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The Coming Rise in Residential Inflation

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  • Marijn A. Bolhuis
  • Judd N. L. Cramer
  • Lawrence H. Summers

Abstract

We study how the recent run-up in housing and rental prices affects the outlook for inflation in the United States. Housing held down overall inflation in 2021. Despite record growth in private market-based measures of home prices and rents, government measured residential services inflation was only four percent for the twelve months ending in January 2022. After explaining the mechanical cause for this divergence, we estimate that, if past relationships hold, the residential inflation components of the CPI and PCE are likely to move close to seven percent during 2022. These findings imply that housing will make a significant contribution to overall inflation in 2022, ranging from one percentage point for headline PCE to 2.6 percentage points for core CPI. We expect residential inflation to remain elevated in 2023.

Suggested Citation

  • Marijn A. Bolhuis & Judd N. L. Cramer & Lawrence H. Summers, 2022. "The Coming Rise in Residential Inflation," NBER Working Papers 29795, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:29795
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    References listed on IDEAS

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    1. Marijn A Bolhuis & Judd N L Cramer & Lawrence H Summers, 2022. "Comparing Past and Present Inflation [Supply and demand in disaggregated Keynesian economies with an application to the covid-19 crisis]," Review of Finance, European Finance Association, vol. 26(5), pages 1073-1100.
    2. John P. Shelton, 1968. "The Cost of Renting versus Owning a Home," Land Economics, University of Wisconsin Press, vol. 44(1), pages 59-72.
    3. Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511, April.
    4. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    5. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
    6. Clinton P. McCully & Brian C. Moyer & Kenneth J. Stewart, 2007. "A Reconciliation between the Consumer Price Index and the Personal Consumption Expenditures Price Index," BEA Papers 0079, Bureau of Economic Analysis.
    7. Cepni, Oguzhan & Güney, I. Ethem & Swanson, Norman R., 2019. "Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 555-572.
    8. Brent W. Ambrose & N. Edward Coulson & Jiro Yoshida, 2015. "The Repeat Rent Index," The Review of Economics and Statistics, MIT Press, vol. 97(5), pages 939-950, December.
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    1. Marijn A Bolhuis & Judd N L Cramer & Lawrence H Summers, 2022. "Comparing Past and Present Inflation [Supply and demand in disaggregated Keynesian economies with an application to the covid-19 crisis]," Review of Finance, European Finance Association, vol. 26(5), pages 1073-1100.
    2. Gern, Klaus-Jürgen & Kooths, Stefan & Sonnenberg, Nils & Reents, Jan & Stolzenburg, Ulrich, 2023. "World Economy Winter 2023: Strong headwinds for global economic activity," Kiel Institute Economic Outlook 109, Kiel Institute for the World Economy (IfW Kiel).
    3. Gern, Klaus-Jürgen & Reents, Jan & Kooths, Stefan & Sonnenberg, Nils & Stolzenburg, Ulrich, 2023. "Weltwirtschaft im Winter 2023: Konjunkturelle Dynamik bleibt vorerst gering [World Economy in Winter 2023: Strong headwinds for global economic activity]," Kieler Konjunkturberichte 109, Kiel Institute for the World Economy (IfW Kiel).

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    More about this item

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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