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Housing Boom and Headline Inflation: Insights from Machine Learning
Author:
Yang Liunull

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,
Di Yang
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, and
Mr. Yunhui Zhao
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Series:
IMF Working Papers
Volume/Issue:
Volume 2022: Issue 151
Language:
English
Publication Date:
28 Jul 2022
DOI:
https://doi.org/10.5089/9798400218095.001
ISBN:
9798400218095
ISSN:
1018-5941
Pages:
45
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
Legal-disclaimer:
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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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.
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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund
Volume 2022: Issue 151