Incorporating Asymmetric Loss for Real Estate Prediction with Area-level Spatial Data
Abstract
We investigate two asymmetric loss functions, namely 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 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.
Cite
@article{arxiv.2410.09673,
title = {Incorporating Asymmetric Loss for Real Estate Prediction with Area-level Spatial Data},
author = {Vaidehi Dixit and Scott H. Holan and Christopher K. Wikle},
journal= {arXiv preprint arXiv:2410.09673},
year = {2024}
}