AI Real Estate True Price Prediction Using on-Ground Video
Accurate real estate and land valuation is a challenging task due to the influence of multiple spatial, environmental, physical, infrastructural, and socioeconomic factors. Conventional property valuation methods often depend on manual inspection, historical transaction data, and expert judgment, which can be time-consuming, subjective, and difficult to scale. This paper proposes Estate Vision AI, an intelligent, video-first real estate and land analysis framework that integrates computer vision, machine learning, geospatial intelligence, and environmental feature analysis to provide automated property assessment and valuation. The proposed system accepts on-ground property or land videos as a primary input and extracts visual and contextual features related to land characteristics, built structures, accessibility, surrounding infrastructure, drainage conditions, vegetation, and other observable property attributes. These features are combined with location-based and market-related information to estimate property price, land value, and development potential. The framework additionally provides analysis of soil-related visual characteristics, water-flow and drainage indicators, flood-risk factors, transportation accessibility, nearby facilities, and utility availability. An automatic valuation mode enables prediction without requiring the user to provide a reference price, while a comparison mode supports comparative analysis between multiple properties. The system also incorporates location-based video retrieval to enhance contextual understanding of properties and surrounding areas. By combining visual, spatial, environmental, and market features within a unified framework, Estate Vision AI aims to reduce dependence on manual valuation, improve scalability and consistency, and provide an explainable decision-support tool for property buyers, sellers, investors, valuers, and planning authorities. The proposed framework demonstrates the potential of multimodal artificial intelligence for developing more efficient, data-driven, and geographically aware real estate valuation systems.