Hedonic Regression

Abstract

Hedonic regression is the estimation of the hedonic price function from observed transactions, yielding empirical estimates of the implicit prices of the characteristics that enter the model. This fifth article in the Foundations section of The Valuation Engineer Journal exercises the full machinery on the issue's eight-comp Pacifica dataset using R's lm() function, with full reported output including coefficients, standard errors, and residuals. The entry frames regression as the bridge between idealized hedonic theory and the finite, messy data of actual appraisal practice, and uses the strong joint fit (adjusted R-squared = 0.93) combined with one large unexplained residual to motivate the latent-variable framing developed in the following entry.