Human emotion recognition in the significance assessment of property attributes

Abstract

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.

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

“…This is due to the challenges in quantifying features and their numerical definition, particularly regarding the behavioral and emotional aspects of market participants that contribute to the formation of real estate prices and values. Consequently, many authors have explored alternative approaches in many fields of a real estate market analysis including those of a feature significance assessment, such as Artificial Neural Network (ANN) utilization [59,62] or a Genetic Algorithm (GA) [63][64][65][66].…”

Energy Performance in Residential Buildings as a Property Market Efficiency Driver

“…The main elements within the real estate market (i.e., market subject, market object, and space) combine and condition each other [32,33]. The nature of the real estate market and the unpredictability of changes in it, as well as the peculiarities of the market players largely shaping it, mean that all decisions made in the context of real estate are subject to risk and uncertainty, which ultimately affects the efficiency of the real estate market [34][35][36][37].…”

Leveraging supplementary modalities in automated real estate valuation using comparative judgments and deep learning

“…So far, ConvNets have been mainly used for classification of visual data (Koch et al, 2020). A real estate related example is the article of Renigier-Biłozor et al (2022), where human emotions generated by looking at real estate images are classified in order to incorporate the detected emotions into a valuation model. In the article of Glaeser et al (2018) ConvNet is used to evaluate the impact of the exterior and, in Poursaeed et al (2018), the interior visual appearance of a building on prices.…”

“…, 2020). A real estate related example is the article of Renigier-Biłozor et al. (2022), where human emotions generated by looking at real estate images are classified in order to incorporate the detected emotions into a valuation model.…”