The attractiveness of a property is one of the most interesting, yet challenging, categories to model. Image characteristics are used to describe certain attributes, and to examine the influence of visual factors on the price or timeframe of the listing. In this paper, we propose a set of techniques for the extraction of visual features for efficient numerical inclusion in modern-day predictive algorithms. We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks. After comparing these techniques as applied to a set of property-related images (indoor, outdoor, and satellite), we conclude the following: (i) the entropy is the most efficient single-digit visual measure for housing price prediction; (ii) image segmentation is the most important visual feature for the prediction of housing lifespan; and (iii) deep image features can be used to quantify interior characteristics and contribute to captivation modeling. The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors. Without any need to replace a human expert in a real-estate appraisal process, we conclude that the techniques presented in this paper can efficiently describe visible characteristics, thus introducing perceived attractiveness as a quantitative measure into the predictive modeling of housing.

翻译：属性的吸引力是最有趣但最具挑战性的模型类别之一。 图像特性用来描述某些属性, 并研究视觉因素对列表价格或时间框架的影响。 在本文中, 我们提出一套技术, 用于提取视觉特征, 以便有效地将数字纳入现代预测算法中。 我们讨论的是香农的变形, 计算重力中心, 使用图像分割, 以及使用进化神经网络等技术。 在比较这些技术, 应用到一系列与属性有关的图像( 室内、室外和卫星) 之后, 我们得出以下结论:(一) 恒星是房价预测最有效的单一数字直观测量尺度;(二) 图像分割是预测住房寿命的最重要视觉特征;(三) 深度图像特征可以用来量化内部特征, 计算重力中心, 使用图像分解, 使用动态神经网络 。 这里选择的40个图像特征包含大量的预测力, 并超越一些最强的元数据预报器。 我们无需替换一个房地产模型, 来替换一个具有吸引力的人类专家, 从而将具有可见度的量化的模型, 我们得出结论。