  We provide conditions that guarantee local rates of convergence in
distribution of iterated random functions that are not nonexpansive mappings in
locally compact Hadamard spaces. Our results are applied to stochastic
instances of common algorithms in optimization, stochastic tomography for X-FEL
imaging, and a stochastic algorithm for the computation of Fr\'echet means in
model spaces for phylogenetic trees.
