We use a rich data set consisting of 123,000 houses sold in Switzerland between 2004 and 2017 to investigate different automated valuation techniques in settings where the models are updated regularly. We apply six methods (linear regression, robust regression, mixed effects regression, gradient boosting, random forests, and neural networks) to both moving window and extending window models. With respect to the criteria of appraisal accuracy and stability, the preferred methods are robust regression using moving windows, gradient boosting using extending windows, or mixed effects regression for either strategy.