Estimation and Hypothesis Testing for Nonparametric Hedonic House Price Functions

22 Pages Posted: 26 Jul 2010

See all articles by Daniel P. McMillen

Daniel P. McMillen

University of Illinois at Chicago - Center for Urban Real Estate

Christian L. Redfearn

University of Southern California - Sol Price School of Public Policy

Abstract

In contrast to the rigid structure of standard parametric hedonic analysis, nonparametric estimators control for misspecified spatial effects while using highly flexible functional forms. Despite these advantages, nonparametric procedures are still not used extensively for spatial data analysis due to perceived difficulties associated with estimation and hypothesis testing. We demonstrate that nonparametric estimation is feasible for large datasets with many independent variables, offering statistical tests of individual covariates and tests of model specification. We show that fixed parameterization of distance to the nearest rapid transit line is a misspecification and that pricing of access to this amenity varies across neighborhoods within Chicago.

Suggested Citation

McMillen, Daniel P. and Redfearn, Christian L., Estimation and Hypothesis Testing for Nonparametric Hedonic House Price Functions. Journal of Regional Science, Vol. 50, No. 3, pp. 712-733, August 2010, Available at SSRN: https://ssrn.com/abstract=1647262 or http://dx.doi.org/10.1111/j.1467-9787.2010.00664.x

Daniel P. McMillen (Contact Author)

University of Illinois at Chicago - Center for Urban Real Estate ( email )

601 South Morgan Street
MC 144
Chicago, IL 60607-7121
United States

Christian L. Redfearn

University of Southern California - Sol Price School of Public Policy ( email )

Los Angeles, CA 90089-0626
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
3
Abstract Views
901
PlumX Metrics