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On the Imputation of Rental Prices to Owner-occupied Housing

Author

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  • Raquel Arévalo
  • Javier Ruiz-Castillo

Abstract

This paper challenges the usual objections to the possibility of applying the rental equivalent approach to determine the weight that nonrental housing services should have in the Consumer Price Index (CPI). Using data from two Spanish household budget surveys, it is shown that market rents can be well represented in terms of an index of housing quality, two geographical variables, and the year of occupancy. This parsimonious empirical model is used to impute a rental value to nonrental housing units, taking into account the possible selection bias induced by systematic differences in housing characteristics between the market rental sector and the nonrental stock. On average, the estimated hedonic values are relatively close to the self-imputations provided in the household surveys by the occupants of such dwellings. Therefore, using either of the two alternatives to assess the importance of nonrental housing services in the CPI have small consequences for inflation. Instead, dropping these services from the CPI creates a downward bias in the measurement of inflation of 0.33 percentage points per year during 1985–1992, and an upward bias of 0.38 percentage points per year during 1993 to 2000. (JEL: C43, D12, R21, C21, E31) Copyright (c) 2006 by the European Economic Association.

Suggested Citation

  • Raquel Arévalo & Javier Ruiz-Castillo, 2006. "On the Imputation of Rental Prices to Owner-occupied Housing," Journal of the European Economic Association, MIT Press, vol. 4(4), pages 830-861, June.
  • Handle: RePEc:tpr:jeurec:v:4:y:2006:i:4:p:830-861
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    Citations

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    Cited by:

    1. Hill, Robert J. & Syed, Iqbal A., 2016. "Hedonic price–rent ratios, user cost, and departures from equilibrium in the housing market," Regional Science and Urban Economics, Elsevier, vol. 56(C), pages 60-72.
    2. Beatty, Timothy K.M. & Larsen, Erling Røed & Sommervoll, Dag Einar, 2010. "Using house prices to compute the price of housing in the CPI," Economics Letters, Elsevier, vol. 106(3), pages 238-240, March.
    3. Ahlfeldt, Gabriel M. & Maennig, Wolfgang, 2015. "Homevoters vs. leasevoters: A spatial analysis of airport effects," Journal of Urban Economics, Elsevier, vol. 87(C), pages 85-99.
    4. HILL Robert J. & STEURER Miriam & WALTL Sofie R., 2018. "Owner Occupied Housing in the CPI and Its Impact On Monetary Policy During Housing Booms and Busts," LISER Working Paper Series 2018-05, LISER.
    5. Laura Blow & Lars Nesheim, 2009. "A retail price index including the shadow price of owner occupied housing," CeMMAP working papers CWP03/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Kosfeld, Reinhold & Eckey, Hans-Friedrich & Lauridsen, Jørgen, 2007. "Disparities in prices and income across German NUTS 3 regions," Volkswirtschaftliche Diskussionsbeiträge 93, University of Kassel, Faculty of Economics and Management.

    More about this item

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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