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作者

来源

JOURNAL OF HOUSING ECONOMICS,Vol.41,P.1-29

语言

英文

关键字

Housing valuation; Forecast combination; Measurement error; SAMPLE SELECTION BIAS; HOUSE PRICE INDEXES; ESTIMATING AGGREGATE LEVELS; US EMPLOYMENT GROWTH; IN-VARIABLES; NONLINEAR MODELS; LOCAL JURISDICTIONS; TAX ASSESSMENT; OUTPUT GROWTH; TIME-SERIES

作者单位

[Glennon, Dennis; Kiefer, Hua; Mayock, Tom] Off Comptroller Currency, Credit Risk Anal Div, 400 7th St SW, Washington, DC 20219 USA. [Mayock, Tom] UNC Charlotte, 400 7th St SW, Washington, DC 20219 USA. Mayock, T (reprint author), Off Comptroller Currency, Credit Risk Anal Div, 400 7th St SW, Washington, DC 20219 USA.; Mayock, T (reprint author), UNC Charlotte, 400 7th St SW, Washington, DC 20219 USA. E-Mail: dennis.glennon@occ.treas.gov; hua.kiefer@occ.treas.gov; thomas.mayock@occ.treas.gov

摘要

In this study we use a large database of real estate transactions to assess the magnitude of measurement error associated with using popular house price indices (HPIs) to value individual properties. In the 4 large U.S. counties that we analyze, we find that the bias associated with using these HPIs to value individual homes increased from near zero in 2005 to between 26% and 113% in 2010. In the second part of the analysis, we use data from Florida to demonstrate that forecast combination methods can be used to improve the accuracy of property-level valuations, in some cases reducing the estimated bias by more than a factor of 3. We find that even the simplest forecast combination method - a simple average - has the potential to significantly improve value estimates.