Reatlas Methodology: Where Our Data Comes From

Reatlas exists to answer one question well: what is actually happening in a given housing market? Answering it credibly means being explicit about where the numbers come from, how they fit together, and how often they change. This page documents our data sources, the structure that unifies them, our update cadence, and how we derive the metrics that aren't reported directly by any single source.

The three primary sources

Reatlas combines three authoritative datasets, each contributing a different layer of the picture.

U.S. Census American Community Survey (ACS). The ACS is the definitive source for demographic and housing-stock data: median household income, homeownership and vacancy rates, occupied and vacant units, education, poverty, and the composition of households. It is a rigorously sampled government survey covering the entire country down to the ZIP-code-equivalent level. ACS is the "who lives here and what can they afford" layer.

Realtor.com market data. For the active state of the for-sale market, we use Realtor.com's monthly market data: median and average listing prices, price per square foot, active and new listing counts, median days on market, pending ratios, and price-reduction shares. This is the "what's for sale, at what price, and how fast it's moving" layer, and it carries the month-over-month and year-over-year change indicators that reveal momentum.

Zillow. Zillow's Home Value Index (ZHVI) and related series contribute a smoothed, whole-stock view of home values that complements listing-based figures. Because ZHVI reflects the entire housing stock rather than only homes that happened to list or sell, it is less volatile and useful for reading longer-run value trends.

We treat these sources as complementary rather than interchangeable. Where they overlap — for example, on price — we preserve each rather than blending them into a single opaque number, so the provenance of every figure stays clear.

How the data is structured

Under the hood, Reatlas organizes everything into a star schema — a warehouse design that separates the entities you filter by (dimensions) from the measurements themselves (facts).

The dimensions are shared across all sources. dim_geography holds every place we cover, keyed by a geographic identifier and its level, so the same identifier can safely represent both a county and a same-numbered ZIP without collision. dim_time holds every time period. dim_metrics holds the definition of each metric — its name, category, units, and data type.

The facts are one table per source: Census facts, Realtor facts, and Zillow facts. Each fact row ties a single metric value to a place, a time period, and a metric definition, along with quality indicators like margins of error and estimation flags. This structure is what lets us line up income from the Census, listing price from Realtor.com, and home value from Zillow for the same place and period, and compute across them consistently.

Geography spans five levels: national, state, metropolitan area, county, and ZIP-code-equivalent (ZCTA). A metric requested at any level is answered from the same unified schema.

Update cadence

Different sources refresh on different clocks, and we follow each source's native rhythm:

• Realtor.com market data — monthly. New listing prices, inventory, and days-on-market figures arrive each month, and this is the fastest-moving layer.
• Zillow — monthly. Home value and related indices update monthly.
• Census ACS — annually. Demographic and housing-stock estimates are released on an annual cycle.

Because our public market pages regenerate on a daily revalidation cycle, they always reflect the latest data that has landed in the warehouse. Every market page notes the period the figures describe so you know exactly how current they are.

Derived metrics

Some of the most useful indicators aren't reported directly by any source — they're computed from combinations of the primary data. Reatlas calculates these transparently from the metrics above:

• Price-to-income ratio divides median listing price by Census median household income to measure affordability.
• Months of inventory relates active listings to the pace of sales to gauge supply-and-demand balance.
• Rent-to-price ratio expresses annual rent as a percentage of home value to indicate rental yield.
• Housing cost burden compares monthly housing costs to household income.
• Market velocity scores how quickly homes are selling, derived from median days on market.

For each derived metric, the formula and interpretation bands are documented in our glossary, so nothing is a black box. Where a derived metric depends on an input we don't yet carry for a given place, we show it as unavailable rather than estimating a value.

Principles

Three commitments guide how we handle data. We are source-transparent: every figure traces back to Census, Realtor.com, or Zillow, and derived metrics show their formulas. We are freshness-honest: pages state the period they describe and refresh on each source's cadence. And we prefer gaps to guesses: when a metric is missing for a geography, we mark it unavailable rather than inventing a number.

Every market page and article on Reatlas ends with the same citation line so that correct attribution is the path of least resistance: Source: Reatlas analysis of U.S. Census American Community Survey (ACS), Realtor.com, and Zillow market data.