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Toward a Destination Visitor Attendance Estimation Model: Whistler, British Columbia, Canada Joe Kelly, Peter W. Williams, Arlene Schieven and Ian Dunn Journal of Travel Research 2006 44: 449 DOI: 10.1177/0047287506286718 The online version of this article can be found at: http://jtr.sagepub.com/content/44/4/449

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Toward a Destination Visitor Attendance Estimation Model: Whistler, British Columbia, Canada JOE KELLY, PETER W. WILLIAMS, ARLENE SCHIEVEN, AND IAN DUNN

The nongated and multiple entry point character of many destinations makes it difficult to accurately estimate visitor attendance. This research describes a modeling procedure for credibly estimating tourist attendance in such destinations. It demonstrates the application of the approach in the mountain tourism destination of Whistler, British Columbia, Canada. This article suggests that while the model is capable of providing timely and relevant attendance estimates for destination managers, its credibility is dependent on access to a substantive base of both audited attendance data concerning anchored visitors and specific behavioral information collected on a systematic basis from footloose visitors in the destination. The findings are offered as a contribution to the growing literature on tourism destination and special event visitor attendance estimation and performance assessment. Keywords: destination performance monitoring; visitor attendance estimation; tourism modeling Reliable tourist attendance information is a core metric for evaluating the performance and competitiveness of tourism destinations (Leiper 1989). It provides a critical part of the foundation required for assessing the performance of tourism attractions, festivals, events, product development, and promotional programs (Burgen and Mules 2000). Tourism operators, destination marketing organizations, and state and community governmental organizations use tourist attendance information as an indicator in making a wide variety of planning, development, and management decisions. Despite its strategic importance, accurately measuring visitor attendance has been a challenging and problematic exercise for tourism managers for decades (Lickorish and Jeffries 1961; Dwyer and Forsyth 1996; Burgen and Mules 2000; Deville and Maumy 2004). It is not unusual for estimates of tourist traffic flows to be challenged on the basis of not only who is represented socioeconomically, geographically, and temporally in the attendance count but also how the actual visitor volume estimates are derived. In recent years, significant progress has been made in establishing credible methods of capturing accurate visitor flow information at international and national scales (Lavelleee 2002; Deville and Maumy 2004). Similarly, more systematic methods for accurately assessing attendance levels at gated events have been advanced (Burgen and Mules 1992; Tyrrell, Williams, and Johnston 2004). However, equally rigorous methods of estimating visitor attendance at ungated tourism destinations have only recently been proposed (Tyrrell

and Johnston 2003). It is in this context that the following model for estimating destination tourism visitor attendance has been prepared. This article’s purpose is to outline the structure of this multifaceted model and illustrate its application in the context of a relatively large tourism destination. For the purposes of this article, tourist destinations are defined as geographically defined communities that purposely focus their management activities on attracting visitors for various types of visitor experiences (Ritchie and Crouch 2003). The findings are offered as a contribution to the growing literature on tourism destination and special event visitor attendance estimation and performance assessment.

THE CHALLENGES OF VISITOR FLOW ESTIMATION IN TOURISM DESTINATIONS It is not surprising that systematic approaches for estimating visitor flows have not been developed for tourism destinations. Given the wide range of destination suppliers generating and hosting visitors, the diverse range of visitors attracted, and the varied set of gated and ungated venues used for their pursuits, estimating overall tourist attendance is challenging. However, a range of strategies has traditionally been used at predetermined times and locations to provide ballpark estimates of overall visitor attendance. These Joe Kelly (MSc Statistics) is a doctoral candidate in the School of Resource and Environmental Management at Simon Fraser University, British Columbia, Canada. His research interests focus on the development of planning and management modeling processes for tourism destinations. Peter W. Williams (PhD Natural Resource Management) is the director of Simon Fraser University’s Centre for Tourism Policy and Research in British Columbia, Canada. His research focuses on the development of management and planning strategies that foster more sustainable forms of tourism development. Arlene Schieven is the vice president of marketing at Tourism Whistler in Whistler, British Columbia, Canada. Her role is to oversee the development and execution of all marketing programs designed by Tourism Whistler, to drive tourism business to the resort. Ian Dunn is the manager of research at Tourism Whistler, British Columbia, Canada. His role is to track and analyze key business indicators, consumer behavior, and competitive intelligence to provide a detailed understanding of Whistler’s performance as a tourism destination. Journal of Travel Research, Vol. 44, May 2006, 449-456 DOI: 10.1177/0047287506286718 © 2006 Sage Publications

