Analysing Competition Among Shopping Alternatives Within the Quebec Metropolitan Area:How GIS can further modelling of consumers destination choice behaviourMarius Thriault, Gjin Biba, Franois Des Rosiers & Paul VilleneuvePROCESSUS : 2nd International Colloquium on the Behavioural Foundations of Integrated Land-use and Transportation ModelsUniversity of Toronto, June 12-15, 2005

OutlineContext of retail market transformations

Methodological approach and database

Main findings:Market area and spatial competition among retail facilitiesStore choice behaviour of consumers

Conclusion

Transformation of Retail MarketsEvolution of retail structure (during the 20th Century)Two main impacts:

Modifying consumer destination choice behaviour and retail industry competitionChanging urban dynamics (i.e. land use, activities location, transportation demand)

Context of Quebec Metropolitan Area (QMA)

Agglomeration of 700 000 persons (7th in Canada and 2nd in Quebec)

12 % of workforce (~ 50 000 persons) employed in retail sector; annual market of 8.2 billion of CAN$

Economic and urban environment marked by: Household purchasing power is 4 % above the provincial average Strong urban sprawl Highly developed motorway network (21 Kilometres / 100,000 inhabitants)

Retail structure (in 2001): 87 shopping centers (including 2,300 stores, Total Floor Area > 11 M sq ft) 44 big boxes (Total Floor Area > 3 M sq ft) Roughly 5,000 individual stores (2,600 on 72 commercial street segments)

Evolution (1990-2001) of the retail structure: Stagnation, even recession, of the number of shopping centers Implementation of several isolated big boxes and power centres Transformation of commercial streets (revitalization)

Sources: Statistic Canada, Quebec City, CRAD

Accessibility to Shops - Consumer Behaviour

199119962001Car81,783,885,5Bus11,37,34,2Walking6,27,89,1Others0,71,11,1Total100,0100,0100,0

Research PurposeObjectives : Identify market areas and analyse spatial competition among commercial streets, shopping centers and big boxesInvestigate consumer behaviour when choosing retail store

Better understand social, economic and spatial impacts of new large retail outlets (big boxes and power centres)

Combining GIS and travel survey may contribute to analyse retail market dynamics and consumer choice behaviour

Methodology: Locating consumers and shopping trips (origin and destination)Estimating markets share for each retail outlet Delineating primary (50%) and secondary (80%) market areasModelling consumer store choice (discrete choice modelling)

Database

Retail Outlets Distribution

Distribution of Trips Among Destinations (O-D, 2001) (Monday to Friday)

Graph1

36.2820669685

9.0297320445

12.14160447

16.3383498511

8.2833720788

17.9248745871

Shopping trips (%)

Neighbourhood centres8.1%

Community centres12.0%

Commercial streets17.4%

Big boxes &Power centres8.1%

Regional & Super-regional centres 18.0%

Small individual stores36.4%

Feuil1

Sous types des centres commerciaux

FrequencyPercent

01493360.9037888984

Neighborhood center16136.5785717199

Community center15386.2726864883578

Center commercial local (45-99)16956.9130062401

Center commercial rgional (100-199)3451.4070720666

Ceneter commercial suprargional (>200)439517.9248745871

Total24519100

Neighborhood shopping centers2214Types of commerces

Community shopping centers2977Nomber of retail entitiesFrequencyPercent

Regional and super regional4395Small individuel store5321889636.3

Commercial streets4006Neighborhood center5422149.0

Big Boxes2031Community center23297712.1

Other individual storesCommercial street54400616.3

Big boxe3420318.3

Supre regional center6439517.9

Total24519100

Retail typesShopping trips (%)

Small individuel store36.3

Neighborhood center9.0

Community center12.1

Commercial street16.3

Big boxe8.3

Supre regional center17.9

Feuil1

0

0

0

0

0

0

Shopping trips (%)

