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Puzzling Divergence of U.S. Rents and User Costs, 1980-2004: Summary and Extensions


  • Thesia I. Garner

    () (U.S. Bureau of Labor Statistics)

  • Randal Verbrugge

    () (U.S. Bureau of Labor Statistics)


This paper constructs, for the five largest cities in the United States, user costs and rents for the same structure, in levels (i.e., measured in dollars). The levels formulation is a major advantage over indexes since one can answer questions like "Is it cheaper to rent or to own?" or "Are houses overvalued?" because such questions are essentially about the levels of rents and house prices and their fundamentals. These new measures are constructed using Consumer Expenditure Survey (CE) Interview data from 1982 to 2002, along with house price appreciation forecasts from Verbrugge (2007a). Characteristics, current market value, and rental equivalence of owner-occupied housing are used in a regression framework to predict the rent associated with a structure with median characteristics in each city. The property value of this median house is used to construct a user cost estimate for this structure. We find that, for the median structure in each city, estimated user costs and rents diverge to a surprising degree, in keeping with the previously noted findings of Verbrugge (2007a). It is not always cheaper to own: user costs sometimes lie well above rents. Finally, the dynamics of the estimated price-to-rent ratio are generally similar to those found in conventional estimates based upon indexes, suggesting that the present study might be useful for scaling or normalizing other estimates.

Suggested Citation

  • Thesia I. Garner & Randal Verbrugge, 2007. "Puzzling Divergence of U.S. Rents and User Costs, 1980-2004: Summary and Extensions," Working Papers 409, U.S. Bureau of Labor Statistics.
  • Handle: RePEc:bls:wpaper:ec070080

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    References listed on IDEAS

    1. Richard A. Ashley & Randal J. Verbrugge, 2009. "To difference or not to difference: a Monte Carlo investigation of inference in vector autoregression models," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(3), pages 242-274.
    2. Crone, Theodore M. & Nakamura, Leonard I. & Voith, Richard, 1998. "Measuring housing services inflation," Working Papers 98-21, Federal Reserve Bank of Philadelphia, revised 01 Jan 1999.
    3. Matthew Chambers & Carlos Garriga & Don E. Schlagenhauf, 2009. "Accounting For Changes In The Homeownership Rate," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(3), pages 677-726, August.
    4. Theodore M. Crone & Leonard I. Nakamura & Richard Voith, 2004. "The CPI for rents: a case of understated inflation," Working Papers 04-17, Federal Reserve Bank of Philadelphia.
    5. Blackley, Dixie M. & Follain, James R., 1996. "In search of empirical evidence that links rent and user cost," Regional Science and Urban Economics, Elsevier, vol. 26(3-4), pages 409-431, June.
    6. Case, Bradford & Pollakowski, Henry O & Wachter, Susan M, 1997. "Frequency of Transaction and House Price Modeling," The Journal of Real Estate Finance and Economics, Springer, vol. 14(1-2), pages 173-187, Jan.-Marc.
    7. Case, Karl E & Shiller, Robert J, 1989. "The Efficiency of the Market for Single-Family Homes," American Economic Review, American Economic Association, vol. 79(1), pages 125-137, March.
    8. Davis, Morris A. & Heathcote, Jonathan, 2007. "The price and quantity of residential land in the United States," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2595-2620, November.
    9. Antonia Diaz & Maria Jose Luengo Prado, 2008. "On the User Cost and Homeownership," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 11(3), pages 584-613, July.
    10. Richard Ashley & Randal Verbrugge, 2009. "Frequency Dependence in Regression Model Coefficients: An Alternative Approach for Modeling Nonlinear Dynamic Relationships in Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 4-20.
    11. Karl E Case & John M Quigley & Robert J Shiller, 2003. "Home-buyers, Housing and the Macroeconomy," RBA Annual Conference Volume,in: Anthony Richards & Tim Robinson (ed.), Asset Prices and Monetary Policy Reserve Bank of Australia.
    12. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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    Cited by:

    1. Christian E. Weller, 2009. "Did Retirees Save Enough to Compensate for the Increase in Individual Risk Exposure?," Working Papers wp206, Political Economy Research Institute, University of Massachusetts at Amherst.
    2. Honglin Wang & Chu Zhang & Weihang Dai, 2013. "Rental Adjustment and Housing Prices: Evidence from Hong Kong's Residential Property Market," Working Papers 012013, Hong Kong Institute for Monetary Research.
    3. Marianna Kudlyak, 2012. "Housing services price inflation," Economic Quarterly, Federal Reserve Bank of Richmond, issue 3Q, pages 185-207.
    4. Jordan Rappaport, 2010. "The effectiveness of homeownership in building household wealth," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 35-65.
    5. Ryan Fox & Peter Tulip, 2014. "Is Housing Overvalued?," RBA Research Discussion Papers rdp2014-06, Reserve Bank of Australia.
    6. Riley, Sarah F. & Ru, Hong Yu & Feng, Qing, 2013. "The User Cost of Low-Income Homeownership," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 43(2).
    7. Sheng Guo & William Hardin, 2015. "Financial and Housing Wealth, Expenditures and the Dividend to Ownership," Working Papers 1506, Florida International University, Department of Economics.
    8. Amendola, Nicola & Vecchi, Giovanni, 2014. "Durable goods and poverty measurement," Policy Research Working Paper Series 7105, The World Bank.

    More about this item


    User Costs; Arbitrage; Transaction Costs; House Price Appreciation; Consumer Expenditure Survey; Forecasting; Inflation Stickiness; Rental Equivalence; CPI;

    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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