How Geography Affects Consumer Behaviour The automobile example Murtaza Haider, PhD Chuck Chakrapani, Ph.D. We all know that where a consumer lives influences his or her consumption patterns and behaviours. For example, marketing research tables are tabulated by city, urban or rural regions. However, we often overlook the subtle spatial factors. For example, do consumers who live in the suburbs of Toronto have similar consumption to those who live in downtown Toronto? A little reflection and observation would probably induce us to say may be not or even no. Yet very little research has been done in to understand the differences. In this article, we explore this aspect of consumer behaviour using automobile ownership in the city of Montreal as an example. The model we present is based on a sample of approximately 27,000 households that reside on the island of Montréal. The analysis shows that apart from the usual determinants of car ownership levels, such as income and household size, other spatial characteristics have a profound impact on auto ownership levels. Canadians and automobiles Most Canadian households own cars. The automobile sector constitutes an integral part of the Canadian economy. Hundreds of thousands of jobs are sustained by the automobile sector. It should, therefore, come as no surprise that the purchase of new cars constitutes the second most significant consumption category in retail (Figure 1). Figure 1. How Canadians Spend their Money As the figure shows, the only item that Canadians spend more money on than new automotive vehicles is food (roughly $60 billion a year). But when we add together the sale of new and old automobiles, other spending on automotive fuels, parts, accessories and repairs, Canadians expenditure on automobiles and related items far exceeds even the amount they spend on food. Even though we are a nation of only 32 million people, we buy about 850,000 vehicles each year, making us bonafide petrosexuals! (Figure 2).
Figure 2. Vehicle Sales in Canada Not all consumer decisions are independent While newspapers, magazines, and trade journals print numerous stories about automobile manufacturers, dealerships, and others involved in the sale of automobiles, little attention is paid to the consumers who purchase cars. This has left a knowledge gap about the determinants of automobile consumption and ownership in Canada. While it is true that specialized trade journals carry articles offering insights into the purchasing behaviour of consumers, a general lack of understanding of motivations and circumstances of car consumers prevail. Some basic questions about why and how many cars households buy are seldom the focus of such articles. Why is this important? Consider a consumer buying cornflakes. Generally speaking, if the same consumer also buys soap, we can assume that the cornflake purchase will have no real bearing on the consumer s buying soap. Many purchase decisions are independent of each other. But this is not always true. For instance, do consumers purchase cars independent of their consumption of other consumer durables, such as housing? Or is the location of the household within the city has an impact on how many cars a household would purchase? Or more importantly, are there other more subtle determinants of car ownership? For instance, does the type of street network in a consumers' neighbourhood impact car ownership level above and beyond the more obvious influences such as demographics? In this article we explore the car ownership decision-making of households in a spatial context where we control not only for socio-demographics, but also for spatial influences such as population density, street topology, and proximity to competing modes of travel, such as public transit. The Montreal example We use Montréal as a case study. A vibrant metropolis of nearly 3.5 million people, Montréal is the second largest retail/housing market in Canada. The island of Montréal is the heart of the Montréal region and serves as the nucleus for social, cultural, and employment activities. With a population of 1.8 million people and an area of nearly 500 km², the Island of Montréal sits in the centre of Montréal region, where the Island of Laval lies to its immediate north and south shore lies to its immediate south (Figure 3). Also shown in the figure are the Metro stops which are presented as black dots.
North Shore Island of Laval Montreal Island South Shore Map layers Metro Stations WATER Land Area Water 0 6 12 18 Kilometers Figure 3 Montreal Region Our analysis here is based on households that reside on the Island of Montreal. The reason behind this constraint is that the Island serves as a good proxy for a large urban market of 1.8 million consumers. Even more importantly, if we can demonstrate that spatial factors play a big role in automobile ownerships even in an area that is as relatively compact as Montreal Island, it would bring forth the importance geography more forcefully than if we considered Montreal as a whole and its suburbs. The primary data used comes from a 5% sample of 27,000 households in the greater Montréal area who were interviewed about their travel behaviour. The survey, which was conducted in 1998, also included questions about car ownership. Data from the 1996 census has been included to compliment the travel behaviour survey.
