The Area Stats section is the demographic and economic profile of the property's submarket. It feeds the AI Summary and informs the rent-growth assumption in the pro-forma.
What's on this card
- Population — total population in the surrounding 1-, 3-, and 5-mile rings.
- Population growth — YoY change.
- Median household income — a critical input for rent affordability.
- Income distribution — share of households in $50k, $100k, $150k+ bands.
- Education — share with bachelor's degree or higher.
- Employment — labor force participation, unemployment rate.
- Household composition — owner-occupied vs renter-occupied; family vs non-family households.
- Age cohorts — share under 25, 25–34, 35–54, 55+.
- Commute — average commute time, share by mode.
Where the data comes from
The base layer is U.S. Census Bureau ACS 5-year estimates at the block-group resolution, aggregated to the rings around the property. Employment data comes from BLS at the metro level, mapped down to the property by NAICS sector mix.
For non-U.S. properties, this section is currently sparse — we're working on Canadian census coverage as a follow-up.
Why these stats matter for underwriting
A few quick rules of thumb the AI Summary uses:
- Median income to rent ratio. Affordability of the in-place rent given local median income; a number above ~30% rent-to-income flags a market-risk note.
- Household formation rate. Strong population + household growth supports rent growth assumptions; flat or shrinking suggests caution.
- Employer diversity. A submarket with one dominant employer carries concentration risk; the summary calls this out when present.
Editing
You can override any field, but Census data is the standard reference and we'd recommend leaving it as the source of truth. The override is most useful for employment when you have access to a local economic-development authority's data with more recent numbers than BLS publishes.
Caveats
- Census ACS lags by 1–2 years, so very recent shifts won't show up. The pro-forma's Year 1 assumptions can compensate.
- Block-group data is more granular than ZIP-level data but can still smooth over micro-neighborhoods. Pair Area Stats with Comps to triangulate.
- Top-line population growth can mask demographic shifts (aging in place, Hispanic/Latino growth, family vs single-household). The full distribution is in the deep-dive page if you need it.