Architects and design professionals create drawings and conceptual designs using data from the present to build structures in the future. Delays happen. In the process, projects get dragged out because of regulatory approvals, weather changes and other unforeseen circumstances. These timing adjustments may make original design data irrelevant.
Furthermore, the interval between design and construction makes projecting costs incredibly difficult. Until recently, these cost forecasts have been guesswork at best.
Traditional forecasting data do not meet today’s standards for accuracy in planning and budgeting. They do not predict market swings or sharp cost escalations well. But advances in technology have resulted in a new, useful tool for architects, engineers and other design professionals; predictive data.
Predictive data enables design professionals to consider all future factors at play within a region, including local labour rates and material costs. This reduces the gap between planned and actual project budget in spite of the timeframes.
What difference does it make? First, traditional forecasts are based on macroeconomic indicators which have been shown to be statistically insignificant. Predictive cost models on the other end disregard theory altogether and are based exclusively on data-driven empirical evidence instead.
This empirical evidence is the result of extensive exploratory data analysis and pattern-seeking visualizations of historical cost data with economic and market indicators. In this approach, only economic indicators that have “proven themselves” in exploratory analysis become candidates for model development, testing, validation and predictive cost estimates.
Second, predictive cost data uses mining techniques and principles to improve traditional econometric modelling practices. Data mining takes advantage of recent increases in computing power, data visualization techniques and statistical procedures in order to find patterns in the drivers of construction material and labor cost changes.
Measures of these drivers and their relationships to each other and construction costs, along with their associated lead or lag times, are represented in an algorithm that predicts future values for a defined material and location. This is a far more robust methodology.
Predictive Data and Design.
For professionals, the ability to use predictive data that accounts for real market conditions (amount of construction versus labor availability) and commodity price impacts on material costs is critical to keeping designs in line with budgets. Predictive data makes the planning done today more realistic for tomorrow.
Construction professionals are already using predictive data to produce more accurate forecasts of construction cost for up to three years before the project breaks ground. Predictive data aims to give clients more confidence in designs and the people who deliver them.
Source; BDC Network
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