Data Sources & Methodology

Transparency

Data Sources & Methodology

We believe in transparency about where our data comes from. Every statistic, rating, and metric on this site comes from one of the sources below — government databases, public datasets, and third-party providers — and each one is named.

Texas Education Agency (TEA)

What we use: School and district accountability ratings (A-F), enrollment counts, domain scores, distinctions, and school-level metadata (grade spans, magnet status, principal).
How we process it: We import TEA accountability data annually after ratings are published (typically August-September). Our pipeline processes the official TEA data files and maps schools to districts.
Data freshness: Updated annually. Current data reflects the 2024-25 academic year.

Redfin

What we use: Median sale prices, year-over-year price changes, homes sold, inventory levels, days on market, and other housing market metrics for Austin-area cities, zip codes, and neighborhoods.
How we process it: Our market stats pipeline imports publicly available Redfin market data monthly from their public S3 datasets. We store historical data points at the city, zip code, and neighborhood level to show trends over time.
Data freshness: Updated monthly. Each city and neighborhood page shows the date of the most recent data point.

National Center for Education Statistics (NCES)

What we use: School district geographic boundaries, federal school and district identifiers (NCES IDs), and demographic data.
How we process it: We import NCES boundary shapefiles to map school districts to geographic areas. This allows us to show which districts serve each city and neighborhood.
Data freshness: Updated annually when NCES publishes new boundary files.

U.S. Census Bureau

What we use: Population, median household income, median age, and population density for cities. For neighborhoods, we show demographics for the ZIP code the neighborhood sits in — including income, housing, education, commute, and employment measures.
How we process it: City population comes from the Census Population Estimates Program, which publishes a single estimate of a town's residents as of July 1 each year. It covers incorporated towns and cities only, so for the handful of unincorporated communities we cover — places with no city government — population instead comes from the American Community Survey, and the page says so. Income, age and land area always come from the survey. For neighborhoods, we use survey figures for the ZIP Code Tabulation Area the neighborhood's centre falls in, matched through the Census Geocoder. Neighborhood figures therefore describe the surrounding ZIP code, not the neighborhood boundary itself, and a large ZIP code may cover areas quite different from the neighborhood.
Data freshness: Population estimates reflect the 2025 vintage, as of July 1, 2025. Survey figures reflect the 2024 release, which averages the five years from 2020 to 2024 rather than measuring 2024 alone. Each city and neighborhood page cites the vintage of the figures shown on it.

Walk Score

What we use: Walk Score, Bike Score, and Transit Score for cities and neighborhoods — measuring walkability, bikeability, and public transit access on a 0-100 scale.
How we process it: Our pipeline queries the Walk Score API using the geographic centroid of each city and neighborhood boundary to retrieve scores. Each entity receives its own score based on its location.
Data freshness: Updated when the community intelligence pipeline runs. Walk Score updates their data continuously.

Google Places

What we use: Amenity counts for grocery stores, retail, dining, healthcare, parks, and entertainment near each city and neighborhood. Notable chain presence (H-E-B, Costco, Whole Foods, etc.).
How we process it: We query the Google Places Nearby Search API using each entity's centroid — 15-mile radius for cities, 3-mile radius for neighborhoods. Results are categorized and counted, with notable national and regional chains identified.
Data freshness: Updated when the community intelligence pipeline runs. Google Places data reflects real-time business listings.

OpenStreetMap

What we use: Parks, nature reserves, and gardens for cities and neighborhoods.
How we process it: We query the OpenStreetMap Overpass API for parks and recreation areas within each entity's geographic bounding box. Park counts and notable park names supplement the Google Places amenity data.
Data freshness: Updated when the community intelligence pipeline runs. OpenStreetMap data is community-maintained and continuously updated.

Our Commitment to Accuracy

Every statistic on Living in Austin comes from one of the sources listed above. We do not invent numbers to fill gaps — where a source has no figure for an area, we leave it out rather than substitute a guess.

Some figures are estimates by nature. Census survey values come from a sample, and the Census publishes a margin of error with each one. In the smallest communities that margin can be a large share of the figure itself, so we round such an estimate to the precision it can carry, show the margin alongside it, and leave it out entirely where the margin is wide enough that the number would say nothing. Walk Score is a model rather than a measurement. We also compute some values from source data — population density, affordability ratios, and percentage breakdowns are calculated by us from the underlying Census figures. Those are derived, not independently reported.

Written commentary — city descriptions, highlights, and our take on an area — is our own editorial judgment, not sourced data.

Data is refreshed on regular schedules (monthly for market data, annually for school ratings). Each data-driven page includes a “Data Sources” section at the bottom citing the specific sources used on that page.

If you notice any data that appears inaccurate or outdated, please let us know.