📊 Data Analytics vs. 🤖 Machine Learning

In affordable housing, we live and breathe data—construction costs, rents, AMI tiers, pro-formas, and development schedules.

But as more tech enters our workflow, a question I hear all the time is:

Nope. They're both powerful—but serve very different purposes.
Let’s break it down with real examples from housing finance and development.

🧩 What is Data Analytics?

Data analytics looks backward. It helps you understand what already happened so you can make better decisions next time.

🔍 It’s about spotting patterns, trends, and outliers.
📉 It uses tools like Excel, Tableau, and Power BI.
📊 You ask specific questions—and use clean, structured data to answer them.

🔧 Real Housing Example: Construction Cost Trends

• What’s the average cost per unit or $ per square foot for Type V vs Type I projects?

• How much did hard costs increase between 2020 and 2024?

• Which development costs typically go over budget: hard costs and soft costs.

You’re reviewing what’s already happened—this is data analytics.

🤖 What is Machine Learning (ML)?

Machine learning looks forward. It uses past data to train models that can make predictions, classifications, or decisions—without being explicitly programmed for each scenario.

It’s like Excel on steroids. You’re not just analyzing data—you’re building models that learn and adapt over time.

🏗 Real Housing Example: Predicting Cost Overruns

You train a model using data from 100+ completed projects:

• Project type and size

• Unit mix and AMIs

• Developer experience

• Change orders and contingency use

• GC and architecture team profiles

It learns from past outcomes—and flags projects most likely to go over budget based on patterns it has seen before.

🏘 Why Should Housing Professionals Care?

Because we’re juggling:

• 🌆 Complex financing structures

• 🧾 Multi-year, multi-layered development schedules

• 🏗 Rising costs and unpredictable bids

• 📅 Tight deadlines and place-in-service timelines

✅ Data Analytics helps you:

• Spot trends in costs, rents, and timelines

• Write stronger internal memos and presentations

• Improve future assumptions in your pro formas

✅ Machine Learning helps you:

• Predict risks (cost overruns, schedule slippage, lease-up delays)

• Flag outliers (unrealistic rent assumptions, developer fees)

• Optimize resource allocation and focus

🚀 Getting Started

You don’t need a PhD or an AI team to benefit. Start here:

• 📁 Track clean, consistent data across projects

• ❓ Ask better questions about past project performance

• 🧰 Explore ML tools that are built for housing pros (more on this soon)

💬 What should I cover next?

What are your biggest data headaches?
Want a breakdown on AI tools for pro forma review, lease-up predictions, or bid comparisons?

You can always email me at [email protected] and let me know—I read every email.