Using Latent Root Regression to Identify Nonpredictive Collinearity in Statistical Appraisal Models
研究了潜在根回归技术能否提升评估系数（特征价格）的时间稳定性，并识别评估模型中共线性的性质与影响。结果发现数据中的共线性大多具有预测性，因此潜在根技术相比普通最小二乘法改进有限。
The objective of this study is to examine the potential benefits of using latent root regression techniques to improve the stability of appraisal coefficients (hedonic prices) over time. Another related objective is to more clearly identify the nature and implications of collinearity present in most appraisal models. The results indicate that the majority of the collinearity present in the data is of a predictive nature, hence latent root techniques will likely show little or no improvement over OLS models.