Design work is tricky from a time perspective. Architects and design professionals create drawings and conceptual designs in the present and the structures are built in the future. And that’s assuming everything goes as planned and the project doesn’t drag out because of approvals, permitting, weather or other unforeseen circumstances. Projecting costs and other economic conditions into the future 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 simply do not predict market swings or sharp cost escalations well. But technology advances have resulted in a new, incredibly useful tool: predictive data.
By using predictive data, design professionals can consider all 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 in to how predictive cost data is an improvement on traditional forecasts. Fair warning: This is going to get a little math-heavy.
Predictive Costs: Macroeconomics and Data Mining Make the Difference
Although based on econometric principles and modeling techniques, predictive cost data differs from traditional econometric forecasts in two ways. First, traditional forecasts are based on macroeconomic theory, even though analysis of those macroeconomic indicators demonstrate them to be statistically insignificant predictors. Predictive cost models 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. 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” 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 modeling practices. Since the 1990s, this family of processes and analyses has evolved from a mix of classic statistical principles, more contemporary computer science and machine learning methods. 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 a statistical algorithm that predict future values for a defined material and location. This is a far more robust methodology.
Predictive data and design
What does all this—the econometric principles, empirical evidence and data mining—mean for design 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. Construction professionals are already using predictive RSMeans data from Gordian to more accurately forecast the cost of construction up to three years before the project breaks ground. By using predictive data, project costs are not only forecasted accurately, but clients have more confidence in designs and the people who deliver them.
Data Makes the Difference
The quest to make the most of a project budget can feel like a long uphill climb. Accurate cost data can make it feel more like a walk in the park. Using trustworthy pricing information helps to find viable, value-creating alternatives. Integrating predictive cost data into the design process keeps today’s plans in line with tomorrow’s financial realities. When it comes to maximizing project budget, accurate data makes all the difference.