*4.5. Benefits and Challenges*

The commonly reported benefit in virtually all studies was the higher accuracy of the models in comparison to the traditional cost estimation techniques. This benefit has not been included in the benefits and challenges analysis because it was included in the Predictive Power section, where it was quantitatively analysed. The next two most mentioned benefits were (1) the suitability of the techniques for real practice, and (2) the possibility of improvement by combining them with other techniques. Cheng et al. [56] concluded that the techniques implemented were suitable for practice, where the authors highlighted that the model can enhance the ability of designers, owners, and contractors in the decisionmaking process leading to higher possibilities to achieve project success. Regarding the improvement in the techniques, Sonmez [55] concluded that the simultaneous use of ANN and MRA could provide satisfactory conceptual models.

Some authors of the publications have found limitations that make predictive analytics in cost estimation an area still in development with drawbacks to address. The main challenges expressed were (1) the need for more data, (2) to generalise models towards location and different project types, and (3) the improvement of attribute weighting. Predictive analytics bases its performance on data. Therefore, it becomes essential for cost modelling to have access to building-projects data. Models use input data to learn and larger data sets would increase their performance [51]. Since construction is an economic activity, the nature of competition does not incentivise sharing information because it is an element of competitive advantage, but individual companies may be able to implement predictive analytics by themselves. Ngo et al. [10] found that construction companies in Singapore do have pertinent data to implement predictive analytics. In this sense, the availability of data is a drawback in research, but, from the perspective of companies, it can be considered as a benefit due to a large amount of data they store from previous projects in the form of contract documents, schedules, drawings, specifications, and images. The second area to overcome, according to researchers, is the need for generalisation about location and typologies. Generalisation means an increase in the number of input parameters, and, therefore, more parameters require more data [86]. So, the increase in generalisation is strongly related to the first challenge—data availability. The third challenge perceived in the studies is the need to improve the techniques. The studies exposed that ANNs need improvement in the methods to optimise the network architecture and CBR needs to address attribute weighting, but other techniques not yet explored in the cost estimating of buildings may provide alternatives that suit the particular circumstances of the estimation case.

#### **5. Conclusions**

Several emergent techniques from predictive analytics have become a major area for researchers seeking to improve the practice of construction-cost estimation in the early stages of projects. Advances in methodology and techniques have become available in the last 20 years, but the explicit benefits and implications for cost-estimation practice have not been sufficiently highlighted to ignite the uptake by the industry. As an initial stimulus for the adoption, a systematic literature review was conducted in this study to investigate how predictive analytics can enhance early-stage cost estimation of buildings, resulting in three main contributions to the body of research:


We found that previously published research identified structured processes to apply predictive analytics on cost estimation, and that the accuracy of the models developed has surpassed that of the traditional practices of building construction-cost estimation. Additionally, the practices for modelling costs with predictive analytics have been structured and well documented. Three main implications can be drawn from this discussion:


Future research perspectives relate to implementation issues of predictive analytics in cost estimation, focusing on investigating the current state of uptake in the industry, and the necessary ground conditions in organisations to deploy them, such as necessary skills of practitioners and decision-makers' awareness regarding the implications of predictive analytics for construction project success. The main limitation possibly influencing the results of the review was identified. There was a possibility of not having found all the relevant papers due to the different words used to describe a concept within predictive analytics in cost estimation. The implementation of backward and forward snowballing contributed to addressing the first limitation identifying papers out of the search performed using the search engines.

**Author Contributions:** Conceptualization, S.L.C.M. and E.D.R.C.; methodology, S.L.C.M., E.D.R.C. and V.G.; validation, S.L.C.M., E.D.R.C., V.G. and J.A.; formal analysis, S.L.C.M. and E.D.R.C.; investigation, S.L.C.M. and E.D.R.C.; resources, S.L.C.M., E.D.R.C., V.G. and J.A.; data curation, S.L.C.M., E.D.R.C., V.G. and J.A.; writing—original draft preparation, S.L.C.M. and E.D.R.C.; writing—review and editing, S.L.C.M., E.D.R.C., V.G. and J.A.; visualization, S.L.C.M. and E.D.R.C.; supervision, E.D.R.C. and V.G.; project administration, E.D.R.C.; funding acquisition, E.D.R.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the New Zealand Earthquake Commission (grant number 18/U777).

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
