1. bookVolume 38 (2022): Issue 1 (March 2022)
    Special Issue on Price Indices in Official Statistics
Journal Details
First Published
01 Oct 2013
Publication timeframe
4 times per year
access type Open Access

Econometric Issues in Hedonic Property Price Indices: Some Practical Help

Published Online: 29 Mar 2022
Volume & Issue: Volume 38 (2022) - Issue 1 (March 2022)<br/>Special Issue on Price Indices in Official Statistics
Page range: 153 - 186
Received: 01 Jul 2020
Accepted: 01 Apr 2021
Journal Details
First Published
01 Oct 2013
Publication timeframe
4 times per year

Hedonic regressions are widely used and recommended for property price index (PPI) measurement. Hedonic PPIs control for changes in the quality-mix of properties transacted that can confound measures of change in average property prices. The widespread adoption of the hedonic approach is primarily due to the increasing availability, in this digital age, of electronic data on advertised and transaction prices of properties and their price-determining characteristics. Yet hedonic PPIs are only as good as the underlying estimated hedonic regressions. Regression-based measures are unusual in official economic statistics. There is little technical support in the international Handbooks and Guides for diagnostic measures and graphical plots for estimated regression equations as applied to PPIs. These diagnostics are essential to the transparency and credibility of hedonic PPI measurement. This article seeks to remedy this.


Balk, B.M. 2008. Price and Quantity Index Numbers, Cambridge: Cambridge University Press.10.1017/CBO9780511720758 Search in Google Scholar

Bokhari, S., and D. Geltner. 2012. “Estimating real estate price movement for high-frequency tradable indices in a scarce data environment”. Journal of Real Estate Finance and Economics, 45 (2): 522–543. DOI: http://dx.doi.org/10.1007/s11146-010-9261-4.10.1007/s11146-010-9261-4 Search in Google Scholar

Brand, M., C. Becker Vermeulen, D. Fischbach, and Y. Carpy. 2017, “Swiss residential property price index: the use of geolocalised information for quality adjustment in location.” Paper presented at the 15th Meeting of the Ottawa Group International Working Group on Price Indices, May 10–12, Eltville, Germany. Available at: https://www.ottawagroup.org/Ottawa/ottawagroup.nsf/4a256353001af3ed4b2562bb00121564/1ab31c25da944ff5ca25822c00757f87/$FILE/Swiss%20residential%20-property%20price%20index%20-%20Becker%20Vermeulen%20paper.pdf (accessed January 2022). Search in Google Scholar

Curry, B., P. Morgan, and M. Silver. 2001. “Hedonic regressions: mis-specification and neural networks”. Applied Economics, 33(5): 6592671. DOI: http://dx.doi.org/10.1080/000368401750106261.10.1080/000368401750106261 Search in Google Scholar

De Haan, J. 2010. “Hedonic price indices: a comparison of imputation, time dummy and re-pricing methods” Jahrbücher für Nationalökonomie und Statistik, 230(6): 772–791. DOI: http://dx.doi.org/10.1515/9783110511123-010.10.1515/9783110511123-010 Search in Google Scholar

De Hann, J., and W.E. Diewert. 2013. “Hedonic regression methods.” In Eurostat et al. (2013) op. cit., chap. 5. DOI: http://dx.doi.org/10-2785/34007. Search in Google Scholar

Diewert, W.E. 1976. “Exact and superlative index numbers”. Journal of Econometrics, 4(2): 115–145. DOI: http://dx.doi.org/10.1016/0304-4076(76)90009-9.10.1016/0304-4076(76)90009-9 Search in Google Scholar

Diewert, W.E. 1978. “Superlative index numbers and consistency in aggregation”, Econometrica, 46: 883–900. DOI: http://dx.doi.org/10.2307/1909755.10.2307/1909755 Search in Google Scholar

Diewert, W.E. 1995. Axiomatic and economic approaches to elementary price indexes, NBER Working Paper Series 5104, National Bureau of Economic Research (NBER), Cambridge MA, May. Available at: https://www.nber.org/system/files/working_papers/w5104/w5104.pdf (accessed January 2022).10.3386/w5104 Search in Google Scholar

Diewert, W.E., S. Heravi, and M. Silver. 2009. “Hedonic imputation indices versus time dummy hedonic indices.” In Price Index Concepts and Measurement, Studies in Income and Wealth, 70, edited by W.E. Diewert, J. Greenlees, and C.R. Hulten. National Bureau of Economic Research, Chicago: University of Chicago Press: 278–337. Search in Google Scholar

