Abstract:

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.