spatial fluctuations in the health of the consumer services sector within a metropolis: a...

Spatial fluctuations in the health of the

consumer services sector within a metropolis:

a business/commercial geomatics analysis

Ken Jones

Centre for the Study of Commercial Activity, Ryerson University, 350 Victoria Street,

Toronto, Ont., Canada M5B 2K3

Issues related to data aggregation have typically limited detailed spatial analyses of the

supply-side of the consumer service sector of the economy. The major restriction has been

the lack of disaggregated, longitudinal data below the metropolitan level (Eppli and

Laposa, 1997). Thus, it has been difficult to examine even downtown/inner city/suburban

changes in the supply-side of the commercial sector. This paper extends previous analyses

by examining the spatial dynamics of the urban consumer service economy at a micro-

geographic scale (Simmons and Yeates, 1998). Three metrics of change have been chosen

to reflect the fluctuations in ‘health’ of over a thousand commercial shopping districts or

nodes in a large metropolitan region These concentrations serve a variety of functions that

are associated with the consumer services sector. In general, each of these shopping areas

incorporates some combination of retailing, financial, personal and business service

activities.

The urban commercial economy under investigation comprises approximately

50 000 stores that are located within the confines of the Greater Toronto Area (GTA).

A database, collected on an annual basis between 1996 and 2001, permits monitoring

of the spatial fluctuations at a variety of scales within the Toronto region. In this

paper, three measures are used to examine change: (i) vacancy rates; (ii) business

type; and (iii) the number of store/tenant turnovers. By examining both spatial and

temporal variations in these attributes across an entire urban system, it is possible to

examine the variability and volatility of an urban retail/commercial landscape across a

number of dimensions, and provide insights into a variety of social and economic

issues. These can include the health of local neighborhoods and the impact of new

retail formats, and the relative performance of different retail real estate assets and

classes.

0305-9006/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved.

doi:10.1016/S0305-9006(02)00092-2

Progress in Planning 60 (2003) 75–92

www.elsevier.com/locate/pplann

E-mail address: [email protected] (K. Jones).

http://www.elsevier.com/locate/pplann

The early literature of urban retail change can be traced to the empirical findings of

researchers in the 1960s and 1970s. Simmons (1964, 1966) developed a model that

examined the associations between urban retail growth and various socio-economic

correlates for Metropolitan Toronto. Retail change was linked to a complex set of temporal

and spatial variables that took into account variations in income, technological

developments, and shifts in demography. These factors affected consumer behaviours

and preferences and led to adjustments in the composition and growth of local retail areas.

Schell (1964) examined urban retail change in Boston, while both Sibley (1976) and Shaw

(1978) examined the long-term patterns of retail change for a selected urban case in

Britain. In these latter two studies, changes in the retail pattern were viewed as the

outcome of the aggregate adaptive behaviours of independent retailers and multi-unit

chains to changes in the retail environment.

More recently, Simmons and Simmons (1997) examined retail change for a four-year

period for a set of 174 retail strips within Metropolitan Toronto. This study concluded that

the economic role of strips was shifting toward the service sector; growth in retail strips

took place in and around the downtown core and in the larger, more specialized strips; and

the turnover rates of retailers in strips approached 15%. Finally, Yeates and Montgomery

(1999) provide one of the few studies that examine a set of supply side indicators of retail

change for some smaller urban centres. This study concluded that the overall high levels of

vacancy and volatility of the local retail system was an indicator of over-storing and

reflected a complex set of factors that included nearness to a major metropolitan market,

new forms of direct competition, and shifts in the health of the local economy.

