Client: Confidential (Housing Resource Platform)
Industry: Real Estate, Housing Education
Service: Data Engineering, Custom Scripting, SQL Database Development
Technologies: Python, Custom Web Scraping, Data Cleaning Tools, SQL

The Challenge

Our client wanted to create the most comprehensive national database of down payment assistance (DPA) programs available to homebuyers across the United States. The goal was to compile programs by state, complete with sponsor contact information, benefit descriptions, and detailed eligibility requirements — all in a searchable format that could power both internal tools and public-facing resources.

While the idea was straightforward, the execution was complex. These programs are published in a wide variety of formats, scattered across local housing agencies, nonprofit sites, and government portals — with no consistent structure or terminology.

Our Approach

Owners Media took a phased approach to solve this challenge — combining research, automation, and custom tooling to deliver a structured and scalable result.

1. Sample Set & Lexicon Mapping

We started by manually collecting a sample set of DPA programs across a handful of states. This allowed us to study the key attributes most programs shared — such as income limits, geographic targeting, repayment terms, and qualifying criteria.

We also analyzed the language and terminology used by different agencies to describe these features. By creating a lexicon of relevant terms (e.g., “grant,” “forgivable loan,” “first-time buyer”), we laid the groundwork for more intelligent data collection at scale.

2. Automated Discovery with Custom Scripts

Next, we built a custom Python script to seek out and extract DPA program information from trusted sources across the internet — including housing authorities, government portals, and nonprofit organizations.

The script returned a wealth of information, but as expected, the data was inconsistent and messy — with varying formats, structures, and levels of completeness.

3. Data Cleaning & Structuring Tool

To make this data usable, we developed an internal tool to clean, standardize, and categorize the incoming program data. This included:

• Parsing program features into consistent fields (e.g., loan type, benefit amount, required credit score)
• Removing duplicate or outdated entries
• Formatting contact details for each program sponsor

The tool then funneled the cleaned data into a well-organized database structured to support fast queries, search filtering, and future scalability.

The Results

• Over 1,000 DPA programs identified and organized across all 50 states
• A structured SQL database with consistent schema, enabling easy querying and future expansion
• Sponsor contact information, benefit summaries, and eligibility requirements for each program
• A scalable backend resource that can power future applications, web tools, or third-party integrations

Conclusion

This project demonstrates how Owners Media blends research, technical creativity, and automation to solve complex data challenges. By turning a messy, decentralized web of information into a structured, actionable database, we helped our client create a valuable housing resource with the potential to support thousands of future homeowners.