Using Data to Future-Proof Your Designs
Design work is tricky from a time perspective. Architects create drawings and conceptual designs in the present, and the structures are built in the future. This is assuming that everything goes as planned and the project doesn’t drag out because of delayed approvals, lagging permits, severe weather, or other unforeseen circumstances. Given all the possible delays, projecting future costs and other economic conditions is complicated. Until recently, these forecasts have been guesswork at best.
Traditional forecasting data, developed during a time of far less computing power and available data, does not meet today’s needs for accurate planning and budgeting. These older methods are simply inadequate for predicting market swings and sharp cost escalations. But technology advances have resulted in a new, incredibly useful tool: predictive data.
By using predictive data, architects can consider future factors at play in a region, including local labor rates and material costs. This makes it much easier to complete a project within the planned budget.
Let’s dig into how predictive cost data is an improvement on traditional forecasts.
Predictive Costs: Macroeconomics and Data Mining Make the Difference
Although based on econometric principles and modeling techniques, predictive cost data differs from traditional forecasts in two ways. First, traditional forecasts are based on macroeconomic theory, even though analysis of those macroeconomic indicators show them to be statistically insignificant predictors. Those indicators were all we had, so we used them. But that is no longer the case. Predictive cost models disregard theory altogether and are based exclusively on data-driven empirical evidence.
This empirical evidence is the result of extensive data analysis and pattern-seeking visualizations of historical cost data with economic and market indicators. This updated approach has been extensively researched and validated by Dr. Edward Leamer, professor of global economics and management at UCLA. Only economic indicators that have “proven themselves” become candidates for model development, testing, validation, and predictive cost estimates.
Second, predictive cost data uses mining techniques and principles to improve traditional econometric modeling practices. Data mining takes advantage of recent increases in computing power, data visualization techniques, and updated statistic procedures in order to find patterns and determine drivers of construction material and labor cost changes. Measures of these drivers and their relationships to each other and to 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 that produces a significant increase in result accuracy.
Predictive Data and Design
What does all this—the econometric principles, empirical evidence, and data mining—mean for architects? 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. General contractors and project managers are already using predictive RSMeans data from Gordian to forecast the cost of construction up to three years before the project breaks ground. By using predictive data, costs are not only forecasted accurately, but project owners have more confidence in designs and the teams that deliver them.
Data Makes the Difference
There has always been a gap between when an architect designs a facility and when the facility is actually built. Historically, that gap has resulted in inaccurate cost estimates. That’s not the fault of anyone involved; the tools available to architects were limited. But technology has improved in recent years. Predictive cost data is closing the gap between the design phase and the building phase, making cost estimates more realistic and improving relationships between architects and project owners.