K. Jones / Progress in Planning 60 (2003) 75–9276

CHAPTER 1

The data

The data used in the analysis are collected and maintained by the Centre for the Study

of Commercial Activity at Ryerson University. Since 1996, an annual survey has been

undertaken for the consumer services sector within the Greater Toronto Area. Table 1

provides a description of the size of this database in terms of number shopping areas and

stores for four distinct shopping environments—retail streets (strips), shopping centres,

underground malls, and power centres. For data collection purposes, each retail area is

located and then its areal dimensions are defined. Then, for each outlet/store, data are

collected with respect to its business type (Standard Industrial Classification code),

business name, address, selling area, and ethnicity. This database is then geo-coded and

digital maps of the retail structure and retail change are generated. This spatial database

permits the monitoring of retail change at a variety of spatial scales, and for specific

business types. In this paper, the tenant data has been aggregated to retail concentrations in

order to provide measures of retail change for each of the four distinct retail environments

over a 6 year period (Table 2).

Table 1 provides an overview of the dimensions and general characteristics of the

database. Since 1996, this database has increased in size as a result of two factors—new

construction activity (such as power centres and underground malls), and an increase in

areal extent of the data collection with the inclusion of suburban retail streets and

convenience shopping centres. The resultant database captures the retail/commercial

structure of major retail streets, all shopping centres in excess of 50 000 ft2 of gross

leasable area, the underground mall system, and the network of power centres within the

Greater Toronto Area. For each of these retail types, information can be generated for the

tenant mix, turnover rates and vacancy levels on a property-by-property basis.

The specific objective of the paper, therefore, is to explore the dynamics of retail

change for each of the four retail environments in the Greater Toronto Area (GTA) for the

1996–2001 period. For each property class, changes in the vacancy rates, business

composition and turnover rates are assessed. As part of this evaluation, GIS (Geographic

Information Systems) has been used to analyze and interpret the general patterns of change

Table 1

Retail composition in the GTA

Year No. of Retail

streets

(strips)

No. of

Stores

No. of

Shopping

centres

No. of

Stores

No. of

Underground

malls

No. of

Stores

No. of

Power

centres

No. of

Stores

1996 212 17 973 537 17 484 24 1115 22 270

1997 212 18 401 570 19 514 24 1135 26 354

1998 260 21 001 609 20 808 30 1252 34 510

1999 278 22 734 637 21 387 33 1276 39 767

2000 301 24 465 663 22 014 33 1291 39 885

2001 302 24 981 676 22 181 33 1291 49 1043

K. Jones / Progress in Planning 60 (2003) 75–92 77

that are shaping the urban commercial economy in the GTA. The results of the analysis

may be used for comparative purposes and bench-marking in other metropolitan areas that

are undergoing rapid market growth and significant commercial innovation.

Table 2

Vacancy rates by retail environment: 1996–2001

Year Retail streets

(strips) (%)

Shopping

centres (%)

Underground

malls (%)

Power

centres (%)

1996 9.7 9.5 9.8 4.6

1997 9.3 10.3 9.2 5.7

1998 9.2 10.8 8.9 4.7

1999 9.1 10.6 8.1 5.4

2000 9.3 9.9 7.4 5.4

2001 8.8 9.1 5.3 6.9

Average 9.2 10.0 8.1 5.5


CHAPTER 2

Commercial strips

Commercial strips present a paradox for while turnover rates, and levels of

vacancy for individual properties, often appear high, many strips appear surprisingly

healthy. This is probably because they are often able to expand and contract rather

easily. This rapidity of change reflects the special role that retail strips play within the

urban retail system, and the vulnerability of independent retailers and commercial

outlets to shifts in local market conditions. From a structural perspective, commercial

strips comprise a variety of types. Across the GTA, collectively they account for

approximately 25 000 business locations and contribute approximately 30 million

square feet of selling space to the commercial inventory (Yeates, 2000). In aggregate,

these retail streets represent the most complex urban shopping classification. They can

comprise major shopping streets (for example, Yonge Street in downtown Toronto);

ethnic retail concentrations (such as St Clair Avenue West), specialized retail areas

(for example, trendy Queen Street West), fashionable shopping districts (such as in

the Yorkville district); ‘historic’ downtowns (such as ‘Main Street’ Unionville or

Newmarket); and small neighborhood shopping streets. The Toronto area is well

served by these shopping areas that in total contribute approximately one quarter of

the retail space in the urban area. Moreover, as Simmons and Simmons (1997) have

noted, these unplanned districts reflect the local demographic and socio-economic

characteristics of their respective communities.

