Predicting Construction Costs under Uncertain Market Conditions: Probabilistic Forecasting Using Autoregressive Recurrent Networks Based on DeepAR

Ghiwa Assaf, Rayan H. Assaad, Islam H. El-Adaway, Mohamad Abdul Nabi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Projects often experience cost overruns due to market uncertainty and price escalations. Traditional cost estimation methods that rely on point estimation are incapable of providing prediction intervals as well as probabilistic assessment. Thus, there is need for an innovative approach to predict the changes and uncertainties in construction material costs. This paper proposes a novel stochastic model to estimate construction material costs by applying probabilistic forecasting using autoregressive recurrent networks. First, price data was collected for four different construction materials. Second, data was divided into a training set (pre-COVID-19) and a testing test (post-COVID-19). Third, the state-of-the-art DeepAR algorithm was implemented to provide probabilistic forecasts for construction material prices under uncertain post-COVID market conditions. The results showed that the proposed stochastic model provides accurate cost estimates with a mean absolute percentage error of 1% for concrete products, of 2% for concrete ingredients, of 3% for paving mixtures and blocks, and of 4% for steel and iron materials. This paper adds to the body of knowledge by proposing a new approach for estimating construction material by providing probabilistic forecasts in the form of Monte Carlo samples that can be used to compute quantile estimates, which offers better protections against rising costs.

Original languageEnglish (US)
Title of host publicationContracting, Delivery, Scheduling, Estimating, Economics, and Organizational Management and Planning in Construction
EditorsJennifer S. Shane, Katherine M. Madson, Yunjeong Mo, Cristina Poleacovschi, Roy E. Sturgill
PublisherAmerican Society of Civil Engineers (ASCE)
Number of pages10
ISBN (Electronic)9780784485286
StatePublished - 2024
EventConstruction Research Congress 2024, CRC 2024 - Des Moines, United States
Duration: Mar 20 2024Mar 23 2024

Publication series

NameConstruction Research Congress 2024, CRC 2024


ConferenceConstruction Research Congress 2024, CRC 2024
Country/TerritoryUnited States
CityDes Moines

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction


Dive into the research topics of 'Predicting Construction Costs under Uncertain Market Conditions: Probabilistic Forecasting Using Autoregressive Recurrent Networks Based on DeepAR'. Together they form a unique fingerprint.

Cite this