Synthetic reality mapping of real estate using deep learning-based object recognition algorithms

Abstract

Artifcial intelligence (AI), encompassing machine learning and deep learning (DL), has penetrated the real estate domain. This research investigated DL’s potential to enhance real estate investment decisions through synthetic reality mapping. A convolutional neural network was used to identify construction phases, namely, excavation, demolition, construction, and restoration. An image dataset was constructed to refne our model’s capacity to formulate an optimal AI-driven strategy tailored to inform real estate investment decisions. We analyzed the technical dimension and investor behavior through surveys and psychological evaluations focusing on how our model’s outputs afect decision-making. We endeavored to determine the potentialities and intricacies of the proposed DL framework, ResNet V2-152. It is efective at real-time visual analysis, with a demonstrated capability of predicting construction stages as derived from intricate project designs. Analysis of a cohort of real estate practitioners revealed a proclivity toward traditional strategies. This indicates a broader consensus within the industry, suggesting that the prospect of AI replacing human insight remains distant. AI has catalyzed paradigmatic shifts across myriad sectors including real estate. Its efcacy in processing vast data repositories is unmatched. However, the role of human judgment in real estate decision-making remains signifcant. The nuanced, context-driven insights from humans represent a contribution wherein the current iteration of AI may be infeasible. Hence, while we acknowledge the advent of AI in redefning the contours of the industry, the conjecture that it may entirely eliminate human intervention in the real estate industry necessitates further contemplation and evidence.