Spatial versus Non-Spatial Determinants of Shopping Center Rents: Modeling Location and Neighborhood-Related Factors

Article excerpt


This study is an attempt to model the economic trade-off between spatial and non-spatial determinants of shopping center rents while assessing the role of neighborhood and location attributes in the rent setting process. For that purpose, two space-related indices, namely the Economic Potential Index (EPI) and the Center Attraction Index (CAI), are designed based on a major origin-destination phone survey and on financial data obtained for eight major shopping centers in Quebec City, Canada. The database, which is processed through a regional GIS, includes 1,007 retail units, representing some 4.4 million square feet of gross leasable area. While findings confirm that the EPI act as a significant determinant of shopping center rents, they also bring out the complexity of the relationships between endogenous and exogenous rent determinants.

Objective and Context of Research

This study develops a model of the economic trade-off between spatial and non-spatial determinants of shopping centre rents while assessing the contribution of neighborhood characteristics (household spending power) and center location attributes (proximity to clientele) to the rent setting process. For that purpose, two space-related indices are designed, namely the Economic Potential Index (EPI) and the Center Attraction Index (CAI), which are successively added to a regression model of retail unit base rents.

The study is part of a research program based on physical and financial information obtained from super-regional, regional and community shopping center managers in Quebec City for the 1998-2000 period. While information was made available for only eight shopping centers, all of which have a mall configuration,1 these include the largest corporate and institutional retail property assets in the region. At this point, some 1,007 shops are included in the database, with base and total rent, gross leasable area (GLA) and retail category being available for roughly 954 of them. Census information on 1996 neighborhood profiles (household composition, age and income) as well as regional data on proximity of and accessibility to jobs and services is processed via a regional GIS. Finally, a 2001 origin-destination (O-D) phone survey provides useful information on daily commuting patterns in the Quebec Metropolitan Area (QMA).

Located 150 miles east of Montreal, Quebec City has a population of roughly 560,000 while the QMA totals about 683,000 inhabitants. Apart from its universally prized historical center and old neighborhoods, Quebec City is a typical North American agglomeration characterized by a highly extensive highway network2 and sprawling residential, retail and industrial developments. The vast majority (73%) of daily trips are car-generated while nearly a third of all trips are for shopping and leisure purposes. Retail activities in the region are particularly well developed, with shopping center complexes that rank among the largest in Eastern Canada. As with most other urban regions throughout Canada and the United States, "big boxes" and "category killers" are a major concern for traditional shopping centers whose hegemony is seriously threatened. In this increasingly competitive context, retail establishments' managers need to better monitor and understand the shopping patterns of individuals and households. Because it is quite representative of any medium-size North American city in terms of both urban form and household travel behavior, and despite some specific features,3 findings about Quebec City's retail market may be extended to several other similar agglomerations.

Location has long been known to play a major role in the retail rent setting process. In particular, center site selection and retail store development has long been driven by primary market data linking income, wealth and location. However, to the best of our knowledge, no record has been found of any research resorting to actual, rather than expected, clienteles in order to assess retail establishments' economic potential and trade area at the entire city level. …