Posting pictures is a necessary part of advertising a home for sale. Agents typically sort through dozens of images from which to pick the most complimentary ones. This is a manual effort involving annotating images accompanied by descriptions (bedroom, bathroom, attic, etc.). When volumes are small, manual annotation is not a problem, but there is a point where this becomes too burdensome and ultimately infeasible. Here, we propose an approach based on computer vision methodology to radically increase the efficiency of such tasks. We present a high-confidence image classification framework, whose inputs are images and outputs are labels. The core of the classification algorithm is long short term memory (LSTM), and fully connected neural networks, along with a substantial preprocessing using 'contrast-limited adaptive histogram equalization (CLAHE) for image enhancement. Since, there is no standard benchmark containing a comprehensive dataset of well-annotated real estate images, we introduce Real Estate Image (REI) database for evaluating the image classification algorithms. Therein we demonstrate empirics based on our proposed framework on the new REI dataset, as well as on the SUN dataset.