Paper: Oct 13,2024
stat.AP
ID:2410.09673
Incorporating Asymmetric Loss for Real Estate Prediction with Area-level Spatial Data
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.
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Paper Author: Vaidehi Dixit,Scott H. Holan,Christopher K. Wikle
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