Housing represents the largest asset and liability, in the form of mortgages, on most national balance sheet. For most households it is their largest investment, and when mortgages are required also represents the largest component of household debt. It is also directly tied to financial markets, both the mortgage market and insurance sector. Although many countries have a rich set of housing censuses and statistics, others have large data gap in this area and therefore struggle to formulate effective policies. This paper proposes an approach to construct a global census of residential buildings using opensource satellite data. Such a layer can be used to assess the extent these buildings are exposed to climate hazards and how their production and consumption, in turn, affect the climate. The approach we propose could be scaled globally, combining existing layers of building footprints, climate and socioeconomic data. It adds to the ongoing effort of compiling spatially explicit and granular climate indicators to better inform policies. As a case study, we compute selected indicators and estimate the extent of residential properties exposure to riverine flood risk for Kenya.