🏗️ Structured vs. Unstructured Data in Housing

🔍 What’s the Difference?

Not all data is created equal.
In affordable housing, two types of data dominate how we make decisions:

• Structured data: Clean, labeled, and stored in tables. Think unit counts, total cost, and draw schedules.

• Unstructured data: Messy but rich. Think meeting notes, emails, PDFs, and community feedback.

Both are powerful — but most of us only use one.

📊 Structured Data = The Usual Suspect

Structured data lives in tools like Salesforce, Excel, or your project management platform.

You already use it to:

• Track budgets

• Compare timelines

• Report on performance

But it only tells part of the story.

🧠 Unstructured Data = The Hidden Goldmine

Unstructured data captures all the nuance:

• Emails about permitting delays

• Memos on design concerns

• Notes from community meetings

• Scanned docs or redlines

It’s harder to analyze — but it often holds the “why” behind project outcomes.

⚙️ Why It Matters

The real power comes when you combine both.

🏗️ Use Cases in Housing

Here’s how to make it work in the real world:

✅ Benchmark Success
Use structured data to find top-performing projects. Then, dig into unstructured records to uncover what made them successful.

🚨 Catch Risks Early
Structured data shows delays. Emails and memos may tell you why—before it shows up in the metrics.

🔎 Analyze at Scale
Cluster projects by performance, risk, or funding patterns using both types of data.

📢 Elevate Community Voice
Extract patterns from resident feedback and tie it to project changes.

🤖 Enter Machine Learning

Structured data works great with predictive models — like forecasting project delays or cost overruns.

But unstructured data?
That’s where NLP (natural language processing) comes in.

Turn messy text into powerful features:

• Tag projects with “early risk signals”

• Flag sentiment in feedback

• Extract patterns from permitting notes

Together, they make your ML models smarter and more grounded in reality.

🎯 Final Takeaway

If you're only using structured data, you're missing half the picture.

Use structured data to measure.
Use unstructured data to understand.
Use machine learning to scale both.

👉 Don’t just track what happened.
Learn why. Predict what’s next.