Regression tree does not perform well
A single decision tree often does not work very well.  Of course you could try to optimize the tree. But I think it is better to change to random forest or gradient boosting if you want to work with trees.
My target is continuous value like house price. I am training a regression tree on it. I use Gradientboostregressor in scklearn, Python. 
My target value(house price) has a L shape distribution: house price on high end is like 10 times higher than price on low end. My regression tree model under-predict high values, and over-predict low values. 
Anything I can do to improve model prediction? I tried to model log(price), then exp(prediction),but not work well. 
Thank you very much.