  Mobile phone data are an interesting new data source for official statistics.
However, multiple problems and uncertainties need to be solved before these
data can inform, support or even become an integral part of statistical
production processes. In this paper, we focus on arguably the most important
problem hindering the application of mobile phone data in official statistics:
detecting home locations. We argue that current efforts to detect home
locations suffer from a blind deployment of criteria to define a place of
residence and from limited validation possibilities. We support our argument by
analysing the performance of five home detection algorithms (HDAs) that have
been applied to a large, French, Call Detailed Record (CDR) dataset (~18
million users, 5 months). Our results show that criteria choice in HDAs
influences the detection of home locations for up to about 40% of users, that
HDAs perform poorly when compared with a validation dataset (the 35{\deg}-gap),
and that their performance is sensitive to the time period and the duration of
observation. Based on our findings and experiences, we offer several
recommendations for official statistics. If adopted, our recommendations would
help in ensuring a more reliable use of mobile phone data vis-\`a-vis official
statistics.
