Local Polynomial Regressions versus OLS for Generating Location Value Estimates

Posted: 18 Mar 2017

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: March 16, 2017

Abstract

We estimate location values for single family houses using a standard house price and characteristics dataset and local polynomial regressions (LPR), a procedure that allows for complex interactions between the values of structural characteristics and the value of land. We also compare LPR to additive OLS models in the Denver metropolitan area with out-of-sample methods. 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 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

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

Suggested Citation

Cohen, Jeffrey and Coughlin, Cletus C. and Clapp, John M., Local Polynomial Regressions versus OLS for Generating Location Value Estimates (March 16, 2017). Journal of Real Estate Finance and Economics, Vol. 54, No. 3, 2017. Available at SSRN: https://ssrn.com/abstract=2934602

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)

Register to save articles to
your library

Register

Paper statistics

Abstract Views
132
PlumX Metrics