COST ESTIMATION PREDICTIVE MODELING: REGRESSION VERSUS NEURAL NETWORK

TLDR

Cost estimation typically predicts labor, material, utilities, or other costs over time using limited data on cost drivers, with conventional regression models commonly employed. This study compares regression and neural network approaches for developing cost estimating relationships (CERs) to assess their performance, stability, and ease of use. The comparison uses both simulated and real-world data sets to evaluate the two modeling techniques. Neural networks outperform regression when data deviate from low-order polynomial assumptions or lack prior knowledge of an appropriate CER, whereas regression offers superior accuracy, lower variability, and easier model creation and examination when a suitable CER is available.

Abstract

ABSTRACT ABSTRACT Cost estimation generally involves predicting labor, material, utilities or other costs over time given a small subset of factual data on “cost drivers.” Statistical models, usually of the regression form, have assisted with this projection. Artificial neural networks are non-parametric statistical estimators, and thus have potential for use in cost estimation modeling. This research examined the performance, stability and ease of cost estimation modeling using regression versus neural networks to develop cost estimating relationships (CERs). Results show that neural networks have advantages when dealing with data that does not adhere to the generally chosen low order polynomial forms, or data for which there is little a priori knowledge of the appropriate CER to select for regression modeling. However, in cases where an appropriate CER can be identified, regression models have significant advantages in terms of accuracy, variability, model creation and model examination. Both simulated and actual data sets are used for comparison.