During the 6 year period under investigation, commercial strips have, in the

aggregate, experienced a remarkably stable vacancy rate of 9.2% (Table 2). However,

as Table 3 indicates, over this period the composition of the retail strips in the

Toronto region changed dramatically. In total, six categories (restaurants, personal

services, other retailing, business services, hair and beauty, and health services) added

over 4000 stores to the retail street inventory. More importantly, these six sectors

accounted for approximately 70% of the new retail development along the strips

during the study period. Clearly, the fabric of commercial strips in the Toronto area

has experienced radical change. In almost every strip, retailing is being replaced with

services. In many cases, non-retailing functions such as restaurants and various

personal and business services now dominate.

The spatial variation in vacancy and turnover rates is, however, considerable

throughout the GTA. In Figs. 1 and 2, the symbols used to reflect the vacancy and

turnover rates are over-laid on a map that illustrates the size of each retail area

(measured in terms of the number of stores). Fig. 1 indicates the average vacancy

rates for the 1996–2001 period—the spatial variability in the overall pattern of

vacancy rates is considerable. For the individual strips, the average annual vacancy

rates vary from a low of 0% to a high of 32%. Moreover, certain areas of the city

exhibit consistently high vacancies. For example, east of the downtown core a cluster

of strips exhibited vacancies in the 24% range over the 6 year period under

investigation. This high rate of change in the area is associated with the changing

local market as the area shifts from low income residential to an area of new


condominiums for ‘young urban professionals’. Other areas of high vacancy rates area

associated with the lower income areas to the north and west of the core area. For

many of these neighborhood shopping streets, annual vacancy rates are in excess of

25%.

Fig. 2 indicates the average turnover rates for the 1996–2001 period. Turnovers

provide a clear measure of the volatility of the urban retail system—in general, the average

annual turnover rate in the GTA is 14.7%. Noteworthy is the constant pattern of turnover

across the region—80% of the commercial strips in the Toronto region experienced yearly

turnover rates between 10 and 20%. These rates do not appear to be clustered in any

particular part of the metropolitan region—high and low rates occur in suburban areas as

randomly as they appear to occur in the city.

Table 3

Net retail change by category: 1996–2001

Sector Strips Shopping

centres

Underground

malls

Power

centres

Book and stationery stores 32 242 28 20

Business services 540 174 4 5

Cleaners 127 132 4 5

Drug stores 52 75 21 4

Financial and insurance industries 272 231 21 15

Florists lawn and garden centres 52 20 2 21

Food/grocery 243 359 17 13

Food and beverage services 1142 737 72 160

General merchandise 37 98 8 20

Hair and beauty services 533 434 9 15

Hardware stores 44 214 10 15

Health services 445 418 10 15

Household and appliance stores 436 263 27 80

Jewellery stores 56 147 10 4

Liquor 39 30 1 4

Men’s clothing 222 2147 7 34

Music stores 28 9 0 4

Other clothing and fabric stores 102 87 8 50

Other retail 636 301 48 38

Personal and household services 722 344 17 4

Recreational services 199 130 22 34

Shoe stores 5 11 22 30

Sporting goods 42 6 22 11

Women’s clothing 84 93 24 39

Total stores 5846 3896 222 618


Fig. 2. Average turnover rates for retail strips: 1996–2001.

Fig. 1. Average vacancy rates for retail strips: 1996–2001.


CHAPTER 3

Shopping centres

The shopping centre system in the GTA comprises approximately 650 centres (with

more than 30 000 ft2 of commercial space) and 21 000 stores. These malls,

accounting for approximately 75 million square feet of space, form a hierarchy of

planned centres according to size and function. In general, there are five classes of

centres: convenience, neighborhood, community, regional, and super-regional (Doucet

and Jones, 1997). Although these centres dominate the consumer service sector in the

GTA (as malls do in every North American metropolis), their primacy, after 50 years

of growth, is now being threatened, and some mall closures, or transformations, have

occurred.

