打印

作者

来源

JOURNAL OF REAL ESTATE FINANCE AND ECONOMICS,Vol.54,Issue3,P.365-385

语言

英文

关键字

Land values; Location values; Semi-parametric estimation; Local polynomial regressions

作者单位

[Cohen, Jeffrey P.; Clapp, John M.] Univ Connecticut, Sch Business, Ctr Real Estate, Storrs, CT 06269 USA. [Coughlin, Cletus C.] Fed Reserve Bank St Louis, St Louis, MO USA. [Clapp, John M.] Univ Reading, Reading, Berks, England. Cohen, JP (reprint author), Univ Connecticut, Sch Business, Ctr Real Estate, Storrs, CT 06269 USA. E-Mail: jeffrey.cohen@business.uconn.edu

摘要

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.