One of the largest problems in the real estate market analysis, which includes valuation, is determining the significance of individual property attributes that may affect value or attractiveness perception. The study attempts to assess the significance of selected attributes of real estate based on the detection and analysis of the emotions of potential investors. Human facial expression is a carrier of information that can be recorded and interpreted effectively via the use of artificial intelligence methods, machine learning and computer vision. The development of a reliable algorithm requires, in this case, the identification and investigation of factors that may affect the final solution of the problem, from behavioural aspects through technological possibilities. In the presented experiment, an approach that correlates the emotional states of buyers with the visualization of selected attributes of properties is utilized. The objective of this study is to develop an original method for assessing the significance of property attributes based on emotion recognition technology as an alternative to the commonly used methods in the real estate analysis and valuation, which are usually based on surveys. The empirical analysis enabled determination of the mainstream property attributes significance from evoked emotions intensity within the group of property clients (represented by 156 respondents). The significance ranking determined on the basis of the unconscious expressed facial emotions was verified and compared to the answers given in a form of questionnaire. The results have shown that the conscious declaration of the attribute ranking differs from the emotion detection conclusions in several cases.

房地产市场分析（包括估值）中最大的问题之一就是确定可能影响价值或吸引力感知的单个房地产属性的重要性。这项研究试图根据对潜在投资者情绪的检测和分析，评估房地产选定属性的重要性。人脸表情是信息的载体，可以通过使用人工智能方法，机器学习和计算机视觉来有效地记录和解释这些信息。在这种情况下，开发可靠的算法需要从行为方面到技术可能性，对可能影响问题最终解决方案的因素进行识别和调查。在提出的实验中，利用一种使购买者的情绪状态与所选属性的可视化相关联的方法。这项研究的目的是开发一种基于情感识别技术的评估属性属性重要性的原始方法，以替代通常基于调查的房地产分析和评估中常用的方法。通过经验分析，可以从一组属性客户（由156个受访者代表）中诱发的情绪强度来确定主流属性的重要意义。验证了基于无意识表达的面部表情确定的显着性排名，并将其与以问卷形式给出的答案进行了比较。