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strategies include using aerial photographs, electronic cordon counts, direct observation tallying schedules, sampling surveys of visitor use patterns, and expert estimates of site or facility utilization (Raybould et al. 2000). Each of these methods is capable of providing snapshot perspectives on specific dimensions of destination attendance. However, no single measurement technique provides a bulletproof approach for measuring attendance. Given the complex and dynamic character of destinations, more robust methods employing multiple lines of evidence are needed to generate credible estimates of attendance. Modeling systems incorporating a combination of auditable visitor count information, augmented with appropriately weighted data from footloose destination visitors, offer a potentially cost-effective and useful approach to overcoming the limitations of traditional estimation procedures. Several visitor flow estimation models have been developed for application in other tourism contexts, but have not been incorporated into a robust destination visitor flow estimation system (e.g., Deville and Maumy 2004; Tyrrell, Williams, and Johnston 2004; Lundgren 2004). Rigorous methods have only recently been developed for estimating visitor attendance at ungated tourism destinations with multiple entry points (Tyrrell and Johnston 2003). This article contributes to the growing literature on visitor attendance estimation by describing a unique model that combines specific information from multiple visitor information sources to generate its estimates of destination visitor attendance. It also outlines approaches for overcoming several of the challenges identified as being typically associated with accurately estimating visitor attendance in a nongated tourism context. These challenges include the following: • defining those attendees included in the estimate of destination visitor attendance; • establishing reliable and credible visitor counting procedures; • identifying representative visitor sampling sites; • identifying systematic visitor sampling procedures; • incorporating survey questions that capture relevant visitor attendance estimation patterns; and • creating an attendance estimation system that provides timely, credible, and relevant information for tourism destination decision makers (Research Resolutions and Consulting Ltd. 2004).

WHISTLER CASE STUDY The tourism destination of Whistler, British Columbia, is used to illustrate an application of the visitor attendance estimation model. Whistler, British Columbia, is one of North America’s premier resort destinations. Within its 12-km length are numerous lakes, wetlands, and alpine areas that are home to a diverse range of recreation venues, accommodation units, and hospitality facilities. The destination’s year-round tourism system is managed by a legally mandated organization— Tourism Whistler. This organization coordinates the marketing and promotion of the destination’s commercial tourism operations. It is responsible for its performance to a board of directors comprising tourism and community members. Tourism Whistler provides its board of directors with several performance indicators, including estimates of overall visitor flows. To do this in a credible fashion, it has developed an

innovative destination flow estimation model that integrates information collected from a range of sources. The Visitor Volume Model estimates the number, mix, and timing of visitor arrivals. The following discussion describes the model’s design and operation as it relates to addressing the overriding challenges of estimating attendance in a tourism destination context.

Rationale and Application The Visitor Volume Model provides Tourism Whistler with an effective tool for describing visitor attendance at the destination. Tourism Whistler uses this tool to track historical trends in the number, timing, and mix of visitors to the resort and to inform more strategic and long-term destination decision making. The system estimates not only absolute visitor numbers but also the mix of visitors and timing of visitor arrivals. These outputs provide Tourism Whistler and its stakeholders with valuable information about the destination’s overall use patterns and trends in peak and shoulder season visitor flows. Such information is of value not only to Tourism Whistler but also to the Resort Municipality of Whistler, which uses this intelligence to help shape some of its infrastructure capacity development decisions.

