# Explanatory variables with many zeros

I am trying to fit a linear model to a price response variable. Many of the predictor variables consist of mainly zeros. For example, one possible predictor variable is "drill holes". Not many parts have a drilled hole, but if they do it would make sense that it affects the price. I am using the caret package in R to train the model and choose the appropriate variables. I have already removed all variables with zero variance.

I have found a lot of literature about count data for a response variable with many zeros and zero-inflation models. But what I am wondering is, how should explanatory variables with many zeros (many are NOT count data) be handled? Is there an appropriate transformation? Or are explanatory variables with many zeros allowable since I am dealing with explanatory variables and not the response variable?