Maximize Project Budgets with Alternative Solutions

Using value engineering to make data driven decisions to maximize budgets without compromising integrity.
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Sponsored by Gordian
By Amanda Voss, MPP

VE Tools

Of paramount importance to the VE process is access to verified and reliable sources of construction cost data. Third-party, independent cost data will provide the most objectivity and allow the entire design team and owner to make accurate, informed decisions. In the end, it is all about identifying the best alternatives.

To effectively value engineer, design professionals need to know where costs lie. To help assess feasible solutions, many architects, owners, engineers and other construction professionals rely on accurate cost data from a reliable industry expert. Accurate cost data is crucial for understanding the facts on the ground, enabling effective decisions and detailing the reasons why a change was or was not recommended.

Accurate third-party cost data on materials, labor, and equipment is an invaluable resource for building reliable rough order of Magnitude estimates. That same data is also great for building client trust. Accurate cost information demonstrates the impact of design decisions and shows project owners that the team has exhausted all of its options in pursuit of creating the best possible design within budget parameters. Sharing objective, third-party cost information with a project owner establishes that the design team is a partner that cares about the bottom line and will not take advantage of ownership.

Accurate third-party cost data can offer the additional benefit of predictive costs. Design work is often complicated by the factor of time. Architects and design professionals create drawings and conceptual designs in the present when the structures outlined in the drawings will be built in the future. The project timeline is often extended beyond expectations because of approvals, permitting, weather, or other unforeseen circumstances. Projecting costs and other economic conditions into the future is problematic. Often, forecasting costs have been based on guesswork at best.

Older methods of traditional forecasting data are often lacking when the market swings or experiences sharp cost escalations. With technological advances, however, today’s design teams have a new, incredibly useful tool: predictive construction cost data. Predictive data models allow design professionals to consider all future factors at play on a regional level, including local labor rates and material costs. This makes it much easier to plan for and complete a project within the established budget. Using trustworthy pricing information helps to find viable, value-creating alternatives.

Macroeconomics versus Data Mining

lthough grounded 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 macroeconomic theory altogether and are based instead on data-driven empirical evidence.

The empirical evidence used in predictive cost models 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 the University of California, Los Angeles. Only economic indicators that have “proven themselves” in exploratory analysis become candidates for model development, testing, validation, and predictive cost estimates.

To further bolster their credentials, 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, contemporary computer science, and machine learning methods. Data mining takes advantage of recent increases in computing power, data visualization techniques, and updated statistical procedures 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 predicts future values for a defined material and location.

Put together, predictive models are founded in a robust methodology.

Good Data Makes All the Difference

The ability to use predictive data that accounts for real market conditions, such as amount of construction versus labor availability, and commodity price impacts on material costs is critical to keeping designs in line with budgets. Econometric principles, empirical evidence, and data mining combine to create a powerful tool for construction professionals, allowing them to use predictive data sets to more accurately forecast the cost of construction up to three years before the project breaks ground. This allows for faster planning and less redesign down the road. By using predictive data, project costs are not only forecasted more precisely, but clients also have greater confidence in the designs and the people who deliver them.

Integrating predictive cost data into the design process also keeps today’s plans in line with tomorrow’s financial realities. When it comes to maximizing a project budget, accurate data makes all the difference.


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Originally published in Architectural Record
Originally published in March 2020