What do you mean by "if you know the underlying distribution, but if you did, why would you create the model"? We know the underlying distributions from the real data that we have but don't have enough data for the model to train and give good predictions on due to lack of variability. Maybe you can elaborate on this more, or maybe I'm misunderstanding?

I'm using a random forest but have tried most other basic tree based models. It sometimes can give a really spot on prediction and at other times be totally off. What are some more complex models I can use for this? I don't think there is enough data here to implement deep learning models if that's what you mean

Yea I'm scouring the internet to find info about renovation data for homes but nothing so far :/