在干扰中忽视空间和时空依赖性可以使黑天看起来灰色
R Kelley Pace1, Raffaella Calabrese2
1LREC Endowed Chair of Real Estate, Department of Finance, Louisiana State University, Baton Rouge, LA USA.
概括
自动估值模型 (AVM) 可能会有价格错误. 这项研究揭示了这些错误的非正常性与房地产定价模型的准确性之间的直接联系.
科学领域:
- 计量经济学 计量经济学
- 金融建模金融建模
- 房地产分析 房地产分析
背景情况:
- 自动估值模型 (AVM) 对于住宅抵押贷款房地产价值估计至关重要.
- AVM 的定价错误往往会产生巨大的后果,这给金融风险管理带来了挑战.
- 在AVM错误中,极端事件或"黑天"使评估和监管复杂化.
研究的目的:
- 调查AVM定价错误的非正常性与模型适合性之间的关系.
- 为了证明错误分布特征和预测准确性之间的直接相关性.
- 提供来自美国房地产市场的经验证据.
主要方法:
- 定价错误分布的理论分析.
- 蒙特卡洛模拟用于在各种条件下测试模型行为.
- 美国住房价格数据的实证分析.
主要成果:
- 在定价错误的非正常性和模型的适合性之间发现了直接的线性关系.
- 学生t分布的估计自由度与模型准确度有很强的相关性.
- 这种关系甚至适用于包含空间和时空依赖性的复杂模型.
结论:
- AVM定价错误的非正常性是房价模型表现的关键指标.
- 了解错误分布,特别是尾部事件,对于稳健的金融风险评估至关重要.
- 这些发现为改善房地产融资中的AVM准确性和可靠性提供了洞察力.
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