  In developing countries, building codes often are outdated or not enforced.
As a result, a large portion of the housing stock is substandard and vulnerable
to natural hazards and climate related events. Assessing housing quality is key
to inform public policies and private investments. Standard assessment methods
are typically carried out only on a sample / pilot basis due to its high costs
or, when complete, tend to be obsolete due to the lack of compliance with
recommended updating standards or not accessible to most users with the level
of detail needed to take key policy or business decisions. Thus, we propose an
evaluation framework that is cost-efficient for first capture and future
updates, and is reliable at the block level. The framework complements existing
work of using street view imagery combined with deep learning to automatically
extract building information to assist the identification of housing
characteristics. We then check its potential for scalability and higher level
reliability. For that purpose, we create an index, which synthesises the
highest possible level of granularity of data at the housing unit and at the
household level at the block level, and assess whether the predictions made by
our model could be used to approximate vulnerability conditions with a lower
budget and in selected areas. Our results indicated that the predictions from
the images are clearly correlated with the index.
