterns and strength of seasonality of this industry. In this analysis, various kinds of measurements are being used and the findings are illustrated by graphs.

This paper first explains the term “seasonality” and commonly used methods to measure seasonality are briefly described. This shall give the reader a basic understanding of the topic. The paper then progresses to describe the particular characteristics of the destination New Zealand with regards to seasonality and to describe the statistical data available. The specific methods used to measure seasonality of the hotel industry sector of New Zealand are discussed, the findings are presented and conclusions are drawn.

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Seasonality

Definition

Seasonality is, according to Butler (1994) “a temporal imbalance in the phenomenon of tourism”. It can be expressed in the number of visitors, the expenditure of the visitors, the traffic on highways and other forms of transportation, the employment or in the number of admissions to attractions. Causes of seasonality may be natural or institutional. Natural causes can be variations in the weather like temperature, rainfall, snowfall, daylight and sunlight, but also the characteristics of the location such as latitude, altitude and distance from the equator or accessibility. Institutional causes on the other side are man-made, as for example actions and policies regarding culture, religion or social life as well as public holidays or specific events. The most important institutionalised factors are school holidays and industrial holidays. Apart from that, also social pressure or fashion, inertia and tradition or even sporting seasons may cause seasonality. Some of the causes are stable over a long period like Christmas holidays, some change at certain intervals such as school holidays, some vary predictably like the timing of Easter and others are unpredictable as for example the weather (Hylleberg, 1992). According to Butler and Mao (1996) there are four different patterns in which seasonality can occur:

- Single peak seasonality, which is the most common and characterised by a single, clearly identifiable and relatively fixed, time-span of peak demand,

- Two peak seasonality, characterised by two clearly identifiable and fixed time-spans of peak demand

- Non peak seasonality, where no time-span of peak demand can be identified

- Dynamic seasonality, characterised by a single or multiple time-spans of demands, which are, however, not fixed.

As for the tourism industry Hartmann (1986) stated that the “reliable and predictable recurrence of tourists has formed the economic base for the development of the tourist industry”. Therefore, he argued, tourism is naturally seasonal. Additionally, the WTO (1984) argued that the more specialised a destination is, the more seasonal it is and that large urban centres experience less seasonality due to a diversified demand.

Measurement

Seasonality can be measured in different ways. Firstly, it is necessary to decide for a category of figures. Usually, it is measured in number of visitors. Other units could be the number of arrivals or departures, the number of overnight stays, the length of stay or the expenditures of the visitors. This then needs to be measured on a daily, weekly, monthly or quarterly basis. For the measurement there are different possibilities. One is the coefficient of seasonal variation in terms of standard deviation. Other possibilities can be the seasonal ratio (the largest value divided by the average) or the seasonal indicator (the average divided by the largest value), which could be seen as the average occupancy rate. Some simple additional calculations would be the difference between maximum and minimum or the maximum divided by the minimum. One can also apply the maximum or the minimum on the total share. Another possibility is the Gini coefficient.