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

40 Pages Posted: 19 Aug 2015 Last revised: 6 Mar 2019

See all articles by Jeffrey Cohen

Jeffrey Cohen

University of Connecticut - School of Business

Cletus C. Coughlin

Federal Reserve Bank of St. Louis - Research Division

John M. Clapp

University of Connecticut - Department of Finance

Date Written: 2015-07-01

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

JEL Classification: C14, H41, H54, R51, R53

Suggested Citation

Cohen, Jeffrey and Coughlin, Cletus C. and Clapp, John M., Local Polynomial Regressions Versus Ols for Generating Location Value Estimates: Which is More Efficient in Out-of-Sample Forecasts? (2015-07-01). FRB St. Louis Working Paper No. 2015-14. Available at SSRN: https://ssrn.com/abstract=2646153 or http://dx.doi.org/10.20955/wp.2015.014

Jeffrey Cohen (Contact Author)

University of Connecticut - School of Business ( email )

368 Fairfield Road
Storrs, CT 06269-2041
United States

Cletus C. Coughlin

Federal Reserve Bank of St. Louis - Research Division ( email )

411 Locust St
Saint Louis, MO 63011
United States

John M. Clapp

University of Connecticut - Department of Finance ( email )

School of Business
2100 Hillside Road
Storrs, CT 06269
United States
860-486-5057 (Phone)
860-486-0349 (Fax)

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