The American Housing Survey (AHS) collects a wealth of detail on housing characteristics that is not available from any other source. However, AHS data identify only very broad geographic areas. This research project explores the possibility of using small-area statistical techniques to generate information for areas not covered by the AHS. This information could prove useful for those preparing for or responding to a disaster. The study looks at nine types of household conditions that might be particularly vulnerable in disasters. The research uses fractional logit to estimate these conditions using variables measured for all metropolitan areas by the annual American Community Survey (ACS). Three types of independent variables were used: ACS approximations of the AHS-measured conditions, covariates to distinguish among metropolitan areas on nonhousing factors, and covariates related to local housing markets and economic conditions. The results are only mildly encouraging. Of the nine equations estimated, only two were statistically meaningful using the chi-squared test. However, the R-squared for each of these two equations indicates that using the predicted values may be a worthwhile improvement over assuming that all metropolitan areas have the same values; that is, using the sample mean. Out-of-sample predictions using these two equations suggest that conditions may vary substantially across metropolitan areas. The next major disaster may very well indicate what AHS information is crucial. If such an event were to occur, however, there may not be time to carry out the desired microdata analysis. In that case, the techniques explored in this paper might serve as a valuable second-best approach.