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Abstract
LandSin, a web application with a back-end database, is developed for global land value estimation by combining polynomial regression and differential privacy models. Leveraging local amenities and property details, LandSin offers key features, e.g., accurate land value and price predictions, affordability and habitability analysis, and terrain insights using Google Maps. In addition, it facilitates useful infographics, helping stakeholders identify economically deprived but habitable areas for balanced regional development. It also supports real estate agencies and community planners in finding habitable land by making data-driven decisions regarding land investments and regional planning, ensuring informed and strategic choices.
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