That distinction isn't real. In that regard, both techniques learn exactly the same thing: a best fit curve. The difference is how complicated that curve needs to be. Unless you are trying to do something specific like computer vision or natural language processing, you really don’t need DL. If you are working with simple tabular data, basic ML like linear regression will be more than enough.

In any case, the feedback for most ML projects is to start with a simple regression technique and only start complicating your models if you aren’t getting what you want. You’d be surprised to see how far a simple non-DL model gets you.

I worked in Zillow’s AI team so I know both AI and real estate analytics problems.