Using shrinkage for data-driven automated valuation model specification – a case study from Berlin

We study whether data-driven AVM specification that combines a flexible-yet-simple regression model with shrinkage estimators considerably improves upon the prediction accuracy of a conventional hedonic model. A rolling window prediction comparison based on all condominium sales in Berlin, Germany, between 1996 and 2013 delivered the following results. The highly parameterised model can result in extreme errors if the flexible model, which employs roughly 3,800 variables, is estimated by OLS and even if shrinkage is applied via Ridge regression. Once the most extreme errors are disregarded, Ridge regression appears as the clear winner of the prediction comparison. It is the only procedure that delivers a considerable reduction in the root mean squared prediction error relative to a parsimonious benchmark model (estimated via OLS). Of the two procedures with variable selection capability, Elastic Net delivers a slightly better prediction performance. Lasso, on the other hand, acts considerably more as a selector and typically sets the bulk of the several thousand coefficients to zero. Both procedures largely agree in terms of which characteristics they frequently select: core characteristics of hedonic pricing such as floor space, building age and location dummies.