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, 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.
|Journal||Journal of Real Estate Finance and Economics|
|Early online date||31 Mar 2021|
|Publication status||E-pub ahead of print - 31 Mar 2021|
- non-normal distributions
- rare events
- automated valuation models
- housing prices