Start Date
4-12-2024 10:30 AM
End Date
4-12-2024 12:00 PM
Description
In the evolving field of real estate analysis and valuation, the synergy between human expertise and machine intelligence is reshaping how we define, analyze, and apply similarity in property valuation. Our presentation, Human-Machine Synergy in Real Estate Similarity Concept, delves into the potential of advanced AI models—specifically leveraging the HELIOS system (Homogeneity Estate Linguistic Intelligence Omniscient Support)—to transform real estate market analysis. By bridging linguistic intelligence with machine learning, we explore the creation of “real estate fingerprints,” unique identifiers derived from the synergistic analysis of property features. This method goes beyond traditional metrics to unlock deeper insights into property similarity, offering more accurate, context-aware market assessments. Our study emphasizes AI’s role in enhancing mass appraisals, ensuring more reliable, homogenous grouping of properties in dynamic market environments. This is particularly crucial in the context of property taxation, where accurate and fair assessments directly impact taxpayers. The application of AI in mass appraisal processes ensures that valuations are not only equitable but also transparent, reducing the potential for disputes and enhancing taxpayer trust in the system. In doing so, we highlight the critical interplay between human judgment and machine precision, fostering innovation in property valuation through a blend of automated and expert-driven decision-making. This presentation aligns with the symposium's focus on AI’s transformative potential, showcasing how human-machine collaboration can lead to anti-fragile valuation systems that not only adapt but improve in the face of uncertainty.
Publication Date
12-4-2024
Recommended Citation
Renigier-Biłozor, Małgorzata PhD and Janowski, Artur PhD, "Human-Machine Synergy in Real Estate Similarity Concept" (2024). International Research Symposium. 3.
https://researchexchange.iaao.org/irs/irs24/sessions/3
Human-Machine Synergy in Real Estate Similarity Concept
In the evolving field of real estate analysis and valuation, the synergy between human expertise and machine intelligence is reshaping how we define, analyze, and apply similarity in property valuation. Our presentation, Human-Machine Synergy in Real Estate Similarity Concept, delves into the potential of advanced AI models—specifically leveraging the HELIOS system (Homogeneity Estate Linguistic Intelligence Omniscient Support)—to transform real estate market analysis. By bridging linguistic intelligence with machine learning, we explore the creation of “real estate fingerprints,” unique identifiers derived from the synergistic analysis of property features. This method goes beyond traditional metrics to unlock deeper insights into property similarity, offering more accurate, context-aware market assessments. Our study emphasizes AI’s role in enhancing mass appraisals, ensuring more reliable, homogenous grouping of properties in dynamic market environments. This is particularly crucial in the context of property taxation, where accurate and fair assessments directly impact taxpayers. The application of AI in mass appraisal processes ensures that valuations are not only equitable but also transparent, reducing the potential for disputes and enhancing taxpayer trust in the system. In doing so, we highlight the critical interplay between human judgment and machine precision, fostering innovation in property valuation through a blend of automated and expert-driven decision-making. This presentation aligns with the symposium's focus on AI’s transformative potential, showcasing how human-machine collaboration can lead to anti-fragile valuation systems that not only adapt but improve in the face of uncertainty.