Attendee Universe Based on Tourism Whistler’s specific mandate and objectives, the model focuses on estimating visitor flows for four primary target groups: commercial accommodation visitors, day-use visitors, visitors staying in second homes, and visitors staying with friends and family. Visitors in this context include anyone visiting the destination from outside of the immediate region.

Multiple Information Sources Because of the diverse range of visitor types and on-site behaviors, the model integrates two main sources of data for its attendance calculations. The first source is aggregate monthly data concerning attendance. These data are collected from many of the destination’s hotels and include information on room nights sold, average length of stay, and average number of people per room. These data are used to calculate the number of commercial accommodation visitors at the destination. These data represent the anchored and core audited foundation on which inferences to the larger population of visitors at the destination are based. The second source is monthly survey data randomly obtained from footloose visitors during their visit to Whistler. Although the survey collects a wide spectrum of information, of particular importance to the model is the information extracted concerning the type of accommodation used and length of stay of these visitors.

Aggregate Hotel Data The first source of data used by the model is aggregate hotel data collected through Tourism Whistler’s Commercial Accommodation Survey (CAS). These data are used to produce a credible baseline estimate of visitor volume information for commercial accommodation visitors. The CAS was developed by Tourism Whistler to track the performance of the resort’s tourism accommodation sector. Surveys are sent on

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JOURNAL OF TRAVEL RESEARCH 451

a monthly basis via e-mail to CAS participants, who represent 75% of all rooms available in Whistler for nightly rental. Participants complete a form that tracks their property’s monthly results in the following categories: room nights sold, room nights occupied, average daily rate, rooms available, area of origin of guests and traveler type (independent traveler, tour and travel, conference and group). Tourism Whistler conducts a series of checks on these data to ensure that they are internally consistent and in line with historical performance. For instance, each participant’s data are audited to make sure that the reported total number of room nights sold equals the sum of room night sales for each area of origin and traveler type. Checks are also made to ensure that monthly occupancy rates are in line with past trends and consistent with other hotels. Tourism Whistler then adds the CAS results to an Excelbased model, which combines each participant’s data to develop a representative sample of resort performance on a monthly basis. The sample totals are then scaled up to develop an estimated total for the entire resort. Using the data collected, Tourism Whistler can estimate total room nights sold, occupancy, average daily rate, revenue per available room, area of origin, and traveler type for the entire resort. These results are compared to previous years to provide an understanding of Whistler’s current performance and develop long-term forecasts for the tourism accommodation sector. The specific results are published in a monthly report that is provided to all participants in the CAS program. In addition, general results are available to the public, media, and interested third parties.

in Whistler for more than 21 days, and not be part of a traveling party with more than 10 people. As well, only 1 member of each traveling party is allowed to complete the survey. Small incentives are also provided to survey participants to enhance response rates. While these procedures help to ensure a representative sample, some systematic biases may still be present. For instance, no controls are currently in place to sample non-English-speaking visitors. Future improvements to the surveying and sampling method, such as employing multilingual interviewers on a consistent basis, would help address this potential bias.

User-Friendly Application The Visitor Volume Model utilizes a user-friendly Microsoft Excel software platform to generate its estimates of destination attendance. The system requires limited data entry by the user and automates all calculation and report generation procedures. Tourism Whistler’s research manager uses the model to assess visitation flows to the destination at the end of the winter and summer seasons (in April and October, respectively). The model is particularly user-friendly, requires little adjustment or calibration to capture shifts in visitor attendance patterns during each season, and provides quick and credible estimates of overall visitor flows. However, as the destination has expanded in the diversity of its visitor markets, attractions, and development nodes, so has its need for more detailed and specific consumer behavior information. The Visitor Volume Model currently lacks the fine detail in visitor estimates needed for such targeted marketing decisions.

