{
  "title": "Housing Boom and Headline Inflation: Insights from Machine Learning",
  "publication": "IMF Working Papers, July 28, 2022",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2022/07/28/housing-boom-and-headline-inflation-insights-from-machine-learning-521324",
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  "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.",
  "sections": [
    {
      "heading": "Summary and context",
      "content": "- Title: Housing Boom and Headline Inflation: Insights from Machine Learning\n- Authors: Yang Liu, Di Yang, Yunhui Zhao\n- Date: July 28, 2022\n- Publication: IMF Working Papers, Working Paper No. 2022/151\n- Pages: 45\n- Volume: 2022\n- Issue: 151\n- DOI: https://doi.org/10.5089/9798400218095.001\n- ISBN: 9798400218095\n- ISSN: 1018-5941\n- Subject tags: Consumer price indexes, Economic forecasting, Housing, Housing prices, Inflation, National accounts, Prices"
    },
    {
      "heading": "Stylized facts on housing and inflation",
      "content": "- Inflation rose during the pandemic against supply chain disruptions and a multi-year boom in global owner-occupied house prices.\n- House prices are presented as a leading indicator of headline inflation in the U.S. and eight other major economies with fast-rising house prices.\n- The analysis focuses on two housing components of headline inflation: rent and owner-occupied housing cost."
    },
    {
      "heading": "Forecasting approach and methodological insights",
      "content": "- The paper applies machine learning methods to forecast inflation in the two housing components (rent and owner-occupied housing cost) of headline inflation.\n- For the vast majority of countries analyzed, machine-learning models outperform the VAR model.\n- The methodological finding suggests potential value for incorporating machine-learning models into inflation forecasting."
    },
    {
      "heading": "Key empirical findings and inferences",
      "content": "- Results suggest that for most of the countries in the sample:\n  - The housing components could have a relatively large and sustained contribution to headline inflation.\n  - Inflation is just starting to reflect the higher house prices, implying further inflationary impact may materialize as housing cost measures respond."
    },
    {
      "heading": "Policy-relevant implications (as conveyed in the source)",
      "content": "- Monitoring owner-occupied house prices and housing cost components (rent and owner-occupied housing cost) is important for understanding near-term headline inflation dynamics in countries experiencing housing booms.\n- Incorporating machine-learning forecasting models may improve inflation forecasts relative to VAR models for these housing-related inflation components.\n\nIMF Working Paper No. 2022/151, July 28, 2022\n\n---\n\n Content in this bundle\n\n- Working Paper\n  - Working Paper (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Working Paper (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2022/07/28/housing-boom-and-headline-inflation-insights-from-machine-learning-521324"
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    "Authors: Yang Liu, Di Yang, Yunhui Zhao",
    "Published: July 28, 2022",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9798400218095.001",
    "Title: Housing Boom and Headline Inflation: Insights from Machine Learning",
    "Authors: Yang Liu, Di Yang, Yunhui Zhao",
    "Date: July 28, 2022",
    "Publication: IMF Working Papers, Working Paper No. 2022/151",
    "Pages: 45",
    "Volume: 2022",
    "Issue: 151",
    "DOI: https://doi.org/10.5089/9798400218095.001",
    "ISBN: 9798400218095",
    "ISSN: 1018-5941",
    "Subject tags: Consumer price indexes, Economic forecasting, Housing, Housing prices, Inflation, National accounts, Prices",
    "Inflation rose during the pandemic against supply chain disruptions and a multi-year boom in global owner-occupied house prices.",
    "House prices are presented as a leading indicator of headline inflation in the U.S. and eight other major economies with fast-rising house prices.",
    "The analysis focuses on two housing components of headline inflation: rent and owner-occupied housing cost.",
    "The paper applies machine learning methods to forecast inflation in the two housing components (rent and owner-occupied housing cost) of headline inflation.",
    "For the vast majority of countries analyzed, machine-learning models outperform the VAR model.",
    "The methodological finding suggests potential value for incorporating machine-learning models into inflation forecasting.",
    "Results suggest that for most of the countries in the sample:",
    "Monitoring owner-occupied house prices and housing cost components (rent and owner-occupied housing cost) is important for understanding near-term headline inflation dynamics in countries experiencing housing booms.",
    "Incorporating machine-learning forecasting models may improve inflation forecasts relative to VAR models for these housing-related inflation components.",
    "**Working Paper**"
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