REAL ESTATE PRICE PREDICTION UNDER ASYMMETRIC LOSS

MICHAEL CAIN^{1} AND CHRISTIAN JANSSEN^{2}

1Department of Economics, University of Salford, The Crescent, Salford M5 4WT, U.K.

2Department of Finance and Management Science, University of Alberta, Edmonton, Canada T6G 2R6

(Received January 5, 1994; revised December 12, 1994)

Abstract. This paper deals with the problem of how to adjust a predictive mean in a practical situation of prediction where there is asymmetry in the loss function. A standard linear model is considered for predicting the price of real estate using a normal-gamma conjugate prior for the parameters. The prior of a subject real estate agent is elicited but, for comparison, a diffuse prior is also considered. Three loss functions are used: asymmetric linear, asymmetric quadratic and LINEX, and the parameters under each of these postulated forms are elicited. Theoretical developments for prediction under each loss function in the presence of normal errors are presented and useful tables of adjustment factor values given. Predictions of the dependent price variable for two properties with differing characteristics are made under each loss function and the results compared.

Key words and phrases: Asymmetric loss, Bayesian prediction, real estate valuation.