Housing Boom and Headline Inflation: Insights from Machine Learning

July 28, 2022

Preview Citation

Format: Chicago

Yang Liu, Di Yang, and Yunhui Zhao. Housing Boom and Headline Inflation: Insights from Machine Learning, (USA: International Monetary Fund, 2022) accessed November 21, 2024

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.

Summary

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.

Subject: Consumer price indexes, Economic forecasting, Housing, Housing prices, Inflation, National accounts, Prices

Keywords: Australia and New Zealand, Caribbean, Consumer price indexes, D. forecasting result, Europe, Forecast, Global, Housing, Housing boom, Housing Price Inflation, Housing prices, Inflation, Machine Learning, Machine learning method, Machine-learning model, North America, Owner-Occupied Housing, Rent, VAR model

Publication Details

Pages:

45
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2022/151
Stock No:

WPIEA2022151
ISBN:

9798400218095
ISSN:

1018-5941

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