  We present an empirical method for estimating the underlying redshift
distribution N(z) of galaxy photometric samples from photometric observables.
The method does not rely on photometric redshift (photo-z) estimates for
individual galaxies, which typically suffer from biases. Instead, it assigns
weights to galaxies in a spectroscopic subsample such that the weighted
distributions of photometric observables (e.g., multi-band magnitudes) match
the corresponding distributions for the photometric sample. The weights are
estimated using a nearest-neighbor technique that ensures stability in sparsely
populated regions of color-magnitude space. The derived weights are then summed
in redshift bins to create the redshift distribution. We apply this weighting
technique to data from the Sloan Digital Sky Survey as well as to mock catalogs
for the Dark Energy Survey, and compare the results to those from the
estimation of photo-z's derived by a neural network algorithm. We find that the
weighting method accurately recovers the underlying redshift distribution,
typically better than the photo-z reconstruction, provided the spectroscopic
subsample spans the range of photometric observables covered by the photometric
sample.
