This study employs an activity-optimizing model. Unlike Hosack's model (2001), it is not concerned with micro-scale elements such as parking and the internal space programs of buildings, which are appropriate to real estate calculations at the scale of a parcel or block. Instead, the model works at a larger geographic scale and a higher level of abstraction. It assumes a fixed quantity of land corresponding to the typical scale of a greenfield secondary plan area in the Toronto metropolitan region; a scale comparable to the districts analyzed in Section 2. Within this land base, it allocates land to general use categories similar to those employed in Section 2, and applies various activity intensity factors to determine the area's development capacity and density under various scenarios.
It should be acknowledged that the Growth Plan's minimum density target applies to a broader scale -- the designated greenfield areas of each upper- and single-tier municipality. Given appropriate land use information, the model could be used to estimate densities at the municipal scale. To better relate to the analysis in Section 2, and to explore how the densities of individual sub-municipal planning areas may contribute to meeting the municipality-wide target, the model's inputs and outputs are framed at the scale of the secondary plan district.
The five-step operation of the model is shown in Fig. 50. First, the gross land area is allocated to general use categories: developable and undevelopable, and within the former category, employment land is separated from everything else. In Step 2, dwellings and population are assigned to the residential land area. In Step 3, land for public facilities (parks and schools) is assigned in proportion to population. As the calculations in Steps 2 and 3 are contingent on one another, the values in each must be brought into balance, so that the amount of land required to house the population, and the amount of land for associated public facilities reach corresponding levels. The number of jobs is quantified in Step 4. Finally, densities are calculated in Step 5. Appendix C.1 contains a detailed description of this process.
Fig. 50: The operation of the model
It is important to note that the model does not in the first instance estimate demand on the part of a forecast population for particular types of housing, as would occur in a land-optimizing model. Instead, as is appropriate in an activity-optimizing model, it does the opposite. On the basis of the input variables, it first allocates land uses in order to determine the supply of dwelling units that can be accommodated on the land base, and then estimates the size of the resident population that is housed. Taking this approach largely detaches the model from determinants of housing demand, including the cost of housing, unemployment rates, immigration rates, and the cost of transportation. However, the use of population, employment, and housing demand forecasts contained in Ontario government's Growth Outlook (Hemson 2005) as input assumptions to the model means that the demand side of the equation is implicitly incorporated. A similar supply-side logic applies to employment lands. In a land-optimizing model, jobs would be calculated in proportion to population based on labour force activity rates, and land allocated as appropriate. In this model, it is assumed that the number of jobs is largely constrained by the capacity of available employment land, and so a substantial proportion of total jobs are derived from employment land area.
Fig. 51: Summary of input variables and data sources
|
Input Variable |
Data sources |
|
Land Allocation |
|
|
Gross land area |
Fixed at 400 hectares |
|
Natural heritage features |
Neptis Greenlands database, which contains all federal, provincial, and municipal greenlands designations |
|
Natural heritage system |
Estimate in accordance with municipal and conservation authority standards |
|
Highways, rail, and utility corridors |
Section 2 |
|
Employment land area |
Section 2 and planning studies |
|
Local rights-of-way |
Section 2 and planning studies |
|
Residential Parcel Area (Population and Dwelling Units) |
|
|
Housing type mix |
Provincial, municipal, and |
|
Average household size, by unit type |
|
|
Average parcel area, by unit type |
|
|
Units per parcel, by unit type |
|
|
Public facilities (Parks and Schools) |
|
|
Area per school, by type |
Municipal plans and |
|
Schools per 1,000 units, by type |
|
|
Park area per 1,000 population |
|
|
Employment |
|
|
Vacancy rate of employment lands |
Planning studies |
|
% of all jobs in mixed-use settings |
Municipal plans and |
|
Employment density by employment type on employment lands |
|
|
Job mix on employment lands |
The input variables were chosen on the basis of parsimony and data availability. Sources include development standards specified in planning documents, demographic information from the census and recent forecasts, housing market information, and empirical evidence drawn from original research, publicly available planning reports, and academic literature. Fig. 51 summarizes the input variables. A full description is found in Appendix C.2.
The outputs of the model are population, employment, and dwelling unit density values calculated on three land bases: net parcel area, developable area, and gross area. These land bases are described in Section 1.4 and Fig. 3.
The aim of the exercise is to calculate the development capacity of urbanized land at its planned, "mature," built-out state -- a hypothetical state in which near-peak occupancy has been achieved, yet substantial redevelopment activity has not taken place. This state is likely to be reached only several decades after original construction. The model ignores the reality that, over the long term, development is phased and changes in market conditions may result in uneven development of land. Indeed, peak employment usually takes longer to reach than peak residential population. After full build-out occurs, the urban fabric will continue to evolve and change. Parcels and buildings are converted to other uses. Buildings are demolished and replaced with other uses or, in periods of economic decline, parcels may remain empty. In some cases, uses are intended to be temporary, as with retail "big boxes" that are slated to be replaced by more intensive uses as an area matures. Incorporating these sorts of temporal processes into the model was deemed unnecessary for this exercise.