Start Date
26-6-2024 11:00 AM
End Date
26-6-2024 12:15 PM
Description
Decisions related to mass real estate valuation often involve properties characterized by unique sets of features, each holding distinct significance for decision-makers. Incorporating this aspect into analyses has proven to be a challenge. Traditional methods of information analysis frequently overlook the synergy among these features and neglect the influence of behavioral aspects intrinsic to decision-makers. In addressing these limitations, this study proposes a novel solution leveraging emotion detection technology through Computer Vision and Neural Networks in the context of mass real estate valuation. The novelty of the presentation lies in proposing new approaches by illustrating that the assessment of real estate features forms a synergistic and inseparable conglomerate (Fusion Features). This underscores the enhanced usability of the results in analyzing specific phenomena, structures, or systems within the context of mass real estate valuation. The importance of this solution becomes evident in the context of mass real estate valuation procedures, where the comprehensive understanding of an real estate features, including emotional aspects, can significantly contribute to more accurate and nuanced market analyses outcomes.
Recommended Citation
Renigier-Bilozor, Malgorzata and Janowski, Artur, "Mass appraisal in modern times: Emotion recognition technology implementation focus" (2024). Mass Appraisal Valuation Symposium. 6.
https://researchexchange.iaao.org/mavs/mavs2024/sessions/6
Mass appraisal in modern times: Emotion recognition technology implementation focus
Decisions related to mass real estate valuation often involve properties characterized by unique sets of features, each holding distinct significance for decision-makers. Incorporating this aspect into analyses has proven to be a challenge. Traditional methods of information analysis frequently overlook the synergy among these features and neglect the influence of behavioral aspects intrinsic to decision-makers. In addressing these limitations, this study proposes a novel solution leveraging emotion detection technology through Computer Vision and Neural Networks in the context of mass real estate valuation. The novelty of the presentation lies in proposing new approaches by illustrating that the assessment of real estate features forms a synergistic and inseparable conglomerate (Fusion Features). This underscores the enhanced usability of the results in analyzing specific phenomena, structures, or systems within the context of mass real estate valuation. The importance of this solution becomes evident in the context of mass real estate valuation procedures, where the comprehensive understanding of an real estate features, including emotional aspects, can significantly contribute to more accurate and nuanced market analyses outcomes.
