After the 2015 Gorkha earthquakes in Nepal, a large-scale building survey was conducted for more than 1 million buildings in the region hit by the quakes. The massive dataset contains geo-coded information of building characteristics such as typology, damage to structural elements and overall damage level. This data was primarily used to categorise damage to houses and identify the affected house-owners’ eligibility for the Government’s housing reconstruction grant.

By utilizing this data, UNDP Nepal together with the Institution of Engineering of the Tribhuvan University undertook a data analytics to prototype a model to help identifying vulnerable buildings among existing building stock outside the earthquake affected area. The high-volume and high-granularity  post-earthquake building structure survey data, as well as soil and slope data contributed to developing and testing the applicability of the model.

It is recommended that further data (pre and post-earthquake) is fed into the model for improved prediction of vulnerability in the future. Overall, the outcome of this study offers significant potential in supporting actions or decisions by the policy makers, municipalities, citizens and other parties who promote disaster risk reduction in Nepal.