Abstract-This research sits at the nexus of machine learning,
geospatial analytics, and real estate informatics, aiming to create
smart, data-driven solutions for property price forecasting. With the
growing datafication of the real estate sector, there is a need for
reliable, explainable, and scalable tools for valuation that evolve
with city dynamics. Current solutions are limited by their
assumption on static datasets, manual estimation, or simple
predictive models that fail to capture geolocation-specific features,
seasonality of markets, or visual property features.To address such
limitations, the present paper introduces the Geo Value Analyzer, a
real-time property valuator based on an extremely accurate
machine learning algorithm. The model accepts structured inputs
like location, area, number of rooms, temporal trends in pricing, and
image information, augmented with sophisticated feature
engineering concepts that combine spatial features, locality scores,
and security indices through external APIs. The system has native
support for SHAP-based explainability, which enables the system to
provide clear justification for every predicted value by highlighting
the contribution of features. Implemented on an interactive web
platform, the system further includes key functionalities like fraud
detection, rent-versus-buy analysis, and a chatbot assistant, giving
users a complete, smart tool for making informed decisions on real
estate.
Keywords-Real estate valuation, machine learning, property price
prediction, explainable AI, SHAP, LIME, geospatial analysis,
temporal data modeling, XGBoost, random forest, linear regression,
housing market trends, automated valuation models (AVM),
interpretable machine learning.

GeoValue Analyzer Real estate valuation Property price prediction Machine learning Geospatial analysis Predictive modeling Housing market Data-driven valuation Location-based analysis Regression models Temporal analysis Explainable AI (XAI) SH