  The valuation of real estates (e.g., house, land, among others) is of extreme
importance for decision making. Their singular characteristics make valuation
through hedonic pricing methods dificult since the theory does not specify the
correct regression functional form nor which explanatory variables should be
included in the hedonic equation. In this article we perform real estate
appraisal using a class of regression models proposed by Rigby & Stasinopoulos
(2005): generalized additive models for location, scale and shape (GAMLSS). Our
empirical analysis shows that these models seem to be more appropriate for
estimation of the hedonic prices function than the regression models currently
used to that end.
