TF-IDF + Multiple Regression Prediction Problem
As you mentioned you could only so much with the body of text, which signifies the amount of influence of text on selling the cars. 
Even though the model gives very poor prediction accuracy, you could ahead to see the feature importance, to understand what are the words that drive the sales. 
Include phrases in your tfidf vectorizer by setting ngram_range parameter as (1,2)
This might gives you a small indication of what phrases influence the sales of a car. 
If would also suggest you to set norm parameter of tfidf as None, to check if has influence. By default, it applies l2 norm. 
The difference would come based the classification model, which you are using. Try changing the model also as a last option.
I have a dataset of ~10,000 rows of vehicles sold on a portal similar to Craigslist. The columns include price, mileage, no. of previous owners, how soon the car gets sold (in days), and most importantly a body of text that describes the vehicle (e.g. "accident free, serviced regularly"). 
I would like to find out which keywords, when included, will result in the car getting sold sooner. However I understand how soon a car gets sold also depends on the other factors especially price and mileage. 
Running a TfidfVectorizer in scikit-learn resulted in very poor prediction accuracy. Not sure if I should try including price, mileage, etc. in the regression model as well, as it seems pretty complicated. Currently am considering repeating the TF-IDF regression on a particular segment of the data that is sufficiently huge (perhaps Toyotas priced at $10k-$20k).
The last resort is to plot two histograms, one of vehicle listings containing a specific word/phrase and another for those that do not. The limitation here would be that the words that I choose to plot will be based on my subjective opinion.
Are there other ways to find out which keywords could potentially be important? Thanks in advance.