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Decoupling land values in residential property prices: smoothing methods for hedonic imputed price indices

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Abstract

A property is a bundled good composed of an appreciating asset, land, and a depreciating asset, structure. This study proposes the use of a hedonic based unobserved components approach where land and structure are viewed as two additive components of the price. The underlying trend in each component is identified via mapping each component’s intrinsic hedonic characteristics and using their different dynamic behaviour. The estimation approach uses a modified form of the Kalman filter. One advantage of the estimation strategy is that the composition of sales at any given period do not lead to abrupt or volatile changes in the estimates of the components. The algorithm is simple to implement and we demonstrate, using three datasets representing different urban settings in two countries, that the method produces land value predictions comparable to the state valuer’s assessments, and price indices comparable to recently published alternatives that rely on exogenous non-market information to disentangle land values. Our monthly indices are smoother than the quarterly counterparts.

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  • Alicia N. Rambaldi & Ryan R. J. McAllister & Cameron S. Fletcher, 2015. "Decoupling land values in residential property prices: smoothing methods for hedonic imputed price indices," Discussion Papers Series 549, School of Economics, University of Queensland, Australia.
  • Handle: RePEc:qld:uq2004:549
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    File URL: https://economics.uq.edu.au/files/46425/549.pdf
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    References listed on IDEAS

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    1. Alicia N. Rambaldi & Cameron S. Fletcher & Kerry Collins & Ryan R.J. McAllister, 2013. "Housing Shadow Prices in an Inundation-prone Suburb," Urban Studies, Urban Studies Journal Limited, vol. 50(9), pages 1889-1905, July.
    2. Andrew Harvey & Siem Jan Koopman, 2000. "Signal extraction and the formulation of unobserved components models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 84-107.
    3. W. Erwin DIEWERT & Jan de HAAN & Rens HENDRIKS, 2011. "The Decomposition of a House Price Index into Land and Structures Components: A Hedonic Regression Approach," The Valuation Journal, The National Association of Authorized Romanian Valuers, vol. 6(1), pages 58-105.
    4. W. Erwin Diewert & Jan de Haan & Rens Hendriks, 2015. "Hedonic Regressions and the Decomposition of a House Price Index into Land and Structure Components," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 106-126, February.
    5. Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, vol. 27(7), pages 1317-1333, May.
    6. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    7. Diewert, Erwin & Shimizu, Chihiro, 2015. "Residential Property Price Indices For Tokyo," Macroeconomic Dynamics, Cambridge University Press, vol. 19(8), pages 1659-1714, December.
    8. Harvey, A., 2008. "Modeling the Phillips curve with unobserved components," Cambridge Working Papers in Economics 0805, Faculty of Economics, University of Cambridge.
    9. Stephen Malpezzi & Larry Ozanne & Thomas G. Thibodeau, 1987. "Microeconomic Estimates of Housing Depreciation," Land Economics, University of Wisconsin Press, vol. 63(4), pages 372-385.
    10. Komunjer, Ivana & Ng, Serena, 2014. "Measurement Errors In Dynamic Models," Econometric Theory, Cambridge University Press, vol. 30(1), pages 150-175, February.
    11. Eurostat, 2013. "Handbook on Residential Property Prices Indices," World Bank Publications - Books, The World Bank Group, number 17280, December.
    12. Knight, John R. & Sirmans, C. F., 1996. "Depreciation, Maintenance, and Housing Prices," Journal of Housing Economics, Elsevier, vol. 5(4), pages 369-389, December.
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    Cited by:

    1. Scott Wentland & Gary Cornwall & Jeremy G. Moulton, 2023. "For What It's Worth: Measuring Land Value in the Era of Big Data and Machine Learning," BEA Papers 0115, Bureau of Economic Analysis.
    2. Robert J. Hill & Alicia N. Rambaldi, 2022. "Hedonic Models and House Price Index Numbers," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 413-444, Springer.
    3. Plant, Lyndal & Rambaldi, Alicia & Sipe, Neil, 2017. "Evaluating Revealed Preferences for Street Tree Cover Targets: A Business Case for Collaborative Investment in Leafier Streetscapes in Brisbane, Australia," Ecological Economics, Elsevier, vol. 134(C), pages 238-249.
    4. Gong Yunlong & de Haan Jan, 2018. "Accounting for Spatial Variation of Land Prices in Hedonic Imputation House Price Indices: a Semi-Parametric Approach," Journal of Official Statistics, Sciendo, vol. 34(3), pages 695-720, September.
    5. repec:bea:wpaper:0209 is not listed on IDEAS
    6. Wentland, Scott A. & Ancona, Zachary H. & Bagstad, Kenneth J. & Boyd, James & Hass, Julie L. & Gindelsky, Marina & Moulton, Jeremy G., 2020. "Accounting for land in the United States: Integrating physical land cover, land use, and monetary valuation," Ecosystem Services, Elsevier, vol. 46(C).
    7. Lyndal Plant & Alicia N. Rambaldi & Neil Sipe, 2016. "Property value returns on investment in street trees: a business case for collaborative investment in Brisbane, Australia," Discussion Papers Series 563, School of Economics, University of Queensland, Australia.
    8. W. Erwin Diewert & Kiyohiko G. Nishimura & Chihiro Shimizu & Tsutomu Watanabe, 2020. "The System of National Accounts and Alternative Approaches to the Construction of Commercial Property Price Indexes," Advances in Japanese Business and Economics, in: Property Price Index, chapter 0, pages 181-219, Springer.

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    More about this item

    Keywords

    urban land prices; housing prices; state-space; unobserved components; Fisher price indices;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • 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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