With the aim of developing a model for estimating building damage from synthetic aperture radar (SAR) data in the L-band, which is appropriate for Peru, we propose a regression discriminant function based on field survey data in Pisco, which was seriously damaged in the 2007 Peru earthquake. The proposed function discriminates among damage ranks corresponding to the severe damage ratio of buildings using ALOS/PALSAR imagery of the disaster area before and after the earthquake. By calculating differences in and correlations of backscattering coefficients, which were explanatory variables of the regression discriminant function, we determined an optimum window size capable of estimating the degree of damage more accurately. A normalized likelihood function for the severe damage ratio was developed based on discriminant scores of the regression discriminant function. The distribution of the severe damage ratio was accurately estimated, furthermore, from PALSAR imagery using data integration of the likelihood function with fragility functions in terms of the seismic intensity of the earthquake.