Geography and automobile ownership Let s return to our basic question whether there is a spatial variation in car ownership in a metropolis such as Montréal. The colour thematic map of car ownership levels in Montréal and surrounding regions (Figure 4) provides the answer. It explicitly reveals significant differences in car ownership levels between areas near the urban core (downtown) versus the areas in the suburbs. Furthermore, neighbourhoods in proximity of the Metro stations (which are shaded as dark blue) exhibit the lowest car ownership rates. The suburban areas on the Island of Montréal and the surrounding regions depict the highest levels of car ownership where households on average own 1.7 cars. Figure 4. Automobile Ownership in Montreal We divided the Island of Montréal into three urban categories: the urban core (neighbourhoods within 5 km of downtown), the inner suburbs (neighbourhoods between 5 km and 15 km of downtown), and
outer suburbs (neighbourhoods located more than 15 km away from downtown). The difference in car ownership is shown in Table 1. Table 1: Automobile ownership on the Island of Montréal Area Cars per Increase over 1000 households Urban Core Urban Core 680 - Inner Suburbs 930 38% Outer Suburbs 1300 91% While it is easy to guess that it is more likely that people who live in relatively remote areas with less access to public transportation, the difference as one moves from the urban core is rather startling. People in the inner suburbs have almost 40% more cars per household compared to household in urban core. An Outer Suburb household in nearly twice as likely to own two cars as a household in the Inner Core! What accounts for these rather large differences? We can start with the usual suspects of car ownership, which are income and demographics. It has been often pointed out that high-income households own more cars than the low-income households. Furthermore, households with children, which are often larger in size than households without children, own more cars as well. Similarly, households with more workers, either full-time or part-time, would end up owning more cars to facilitate mobility of workers to their workplaces. In marketing research studies we customarily include such factors. Yet, important as they are, these factors are not sufficient predictors of why a household would own no car, one car, or two or more cars. In fact, a complex structure of spatial interdependencies also influences car ownership levels in a neighbourhood. Modelling the drivers of automobile ownership To determine the probability of a household owning no car, one car, or two or more cars, we used a multinomial logit model. (As most readers would know. Multinomial logit models use a categorical dependent variable. Car ownership is a typical categorical variable, which can be expressed as 0, 1, or 2 to capture the three categories of car ownership explained above: 0, 1 and 2 or more.) For predictor variables we used number of children, low vs. high income, and number of fulltime and part-time workers in a household. Because we hypothesized that the standard demographic variables are not likely to be sufficient, we added spatial variables to the list: inner suburb, outer suburb, population density of different areas, local street pattern and location of employment of workers. Figure 4 also reveals something that is not common knowledge: car ownership levels are the lowest near Metro stations. Not only do differences in car ownership between urban and suburban neighbourhoods exist, but the household location near Metro stations is also a very strong predictor of car ownership. Previous studies have shown that population density in neighbourhoods surrounding the Metro stations and along the Metro lines is much higher than it is in other parts of the city. As well, the households predisposed to using public transit for their daily commutes would locate in neighbourhoods near public transit. Similarly, since public transit offers good service to destinations in downtown, often commuters working in downtown use public transit for their commutes. Along the same lines, we can argue that workers whose jobs are located near the Metro stations are also more likely to commute by public transit rather than driving.
