I’m kicking off a five-part AI build that will ultimately cover land value estimation, risk assessment, ROI prediction and more, but the very first milestone is a robust Land Value Estimation engine. Everything you need to understand the larger vision, data architecture and success metrics is spelled out here: https://docs.google.com/document/d/17X3UJzXx-gG6x1z1YbbiOpAu_Gp1BrCBLJU0oIuKscs/edit?usp=sharing Scope of this initial module • Work only with historical land price data for now; I’ll layer in geographical or environmental feeds once the core model proves reliable. • Keep the geographical focus tight—one defined local area—so we can reach production-ready accuracy quickly, then scale regionally. • Deliver a trained model (Python preferred), well-commented code and a concise report that explains feature engineering, validation metrics and key insights. If you package the model behind a lightweight API or Jupyter notebook demo, even better. Acceptance criteria 1. Mean Absolute Percentage Error meets or beats the benchmark target in the doc. 2. Reproducible pipeline: raw data → cleaning → feature set → model training. 3. Clear hand-off materials so another engineer can extend the code to the remaining four modules without guesswork. If building predictive models on real-world price data is your sweet spot, dive into the doc and tell me briefly how you’d approach feature selection and validation for this local-area prototype.