Local Polynomial Regressions versus OLS for Generating Location Value Estimates: Which is More Efficient in Out-of-Sample Forecasts?

John Clapp (), Jeffrey Cohen and Cletus Coughlin

No 2015-14, Working Papers from Federal Reserve Bank of St. Louis

Abstract: As an alternative to ordinary least squares (OLS), we estimate location values for single family houses using a standard housing price and characteristics dataset by local polynomial regressions (LPR), a semi-parametric procedure. We also compare the LPR and OLS models in the Denver metropolitan area in the years 2003, 2006 and 2010 with out-of-sample forecasting. We determine that the LPR model is more efficient than OLS at predicting location values in counties with greater densities of sales. Also, LPR outperforms OLS in 2010 for all 5 counties in our dataset. Our findings suggest that LPR is a preferable approach in areas with greater concentrations of sales and in periods of recovery following a financial crisis.

Keywords: Land Values; Location Values; Semi-Parametric Estimation; Local Polynomial Regressions (search for similar items in EconPapers)
JEL-codes: C14 H41 H54 R51 R53 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2015-07-01
New Economics Papers: this item is included in nep-for and nep-ure
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Citations: View citations in EconPapers (2)

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DOI: 10.20955/wp.2015.014

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