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Commercial Property Price Indexes: Problems of Sparse Data, Spatial Spillovers, and Weighting

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  • Mick Silver
  • Brian Graf

Abstract

Transaction-price residential (house) and commercial property price indexes (RPPIs and CPPIs) have inherent problems of sparse data on heterogeneous properties, more so CPPIs. In an attempt to control for heterogeneity, (repeat-sales and hedonic) panel data regression frameworks are typically used for estimating overall price change. We address the problem of sparse data, demonstrate the need to include spatial price spillovers to remove bias, and propose an innovative approach to effectively weight regional CPPIs along with improvements to higher-level weighting systems. The study uses spatial panel regressions on granular CPPIs for the United States (US).

Suggested Citation

  • Mick Silver & Brian Graf, 2014. "Commercial Property Price Indexes: Problems of Sparse Data, Spatial Spillovers, and Weighting," IMF Working Papers 2014/072, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2014/072
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    Cited by:

    1. Reuben Ellul & Jude Darmanin & Ian Borg, 2019. "Hedonic house price indices for Malta: A mortgage-based approach," CBM Working Papers WP/02/2019, Central Bank of Malta.
    2. Mick Silver, 2016. "How to Better Measure Hedonic Residential Property Price Indexes," IMF Working Papers 2016/213, International Monetary Fund.
    3. Robert J. Hill & Miriam 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, International Association for Research in Income and Wealth, vol. 66(3), pages 736-751, September.
    4. Ian Borg & Jude Darmanin & Reuben Ellul, 2019. "Hedonic house price indices for Malta: A mortgage-based approach," CBM Working Papers WP/04/2019, Central Bank of Malta.

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