Spencer Dobbs | Senior Analytics Engineer

The Unique Challenges of Real Estate Analytics

Real estate isn't like other industries when it comes to data. Unlike e-commerce with its clean transaction logs or SaaS with its user events, real estate data is messy, distributed, and often trapped in legacy systems.

Lesson 1: Master Your Sources

Before building anything fancy, you need to deeply understand your source systems. In our case, this meant:

Property management systems with decades of history

Accounting platforms with complex chart of accounts

Third-party data providers for market intelligence

IoT sensors for smart home devices

Each source has its quirks, its gaps, and its gotchas.

Lesson 2: Build for Trust

When your analytics inform billion-dollar decisions, trust isn't optional—it's existential. We invested heavily in:

Data quality monitoring and alerting

Comprehensive documentation

Lineage tracking for every metric

Regular validation against source systems

Lesson 3: Think in Products, Not Reports

The shift from "report factory" to "data product" mindset transformed our impact. Instead of responding to ad-hoc requests, we built self-serve platforms that empowered stakeholders to answer their own questions.

Looking Forward

The future of real estate analytics is exciting. With Gen AI, we're exploring natural language interfaces to our data products. Imagine asking "Which markets have the best risk-adjusted returns for acquisitions?" and getting an instant, data-backed answer.

That's the future we're building toward.