Introduction

Real estate decisions are fundamentally spatial. Whether identifying where to build, invest, or market, success hinges on precisely understanding place—and how people relate to it. At F5P, spatial analysis isn’t just a step in our process; it’s the foundation. Our data-driven insights, forecasts, and maps harness spatial data science techniques to reveal not only what’s happening in the housing market, but precisely where it's happening and why these geographic variations matter.

Behind every analysis is a critical decision: which spatial unit to use? From ZIP codes and Census tracts to Core-Based Statistical Areas (CBSAs), spatial units aren't just shapes on a map. The unit chosen directly influences how we interpret housing trends, compare markets, and deliver actionable insights.

At F5P, we have deep knowledge of how these geographic units are defined as well as how they evolve and interact. In this post, we’ll explain our approach to selecting and utilizing appropriate spatial units, demonstrating why careful geographic choices are essential for accurate, impactful, and spatially aware real estate analytics.

What is a spatial unit of analysis?

Spatial units are fundamental to any geospatial analysis.

When analyzing data, whether local demographics or nationwide real estate trends, we aggregate information into defined geographic areas such as states, counties, or ZIP codes. Aggregating data into these units contextualizes it, enabling meaningful comparisons. For example, reporting 100,000 crimes in a city last year tells us little about local safety. However, aggregating crime incidents by census tract offers a more granular view, highlighting spatial patterns and identifying areas potentially riskier for investment.

Spatial units typically follow hierarchical structures. At F5P, we primarily rely on two spatial hierarchies: the statistically-defined Census Statistical Hierarchy and the economics-based Core-Based Statistical Areas Hierarchy.

The Census statistical hierarchy is structured to balance statistical accuracy and consistency with established political boundaries. It provides complete coverage across the U.S. at each hierarchical level, facilitating detailed demographic comparisons. This hierarchy is defined as:

• Nation → State → County → Census Tract → Block Group → Block

Another important hierarchy we use is based on Core-Based Statistical Areas (CBSAs). Unlike purely administrative or population-based units, CBSAs reflect economic activity and commuting patterns, frequently spanning multiple states. They are particularly useful for understanding regional economies, labor markets, and related housing demand. At F5P, our market analyses leverage CBSAs due to their economic coherence. The CBSA hierarchy is:

• Nation → CBSA (”market”) → County → Census Tract

Why choosing the right unit matters

Selecting an appropriate spatial unit involves tradeoffs between granularity, clarity, and data compatibility. Census tracts offer detailed local insights but can produce noisy data, obscuring broader regional trends. Conversely, CBSAs clearly depict macroeconomic dynamics yet may overlook local nuances. At F5P, we tailor spatial units specifically to the analytical question, ensuring our internal reports, predictive models, and recommendations align with the intended scale and complexity of the decision.

Consider a site-selection scenario for a luxury condo development targeting high-income remote workers in the Dallas-Fort Worth CBSA. Initially, we analyze CBSA-level data to gauge broad economic indicators, such as employment growth and income distribution. We then zoom into Census tracts to pinpoint neighborhoods populated by college-educated professionals aged 25-45 earning $150K or more, particularly those with higher remote-work rates. Further refining this, we examine block-group data to discern hyper-local patterns: are high earners clustered around transit hubs or amenities? Finally, we integrate additional datasets—building footprints, Points of Interest (POIs), and walkability scores—to precisely locate optimal streets and sites offering immediate access to amenities like restaurants, parks, and co-working spaces. This demonstrates the power of spatial granularity: more detailed data yields more actionable insights in this example.

Conversely, consider a fund identifying top markets nationwide for student housing development. Beginning from a broad market scope, CBSA-level analysis is ideal as it captures economic ecosystems supporting university environments. For example, the Austin-Round Rock-Georgetown CBSA encompasses UT Austin and the wider technology-driven economy, which attracts graduate students and creates spillover housing demand. A state-level analysis might obscure smaller yet lucrative markets such as College Station-Bryan, Texas. Likewise, a Census tract-level analysis might overly narrow the scope, missing regional economic drivers such as job growth and multi-university clusters.

These examples underscore a critical principle at F5P: match the spatial unit to the scale of your decision. Granular local insights best serve site-specific decisions, while broader economic units effectively support regional comparisons.

Conclusion: the F5P advantage

At F5P, selecting the right spatial unit isn’t just technical, it's essential. Our expertise in Census and other geographies allows us to match spatial detail to the specific needs of each analysis, ensuring our insights are accurate and actionable.

The result: analytics that clarify not only what’s happening in the real estate market, but exactly where it’s happening and how to leverage this information in your business.