Temporal Aspects in Machine Learning
It's not really possible to adress concept drift in general. But I can bring two similar answers for drift of houses prices :

As other prices the drift is usually well measured and studied. As one would correct price for inflation, one can correct past house prices with a housing index (typically this index for the US). It will help your model having prices that are comparable over years.

Another way to tackle drift is to consider a ratio with a relevant variable that has a similar drift. For housing price, that might be median income of the neighborhood. This will give you a variable that is less sensitive to the overall drift.


As you can see those two methods are pretty much equivalent here in practice, as it mainly consist in correcting features and eventually, targets. The main difference is that in the first case you talk about dollars directly which is often more business oriented. Application of those methods can get a bit difficult if you try to use your model to predict the future and need to project housing index or median wage.