Diewert, W.E., K. Nishimura, C. Shimizu, and T. Watanabe. 2020. Property Price Index: Theory and Practice, Japan: Springer Japan.10.1007/978-4-431-55942-9 Search in Google Scholar

European Commission (EC), Eurostat. EC, 2017. Technical Manual on Owner-Occupied Housing and House Price Indices, Luxembourg: Eurostat. Available at: https://ec.europa.eu/eurostat/documents/7590317/0/Technical-Manual-OOH-HPI-2017/ (accessed January 2022). Search in Google Scholar

Eurostat, European Union, International Labor Organization, International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations Economic Commission for Europe, and The World Bank. 2013. Handbook on Residential Property Prices Indices (RPPIs), Luxembourg: European Union. Available at: https://ec.europa.eu/eurostat/documents/3859598/5925925/KS-RA-12-022-EN.PDF. Search in Google Scholar

Eurostat 2017. Commercial Property Price Indicators: Sources, Methods and Issues, Luxembourg: Eurostat. Available at: https://ec.europa.eu/eurostat/documents/7870049/8545612/KS-FT-16-001-EN-N.pdf/9e4bbc9b-8c6f-44a9-b686-1083a7a8fa0f. Search in Google Scholar

Fenwick, D. 2013. “Uses of residential property price indices.” In Eurostat et al. 2013 op. cit., chap. 2. DOI: http://dx.doi.org/10-2785/34007. Search in Google Scholar

Fuerst, F., and C. Shimizu. 2016. “The rise of eco-labels in the Japanese housing market”, Journal of Japanese and International Economy, 39: 108–122. DOI: http://dx.doi.org/10.1016/j.jjie.2016. Search in Google Scholar

Geltner, D. 1993. “Temporal aggregation in real estate return indices”, Journal of the American Real Estate and Urban Economics Association 21(2): 141–166. DOI: http://dx.doi.org/10.1111/1540-6229.00605.10.1111/1540-6229.00605 Search in Google Scholar

Giles, D.E. 2011. Interpreting dummy variables in log-linear regression models: exact distributional results, University of Victoria, Department of Economics, Econometrics Working Paper EWP 1101, January. Available at: https://www.researchgate.net/publication/228954943_Interpreting_Dummy_Variables_in_Semi-Logarithmic_Regression_Models_Exact_Distributional_Results (accessed January 2022). Search in Google Scholar

Greenstone, M. 2017. “The continuing impact of Sherwin Rosen’s “Hedonic prices and implicit markets: product differentiation in pure competition,” Journal of Political Economy, 125(6): 1891–1902. Available at: https://static1.squarespace.com/static/585017d2bebafbe412f1a838/t/6033d2f812ec930bb253a046/1614009365638/The-Continuing-Impact.pdf.10.1086/694645 Search in Google Scholar

Heiss, F. 2020. Using R for introductory econometrics, 2nd Edition, Düsseldorf, Germany: Florian Heiss, URfie.net. Search in Google Scholar

Heravi, S., and M. Silver. 2007. “Different approaches to estimating hedonic indexes”, In Hard-to-Measure Goods and Services: Essays in Honor of Zvi Griliches, Studies in Income and Wealth, edited by E. Berndt and C. Hulten, 67: 235–268, National Bureau of Economic Research, Chicago: University of Chicago Press.10.7208/chicago/9780226044507.003.0009 Search in Google Scholar

Hill, R.E. 2013. “Hedonic price indices for residential housing: a survey, evaluation and taxonomy.” Journal of Economic Surveys, 27(5). DOI: http://dx.doi.org/10.1111/j.1467-6419.2012.00731.x.10.1111/j.1467-6419.2012.00731.x Search in Google Scholar

Hill, R.J., and D. Melser. 2008. “Hedonic imputation and the price index problem: An application to housing”, Economic Inquiry, 46: 593–609. DOI: http://dx.doi.org/10.1111/j.1465-7295.2007.00110.x.10.1111/j.1465-7295.2007.00110.x Search in Google Scholar

Hill, R.J., and M. Scholtz. 2017. “Can geospatial data improve house price indices? a hedonic imputation approach with splines”, Review of Income and Wealth, 64(4): 737–756. DOI: http://dx.doi.org/10.1111/roiw.12303.10.1111/roiw.12303 Search in Google Scholar

