2024/10/12 by Vaidehi Dixit, Dixit, Vaidehi, Scott H. Holan +3
Earth and Planetary Sciences · Engineering · Environmental Science · #3D Modeling in Geospatial Applications #3D Surveying and Cultural Heritage #Applications (stat.AP) #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications
paper · pdf · doi:10.48550/arxiv.2410.09673
openalex publication_date 2024/10/12 · openalex created_date 2024/10/20 · openalex updated_date 2026/07/28
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.