# Multiple XGBoost models or just 1 for a cetain type of category?

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.

• What if the "rentals" variable are around \$1000 and the "sales" variables are \$300,000 combining the two output types give me different errors, an error on the "rentals" might be of magnitude \$100 but the error on the "sales" might be \$5,000 dollars and combining them (I expect) gives me "incorrect" RMSE results. Mar 11, 2020 at 17:12