AI Property Valuation Model

I have assembled a multi-city database of land and building transactions containing location, area, property type, deal value and several years of historical trends. My goal is to turn this raw information into a production-ready AI/ML model that can return an accurate market rate per sq. ft. (or per acre) for any given query. Scope • Cover both residential and commercial assets. • Use location & area, property type & transaction value, and historical trends as core features. • Deliver a workflow that I can refresh easily once a month when new records are appended. What I need from you 1. A well-documented training pipeline in Python (scikit-learn, XGBoost, LightGBM, TensorFlow or similar—open to your recommendation) with clear preprocessing steps and feature engineering logic. 2. Reproducible notebooks or scripts plus an exportable model file. 3. Evaluation report with cross-validation and error metrics (MAPE/RMSE) demonstrating robust generalisation across cities and sub-localities. 4. Simple instructions or an automated script for retraining so my team can keep the model current after each monthly data update. 5. (Optional but welcome) an API or lightweight microservice wrapper that takes location & property details as input and returns the predicted rate. I will provide the cleaned dataset in CSV or database form and will be available to clarify domain nuances throughout the project. If you have experience building scalable price-prediction or valuation engines, let’s talk about the best approach and timelines. Deliverables 1.) Cleaned and processed dataset. 2.) Final trained model + all source code. 3.) Documentation on how to run, retrain, and deploy the model. 4.) Dashboard/web app for non-technical team members to input property details and see predictions.

Project ID: 39730699