Note: The forecast models mentioned are at the end of this text.
There are only six primary building design categories on the
planet. The fact that buildings have been classified by appearance and internal
activity (such as a Greek Revival bank) has concealed a more fundamental
classification that makes the efficient use of land mathematically predictable.
A building category may be occupied by any activity when
they both comply with local zoning and building codes. The categories
themselves just have greater and lesser shelter capacity potential based on
their design specification topics and assigned values. (“Shelter capacity” is
gross building area per buildable acre.)
The most common building design category is G1. It includes
all buildings served by a surface parking lot around, but not under, the
building. I’m including a G1 model and two versions of its design specification
template to explain the concept of consistent development capacity prediction
and regulation.
The entire software package is based on a single concept. A
user can accurately predict gross building area options for any given land area
based on the values entered in a design specification template. The template is
related to the building design category under consideration. When gross
building area options can be forecast, the user can predict anything that is a
function of a building square foot such as: construction cost, profit
potential, revenue potential, financing expense, population, traffic
generation, and so on. This is a great boost to project economic development,
but there is another macro consideration.
A city has a total expense per acre that is offset by its
total revenue per acre. Revenue potential is a function of the gross building
area present and the activity sheltered within. The combination of shelter
capacity and activity produces real estate tax, income tax, and other
miscellaneous income to offset total municipal expense per acre. The challenge
is to correlate land use allocation with shelter capacity to produce revenue
per acre that will be equal to a city’s expense per acre as it ages.
The best municipal economic development combines project
emphasis with a correlated, comprehensive land use and shelter capacity plan
for economic stability throughout the city. This means that a city must
understand the productivity of each acre. Productivity is primarily produced by
a combination of shelter capacity and activity per acre. The challenge is to
balance shelter capacity with occupant activity throughout a city to reach its
revenue target. Unfortunately, a city has not been able to measure or accurately
predict the shelter capacity of each acre in its portfolio. This has prevented
an accurate assessment of the revenue potential of each acre when occupied by gross
building area and activity options.
An economic plan grows one project at a time, but it will
continue to be a haphazard benefit to its community until the physical growth
of shelter capacity can be correlated with the social and economic implications
of occupant activity per square foot of gross building area potential. At this
point the final physical composition, context, and appearance added will
symbolize the development decisions behind the health, safety, and quality of
life provided.
The following are two examples of design specification
decisions that lead to gross building area and shelter capacity options for a
given gross land area. The design specification template lists the G1 cell
content that must be managed with optional value decisions to produce intended
physical, social, psychological, environmental, and economic results on a
consistent basis.
Non-Residential
Shelter Capacity
A G1.L1 forecast model is attached and applies when gross
land area (GLA) is given and gross building area options (GBA) are to be found.
(A G1.B1 forecast model applies when a gross building area objective is given
and buildable land area options for the gross building area objective are to be
found.) When values are entered in all shaded boxes of the G1.L1 forecast
model, the algorithm and master equation in the model calculate gross building
area options for the land specified in cell F3 based on the floor quantity
options entered in cells A44-A53. (Note that 30% open space has been entered in
cell F11. This value produces an impervious cover limit of 70% in cell F12. The
storm sewer capacity present or planned should equal this impervious cover
percentage unless storm detention and/or retention are introduced.)
Based on all specification values entered, the gross
building area options forecast are contained in cells B44-B53 of the Planning
Forecast Panel and are a function of the floor quantity options entered in
cells A44-A53. (Please note that the GBA options increase at a decreasing rate
per floor, and that building footprint options (BCA) decline at an increasing
rate per floor until the footprint eventually becomes too small to be
realistic. This is a G1 Design Category characteristic that I won’t stop to
explain.)
A change to one of more of the values entered in the shaded
boxes of the design specification template produces a new forecast of
development capacity options in the Planning Forecast Panel of the G1.L1 model.
The building footprint area (BCA), gross parking area (GPA), and parking
quantity options (NPS) predicted in Columns C-E are a few of the many that can
be calculated once gross building area alternatives (GBA) can be forecast.
The Implications Module measures the shelter capacity
(SFAC), intensity (INT), intrusion (INTR), and dominance (DOM) implied by the design
specification values entered and the gross building area options (GBA)
calculated in cells B44-B53.
Residential Apartment
Shelter Capacity
The G1.L1 forecast model predicted gross building area
options when occupied by non-residential activities. The results in the
Planning Forecast Panel (PFP) and Implications Module (IMP) were a function of
the values entered in its 27 shaded design specification boxes.
A G1.L1.R3 forecast model is included to illustrate an R3 apartment
specification module. It must be added to the G1.L1 specification template when
R3 apartment occupancy is planned. This module is presented in cells A34-J45.
The 52 values entered in the shaded boxes of the G1.L1.R3 design
specification are again used by an architectural algorithm and master equation
to predict gross building area potential in cells B56-B65.
The values entered in the R3 Apartment Module are used to
convert the gross building area and building footprint options predicted in
columns B and C of the Planning Forecast Panel. The number of dwelling units
implied is forecast in Column D of the Planning Forecast Panel. Additional
parking and garage predictions are included in Columns E-H based on the
specification values entered. A breakdown of dwelling unit quantities by
bedroom type is included on lines 70 and 71.
The Implications Module of the G1.L1.R3 forecast model is
located in cells J56-M65. It translates gross building area options into the following
implication categories: (1) shelter capacity per acre, (2) intensity, (3)
intrusion, (4) domination, and (5) dwelling unit density per shelter acre
(dSHAC).
Conclusion
A forecast model permits all interested public
and private parties to discuss a common set of correlated design specification
topics and values with predictable outcomes and implications. This removes
surprises, builds trust, and produces consensus by using a common mathematical
language with the capacity to repeat success and avoid failure. 
