Predictive Analytics for Early-Stage Construction Costs Estimation
Low accuracy in the estimation of construction costs at early stages of projects has driven the research on alternative costing methods that take advantage of computing advances, however, direct implications in their use for practice is not clear. The purpose of this study was to investigate how pre...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/12/7/1043 |
| _version_ | 1797440501911126016 |
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| author | Sergio Lautaro Castro Miranda Enrique Del Rey Castillo Vicente Gonzalez Johnson Adafin |
| author_facet | Sergio Lautaro Castro Miranda Enrique Del Rey Castillo Vicente Gonzalez Johnson Adafin |
| author_sort | Sergio Lautaro Castro Miranda |
| collection | DOAJ |
| description | Low accuracy in the estimation of construction costs at early stages of projects has driven the research on alternative costing methods that take advantage of computing advances, however, direct implications in their use for practice is not clear. The purpose of this study was to investigate how predictive analytics could enhance cost estimation of buildings at early stages by performing a systematic literature review on predictive analytics implementations for the early-stage cost estimation of building projects. The outputs of the study are: (1) an extensive database; (2) a list of cost drivers; and (3) a comparison between the various techniques. The findings suggest that predictive analytic techniques are appropriate for practice due to their higher level of accuracy. The discussion has three main implications: (a) predictive analytics for cost estimation have not followed the best practices and standard methodologies; (b) predictive analytics techniques are ready for industry adoption; and (c) the study can be a reference for high-level decision-makers to implement predictive analytics in cost estimation. Knowledge of predictive analytics could assist stakeholders in playing a key role in improving the accuracy of cost forecast in the construction market, thus, enabling pro-active management of the project owner’s budget. |
| first_indexed | 2024-03-09T12:09:06Z |
| format | Article |
| id | doaj.art-d90e7beda4704bf780a7a45e92ff1091 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T12:09:06Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-d90e7beda4704bf780a7a45e92ff10912023-11-30T22:55:27ZengMDPI AGBuildings2075-53092022-07-01127104310.3390/buildings12071043Predictive Analytics for Early-Stage Construction Costs EstimationSergio Lautaro Castro Miranda0Enrique Del Rey Castillo1Vicente Gonzalez2Johnson Adafin3Department of Civil and Environmental Engineering, University of Auckland, Auckland 1010, New ZealandDepartment of Civil and Environmental Engineering, University of Auckland, Auckland 1010, New ZealandConstruction Engineering and Management, Faculty of Engineering—Civil and Environmental Engineering Department, University of Alberta, Edmonton, AB T6G 2R3, CanadaDepartment of Quantity Surveying and Construction Management, Northland Polytechnic, Auckland 1010, New ZealandLow accuracy in the estimation of construction costs at early stages of projects has driven the research on alternative costing methods that take advantage of computing advances, however, direct implications in their use for practice is not clear. The purpose of this study was to investigate how predictive analytics could enhance cost estimation of buildings at early stages by performing a systematic literature review on predictive analytics implementations for the early-stage cost estimation of building projects. The outputs of the study are: (1) an extensive database; (2) a list of cost drivers; and (3) a comparison between the various techniques. The findings suggest that predictive analytic techniques are appropriate for practice due to their higher level of accuracy. The discussion has three main implications: (a) predictive analytics for cost estimation have not followed the best practices and standard methodologies; (b) predictive analytics techniques are ready for industry adoption; and (c) the study can be a reference for high-level decision-makers to implement predictive analytics in cost estimation. Knowledge of predictive analytics could assist stakeholders in playing a key role in improving the accuracy of cost forecast in the construction market, thus, enabling pro-active management of the project owner’s budget.https://www.mdpi.com/2075-5309/12/7/1043buildingscost estimationpredictive analyticssystematic literature review |
| spellingShingle | Sergio Lautaro Castro Miranda Enrique Del Rey Castillo Vicente Gonzalez Johnson Adafin Predictive Analytics for Early-Stage Construction Costs Estimation Buildings buildings cost estimation predictive analytics systematic literature review |
| title | Predictive Analytics for Early-Stage Construction Costs Estimation |
| title_full | Predictive Analytics for Early-Stage Construction Costs Estimation |
| title_fullStr | Predictive Analytics for Early-Stage Construction Costs Estimation |
| title_full_unstemmed | Predictive Analytics for Early-Stage Construction Costs Estimation |
| title_short | Predictive Analytics for Early-Stage Construction Costs Estimation |
| title_sort | predictive analytics for early stage construction costs estimation |
| topic | buildings cost estimation predictive analytics systematic literature review |
| url | https://www.mdpi.com/2075-5309/12/7/1043 |
| work_keys_str_mv | AT sergiolautarocastromiranda predictiveanalyticsforearlystageconstructioncostsestimation AT enriquedelreycastillo predictiveanalyticsforearlystageconstructioncostsestimation AT vicentegonzalez predictiveanalyticsforearlystageconstructioncostsestimation AT johnsonadafin predictiveanalyticsforearlystageconstructioncostsestimation |