Hill, R.J., A.N. Rambaldi, and M. Scholtz. 2020 “Higher frequency hedonic property price indices: a state-space approach”, Empirical Economics. DOI: https://doi.org/10.1007/s00181-020-01862-y.10.1007/s00181-020-01862-y Search in Google Scholar

Hill, R.J., and M. Steurer. 2020. “Commercial property price indices and indicators: review and discussion of issues raised in the CPPI Statistical Report of EUROSTAT (2017)”, Review of Income and Wealth. DOI: http://dx.doi.org/10.1111/roiw.12473.10.1111/roiw.12473 Search in Google Scholar

Hill, R.J., M. Scholtz, C. Shimizu, and M. Steurer. 2018. “An evaluation of the methods used by European countries to compute their official house price indices.” Economie et Statistique, (500, 501, 502). DOI: http://dx.doi.org/10.15396/eres2018_201.10.15396/eres2018_201 Search in Google Scholar

Iacobucci, D., M.J. Schneider, D.L. Popovich, and G.A. Bakamitsos. 2016. “Mean centering helps alleviate “micro” but not “macro” multicollinearity”, Behavioral Research, 48: 1308–1317. DOI: 10.3758/s13428-015-0624-x.10.3758/s13428-015-0624-x26148824 Search in Google Scholar

IMF, International Monetary Fund. 2020. RPPI Practical Compilation Guide, Washington DC: IMF. Available at: https://www.imf.org/en/Data/Statistics/RPPI-guide (accessed July 2020). Search in Google Scholar

Kennedy, P. 1981. “Estimation with correctly interpreted dummy variables in log-linear equations”, American Economic Review 71(4): 801. Available at: www.jstor.org/stable/1806207 (accessed January 2022). Search in Google Scholar

Kennedy, P.E. 2008. A Guide to Econometrics, 6th Edition, Oxford: Wiley-Blackwell. Search in Google Scholar

Leventis, A. 2008. Revisiting the differences between the OFHEO and S&P/Case-Shiller house price indexes: new explanations, Office of Federal Housing Enterprise Oversight (OFHEO), Available at: https://fhfa.gov/PolicyProgramsResearch/Research/PaperDocuments/20080115_RP_RevisitingDifferencesOFHEOSPCaseShillerHPI_N508.pdf (accessed 28 January, 2022). Search in Google Scholar

Maddala, G.S., and K. Lahari. 2009. Introduction to Econometrics, 4th Edition, Chichester, England: John Wiley and Sons. Search in Google Scholar

Mehrhoff, J., and E. Triebskorn. 2016. How should we measure residential property prices to inform policy makers? Narodowy Bank Polski Workshop: Recent Trends in the Real Estate Market and its Analysis, August 9, Poland. Available at: https://papers.ssrn.-com/sol3/papers.cfm?abstract_id=2842531 (accessed January 2022). Search in Google Scholar

OECD. 2009. Measuring Capital – OECD Manual 2009, Second edition, Paris: OECD Publishing. Available at: https://www.oecd-ilibrary.org/docserver/9789264068476-en.pdf?expires=1610123185&id=id&accname=guest&checksum=2335110A437CEF603F6E28E38485ECCB (accessed January 2022). Search in Google Scholar

O’Hanlon, N. 2011. “Constructing a national house price index for Ireland,” Journal of the Statistical and Social Inquiry Society of Ireland, 40: 167–196. Available at: http://www.tara.tcd.ie/bitstream/handle/2262/62349/o%27hanlon%20pdf.pdf?sequence=1&isAllowed=y (accessed January 2022). Search in Google Scholar

Oladunni, T., and S. Sharma. 2018. “H2O deep learning for hedonic pricing”, International Journal of Computers and their Applications, 25(1): 1–9. Available at: https://www.researchgate.net/publication/342914955_H2O_Deep_Learning_for_Hedonic_Pricing (accessed January 2022). Search in Google Scholar

Rambaldi, A.N., and C.S. Fletcher. 2014. “Hedonic imputed price indices: the effects of econometric modeling choices”, The Review of Income and Wealth 60: s423–s448. DOI: http://dx.doi.org/10.1111/roiw.12143.10.1111/roiw.12143 Search in Google Scholar

