I am building a model to predict, say house prices. Within my data I have sales and rentals. The
Y variable is the price of either the sales or rentals. I also have a number of
X variables to predict
Y, such as number of bedrooms, bathrooms, meters squared etc.
I believe that the model will firstly make a split on the variable "sales" vs "rentals" as this would reduce the loss function - RMSE - the most.
Do you think it is best to train 2 models one for "sales" and the other for "rentals"? The RMSE for the model is quite high and this is in part due to the incorrect "Sales" predictions.