Gordian Releases Predictive Technology in RSMeans Data Online
Wednesday, July 11th, 2018
Gordian, the leading provider of facility and construction cost data, software and expertise, announced the availability of Predictive Cost Data in RSMeans Data Online. The advanced predictive technology delivers accurate, location-specific costs for more than 100 different types of construction projects up to three years into the future.
The foundation of Predictive Cost Data is more than 10 billion data points from 15 years of historical RSMeans data. Gordian’s Data Scientists applied advanced analytics with more than one thousand indexes, including government indexes and information from data aggregator, Moody’s. Through a rigorous statistical program, a unique algorithm was produced for each material and labor segment, such as steel, wood and concrete. Thorough back testing from the last 10 years resulted in an unparalleled accuracy of three percent to actual cost indexes.
“Most of the tools available today make it difficult to plan and budget for future construction, especially more than one year out,” said Noam Reininger, Gordian’s Chief Data Officer. “Going forward, you no longer have to plan tomorrow’s project with yesterday’s data because our Predictive Cost Data has accuracy these previous methods lack. It is a game changer.”
For more than 75 years, RSMeans data from Gordian has been the leader for construction cost data, and the predictive technology provides the capability to effectively plan and estimate future construction projects. Until this point, owners and construction executives had few options outside of relying on current and historical costs, their own experience and studying trends to forecast building costs.
These capabilities make it possible to determine when and where costs and conditions are most favorable for construction projects. With a few clicks, the cost of materials in a specific region during a particular quarter is visible, helping organizations prioritize the timing and even locations of projects.
Reininger continued: “Over time, material prices change substantially. Continually attempting to stay abreast of trends or only factoring in general inflation to try to account for these differences simply falls short. With Predictive Cost Data, our algorithm allows for a level of accuracy never before possible.”