Visitor Survey Data The second source of data used by the Visitor Volume Model is obtained from Tourism Whistler’s visitor intercept surveys. These surveys are designed to capture key behavioral characteristics of footloose visitors in the destination. They are conducted at high-traffic strategic locations throughout the destination. The data collected are used to establish ratios of commercial accommodation attendance to other types of visitation (i.e., day-only visitors, visitors staying in second homes, and visitors staying with friends or family). These ratios are then used to adjust the overall baseline attendance level derived from hotel registrations upward to account for other types of visitors. The intercept surveys are systematically conducted on a daily basis by interviewees trained and directed by Tourism Whistler staff. At least 15 surveys are conducted per day in both the winter (December 1 to April 30) and summer (June 1 to October 15) seasons. During the winter season, visitors are intercepted and surveyed at two high-traffic ski lodges on Whistler and Blackcomb Mountains. Interviewing occurs on alternate days at these two sites throughout the season. During the summer season, surveying occurs at highly frequented locations within the destination’s primary commercial village zones. It is assumed that in the winter and summer seasons, visitors will have a high likelihood of visiting these areas at least once during their trip to the destination. Several screening criteria are used to ensure that the sample of survey responses is representative of the overall visitor population. In particular, survey participants must be 18 years or older, be living outside of the immediately surrounding Pemberton to Squamish travel corridor, not be staying

VISITOR VOLUME MODEL ESTIMATION PROCEDURES A more detailed description of the Visitor Volume Model’s structure, data integration, and attendance estimation procedures is provided in the following sections.

Accommodation Attendance Estimation The comprehensive and reliable information collected from Tourism Whistler’s CAS is used as the starting point for attendance estimation. It is adjusted upward to reflect estimated commercial accommodation usage in those rental properties not participating in the CAS. The magnitude of this adjustment is derived from Tourism Whistler’s visitor intercept surveys, which specify the relative proportion of visitors using various forms of accommodation. The model’s procedure for estimating the number of visitors staying in paid accommodation is expressed as follows: VisitorsP =

Room Nights Sold × Avg People Per Room Avg Length Of Stay

(1)

The total number of visitors staying in paid accommodation is then converted to a measure of the average number of visitors staying in paid accommodation per day: Avg Visitors Per DayP =

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VisitorsP × Avg Length Of Stay 365 Days

(2)


452

MAY 2006 TABLE 1 ANNUAL NUMBER OF VISITORS AT WHISTLER

Year 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04

Day Only 360,067 393,914 481,100 660,953 864,348 567,957 637,414 677,098 540,419

(26%) (26%) (27%) (31%) (37%) (27%) (31%) (35%) (30%)

Second Home 42,556 46,165 71,002 86,697 59,120 58,316 63,293 32,637 43,363

Friends and Relatives

(3%) (3%) (4%) (4%) (3%) (3%) (3%) (2%) (2%)

89,887 81,177 123,739 181,396 155,143 130,698 134,912 112,493 109,509

Other Attendee Visitation Estimation Beyond patrons staying in commercial accommodation, there are other visitors at the resort. These include day-only visitors, visitors staying in second homes, and visitors staying with friends and family. Estimates of their presence in the destination are calculated by multiplying the estimates of the average number of visitors staying in paid accommodation per day by the ratio of survey respondents of visitor type k to survey respondents staying in paid accommodation. For example, assume that the hotel data revealed that there were on average 10,000 visitors staying in paid accommodation per day during the course of a season. Also assume that the survey data indicated that for every 100 footloose respondents intercepted, 75 stayed in paid accommodation, 10 stayed with friends and relatives, 5 stayed in second homes, and 10 were day-only visitors. Then the number of visitors that stayed with friends and relatives per day is calculated by multiplying 10,000 by the ratio of 10 to 75, giving an estimate of about 1,300 visitors per day. Such ratio-based approaches to visitor flow estimation have been used in other more macro-level tourism contexts and have been modified for application in a destination context (Denton and Furse 1993; Leiper 1989; Lavellee 1995, 2002). The general estimation procedure used for calculating the volume of these noncommercial accommodation visitors is expressed as follows: Avg Visitors Avg Visitors Num Survey Respondentsk = × Per Dayk Per DayP Num Survey RespondentsP

