Document Type

Closed Project

Publication Date

Winter 2007


Dundar Kocaoglu

Course Title

Decision Making in Engineering and Technology Management

Course Number

ETM 530/630


The construction industry can be, roughly, divided into four construction end-product categories – residential (homes/town-homes), commercial (retail/apartments), industrial (manufacturing/warehouses/storage) and mixed-use (multi-story with lower levels retail and upper levels condominiums). The “decision” to build (or not) is based is based on number of factors – some analytical, some not so analytical, i.e. “gut feelings”. It is the authors opinion that the “build/no-build” decision is entirely too non-analytical and unquantified. Decisions are made based on wild guesses, hunches and “gut-feelings”. This paper proposes to apply decision making theory, develop a decision model and document the decision making process. This analysis then can be used to quantify, the expected net income and the probability of such net income, on the project prior to making the “build/no-build” decision. Specifically, this paper analyzes the mixed-use category of construction end-product. The analysis uses costs and revenues to determine net incomes. Costs include land (dirt), hard costs, soft costs, construction loan interest and sales commissions. The revenue category is limited to condominium sales only. Initially, a uniformally random distribution for cost and revenues was assumed. It was determined that the results attained could be refined, to provide a more accurate estimation of net income, if the distribution was supplemented with Pairwise Comparison. Ten expert opinions were gathered to first, estimate the ranges about the cost and revenue components. The experts were provided an initial estimate, from which they estimated a range (lower and upper bound), around the initial estimate. The range for each component was divided into three equal intervals. Using a Pairwise Comparison, the intervals were evaluated by the experts. Probabilities were then computed for each possible outcome of net income. The probabilities were then plotted against the net income. The resultant output, the probability vs. net income graph (and associated table), are the tools that quantify the data and graphically depict, for the decision maker, the probability of economic success as predicted from expert opinion. The use of the Pairwise Comparison Technique indicated a slightly different, yet more robust (in the opinion of these authors) result, than the random distribution model had the Pairwise Comparison not been made.


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