AI-Driven Property Value Predictor

please review project details here https://docs.google.com/document/d/18J_UF2inYrq3XeOUdV7rQXGM_aCYhzpym7QihlKfjfc/edit?usp=sharing

Job Title: Data Scientist / AI Engineer (Real Estate Forecasting & Flask Integration)
Company / Project

Project Title: AI-Enhanced Pre-Foreclosure Property Investment Platform
Owner: [Your Name / Company Name]
Deadline: July 1, 2025 (Phase I: AI Model v1.0)
Website (in development): homeprices.cashprohomebuyers.com

We’re building an intelligent real estate platform that empowers homeowners in pre-foreclosure and property investors with AI-driven home value predictions, interactive dashboards, and real-time property insights.
The backend is built in Flask (Python), and we’re ready to integrate a robust machine learning pipeline for predictive analytics.

Objective

Develop and deploy a predictive model that forecasts future property values (6mo, 1yr, 1.5yr, 2yr) using comparative market data, loan info, and local economic trends.

The model will power both the Seller and Buyer dashboards, providing dynamic visual forecasts, ROI analytics, and confidence intervals.

Core Responsibilities
1. Data Engineering & Integration

Build pipelines to collect, clean, and normalize data from:

PropStream (comparables, sale history, loan balance)

Zillow/Redfin APIs (pricing & trends)

County assessor data and optional macroeconomic feeds

Handle missing data, outliers, and inconsistent property features.

Engineer a unified dataset combining property + market-level variables.

2. Feature Engineering

Create new features such as:

Equity spread, price/sqft, DOM (days on market)

Rolling averages of appreciation rates

Local neighborhood appreciation indices

Encode categorical variables and scale continuous features.

Implement temporal splits for model validation.

3. Model Development

Develop two complementary prediction models:

XGBoost / LightGBM for short-term (6–12 months)

LSTM / Transformer (TensorFlow) for long-term (12–24 months)

Train models using time-series regression and feature importance tracking.

Produce confidence intervals for all predictions.

4. Model Evaluation

Validate with metrics (RMSE, MAPE, R²).

Compare model performance by:

Region (ZIP/City)

Property type

Forecast horizon

Provide feature importance visualizations and drift tracking.

5. Deployment

Package models into a Flask-based REST API:

/predict endpoint accepts JSON input and returns forecasts:

{
"6mo": {"value": X, "ci": [A,B]},
"1yr": {"value": X, "ci": [A,B]},
"1.5yr": {"value": X, "ci": [A,B]},
"2yr": {"value": X, "ci": [A,B]}
}


Optimize for performance and caching (Redis preferred).

Document API schema and connect to the frontend dashboards.

6. Visualization & Dashboard Integration

Output prediction data for display in:

Seller dashboard (forecast chart + equity analysis)

Buyer dashboard (ROI %, forecasted appreciation)

Deliver results as JSON + Plotly-ready datasets.

7. Maintenance & Monitoring

Implement automated retraining pipelines.

Track model accuracy, drift, and versioning (using MLflow or similar).

Schedule weekly or monthly retraining using updated PropStream data.

Tech Stack
Layer Preferred Tools
Programming Python 3.10+, Flask
Data Pandas, NumPy, SQLAlchemy
Modeling XGBoost, LightGBM, TensorFlow/Keras
Visualization Plotly, Matplotlib
Storage PostgreSQL or Firebase
Deployment AWS Lambda / EC2 / GCP
Auth / API JWT, Flask-RESTful
Tracking MLflow, DVC
Deliverables

Cleaned and merged dataset (CSV + schema)

Feature engineering scripts

Trained models (XGBoost & LSTM)

Flask REST API with /predict endpoint

Model evaluation report (RMSE, MAPE, confidence intervals)

Integration guide for dashboards (JSON schema + example plots)

Retraining pipeline (ETL + model update)

Documentation (Markdown or PDF)