(3)

The number of visitors of type k was then estimated by multiplying the average number of visitors staying at the resort per day by 365 days, and dividing by the average length of stay: VisitorsP =

Avg Visitors Per Dayk × 365 Days Avg Length Of Stayk

(4)

where Avg Length of Stayk is the average length of stay of visitor type k as reported by survey respondents. Although ratio-estimation methods (like the one used in Equation 3) produce statistically biased estimates of true population totals, they often generate estimates that have much lower variance and therefore greater accuracy than those produced by simple inflation methods (Levy and Lemeshow, 1991). Ratio-based methods are particularly favored when

(7%) (5%) (7%) (9%) (7%) (6%) (7%) (6%) (6%)

Paid Accommodation 875,200 983,222 1,098,078 1,197,731 1,235,415 1,308,709 1,238,359 1,125,530 1,126,743

(64%) (65%) (62%) (56%) (53%) (63%) (60%) (58%) (62%)

Total 1,367,710 1,504,478 1,773,919 2,126,777 2,314,026 2,065,680 2,073,978 1,947,758 1,820,034

there is high correlation between the two variables used to develop the ratio. This is certainly the case in Equation 3, where the average number of visitors staying in paid accommodation per day is highly correlated with the average number of day-only visitors, visitors staying in second homes, and visitors staying with friends and family.

Model Application Tourism Whistler uses the information generated by the Visitor Volume Model to inform its stakeholders of the changing character of the area’s performance with respect to overall attendance volume and market mix. Tables 1 and 2 provide an illustration of the type of output generated by the model for the period from 1995-96 to 2003-04. The estimates are for annual reporting periods extending from May of one year to April of the next. They cover Whistler’s summer season (May to October) and winter season (November to April). The model generates outputs for both the total number of visitors (Table 1) and the average number of visitors per day at the resort (Table 2). Both these metrics are useful for tracking trends in overall resort attendance. Whereas the total number of visitors is a valuable measure for assessing the impact of various decisions and events on overall destination performance, the average number of visitors per day is beneficial for assessing carrying capacity issues. The model is also capable of vividly illustrating the impact of various external events on the destination’s performance. For instance, the collateral damage to tourism flows created by the highly promoted Millennium Y2K computer meltdown scare, the 9/11 North American terrorism strike, and the global SARS outbreak can be mapped against seasonal and yearly shifts in visitor volumes (Figure 1). Such estimations help Tourism Whistler and its partners contextualize the full impact of these events on the destination’s overall visitor performance in specific years. Other Tourism Whistler models are designed to identify the effectiveness of various marketing programs designed to stimulate growth in various markets after these events.

Model Validation To test the validity of the Visitor Volume Model, its outputs were compared to four existing data sources: annual hotel tax revenue, total skier visits, total golf rounds, and annual traffic counts (Resort Municipality of Whistler 2004). The

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JOURNAL OF TRAVEL RESEARCH 453 TABLE 2 ESTIMATED AVERAGE NUMBER OF VISITORS PER DAY AT WHISTLER Year

Day Only

1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04

984 1,079 1,318 1,811 2,362 1,556 1,746 1,855 1,477

Second Home

(10%) (11%) (11%) (12%) (16%) (11%) (12%) (14%) (12%)

484 543 770 1,056 743 703 766 557 671

Friends and Relatives

(5%) (6%) (6%) (7%) (5%) (5%) (5%) (4%) (5%)

