Ah ok. On the first point I guess whoever you are looking for will need to spend a considerable amount of time building/finding a dataset to train a model.

On the second point, I might have incorrectly assumed you were familiar with the Zillow controversy around price prediction.

The TL;DR is that the ML team used a model to forecast prices using a tool made by Facebook called Prophet. The model was probably accurate enough for displaying a rough prediction on a website. Another team in Zillow started using these price predictions to flip houses and lost a whole bunch of money since the model was not designed to do this.

A lot of armchair data scientists quickly pointed the finger at Prophet for being a "bad" model. The reality is all models are bad if they are used for the wrong reason. In this case, the team flipping houses likely didn't listen to the data science team when they said the model shouldn't be used for that purpose.

This is why it's a good idea to know how the model outputs are going to be used. The obvious answer is always "as accurate as possible" but sometimes that might not be accurate enough...

Hope this helps!