Inflation has beenrisingduringthe pandemic against supplychain disruptions and a multi-yearboom in globalowner-occupied house prices.Wepresent somestylized facts pointingto house prices as a leading indicator of headline inflation in the U.S.and eight other majoreconomies with fast-risinghouse prices.Wethen apply machine learningmethods to forecastinflation in two housingcomponents (rent and owner-occupiedhousing cost) of the headline inflation anddrawtentative inferences about inflationary impact.Ourresults suggest that for most of these countries, the housingcomponentscouldhave a relatively large andsustained contributionto headline inflation,as inflation is just starting to reflect the higherhouse prices. Methodologically, for the vast majority of countrieswe analyze, machine-learningmodels outperform theVARmodel, suggestingsome potentialvalue for incorporating such models into inflation forecasting.