Automated valuation models (AVMs) are widely used by financial institutions to estimate the property value for a residential mortgage. The distribution of pricing errors obtained from AVMs generally show fat tails (Pender 2016; Demiroglu and James Management Science, 64(4), 1747–1760 2018). The extreme events on the tails are usually known as “black swans” (Taleb 2010) in finance and their existence complicates financial risk management, assessment, and regulation. We show via theory, Monte Carlo experiments, and an empirical example that a direct relation exists between non-normality of the pricing errors and goodness-of-fit of the house pricing models. Specifically, we provide an empirical example using US housing prices where we demonstrate an almost perfect linear relation between the estimated degrees-of-freedom for a Student’s t distribution and the goodness-of-fit of sophisticated evaluation models with spatial and spatialtemporal dependence.

金融机构广泛使用自动估值模型 (AVM) 来估算住宅抵押贷款的房产价值。从 AVM 获得的定价误差分布通常呈现肥尾状（Pender 2016；Demiroglu and James Management Science, 64 (4), 1747–1760 2018）。尾部的极端事件通常被称为金融界的“黑天鹅”（Taleb 2010），它们的存在使金融风险管理、评估和监管变得复杂。我们通过理论、蒙特卡洛实验和实证例子表明，定价误差的非正态性与房价模型的拟合优度之间存在直接关系。具体来说，我们提供了一个使用美国房价的实证示例，其中我们证明了学生t分布的估计自由度与具有空间和时空依赖性的复杂评估模型的拟合优度之间几乎完美的线性关系。