Developing GeoAI ıntegrated mass valuation model based on LADM valuation ınformation Great Britain country profile

Abstract

Access to information pertaining to land facilitates strategic planning for individuals, businesses, and governmental entities and allows them to anticipate and address contemporary challenges such as climate change, housing requirements, and economic prosperity. Improvements in information technologies contribute to data creation, analysis, and dissemination processes that enable gaining knowledge about land-related events. Greater use of location data within property technologies gives rise to an efficient and sustainable land administration system. GeoAI can contribute greatly to the solution of spatial problems by extracting information from complex data through geospatial intelligence. Property valuation requires a spatially explicit model to evaluate locational factors with spatial analyses. In this study, a Land Administration Domain Model (LADM)-based mass valuation model is developed for Great Britain by integrating GeoAI techniques. First, the LADM Valuation Information package is matched with the country's current organizational structure and it is extended according to the needs to create a country profile. After developing the conceptual model, a database schema is created and automatic conversion to the physical model is conducted. Loading different data sources into the database, a mass valuation application is carried out through spatial analysis and Random Forest regression analysis. As a result, a highly accurate and holistic valuation model is implemented based on the international land administration standards.