The process of transitioning from a conceptual design to a detailed one is generally well understood. However, the complexity inherent in cost estimation during this process often must be clarified. This complexity primarily stems from the evolving nature of building design.
Here are a couple of quotes from Confucius:
Cost rests on the design: as the blueprint unfolds, so does the clarity of expenditure.
Early estimates are like viewing a mist-veiled mountain from afar; later estimates, like beholding its clear paths at the mountain's base.
Conducting a detailed cost estimation at the conceptual stage is challenging due to the absence of specific critical data components (e.g. structural, mechanical and electrical systems still need to be designed, facade materials still need to be chosen, etc.). Therefore, in the early stages, cost estimators typically stick to 'order of cost' estimates. This involves applying statistical or historical rates, such as cost per square foot ($/sqft), to broad metrics like Gross Internal Area or Net Internal Area. As the project advances, these estimates become increasingly detailed and accurate. For instance, during the pre-construction phase, actual rates and specific data replace statistical averages. Those actual rates are costs associated with procurement and construction: $/elements, $/trades, $/WBS (Work Breakdown Structure).
The transition between a project's early and later phases involves a mix of methods. Some aspects become more detailed earlier in the process, while others do so later. This variability adds to the complexity and messiness of the cost estimation process, demanding both flexibility and precision from the estimators.
Maintaining a centralised cost database is crucial to address the complexities of cost estimation, yet this presents its own challenges. The database should comprise at least two essential components:
However, the challenge lies in the continual updating of these components. Keeping historical data and current rates in sync is arduous, primarily due to the dynamic nature of construction costs and market conditions. Additionally, there needs to be a synergistic bridge between these two components. This bridge would facilitate the enhancement of each dataset through cross-referencing and validation, ensuring that historical benchmarks inform current rates and vice versa.
Another vital consideration is the regional differentiation in costs. The database should not only segregate data by region but also adapt to regional market fluctuations and specific construction practices. This regional separation ensures that estimators have access to relevant and accurate information tailored to their specific geographic context.
In essence, the toughness of a cost database hinges on its ability to stay current, interconnected, and regionally specific. These elements together form a robust framework for accurate and reliable cost estimation in the dynamic construction field.
