It looks like a classical email classification problem: Spam or Ham.
In the following I will use some specific slang, do not worry, look it up in Google, if you will see specific terms you do not know.
Prerequisite: ideally your dataset will be "balanced", having, say, 50% cars and 50% houses, could be 40/60 as well. Problems will arise, when one class will be say < 10%.
So called "bag of words" can be seen as a start.
- Your dataset consist of two columns, say, "description" and "label"
- Label has only two values car or house
- Tokenize your decription column (single words)
- Cleanse the desc column: remove stop words (like and, the , a - as they do not have any value), remove punctuation, possibly stem the words, remove numbers
- calculate document-term-matrix, possibly use TF-IDF
By the end of this operation you will have a dataset with a label column (car or house) and long number of word (or even n-gram, e.g. "great_view" is a bi-gram) columns containing binary values:
Label; vehicle, balcony, wheel, ..., ...., great_view
Car, 1, 0, 1, ....., ...., ..., 0
House, 0, 1, 0, ..., ..., 1
Then use naive bayes or logistic regression as a start to train your model.
Pre-process every new description as above and use your trained model to assign a probability to be a "car" or a "house", check the confusion matrix, maybe adjust the threshold .
Everything I described can be done e.g. in R or Python.
In R use the text mining package "tm".