  We present the methodology and data behind the photometric redshift database
of the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We adopt a hybrid
technique, empirically estimating the redshift via local regression on a
spectroscopic training set, then fitting a spectrum template to obtain
K-corrections and absolute magnitudes. The SDSS spectroscopic catalog was
augmented with data from other, publicly available spectroscopic surveys to
mitigate target selection effects. The training set is comprised of $1,976,978$
galaxies, and extends up to redshift $z\approx 0.8$, with a useful coverage of
up to $z\approx 0.6$. We provide photometric redshifts and realistic error
estimates for the $208,474,076$ galaxies of the SDSS primary photometric
catalog. We achieve an average bias of $\overline{\Delta z_{\mathrm{norm}}} =
5.84 \times 10^{-5}$, a standard deviation of $\sigma \left(\Delta
z_{\mathrm{norm}}\right)=0.0205$, and a $3\sigma$ outlier rate of $P_o=4.11\%$
when cross-validating on our training set. The published redshift error
estimates and photometric error classes enable the selection of galaxies with
high quality photometric redshifts. We also provide a supplementary error map
that allows additional, sophisticated filtering of the data.