Rosen, S. 1974. “Hedonic prices and implicit markets: product differentiation in pure competition.” Journal of Political Economy, 82(1): 34–55. Available at: https://www.jstor.org/stable/1830899?seq=1 (accessed January 2022).10.1086/260169 Search in Google Scholar

Shimizu, C., K.G. Nishimura, and T. Watanabe. 2010. “House prices in Tokyo – a comparison of repeat-sales and hedonic measures”, Journal of Economics and Statistics, 230(6): 792–813. DOI: http://dx.doi.org/10.1515/9783110511123-011.10.1515/9783110511123-011 Search in Google Scholar

Silver, M. 2002. “The use of weights in hedonic regressions: the measurement of quality-adjusted price changes.” researchgate.net. Available at: https://www.researchgate.net/publication/238355850_The_Use_of_Weights_in_Hedonic_Regressions_the_Measurement_of_Quality-Adjusted_Price_Changes (accessed January 2022). Search in Google Scholar

Silver, M. 2015. “The degree and impact of differences in house price index measurement”, Journal of Economic and Social Measurement, 39: 305–328. DOI: http://dx.doi.org/10.3233/JEM-150406.10.3233/JEM-150406 Search in Google Scholar

Silver, M. 2016. How to better measure hedonic residential property price indices, IMF Working Paper WP/16/213, Washington D.C. Available at: https://www.researchgate.net/publication/309786163_How_to_Better_Measure_Hedonic_Residential_Property_Price_Indexes (accessed January 2022).10.5089/9781475552249.001 Search in Google Scholar

Silver, M. 2018. “How to measure hedonic residential property price indices better”, Eurostat Review on National Accounts and Macroeconomic Statistics Indicators (EURONA), volume 1: 35–66. Available at: https://www.researchgate.net/publication/326186402_How_to_Measure_Residential_Property_Price_Indices_Better#fullTextFileContent (accessed January 2022). Search in Google Scholar

Silver, M. 2019. “Data Sources for CPPIs: An overview and strategy”, Paper presented at the European Commission International Conference on Real Estate Statistics, 20–22 February, Luxembourg. Available at: https://www.researchgate.net/publication/342601082_Data_Sources_for_CPPIs_An_Overview_and_Strategy (accessed January 2022). Search in Google Scholar

Silver, M., and B. Graf. 2014. Commercial property price indexes: problem of spare data, spatial, spillovers, and weighting, IMF Working Paper. WP/14/73. Washington D.C. DOI: http://dx.doi.org/10.2139/ssrn.2445456.10.2139/ssrn.2445456 Search in Google Scholar

Silver, M., and S. Heravi. 2001. “Scanner data and the measurement of inflation”, The Economic Journal, 111, 472: 383–404. DOI: http://dx.doi.org/10.1111/1468-0297.00636.10.1111/1468-0297.00636 Search in Google Scholar

Silver, M., and S. Heravi. 2005. “A failure in the measurement of inflation: results from a hedonic and matched experiment using scanner data”, Journal of Business and Economic Statistics, 23(5): 269–281. DOI: http://dx.doi.org/10.1198/073500104000000343.10.1198/073500104000000343 Search in Google Scholar

Silver, M., and S. Heravi. 2007. “Why elementary price index number formulas differ: Evidence on price dispersion.” Journal of Econometrics, 140(2): 874–883. DOI: http://dx.doi.org/10.1016/j.jeconom.2006. Search in Google Scholar

Solon, G., S.J. Haider, and J.M. Wooldridge. 2015. “What are we weighting for?” Human Resources, Spring, 50, 2, 301–316. See also National Bureau of Economic Research (NBER) Working Paper 18859, February 2013. Available at: https://www.nber.org/system/files/working_papers/w18859/w18859.pdf (accessed January 2022).10.3368/jhr.50.2.301 Search in Google Scholar

Triplett, J.E. 2006. Handbook on Hedonic Indices and Quality Adjustments in Price Indices: Special Application to Information Technology Products. Paris: OECD Publishing.10.1787/9789264028159-en Search in Google Scholar

Van Garderen, K.J., and C. Shah. 2002. “Exact interpretation of dummy variables in semi-logarithmic equations”, Econometrics Journal 5: 149–159. DOI: https://doi.org/10.1111/1368-423X.00078.10.1111/1368-423X.00078 Search in Google Scholar

Wooldridge, Jeffrey M. 2013. Introductory Econometrics: A Modern Approach, 5th Edition, Mason OH: South Western, Cengage Learning. Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo