Multimodal property video neuroanalytics.

With the development of smart technologies and smart buildings, more multimodal big data are being generated in buildings. The availability of more detailed and real-time buildings data on well-being and other aspects is, however, still inadequate. The collection of such data should preferably be low-cost. Our innovative Multimodal Property Video Neuroanalytics (MOVE) can provide such data. The research aims to develop the MOVE designed to examine the property, its context, and potential investors' emotional, affective, and physiological states (MAPS) by combining the circumplex model of affect, the somatic marker hypothesis, regression and multiple criteria analysis, and neuromarketing and recommender methods. The link between the built environment and emotions can be deep and multifaceted. We developed the MOVE by integrating text, biometrics, audio, and image analysis technologies, regression and multi-criteria analysis methods. The MOVE contains multimodal analysis, fusion, regression, property decision support, digital evidence-based recommendations, and value analysis subsystems. Using the proposed MOVE, the perceived investment value is determined and evidence-based recommendations are provided on how to increase the property value. To improve green building ratings, stakeholders should use potential investors' and users' MAPS data, which adequately reflect the quality of green buildings. We suggest including a new emotional dimension in green building rating systems.