  We investigate the potential and accuracy of clustering-based redshift
estimation using the method proposed by M\'enard et al. (2013). This technique
enables the inference of redshift distributions from measurements of the
spatial clustering of arbitrary sources, using a set of reference objects for
which redshifts are known. We apply it to a sample of spectroscopic galaxies
from the Sloan Digital Sky Survey and show that, after carefully controlling
the sampling efficiency over the sky, we can estimate redshift distributions
with high accuracy. Probing the full colour space of the SDSS galaxies, we show
that we can recover the corresponding mean redshifts with an accuracy ranging
from $\delta$z=0.001 to 0.01. We indicate that this mapping can be used to
infer the redshift probability distribution of a single galaxy. We show how the
lack of information on the galaxy bias limits the accuracy of the inference and
show comparisons between clustering redshifts and photometric redshifts for
this dataset. This analysis demonstrates, using real data, that
clustering-based redshift inference provides a powerful data-driven technique
to explore the redshift distribution of arbitrary datasets, without any prior
knowledge on the spectral energy distribution of the sources.
