Methods to estimate compound flooding at the household level are largely nonexistent outside of complex computational models. The exclusion of topographic depressions, and therefore pluvial flooding, from leading flood hazard maps is also underestimating potential exposure. Furthermore, national level exploratory analyses have yet to capture local variability in exposure and social vulnerability, which is necessary for local stakeholders to identify the inequitable distribution of flood risks. Using high resolution elevation data to approximate event specific inundation from both pluvial and fluvial sources, in conjunction with a localized social vulnerability index, we created a methodology to estimate flood risk at the household level. Our analysis uses the 2015 Memorial Day Flood in Austin, Texas as a case study and proof of concept of our estimation methodology. We show that the inclusion of pluvial flood sources increases inundation extents, with 37% of the Census Block Groups in the study area experiencing flooding from only pluvial sources. Furthermore, averaging flood depths, and therefore exposure estimates, to geographical and cartographic boundaries (e.g., Census Block Groups), masks household variability, with 80% of the Census Block Groups in the study area having a coefficient of variation around the mean flood depth exceeding 100%. Comparing our pluvial flooding estimates to a 2D hydrodynamic physical-based model, we classified household exposure accurately for 92% of the parcels. Our methodology can be used as a tool to estimate the impacts of inland compound flooding on household risk in order to provide a first estimate of storm specific risk.