I&T Solution

Reference No.

S-0349

Solution Name

Predictive Modelling of Building Cost Estimate

Solution Description

Building cost estimation has been one of the most important topics in surveying and the construction industry.  It involves numerous steps of procedures from budget planning, building cost estimation to budget cost approval and cash management.  While there may sometimes be a long time gap between budget cost approval and settlement with contractors, there incurs a significant monetary loss in terms of budget utilization and idle time value of money.  Also, under-estimating the cost at preliminary stage could lead to extra time and monetary efforts for budgeting and funding at later stages.  Therefore, there exists a need in leveraging big data analytics and state-of-the-art predictive models to better predict the building cost estimate, so as to efficiently allocate the wasted time and human efforts to perform other tasks.

Within a rather traditional construction industry, StatLytics sees the opportunities in introducing and adopting big data analytics, artificial intelligence, machine learning and predictive modelling in building cost estimation.  In early stages, machine learning and latest algorithms in predictive analytics are expected to improve the high-level building cost estimation by a lot, throughout learning from previous sources of errors and gaining insights from all past data.  In intermediate stages, with a good foundation in data platform and high-level estimates, big data analytics could come along and improve the prediction algorithms, e.g. obtaining tremendous data from the Internet for analytics and predicting general building cost price trend.  In later stages, artificial intelligence could be introduced by using image processing or text analytics to learn how complex a building is.

Application Areas

Budget Cost Management

Technologies Used

Artificial Intelligence (AI)
Data Analytics
Machine Learning
Predictive Analytics

Use Case

Several improvements are seen important for the departmental use case.  The most significant one would be an improvement in project cost estimation.  This facilitates better project and budget management, reduces extra human efforts in the pre-approval of cost funding and minimizes monetary loss associated with time value of money.  Others related improvements include, but not limited to, construction data management, digitalization within the department, streamlining processes and procedures and better cross-team or cross-departmental data exchange and integration.

Company / Organisation Name

StatLytics Limited

Email Address

info@statlytics.co

Telephone Number

91433720

Website

https://www.statlytics.co/

Address

West Wing 2/F, 822 Lai Chi Kok Road, Cheung Sha Wan, Kowloon

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