Application of Data Mining Techniques and Hedonic Pricing Methods to Determine the Real Estate Land Prices in the Chengalpattu District

Real estate valuation is of considerable significance to the nation's economy. However, the lack of clarity on the variables affecting the price of land is a significant challenge in real estate valuation. This study used a data mining approach to determine variables that significantly influence pricing in the Chengalpattu district of Tamil Nadu using the CHAID and C&RT algorithms as a classification approach. Twenty-one parameters were initially considered based on the actual market prices, and data was collected for 992 residential land parcels. The decision tree algorithms revealed that nine of the twenty-one factors were significant. Utilization of these nine factors in predicting land prices using the hedonic pricing model (HPM) resulted in an 83% price prediction accuracy. The distance of the land parcel from the central business district, the universities, bus stops, highways, hospitals, mountains, industries, commercial amenities, and MRTS were the significant factors influencing the land price. In conclusion, combining the two decision tree methods, CHAID and C&RT, and utilizing the HPM as valuation techniques are better suited for property price prediction.