創新及科技解決方案

解決方案描述

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.