An important variable in assessing risk of a commercial/residual building is the actual age of the building. While this seems obvious, obtaining the true age of the building can be challenging. COUNTRY has made use of data available but would like to explore machine learning techniques for assessing the age of a commercial/residual building.

Using available data sources, research modeling techniques and deploy best model that predicts a commercial/residual building’s age. This project builds on existing research done in this space, and COUNTRY is particularly interested in researching how image classification techniques complement modeling approaches on structured data to obtain a robust commercial/residual building age model.

Students: Boting Li, Chengzhuang Zheng, Ziqin Xiong

Supervisors: Lois She-Tom (COUNTRY Financial), Matt Morris (COUNTRY Financial)