In our model we use low density and high-density as explanatory variables and we have used medium density neighbourhoods as the base case for comparison. We have included dummy variables to control for employment locations in downtown or near Metro stations for one or more workers in the household. Lastly, we have categorized the neighbourhood street network patterns as grid structure or non-grid structure. Previous research has shown that households residing in neighbourhoods with grid-like street patterns often depict lower car ownership levels. A variable has been included in the model to account for the impact of street topology on car ownership levels. The results of the model presented in Table 2. The first column reports the relative risk ratios. For example, a household with 2 children is 50% more likely ( relative risk = 1.51) than a household with no children; a household with an additional full time worker is 185% more likely ( relative risk = 2.85) to own a car. The relative risks of owning 2 or more cars against no car is shown at the second part of the same table. But now let s look at spatial determinants. Table 2: Output from the multinomial logit model of car ownership levels in the Island of Montréal Owning 1 car against 0 Owning 2 cars against 0 Variables RRR Std. Err. RRR Std. Err. Inner Suburb 1.54 0.08 2.09 0.16 Outer Suburb 1.93 0.13 3.37 0.32 One child 1.14 0.06 1.13 0.07 Two children 1.51 0.10 2.00 0.15 Three children 1.33 0.15 1.97 0.24 Four children 1.24 0.26 1.59 0.40 Low-income household 0.69 0.03 0.55 0.03 High-income household 1.60 0.12 2.48 0.20 Low Population density 1.31 0.11 1.77 0.17 High Population Density 0.75 0.03 0.63 0.04 Grid-like Street Pattern 0.76 0.03 0.64 0.03 Fulltime Workers 2.85 0.08 7.84 0.26 Part time Workers 1.75 0.09 3.53 0.22 Employed in Downtown 0.88 0.05 0.59 0.04 Employed Near Metro Station 0.81 0.05 0.76 0.05 As expected, households living in low population density areas are 1.3 times more likely to own a car than the households living in medium density neighbourhoods. On the other hand, residents of highdensity neighbourhoods are 25% less likely to own a car than the residents of medium density neighbourhoods. The employment location of workers also confirmed our hypothesis that if workers are employed in downtown or near Metro stations, they are less likely to own vehicles. In this regard a household with a worker employed in downtown is 12% less likely to own a car than a household who does not have any workers employed in downtown. We now return to the most important question raised earlier about the differences between urban and suburban households. The model reveals that households residing in inner suburbs are 1.54 times more likely to own one car than households living in the urban core. Similarly, households living in the outer suburbs are 1.93 times more likely to own one car than households living in the urban core. These results indicate that while we control for the differences in income, household size, and the
location of their homes and employment destinations, still households living in the suburbs are more likely to own a car than households living in the urban core in Montréal. The second set of results compare the outcome of owning two or more cars against the outcome of owning no car. The results are similar to the ones observed for the outcome of owning one car against none. The differences are even more pronounced here. For instance, while the households in Inner suburbs are 1.5 times as likely as the Urban Core to own a single car, household of Outer Suburbs are twice as likely to own more than one car. Comparing households with no children, we observe that households with two children are 2 times more likely to own two vehicles than owning zero vehicles. Implications of target marketing The analysis presented here has important lessons for targeting marketing campaigns. An advertisement in a local daily or local television channel will not be able to target the consumers who are more likely to purchase one or more cars. Households residing in downtown or near the Metro stations are predisposed to commute by public transit. Their decision to locate near Metro stations or in downtown may have been predicated on their desire to commute by public transit rather than by private transportation. Marketing campaigns that do not distinguish between households who are predisposed to travelling by public transit and households who are likely to own one or more cars would be less effective in getting value for the investments. Unlike the demand for cereal or shampoos, the demand for automobile differs significantly over the urban landscape. It is therefore prudent for the marketing professionals to account for spatial heterogeneity in their marketing campaigns for products whose demand varies by location. Acknowledgments The authors would like to acknowledge the contribution of Mr. Andrew Carter who conducted the spatial analysis for street topology while he was a Masters student at McGill University. The primary database was developed by Agence Métropolitaine de Transport from the origin-destination survey of households in Montréal region in 1998. Murtaza Haider initiated this research in 2004 while he was a professor at McGill University. ----- Murtaza Haider is an Associate Professor of Real Estate Management at the Ted Rogers School of Management at Ryerson University. He can be reached at murtaza.haider@ryerson.ca. Dr. Chuck Chakrapani is the President of Leger Analytics.