Even if your target variable is a numeric one, sometimes it’s better to use classification methods instead of approximation ones. For instance if you have mostly zero target values and just a few non-zero values. Change the latter to 1, in this case you’ll have two categories: 1 (positive value of your target variable ) and 0. You can also split numerical variable into multiple subgroups : apartment prices for low, medium and high by equal subset width and predict them using classification algorithms. This process is called discretization.
Both types of models are common in machine learning, so a good understanding of when to use which is important.