Incorporating Asymmetric Loss for Real Estate Prediction With Area-Level Spatial Data

Abstract

We investigate two asymmetric loss functions, namely linear exponential (LINEX) loss and power divergence loss for optimal spatial prediction with area-level data. With our motivation arising from the real estate industry, namely in real estate valuation, we use the Zillow Home Value Index (ZHVI) for county-level values to show the change in prediction when the loss is different (asymmetric) from a traditional squared error loss (symmetric) function. Additionally, we discuss the importance of choosing the asymmetry parameter and propose a solution to this choice for a general asymmetric loss function. Since the focus is on area-level data predictions, we propose the methodology in the context of conditionally autoregressive (CAR) models. We conclude that the choice of the loss functions for spatial area-level predictions can play a crucial role and is heavily driven by the choice of parameters in the respective loss.