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arXiv · 2410.09673

Incorporating Asymmetric Loss for Real Estate Prediction with Area-level Spatial Data

Vaidehi Dixit·Scott H. Holan·Christopher K. Wikle
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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.

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Vaidehi Dixit, Scott H. Holan, Christopher K. Wikle. 2024-10-12. Incorporating Asymmetric Loss for Real Estate Prediction with Area-level Spatial Data. https://arxiv.org/abs/2410.09673

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