Several factors have been cited as being associated with these closures and

transformations. These include: competition from big-box retailers, particularly since

1990; over-saturation of the North American market with commercial space; the

emergence of e-tailing; and changes in consumer shopping behaviours and

preferences. Under-performing and/or declining shopping centre properties are often

referred to as greyfield properties (Insausti et al., 2000). Typical signs of declining

health include: (1) high vacancies; (2) increases in tenant turnovers; (3) loss of anchor

tenants and national chains; and (4) decline and/or lack of reinvestment in the

physical structure of the malls.

From 1996 to 2001, shopping centres in the GTA experienced an average vacancy

rate of 10% (Table 2). However, when malls are examined according to their size,

major variations in vacancy rates are noted. There is a significant change in vacancy

rates between malls above and below 700 000 ft2, with the vacancy rate of large

regional malls normally below 6%. Changes in the composition of tenant mix of the

shopping centres are less pronounced than experienced in commercial strips (Table 3).

Moreover, three retail categories actually showed a net decline in the number of mall

stores—men’s fashion, book and hardware stores. These decreases were the direct

outcome big-box competition in the case of books and hardware, and a major decline

in fashion spending in the case of men’s fashion. Major increases were noted in the

following sectors—restaurants, hair and beauty, health services, food retailing,

personal services, other retailing, housewares and financial services. These eight

categories accounted for 77% of the net change in shopping centre tenants and reflect

the increase in convenience-oriented shops and a major shift toward services across

the entire urban shopping centre network.

Major spatial variations occur in the health and stability of the shopping centre

system. In Figs. 3–7, centre size is linked to symbols that are used to indicate average

vacancy and turnover rates across the network of shopping centres. For clarity, the

shopping centre system is subdivided into two classes: those with less than

1 000 000 ft2 of commercial space, generally referred to as convenience and

neighborhood centres; and those with more than 100 000 ft2, generally referred to as

community and regional centres.


Fig. 3. Average vacancy rates for shopping centres , 100,000 square feet: 1996–2001.

Fig. 4. Average vacancy rates for shopping centres . 100,000 square feet: 1996–2001.


Fig. 5. Average turnover rates for shopping centres , 100,000 square feet: 1996–2001.

Fig. 6. Average turnover rates for shopping centres . 100,000 square feet: 1996–2001.


Fig. 3 presents the average vacancy rates for the 1996–2001 period for the 442 smaller

centres within the urban area. Although these malls record an average vacancy rate of

9.6%, there is considerable spatial variability, with pockets of higher vacancies noted in

the eastern and northern suburbs, and smaller centres in the central area exhibiting lower

vacancy rates.

Fig. 4 illustrates the pattern of vacancies for the set of 159 larger centres. The

distribution of the vacancy rates for these malls exhibits a much more regular pattern

and surprisingly, in aggregate, the larger malls experienced a higher average vacancy

of 10.5%. Upon closer inspection, for this larger category, the pattern of vacancies is

somewhat bipolar. Twelve malls experienced extremely high vacancy rates—in excess

of 27%. Typically, these malls are situated in the suburban areas and tended to serve

either new developing residential markets, or are located in highly competitive

clusters. Conversely, the 10 large super-regional malls (those in excess of 900 000 ft2)

consistently exhibited vacancy rates in the 5% range. This trend points out the

obvious importance of accessibility and shopping centre attraction to the overall

success of planned shopping centres.

The annual turnover rate for the entire of shopping centre network was

approximately 16% for the 1996–2001 period. Unlike vacancy rates, the spatial

variation in turnover rates is similar for both large and small malls (Figs. 5 and 6). In

both cases, approximately 25% of the centres had turnover rates in excess of 20%.

Fig. 7. Average vacancy rates for the underground mail system: 1996–2001.


