Enhancing Real Estate AI Predictive System
Budget: $2 – $8 USD
Python Data Scientist Needed — Real Estate AI Price Prediction & Automation Platform
Website: https://homeprices.cashprohomebuyers.com
Platform Type: AI-Powered Real Estate Valuation & Cash Offer System
Tech Focus: Python (Machine Learning, Data Analysis, Automation, API Integration)
Project Overview
We’re seeking a Python Data Scientist / Machine Learning Engineer to enhance and automate our real estate cash-offer web app.
The platform uses property data to generate AI-based valuations for buyers and sellers.
Your role will focus on data pipeline automation, price prediction modeling, and backend logic that powers instant AI evaluations and financial reports.
Scope of Work
1. Admin → Estimate Integration
URL: Admin Panel
When an admin clicks a user, it should auto-load the Estimate page below with prefilled property details:
Estimate Page
2. AI Evaluation Generation
Implement a backend API or model that triggers when clicking “Get AI Evaluation.”
It should:
Use available property data (address, sqft, comparables, etc.)
Generate a cash offer and return predicted value range.
Result displayed dynamically on the estimate page.
3. Machine Learning Models (Core Feature)
Develop or refine price prediction models for:
3-month
6-month
9-month market outlooks
Remove older forecast options (6 years, 1 year, 1.5 years).
Model input data should include:
Property characteristics (sqft, lot size, bed/bath, build year)
Market comps (local sales, Redfin/Realtor data if accessible)
Time-series trend indicators
You may use XGBoost, RandomForest, or LSTM (your choice) depending on accuracy vs performance.
4. Financial Report Automation
URL: GA Properties
When “Financial Report” is clicked:
Generate a backend summary using AI evaluation results.
Output follows logic from these algorithm docs:
Cash Offer Algorithm: Google Doc
Buyer Price Algorithm: Google Doc
Display or export results to the admin dashboard as a dynamic report or downloadable PDF.
5. Data Enhancement Features
Verify and maintain working external links for Realtor and Redfin data pages:
GA Properties
NC Properties
Add pagination at the top and sort by price (ascending/descending) functionality for property listings.
6. Property Display Improvements (Optional UI)
URL: Property Page
Display property image under address.
Format property attributes in a 4x4 grid matrix similar to PropStream
.
Deliverables
Backend API for AI-generated cash offers
Working ML models for 3m, 6m, 9m projections
Admin-to-estimate page automation
Financial report generation using provided algorithms
Data verification for Realtor/Redfin links
Basic sorting, pagination, and property layout logic
Skills Required
Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch)
Machine Learning & Predictive Modeling (Real Estate Valuation preferred)
API Development (FastAPI or Flask)
Data Cleaning, Feature Engineering, and Model Optimization
Website: https://homeprices.cashprohomebuyers.com
Platform Type: AI-Powered Real Estate Valuation & Cash Offer System
Tech Focus: Python (Machine Learning, Data Analysis, Automation, API Integration)
Project Overview
We’re seeking a Python Data Scientist / Machine Learning Engineer to enhance and automate our real estate cash-offer web app.
The platform uses property data to generate AI-based valuations for buyers and sellers.
Your role will focus on data pipeline automation, price prediction modeling, and backend logic that powers instant AI evaluations and financial reports.
Scope of Work
1. Admin → Estimate Integration
URL: Admin Panel
When an admin clicks a user, it should auto-load the Estimate page below with prefilled property details:
Estimate Page
2. AI Evaluation Generation
Implement a backend API or model that triggers when clicking “Get AI Evaluation.”
It should:
Use available property data (address, sqft, comparables, etc.)
Generate a cash offer and return predicted value range.
Result displayed dynamically on the estimate page.
3. Machine Learning Models (Core Feature)
Develop or refine price prediction models for:
3-month
6-month
9-month market outlooks
Remove older forecast options (6 years, 1 year, 1.5 years).
Model input data should include:
Property characteristics (sqft, lot size, bed/bath, build year)
Market comps (local sales, Redfin/Realtor data if accessible)
Time-series trend indicators
You may use XGBoost, RandomForest, or LSTM (your choice) depending on accuracy vs performance.
4. Financial Report Automation
URL: GA Properties
When “Financial Report” is clicked:
Generate a backend summary using AI evaluation results.
Output follows logic from these algorithm docs:
Cash Offer Algorithm: Google Doc
Buyer Price Algorithm: Google Doc
Display or export results to the admin dashboard as a dynamic report or downloadable PDF.
5. Data Enhancement Features
Verify and maintain working external links for Realtor and Redfin data pages:
GA Properties
NC Properties
Add pagination at the top and sort by price (ascending/descending) functionality for property listings.
6. Property Display Improvements (Optional UI)
URL: Property Page
Display property image under address.
Format property attributes in a 4x4 grid matrix similar to PropStream
.
Deliverables
Backend API for AI-generated cash offers
Working ML models for 3m, 6m, 9m projections
Admin-to-estimate page automation
Financial report generation using provided algorithms
Data verification for Realtor/Redfin links
Basic sorting, pagination, and property layout logic
Skills Required
Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch)
Machine Learning & Predictive Modeling (Real Estate Valuation preferred)
API Development (FastAPI or Flask)
Data Cleaning, Feature Engineering, and Model Optimization
Related categories:
PHP
Python
Django
Software Architecture
Data Science
Data Analysis
API Development
AI Development