The　spatial　mismatch　between　jobs　and　housing　in　cities　creates　long　daily　travels　that　exacerbate　climate　change,　air　pollution,　and　traffic　congestion.　Yet,　not　enough　research　on　occupational　differences　has　been　done.　This　study　first　applies　the　Hidden　Markov　Mixture　Model　(H3M)　to　model　travel　patterns　for　different　occupation　groups　in　Hong　Kong.　Then,　the　Variational　Bayesian　Hierarchical　EM　algorithm　is　used　to　identify　common　lifestyle　clusters.　Next,　a　binary　logistic　regression　is　developed　to　examine　whether　the　lifestyle　clusters　can　be　explained　by　jobs-housing　balance.　This　study　is　among　the　first　to　consider　travel　patterns　as　a　Markov　process　and　apply　H3M　to　examine　jobs-housing　balance　by　fine-grain　occupation　group.　The　method　is　transferable　and　universally　applicable;　and　the　results　provide　occupation-specific　insights　on　jobs-housing　balance　in　an　Asian　context.　The　research　findings　suggest　that　different　occupation　groups　have　different　travel　patterns　in　Hong　Kong.　Two　lifestyle　clusters,　“balanced　and　compact　activity　space”　and　“work-oriented　and　extensive　travels”,　are　unveiled.　Notably,　the　latter　is　associated　a　lower　level　of　jobs-housing　balance.　Some　occupations　in　the　quaternary　industry　(“information　and　communications”,　“profession,　science　and　technology”,　“real　estate”,　and　“finance　and　insurance”)　are　having　more　serious　jobs-housing　imbalance.　The　paper　concludes　with　a　discussion　on　improving　the　occupation-specific　jobs-housing　balance　in　accordance　with　Hong　Kong’s　future　development　goals.　©　2023,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.