However, upon closer inspection, a larger proportion of the small centres (21.8%) had

annual tenant turnovers of less than 10%, while for the larger centres this figure

decreased to 16.3%. It should be noted that tenant turnovers in many instances could

be viewed as a good thing as the need to refresh retail properties is constant as both

market and competitive pressures are always in flux. This is particularly evident with

the group of super-regional malls that collectively experienced a relatively high

annual turnover of 13.3%.


CHAPTER 4

Underground malls

One dominant element of Toronto’s retail economy is the underground retailing system

(Jones, 1998). Currently, this network of linked retail spaces comprises 33 malls with over

1250 tenants, providing 3 million square feet of commercial space in the downtown. This

extensive, inter-linked, network of malls serves the large daytime work force of

approximately 200 000 who are employed in the various office complexes in the

downtown area. Most of the office buildings are liked directly to these underground

commercial spaces, and these, in turn, are linked to a subway system that serves the City of

Toronto, and a GO-train commuter railroad system that connects the outer suburbs with

the downtown area (Yeates and Jones, 1998).

During the 1996–2001 period, the underground system experienced an average annual

vacancy rate of 8.2%. Moreover, over this 6 year period the system experienced a

consistent decline in vacancies from a high of 9.8% in 1996 to a low of 5.3% in 2001. This

decline reflects the boom economy of the late 1990s and the heavy reliance of the

underground merchants on the health of the financial services sector during the period.

From a tenant mix perspective, the changing tenant composition of the underground

system mirrors the general findings of both shopping centres and commercial strips. Net

increases occurred restaurants, financial and personal services, with moderate growth in

certain specialty retail activities such as jewellery and men’s fashion. A number of retail

Fig. 8. Average turnover rates for the underground mail system: 1996–2001.


categories, such as books, women’s fashion, shoes, recreation and sporting goods, exhibit

small declines.

The spatial variation in vacancy and turnover rates for the underground mall system are

illustrated in Figs. 7 and 8. The system experienced an average vacancy of 8.1% over the 6

years of study. As Fig. 7 illustrates, the vacancy rates ranged from a low of 0% to a high of

77% for the 33 mall properties. In general, the smaller, more peripheral malls in the

underground complex experienced higher vacancies—due undoubtedly to poor access to

the main concentrations of the daytime workforce, and limited access to the main flow of

consumer traffic. Fig. 8 depicts the annual tenant turnover rates in the underground

complex. On average, 17.8% of the tenants turned over on an annual basis. The pattern of

tenant turnover was more regular and ranged from a high of 100% to a low of less than 6%.

More importantly, the five larger properties in the system (i.e. those directly linked to the

head offices of the five major banks) experienced much lower turnover rates (i.e. in the 8%

range).


CHAPTER 5

Power centres

The recent establishment of big-box retailers and associated power centres has altered

dramatically the competitive retail landscape of the Greater Toronto Area (Jones and

Doucet, 2001). Big-box activity exploded onto the GTA scene in the early 1990s. They

include a variety of retail forms that are based on: low prices, achieved through low land

costs (often unused industrially zoned land) and labour inputs, and/or, a wide selection of

brand-name merchandise in large stores monitored minute-by-minute with highly

computerized sales/inventory/ordering systems. Their real innovation lies, therefore,

with the magnitude of the scale economies of their enterprises, which, in the first flush of

competition, generate enormous efficiency advantages over smaller operators. Power

centres are essentially commercial real estate developments consisting of two or more big-

boxes sharing the same parking facilities. The concentration of big-boxes into power

centres is basically a post-1994 phenomenon, though Crossroads Power Centre (Weston

Rd/401), established in 1987, is generally regarded as the first power centre in the GTA.

The number power centres in the Toronto region increased from 22 in 1996 to 49 in

2001, and the number of power centre tenants has increased four fold (Table 1). The

growth of this retail form has been dramatic and has provided direct competition to the

existing shopping centre system. By 2001, power centres in the GTA contributed over

1000 stores to the retail inventory and accounted for over 17 million square feet of space.

