Coimbatore Property Valuator: A Hybrid Machine Learning and Offline Market-Intelligence Framework for Property Valuation Real Estate Market and Trend Prediction Model
TL;DR
A local framework for property valuation in Coimbatore, India is presented, namely, the Coimbatore Property Valuator (CPV), which illustrates the complementary use of statistical prediction and domain rules in a practical application to property valuation.
Abstract
Determination of property value is complicated due to factors of locality, property type, area, accessibility, physical characteristics and market conditions. In this paper, a local framework for property valuation in Coimbatore, India is presented, namely, the Coimbatore Property Valuator (CPV). The system includes a data-acquisition and machine-learning module, and an offline evaluation and market-intelligence module. Property listings are automatically harvested from the web, cleaned, standardized, and enhanced with a locality knowledge base, which provides attributes and context on the locality's micro-market, zone, soil, connectivity, and appreciation. XGBoost, LightGBM and CatBoost are the algorithms used for separate training of Sale and Rent/Lease pipelines. They make predictions using a weighted log-space ensemble with weights of 0.40, 0.35 and 0.25, respectively. The models are based on area, BHK, market statistics, geographic attributes and property-category features. The mobile app also offers an offline valuation engine that uses processed data to compute locality market-rate baselines and uses transparent, rule-based adjustments. It calculates and compares the seller's asking price with a "fair value" range and categorizes the outcome as Great Deal, Fair Market Deal or Overpriced. It also provides locality connectivity and rule-based appreciation projections. The documented project training run gave an ensemble R² of 0.8510 and 0.8567 for Sale and Rent/Lease, respectively. These values are based on the actual run and are not meant to be viewed as absolute market values. CPV fills in the gaps of previous research by integrating local market information, various transaction methods, ensemble prediction, transparent market adjustment, and offline deployment in a unified framework. It illustrates the complementary use of statistical prediction and domain rules in a practical application to property valuation.