PDF(Pubmed)
Abstract：
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.
摘要：
自动估价模型（AVM）被金融机构广泛用于估计住宅抵押贷款的财产价值。从AVM获得的定价误差分布通常显示为肥尾(Pender2016；Demiroglu和JamesManagementScience，64(4),1747-17602018)。尾部的极端事件通常被称为“黑天鹅”（Taleb2010）在金融和他们的存在复杂的金融风险管理，评估，和监管。我们通过理论证明，蒙特卡罗实验,以及一个经验例子，即定价误差的非正态与房屋定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住房价格的实证例子，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。