Over the 1996–2001 period, vacancy rates in power centres averaged 5.5%—the lowest

for any retail format. This relatively low value reflects the continual demand for power

centre space over the later half of the 1990s and the associated market growth in suburban

areas of the in the GTA—the principal source of demand for the power centre retailers.

From a structural perspective, the power centres show growth in virtually every

category. But, unlike the other three commercial forms, much of the growth is associated

with retail activity. Sectors of major growth include housewares, women’s clothing, other

retailing, other clothing (unisex), men’s clothing, shoes, general merchandise (Wal-Mart),

books and hardware (Home Depot), as well as family style restaurants. Thus, power

centres have become significant nodes of retail activity, while planned shopping centres

and commercial strips have become more service-oriented. Since 2000, however, power

centres have begun to augment their retail offerings with a limited assortment of business

and personal services.

Fig. 9 provides evidence of the general health and distribution of power centres in the

Toronto region. Power centres are primarily a suburban phenomenon, with most of the

locations situated in close proximity to major expressways. Initially, they were located to

serve the expanding residential markets in the outer suburbs of the GTA, but increasingly

some new power centres have emerged in more central locations where developers have

taken advantage of the availability of low cost parcels of vacant industrial properties. The

sequence of occupancy is quite simple—as the economy restructured and industrial firms

left the Toronto area in the early 1990s, a number of relatively low cost industrially zoned

parcels were converted to power centres without much planning opposition due to the need


to re-cycle the properties. There is also a tendency for power centres to cluster at major

expressway/highway intersections, creating power nodes.

Power nodes, being composites of power centres, are undoubtedly the big-box related

innovative wrinkle of the early 21st century. They encompass nearly all types of consumer

services—retail stores, general services (particularly entertainment and restaurants), and

the broad range of consumer-oriented financial services. Power centres within major

power nodes are, in particular, being planned by developers to include major retail and

entertainment anchors that will (hopefully) generate crossover shopping/spending (that is,

externalities). Thus, whereas malls are designed to generate externalities through internal

pedestrian traffic flow, power centres within some nodes are now being designed to

generate externalities in what has hitherto been a single-purpose, entirely auto-oriented,

destination environment.

Since power centres are a relatively new feature on the commercial landscape, and

many of the existing centres are still underdevelopment, an assessment of vacancy rates

has not been undertaken for this class of property. However, turnover rates have been

examined (Fig. 9). Though turnover rates are generally quite low—ranging from a low of

0% to a high of 20%—there are some distinct spatial variations. The highest turnover rates

are associated with a set of power centres in the southwest area of the region, and in the

area of the Highway 7-400 power node in the northwest. This tendency appears to reflect a

more competitive big-box (power centre) environment in the western portion of the GTA.

Fig. 9. Average turnover rates for power centres: 1996–2001.


CHAPTER 6

Conclusion

This paper has provided two distinct outcomes. First, the results provide new insights

into the dynamics of the urban consumer services sector. The analysis of the Toronto

region has revealed the complex nature and the speed of change that operates within a

contemporary metropolitan consumer service economy—particularly consequent to the

new big-box/power centre commercial innovation. More importantly, the use of

disaggregate information permits identification of the extremes in both the permanence

and the functional structure of our commercial strips, shopping centres, and power centres.

The results of the study reinforce the need for policy makers, investors, retail analysts and

urban planners to develop a greater spatial understanding of the variability and speed of

change of the urban service economy. Frequently, policies and investment decisions that

relate to commercial environments are made without detailed knowledge of the economic

health and direction of change.

Secondly, the paper illustrates the importance of business/commercial geomatics and

large longitudinal, spatial databases as necessary prerequisites for informed decision-

making. What is required is the discipline and allocation of resources to create and

maintain annual inventories of the commercial economy and link these with other socio-

economic databases. When these databases are fused, powerful insights can be generated

on a year-over basis and the dynamics of the commercial spatial economy can be traced,

interpreted and projected.

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