1,102 1,023 1,583 2,525 1,993 1,675 1,701 1,504 1,405

Paid Accommodation

(12%) (10%) (13%) (17%) (13%) (11%) (12%) (12%) (11%)

6,827 7,175 8,225 9,536 9,861 10,850 10,010 9,138 8,861

(73%) (73%) (69%) (64%) (66%) (73%) (70%) (70%) (71%)

Total 9,397 9,820 11,896 14,928 14,959 14,784 14,223 13,054 12,414

FIGURE 1 PERCENTAGE CHANGE IN NUMBER OF VISITORS AT WHISTLER

50.0% 40.0% Millenium % Change from Previous Year

30.0% 20.0% 10.0% 0.0% −10.0% −20.0% −30.0% −40.0% September 11

SARS Nov-03

May-03

Nov-02

May-02

Nov-01

May-01

Nov-00

May-00

Nov-99

May-99

Nov-98

May-98

−50.0%

Month

first source, hotel tax revenue, is the 2% share that the Resort Municipality of Whistler receives from the 10% hotel tax collected from all hotel room revenues under the provincial Hotel Room Tax Act. These revenues give an accurate measure of the demand for commercial accommodation in Whistler. In the eight-year period from 1996 to 2003, there was a 0.90 correlation between hotel tax revenue and the average number of visitors staying in paid accommodation (from the Visitor Volume Model). Although some discrepancies between these two series were expected because of changes in room rates, the overall trends were consistent (Figure 2). The data on total skier visits and total golf rounds give reliable measures of visitor attendance to key winter and summer attractions in Whistler. In the nine winter seasons from 1995/96 to 2003/04, there was a 0.93 correlation between total skier visits and the average number of visitors per day at Whistler (from the Visitor Volume Model). In the nine summer seasons from 1995 to 2003, there was also a 0.93

correlation between total golf rounds and the average number of visitors per day. The similar trends observed between the model’s outputs and these data series give external validation that the model produces consistent and reliable results (Figures 3 and 4). The traffic count data were obtained from the British Columbia Ministry of Transportation’s permanent count station located on Highway 99 ten kilometers north of Squamish, British Columbia (this station is approximately 50 km south of Whistler). These traffic counts are considered highly correlated with visitor volumes because the vast majority of visitors who travel to Whistler use this highway to access the resort. In the 7-year period from 1996 to 2002, there was a 0.87 correlation between the average annual daily traffic count and the average number of visitors per day at Whistler (from the Visitor Volume Model). Although some minor discrepancies exist (particularly in 2002), the overall trends in these two data series are remarkably consistent (Figure 5).

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MAY 2006 FIGURE 2 COMPARISON OF VISITOR VOLUME MODEL TO HOTEL TAX DATA