Supre regional center18%

Feuil2

Feuil3

Delineation of Market Areas

Primary and Secondary Market Areas (2001)

Commercial Streets with Neighbourhood Shopping Center24 segments; 1,645 individual shops Primary Market Area = 5.6 sq. KmAverage Control of Market = 12.3 %Secondary Market Area = 30 sq. KmAverage Control of Market = 6.8 %

Commercial Streets Without Shopping Center42 Segments; 1,062 individual shops Primary Market Area = 4.8 sq. KmAverage Control of Market = 9.4 %Secondary Market Area = 20.5 sq. KmAverage Control of Market = 8.3 %

Community Shopping Centres23 centres; 686 individual shops Primary Market Area = 7.7 sq. KmAverage Control of Market = 11.9 %Secondary Market Area = 54.1 sq. KmAverage Control of Market = 9.5 %

Regional and Super-regional Shopping Centres6 centres; 1,124 individual shops Primary Market Area = 47.8 sq. KmAverage Control of Market = 14.2 %Secondary Market Area = 297.1 sq. KmAverage Control of Market = 9.9 %

Big Boxes Grocery & Beverage11 stores; Total Floor Area=76 710 sq. mPrimary Market Area = 19.3 sq. KmAverage Control of Market = 3.8 %Secondary Market Area = 82.6 sq. KmAverage Control of Market = 1.6 %

Big Boxes - Renovation Products6 stores; Total Floor Area = 48 946 sq. m. Primary Market Area = 66.4 sq. KmAverage Control of Market = 1.3 %Secondary Market Area = 117.6 sq. KmAverage Control of Market = 0.9 %

Big Boxes - Car related products (Canadian Tire)5 stores; Total Floor Area = 38 785 sq. m. Primary Market area = 11.0 sq. KmAverage Control of Market = 2.0 %Secondary Market Area = 39.6 sq. KmAverage Control of Market = 1.0 %

Big Boxes - Mixed Products (WalMart)3 stores; Total Floor Area 31 850 sq. mPrimary Market Area = 33.8 sq. KmAverage Control of Market = 6.6 %Secondary Market Area = 56.5 sq. KmAverage Control of Market = 5.7 %

Others Big boxes5 stores; Total Floor Area = 21 074 sq. m.Primary Market Area = 10.3 sq. KmAverage Control of Market = 2.9 %Secondary Market Area = 48.0 sq. KmAverage Control of Market = 0.8 %

Modelling Consumers Destination ChoiceWe investigate consumer destination choice (type of outlet) rather than consumer behaviour within commercial spaceMultinomial Logistic Regression Model Adjusted using O-D Survey Data

Attributes of Trips by Retail Form (%)

Type of Shop Choice: MNL ParametersSignificant levels: -- non significant; * 0.1; ** 0.5; *** 0.01Reference is Big Boxes - Power Centres [Figures present odds ratios]

ConclusionMethodology: Combining GIS and Statistical Analysis was efficient for modeling both spatial and non-spatial determinant of retail trade market

Competition among retail facilities and consumer behaviour: Commercial streets (week days, resist fairly well to competition): Multi-purpose trips especially Leisure and Restaurant, Lone personsIntegration of small shopping centres yields a comparative advantageCommunity shopping centres (threatened by big boxes development):Grocery, Single - parent families, RetiredTheir endurance is mostly related to strategic locationRegional & super-regional shopping centres (yet very competitive):Proximity to workplaces and bus routes; Large choice of products, WomenOne of their last competition strategy may be to agglomerate with power centres Big Boxes (extending their geographic and economic market shares): Car trips, Men, Workers, Households with children The most competitive are in grocery, mixed products and renovation sectors

Retail evolution, impacts on transportation and urban dynamics:Big boxes are threatening for shopping centres (mostly at neighbourhood and regional levels)Increases car use and demand for new transportation infrastructuresStrong competition means potential readjustment of commercial real estate values, activities redistribution (or relocation), traffic, road infrastructures