Semi-Parametric Interpolations of Residential Location Values: Using Housing Price Data to Generate Balanced Panels

John Clapp (), Jeffrey Cohen and Cletus Coughlin

No 2014-50, Working Papers from Federal Reserve Bank of St. Louis

Abstract: We estimate location values for single family houses by local polynomial regressions (LPR), a semi-parametric procedure, using a standard housing price and characteristics dataset. As a logical extension of the LPR method, we interpolate land values for every property in every year and validate the accuracy of the interpolated estimates with an out-of-sample forecasting approach using Denver sales during 2003 through 2010. We also compare the LPR and OLS models out-of-sample and determine that the LPR model is more efficient at predicting location values. In a balanced panel application, we use GMM estimation to examine how the location value estimates are affected by airport infrastructure investments.

Keywords: Land Values; Semi-Parametric Estimation; Local Polynomial Regressions; Balanced Panel; Fixed Effects (search for similar items in EconPapers)
JEL-codes: C14 H41 H54 R51 R53 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2014-12-12
New Economics Papers: this item is included in nep-ure
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Citations: View citations in EconPapers (1)

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