Making data-driven decisions on the impacts of rising construction commodity prices

Steve Willcock
Raw material prices have escalated significantly since the onset of the COVID-19 pandemic, creating great uncertainty in predicting construction prices.

COVID-19 disrupted typical market conditions, leading to an imbalance in supply and demand for many commodities. Early in the pandemic as construction and manufacturing operations slowed, demand for many materials decreased. As a result, producers scaled back their operations.

As businesses adapted to new regulations, operations in construction and manufacturing resumed and demand for materials quickly spiked. However, production for some materials did not resume at the same rate, and stockpiles began to diminish. With producers unable to meet the sudden resurgence in demand, materials such as steel and lumber became scarce and prices rose sharply.

The rise in US residential construction has been spectacular, with the latest government figures showing the residential construction market has grown by more than $138 billion per month from March 2020 to March 2021, a 23% increase. This growth has been a critical factor in escalating lumber prices, as increased demand from residential projects has exacerbated the range of supply-side challenges during the pandemic.

Conversely, during the same period, non-residential construction declined by more than 7%, equal to approximately $62 billion less construction activity per month.

Current pricing trends

Over the last year, prices for many construction goods have accelerated faster than general historical trends. Steel and lumber prices have escalated sharply since early 2020. As construction projects and the wider economy have come back online during the recovery from COVID-related shutdowns, demand has exceeded producers’ ability to supply materials.

The spikes in steel and lumber prices are believed to be short-term mismatches in supply and demand.

Scaling up production for lumber has been challenging due to issues such as beetle plagues and wildfires, which have reduced the inventories of many lumber mills in the Pacific Northwest, combined with a longstanding lack of excess production capacity[1] and labor[2] at lumber mills.

An expected increased production capacity of steel in the near future could alleviate some pressure in the market as new mills come online[3]. Furthermore, steel and lumber prices could reduce if the Association of General Contractors (AGC)[4] are successful in convincing the Biden administration to eliminate tariffs on steel[5] and lumber[6].

A longer-term concern exists with copper, as demand for copper is expected to surge from growth in production of electric vehicles, data centers and renewable power infrastructure, which could create a long-term shortage and drive up the price.


Project owners should be aware of the current inflated prices of the materials mentioned in this report and monitor pricing trends over the coming months.

Faithful+Gould produces a monthly Construction Data Intelligence Report that construction consumers can use to track the latest information on material price movements and a range of other market indicator information.

The current pricing levels for steel and lumber are likely not sustainable in the long term and a balancing in market forces is expected to bring them back down; however, it remains difficult to accurately determine timelines.

We are hearing frequent reports of steel and lumber vendors not being able to guarantee a quoted price to contractors for more than a few days, which naturally will lead to contractors accounting for further risk in the numbers that they pass on to project owners. It is also important to keep in mind that a percentage change in raw material prices does not always translate directly to a similar change in a project’s final cost. Other elements such as labor cost, equipment rates, material fabrication and installation will not necessarily change cost at the same rate as the raw material price.

Historically, the largest impact on the price owners will pay for construction projects is the level of competition in the construction market. With non-residential construction down more than $62 billion/month during the pandemic, non-residential contractor backlog has been depleted significantly. Architectural billings had also greatly reduced during the pandemic but has started showing significant signs of recovery since March.


The reduced activity led to a notable increase in competition among contractors for work, which has kept bid prices suppressed through 2020 and early 2021, despite the rapid escalating commodity prices. The following chart plots the year-over-year change of input prices and the effective ‘bid price’ of non-residential construction over the last year.

These dual impacts of reduced non-residential construction activity and increasing material prices are creating a challenging environment for contractors to deliver jobs profitably. Anecdotally, Faithful+Gould is also noting greater concerns from contractors regarding material lead time, in addition to the known loss of available productive working time on site because of social distancing measures.

The long period of reduced non-residential construction activity and increasing material prices have created real pressures on contractors’ business models as projects have cost them more to complete. In many cases they’ve reduced bid prices to win work. Now that non-residential construction activity is picking up, the willingness of contractors to absorb further impacts of material cost increases will be low.

Experience tells us that if a contractor doesn’t have enough funds in their contract to deliver the required scope, the chances of a satisfactory outcome on a project are likely to be low for all involved. Equally, it becomes unaffordable for owners if the costs are rising too fast for projects already underway. A balance of these factors needs to be achieved for an owner to receive the value they need on a project—we believe a critical tool in achieving this balance is better data to make an informed decision.  

Predicting the exact timing of a drop in metal and lumber pricing is a difficult and risky game. Even if perfect information existed on this topic, it would not be in the best interest of many projects to move their schedule to accommodate reduced commodity prices or changes in bid activity.

We recommend that owners look closely at the basis for the contingency budgets they are carrying on their project to make an informed decision about how much risk the project is willing and able to bear on material price increases. 

We recommend two strategies to help make data-driven decisions on the impact of commodity prices:

  1. Understand your Estimate - A project estimate that breaks out the labor, materials and equipment components across all the buy-out phases of a job.
  1. Risk Simulation - Combining the latest market data analysis and forecasts with a Monte Carlo risk simulation to quantify the range of potential impacts on the project.

By understanding the elements of the project estimate that are susceptible to commodity price variation, a project team can isolate the size of the risk that commodity escalation has on their project. Then the team can review the latest data from a range of sources, such as government producer price indexes (PPI), commodity futures markets and direct vendor relationships. Developing this data into forecast models with associated confidence intervals provides an excellent starting point for a project team to apply their own expert judgment and experience to define well-informed probability distributions in a risk model.

The use of Monte Carlo simulation techniques provide a probabilistic answer (i.e., a calculation based on a statistical distribution) to a deterministic problem (e.g. the change in project electrical cost based on the change in copper prices between now and the planned end of July buy-out date).

The analysis involves running thousands of randomized simulations of a project scenario, shaped by the probability distributions that the project team developed, after reviewing the impacts of the best available data and forecast models. Thankfully, the software does the hard work and the simulations run close to instantaneously. The main effort is to understand how the data relates to project costs, then gather a knowledgeable team to collaborate in developing the cost ranges and probability distributions.

This gives the project team a better understanding of the range of impacts on their job to make a data-driven decision on the calculated project risk. Then they can consider if they can afford to proceed, or if they need to consider alternatives like value engineering or alternative procurement strategies.







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