• Ananya R Chandhra
• Harshitha HV
• Harshitha VS
• Manasa D

The rapid growth of the global real estate market has increased the need for accurate and easily accessible house price prediction tools. However, most existing systems rely solely on structured numerical data and operate in a single language, limiting their usability for diverse linguistic communities. This project presents a Multilingual House Price Prediction System that integrates Machine Learning and Natural Language Processing (NLP) to deliver accurate, region-specific property price estimates while supporting multiple languages. The system analyses key factors such as location, property size, nearby amenities, and market trends using advanced predictive algorithms. To enhance accessibility, a multilingual interface powered by NLP enables users to interact with the system and receive explanations in their preferred language. By combining predictive analytics with language translation capabilities, the proposed system improves inclusivity, user experience, and decision-making for buyers, sellers, and investors across different regions.

House Price Prediction, Machine Learning, Multilingual System, Natural Language Processing (NLP), Real Estate Analytics, Language Translation, Predictive Modelling, Regression Algorithms, Regional Market Analysis, Geospatial Features, User Accessibility, Multi-Regional Data, Intelligent Decision Support, Property Valuation, Human–Computer Interaction (HCI).

"Multilingual House Price Forecasting using Machine Learning and NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.a121-a129, December-2025, Available :http://www.jetir.org/papers/JETIR2512016.pdf

"Multilingual House Price Forecasting using Machine Learning and NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. ppa121-a129, December-2025, Available at : http://www.jetir.org/papers/JETIR2512016.pdf