$4,500,000

140,000

$4,000,000 120,000 $3,500,000 100,000 $3,000,000 80,000

$2,500,000 Hotel Tax $2,000,000

60,000

Hotel Tax Revenue

Number of Visitors per Day at Paid Accommodations

Visitor Volume Model

$1,500,000 40,000 $1,000,000 20,000 $500,000

0

$0 1996

1997

1998

1999

2000

2001

2002

2003

FIGURE 3 COMPARISON OF VISITOR VOLUME MODEL TO SKIER VISITS DATA

120,000

2,500,000

Skier Visits 100,000

80,000 1,500,000

Visitor Volume Model 60,000

1,000,000 40,000

500,000 20,000

0

0 Winter 1995/96

Winter 1996/97

Winter 1997/98

Winter 1998/99

Winter 1999/00

Winter 2000/01

Winter 2001/02

Winter 2002/03

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Winter 2003/04

Whistler-Blackcomb Skier Visits

2,000,000 Number of Visitors per Day at Whistler

454


JOURNAL OF TRAVEL RESEARCH 455 FIGURE 4 COMPARISON OF VISITOR VOLUME MODEL TO GOLF ROUNDS DATA

120,000

90,000 Visitor Volume Model 80,000

80,000

60,000 Golf Rounds 50,000

60,000 40,000

Total Golf Rounds

Number of Visitors per Day at Whistler

100,000 70,000

40,000

30,000

20,000 20,000 10,000

0

0 Summer Summer Summer Summer Summer Summer Summer Summer Summer 1995 1996 1997 1998 1999 2000 2001 2002 2003

FIGURE 5 COMPARISON OF VISITOR VOLUME MODEL TO TRAFFIC COUNT DATA

200,000

9,000 Traffic Count

8,000

Number of Visitors per Day at Whistler

160,000

7,000

140,000 6,000 Visitor Volume Model

120,000

5,000 100,000 4,000 80,000 3,000 60,000

Average Annual Daily Traffic Count

180,000

2,000

40,000

1,000

20,000 0

0 1996

1997

1998

CONCLUSIONS The Visitor Volume Model provides a relatively robust method for addressing some of the challenges currently

1999

2000

2001

2002

associated with monitoring destination tourism attendance. While the model does identify the types of information that can be collected and used effectively to improve destination attendance estimates, it does not have explanatory capabilities. As a result, it can only suggest correlations between

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456

MAY 2006

management measures taken by Tourism Whistler to influence visitor attendance flows, as opposed to identifying explicit cause-and-effect relationships. In a Whistler context, the model is considered to be a relevant, credible, and costeffective method of monitoring visitor flows. It has become an important tool in Tourism Whistler’s performance-monitoring program. While the model provides an innovative framework for establishing such attendance estimation systems in other destinations, it is quite possible that its data requirements will be difficult to meet in other places. The model’s operation is built on a foundation of audited commercial accommodation visitor volumes. While provision of the visitor visitation data is voluntary for commercial accommodation providers in the destination, most of them provide their information because of the beneficial information the model generates (only participants who provide their data receive the confidential estimates). The involvement of these commercial interests for about a decade in providing their proprietary data attests to the value and credibility of the model’s outputs. Unless other destination management organizations have ready access to such data through hotel tax requirements, they may not be able to obtain this critical input information. While the responsibility for collecting source data is acknowledged as being important to measuring performance, most commercial accommodation providers consider such information proprietary and do not divulge their visitation figures. Similarly, Tourism Whistler has made a conscious investment in acquiring visitor survey information in a systematic and comprehensive fashion to better understand the characteristics of other visitors that are needed for calibrating the Visitor Volume Model. This requires a commitment of human and fiscal resources for such data collection. Many destinations do not have the budgetary resources or systems in place to collect such information. However, Tourism Whistler’s positive experience with respect to utilizing the model’s output for planning and management purposes makes funding such data collection worthwhile. Of course, there is always the need for more detailed visitor travel behavior information that would make the model more robust and useful for other applications, but such expenditures are not warranted given the current needs of the organization. Other destinations might follow Tourism Whistler’s approach to rationalizing how much data collection is warranted, given the precision needed to meet their objectives. Given the growing global competitiveness for destination travel markets, the availability of credible and relevant attendance volume information is an increasingly important input into the strategic decision-making activities of destination management organizations. While there is considerable variation in the capacity of tourism destinations to make use of a common modeling system, the Visitor Volume Model provides a framework for constructing a credible and useful approach for putting together the pieces needed. Future research initiatives that would help to move destination visitor attendance estimation procedures forward include the following:

• revealing the sensitivity of changes in commercial accommodation use to shifts in attendance levels at other destination visitor venues; • establishing more efficient and effective survey and sampling methods of acquiring the relevant information needed for modeling purposes; • enhancing methods to model visitor flows for more detailed market segments; and • incorporating cause-and-effect relationships to forecast the impacts of key planning and management decisions and other events on visitor flows.

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• determining other key data sources (beyond commercial accommodation data) that are available as a base for model estimations;

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Toward a Destination Visitor Attendance Model