Background
A global real estate intelligence firm partnered with Coaldev to develop a predictive system that analyzes decades of property data to forecast which assets are most likely to sell within specific timeframes.
The challenge involved combining over 40 years of historical property data with real-world “stress indicators” such as inheritance, bankruptcy, and foreclosure events. These data sources provided critical predictive signals about market behavior. The client also required a robust end-to-end architecture that could manage over 200 million records, featuring flexible search, enrichment, and lead discovery capabilities.
Coaldev’s goal was to deliver a scalable analytics platform that not only predicted property sale probability but also enabled direct lead generation for investors, brokers, and financial institutions.
Challenges
Building a predictive engine for real estate required orchestrating massive datasets, complex historical signals, and high-speed search expectations within one unified system. The platform needed to merge decades of structured and unstructured data while still delivering instant, relevance-driven results to end users. Below are the major challenges Coaldev resolved while developing this large-scale analytics and prediction environment.
Managing and querying massive datasets exceeding 200 million rows.
Integrating heterogeneous data (historical, financial, ownership).
Implementing high-performance text search with fuzzy matching.
Ensuring near-real-time indexing and search across distributed clusters.
Solution
Coaldev employed ElasticSearch as the system’s backbone, chosen for its speed, flexibility, and relevance-based search capabilities. The solution integrated multiple data pipelines, scrapers, and ML models into one unified environment hosted on Linode Cloud using Kubernetes orchestration.
Coaldev designed a multi-layered architecture combining data ingestion, predictive analytics, and user-friendly search.
1. Data Integration
Developed automated scrapers and ETL workflows to consolidate property and financial data from multiple public and proprietary sources.
2. Predictive Modeling
Created machine learning pipelines using Scikit-learn to calculate sale probability scores, factoring in both static property features and dynamic stress indicators.
3. ElasticSearch-Based Search Engine
Implemented fuzzy matching, custom ranking, and stress-level filtering for rapid lead discovery and prioritization.
4. Scalable Infrastructure
Deployed the entire system on Linode Cloud using Kubernetes clusters for distributed indexing, fast updates, and seamless scaling.
5. Interactive Interface
Designed a React.js frontend providing instant access to predictive scores, property insights, and lead lists with map-based navigation.
This unified system offered both analytical power and operational speed, turning historical data into actionable, real-time insights.
Results
The delivered system transformed real estate data into actionable insights with predictive accuracy.
Key outcomes
Predictive scoring models: Accurately forecast the likelihood of property sale within given timeframes.
Lead generation workflows: Identified high-probability sellers and enriched owner contact data.
ElasticSearch-powered interface: Enabled instant search, fuzzy address matching, and stress factor filtering.
Scalability: Achieved high-speed query performance on datasets of 200+ million records.
Technical Overview
- Backend: Python (Django), ElasticSearch, Kubernetes (K8s)
- Frontend: React.js
- Hosting: Linode Cloud (VPS Cluster)
- Machine Learning: Scikit-learn, AI/ML Pipeline Integration
Live Link: https://www.dataflik.com/
