PropVista: Intelligent Property Selection System

Property selection is a complex process involving financial, legal, and technical considerations. However, traditional approaches often overlook critical factors such as soil quality, environmental conditions, and lifestyle suitability. This paper presents PropVista, an intelligent property selection system that integrates Artificial Intelligence (AI), Machine Learning (ML), and IoT-based soil analysis to enhance decision-making. The system evaluates properties using parameters such as Safe Bearing Capacity (SBC), soil moisture, environmental risks, and user preferences. A weighted scoring model is applied to generate accurate and personalized recommendations. The system also incorporates features such as lifestyle suitability analysis and future development prediction to support long-term investment decisions. The implementation demonstrates improved accuracy, faster response time, and enhanced user experience. The proposed system reduces dependency on experts and provides a transparent, data-driven approach to property evaluation.

@article{ijter2026212605149913,
  author = {Rohan Anil Bondre and Jagruti Patil and Pragati Gaikwad and Namrata Kedar and Dr.Shwetkranti Taware},
  title = {PropVista: Intelligent Property Selection System},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2026},
  volume = {2},
  number = {5},
  pages = {262-272},
  doi =  {10.64823/ijter.2605021},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212605149913/propvista-intelligent-property-selection-system},
  abstract = {Property selection is a complex process involving financial, legal, and technical considerations. However, traditional approaches often overlook critical factors such as soil quality, environmental conditions, and lifestyle suitability. This paper presents PropVista, an intelligent property selection system that integrates Artificial Intelligence (AI), Machine Learning (ML), and IoT-based soil analysis to enhance decision-making. The system evaluates properties using parameters such as Safe Bearing Capacity (SBC), soil moisture, environmental risks, and user preferences. A weighted scoring model is applied to generate accurate and personalized recommendations. The system also incorporates features such as lifestyle suitability analysis and future development prediction to support long-term investment decisions. The implementation demonstrates improved accuracy, faster response time, and enhanced user experience. The proposed system reduces dependency on experts and provides a transparent, data-driven approach to property evaluation.},
  keywords = {Artificial Intelligence, Property Recommendation System, IoT-based Soil Analysis, Decision Support System, Real Estate Analytics},
  month = {May},
}

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