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

May 1, 2014

Preview Citation

Format: Chicago

Mick Silver, and Brian Graf. Commercial Property Price Indexes: Problems of Sparse Data, Spatial Spillovers, and Weighting, (USA: International Monetary Fund, 2014) accessed December 26, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary

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).

Subject: Econometric analysis, Inflation, Land prices, Logit models, Price indexes, Prices, Spatial models

Keywords: Commercial Property Price Indexes, Dummy variable, Errors in Measurement, House Price Indexes, Index Number Weights, Inflation, Inflation estimate, Land prices, Logit models, Math, Moody's aggregation process, Moody's CPPIs, Price indexes, Price inflation, RCA building block index, RCA CPPI weight, Spatial Econometrics, Spatial models, Spatial Panel Regressions, Transaction price, WP

Publication Details

Pages:

30
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2014/072
Stock No:

WPIEA2014072
ISBN:

9781484364543
ISSN:

1018-5941

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