Housing Boom and Headline Inflation: Insights from Machine Learning

Yang Liu, Di Yang and Yunhui Zhao

No 2022/151, IMF Working Papers from International Monetary Fund

Abstract: Inflation has been rising during the pandemic against supply chain disruptions and a multi-year boom in global owner-occupied house prices. We present some stylized facts pointing to house prices as a leading indicator of headline inflation in the U.S. and eight other major economies with fast-rising house prices. We then apply machine learning methods to forecast inflation in two housing components (rent and owner-occupied housing cost) of the headline inflation and draw tentative inferences about inflationary impact. Our results suggest that for most of these countries, the housing components could have a relatively large and sustained contribution to headline inflation, as inflation is just starting to reflect the higher house prices. Methodologically, for the vast majority of countries we analyze, machine-learning models outperform the VAR model, suggesting some potential value for incorporating such models into inflation forecasting.

Keywords: Housing Price Inflation; Rent; Owner-Occupied Housing; Machine Learning; Forecast; machine-learning model; machine learning method; housing boom; D. forecasting result; Inflation; Housing prices; Housing; Consumer price indexes; Global; Europe; Australia and New Zealand; North America; Caribbean; VAR model (search for similar items in EconPapers)
Pages: 45
Date: 2022-07-28
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-mon and nep-ure
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