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A spatial model averaging approach to measuring house prices

Author

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  • Ryan Greenaway-McGrevy

    (University of Auckland)

  • Kade Sorensen

    (University of Auckland)

Abstract

We present a novel approach to the estimation of hedonic imputation (HI) price indices for real estate markets using a new Mallows model averaging (MMA) estimator that is robust to spatial dependence. The spatial MMA (SMMA) method is explicitly designed to minimize the quadratic forecast loss of the imputed sales transactions that comprise the HI index when sales transactions are spatially correlated. We apply the SMMA HI approach to a sales transaction dataset for three geographic real estate suburbs of Auckland, New Zealand. The SMMA HI price indices outperform conventional OLS HI methods, exhibiting more accurate out-of-sample prediction and tighter in-sample confidence intervals. The SMMA HI method is expected to offer practitioners enhancements in constant-quality price index accuracy in data sparse environments where model overfitting is a concern, such as high frequency price measurement or highly localized geographies.

Suggested Citation

  • Ryan Greenaway-McGrevy & Kade Sorensen, 2021. "A spatial model averaging approach to measuring house prices," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-32, December.
  • Handle: RePEc:spr:jospat:v:2:y:2021:i:1:d:10.1007_s43071-021-00013-4
    DOI: 10.1007/s43071-021-00013-4
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    More about this item

    Keywords

    Hedonic Imputation Price Index; House prices; Model averaging; Mallows model averaging;
    All these keywords.

    JEL classification:

    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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