The appraisal of large amounts of properties is often entrusted to Automated Valuation Models (AVM). At one time, only econometric models were used for this purpose. More recently, also machine learning models are used in mass appraisal techniques. The literature has devoted much attention to assessing the performance capabilities of these models. Verification tests first train a model on a training set, then measure the prediction error of the model on a set of data not met before: the testing set. The prediction error is measured with an accuracy indicator. However, verification on the testing set alone may be insufficient to describe the model’s performance. In addition, it may not detect the existence of model bias such as overfitting. This research proposes the use of cross validation to provide a more complete and effective evaluation of models. Ten-fold cross validation is used within 5 models (linear regression, regression tree, random forest, nearest neighbors, multilayer perception) in the assessment of 1,400 properties in the city of Turin. The results obtained during validation provide additional information for the evaluation of the models. This information cannot be provided by the accuracy measurement when considered alone.
The Cross Validation in Automated Valuation Models : A Proposal for Use
Valier, Agostino
2020-01-01
Abstract
The appraisal of large amounts of properties is often entrusted to Automated Valuation Models (AVM). At one time, only econometric models were used for this purpose. More recently, also machine learning models are used in mass appraisal techniques. The literature has devoted much attention to assessing the performance capabilities of these models. Verification tests first train a model on a training set, then measure the prediction error of the model on a set of data not met before: the testing set. The prediction error is measured with an accuracy indicator. However, verification on the testing set alone may be insufficient to describe the model’s performance. In addition, it may not detect the existence of model bias such as overfitting. This research proposes the use of cross validation to provide a more complete and effective evaluation of models. Ten-fold cross validation is used within 5 models (linear regression, regression tree, random forest, nearest neighbors, multilayer perception) in the assessment of 1,400 properties in the city of Turin. The results obtained during validation provide additional information for the evaluation of the models. This information cannot be provided by the accuracy measurement when considered alone.
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