ABSTRACT

Point estimates from Automated Valuation Models (AVMs) represent the most likely value from a distribution of possible values. The uncertainty in the point estimate – the width of the range of possible values at a given level of confidence – is a critical piece of the AVM output, especially in collateral and transactional situations. Estimating AVM uncertainty, however, remains highly unstandardised in both terminology and methods. In this paper, we present and compare two of the most common approaches to estimating AVM uncertainty – model-based and error-based prediction intervals. We also present a uniform language and framework for evaluating the calibration and efficiency of uncertainty estimates. Based on empirical tests on a large, longitudinal dataset of home sales, we show that model-based approaches outperform error-based ones in all but cases with very highest confidence level requirements. The differences between the two methods are conditioned on model class, geographic data partitions and data filtering conditions.

摘要

来自自动评估模型（AVM）的点估计值代表了可能值分布中最可能的值。点估计的不确定性（在给定的置信度下可能值范围的宽度）是AVM输出的关键部分，尤其是在抵押和交易情况下。然而，在术语和方法方面，估计AVM的不确定性仍然高度标准化。在本文中，我们介绍并比较了两种最常见的估计AVM不确定性的方法-基于模型的预测间隔和基于误差的预测间隔。我们还提供了统一的语言和框架，用于评估不确定性估计的校准和效率。根据对大型纵向房屋销售数据集的经验检验，我们表明，除了具有极高置信度要求的情况外，基于模型的方法在所有情况下均优于基于错误的方法。两种方法之间的差异取决于模型类，地理数据分区和数据过滤条件。