Factors Influencing Visitor's Choices of Urban Destinations in North ...

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Attraction Elasticities for Ottawa and TorontoThis section of the report focuses on the range of attraction elasticities for Toronto andOttawa that were found to be significant in our previous analysis. These elasticity valuesgive a sense of the impact of adding an attraction of the listed type relative to the existingattraction base and visitor count in each city. For example, a 1% increase in the numberof amusement parks (Q3) would yield a 0.94% to 1.48% increase in visitations to Ottawadepending on what model is used. The elasticity measure for Toronto would be less.Table 20: Attraction Elasticities for Toronto and OttawaType of Attraction Ottawa TorontoAmusement Parks (Q3) 0.94 ≤ ap ≤1.48 0.33 ≤ ap ≤ 0.52Shopping Areas (Q3) 0.13 ≤ sa ≤ 0.17 0.05 ≤ sa ≤ 0.06Specific Structures (Q3) 0.21 ≤ ss ≤ 0.60 0.22 ≤ ss ≤ 0.63Casinos (TC) 0.08 ≤ ca ≤ 0.09 ca = 0.03Popular Entertainment (Q2) pe = 0.11 pe = 0.04Popular Entertainment (Q1) pe = 0.13 pe = 0.04Shopping Areas (Q1) sa = 0.24 sa = 0.08Note: ap-amusement parks, sa-shopping areas, ss-specific structures, ca-casinos, pe-popular entertainmentSource: Global Insight, Inc.Table 21: Visitor Impact for Ottawa and TorontoType of AttractionVisitor ImpactAmusement Parks (Q3) 4.52 ≤ ap ≤ 6.69Shopping Areas (Q3) 0.61 ≤ sa ≤ 0.81Specific Structures (Q3) 1.02 ≤ ss ≤ 2.87Casinos (TC) 0.39 ≤ ca ≤ 0.43Popular Entertainment (Q2) pe = 0.52Popular Entertainment (Q1) pe = 0.60Shopping Areas (Q1) sa = 1.15Note: ap-amusement parks, sa-shopping areas, ss-specific structures, ca-casinos, pe-popular entertainmentSource: Global Insight, Inc.From this analysis, Global Insight's findings are:• A smaller city like Ottawa has a lower attraction base than Toronto.Consequently, the addition of a tourist attraction will generate a higher marginalresponse in percentage terms in Ottawa than in Toronto.• The response from adding amusement parks and specific structures is higher thanthe response from adding shopping areas and casinos in both cities.• The response from adding amusement parks and casinos in both Ottawa andToronto is lower than the North American average.• The response from adding specific structures and shopping areas in both Ottawaand Toronto is lower than the North American average for all five models.31
• Although the elasticity values are different among cities because the startingvalues for the number of attractions and for the number of visitors are different,keep in mind that the actual visitor impact will be the same. This result derivesfrom the cross-sectional nature of the analysis, which yields the same impact for agiven change in the base attraction count for all cities. For example, a 1%increase in the number of new amusement park (Q3) in Ottawa would yield a0.94% to 1.48% increase in visitations to Ottawa depending on what model isused. The percent increase in visitations would result in an increase of 4.5 to 6.7total visitors, depending on the model used. For Toronto, the 1% increase wouldyield a 0.33% to 0.52% increase in visitations, and the impact on the total numberof visitors would be in the same range of 4.5 to 6.7 visitors.Marketing Budgets ElasticitiesThe data for marketing budgets was available for only 33 North American cities insteadof 50. Model 5C includes an estimated coefficient for the marketing budgets. Usingformula (7) for the elasticity calculation, individual elasticities can be estimated for all 33cities.Elasticity city = β*(MB 0 / V 0 ) (7)Where:MB 0 - a current amount spent on marketing in a particular city;V 0 - the current number of visits in a particular city;β - the estimated coefficient from the model equation.If the marketing budget in Atlanta increases by 1%, the number of visits to Atlanta willincrease by 0.08%. It is worthwhile to note that Chicago and Seattle would have thelowest response to the increase in the existing marketing budgets, while Las Vegas andMontreal would benefit the most from additional spending on marketing. One of thereasons for such a low response in both Chicago and Seattle is perhaps that these citiesalready have a good return on their current marketing budgets (a high ratio of V 0 /MB 0 )and thus will have a lower response from increasing marketing budgets relative to othercities.32
Page 2 and 3: Table of ContentsI. EXECUTIVE SUMMA
Page 4 and 5: I. Executive SummaryHighlightsThe r
Page 6 and 7: Next StepsThis project has surfaced
Page 8 and 9: IV.coefficients from these models p
Page 10 and 11: series, simple autoregressive and
Page 12 and 13: Arts & Culture Museums Historymuseu
Page 14 and 15: The tourist visitations data was al
Page 16 and 17: Hotel Room Count and Hotel Property
Page 18 and 19: Table 9: Michelin’s RatingsRating
Page 20 and 21: value below one suggests that the s
Page 22 and 23: Table 13: Individual Attractions Th
Page 24 and 25: Table 16: Significant Results 14Equ
Page 26 and 27: The adjusted R 2 for this model is
Page 28 and 29: • On average across all cities, b
Page 30 and 31: Model 5C 17 : In Model 5C, the numb
Page 32 and 33: The model overpredicts the number o
Page 36 and 37: Table 22: Marketing Budget Elastici
Page 38 and 39: VII.Technical AppendixA. Attraction
Page 40 and 41: • Metropolitan Opera House in New
Page 42 and 43: Model 3: Three-Star Quality-Rated A
Page 44 and 45: Model 5B: Three-Star Quality-Rated
Page 46 and 47: 3. The cognitive perspective involv
Page 48 and 49: also found that almost half of the
Page 50 and 51: Also, the model seems to completely
Page 52 and 53: strengths and its weaknesses, devel
Page 54 and 55: Items from QuestionnaireTable 23: M
Page 56 and 57: Tourism Demand/Econometric Modellin
Page 58 and 59: Table 25: Types of Explanatory Vari
Page 60 and 61: Results: Lim and McAleer found that
Page 62 and 63: Richards, Greg. (2002). “Tourism
Page 64 and 65: Figure 4: Overseas Visitors to U.S.
Page 66 and 67: Figure 6: Cities Person-Trips Volum

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