Facts Fun About Houses

Using Facts Fun About Houses: What It Actually Does and How to Use It

Facts Fun About Houses is a structured dataset and API that pulls verified residential data—square footage, construction year, lot size, school district boundaries, historical sales, and basic amenity counts—into a queryable format. It is not a real-time MLS feed. The data is refreshed on a monthly cadence, which matters more than most people realize when they start integrating it into a production app. You can grab the dataset from their public releases page. The CSV export covers about 14 million records across the US, split into individual household rows and aggregated neighborhood summaries. The JSON API key costs $29/month for the first 50,000 requests, then scales from there. I went with the CSV dump for my project because I was running batch transforms on 300,000+ rows before the API ever saw anything, and hitting an endpoint for every row would have been absurdly slow and expensive. Download the zip, extract it to a working directory, and run a quick validation check. A lot of people skip this and immediately assume the data is clean. It isn't always. I ran into a problem last year where roughly 4% of the California entries had the square footage recorded in meters instead of feet. The field wasn't flagged, the schema said "sqft," and I only caught it when a client's listing showed a 2,400 sqft home in San Mateo as actually being around 223 sqft after conversion. The workaround was straightforward: I cross-referenced the affected entries against county assessor data using the APN field, which is reliably present in the CA subset, and wrote a small pandas script that caught the outlier distribution (anything under 400 sqft in a state where the median is roughly 1,800 sqft) and reclassified those records manually.

How to Work With the Data Without Losing Your Mind

The biggest mistake beginners make is treating the schema as fixed. The field names changed in the v3.2 release without a migration guide. "YearBuilt" became "year_built_raw" and they added a separate "year_assessed" field that captures when the county last evaluated the property, which is not the same thing. If you're writing parsers or ETL pipelines, lock your dependency to a specific version number and note it in your documentation. I wasted a solid afternoon debugging why my year-over-year appreciation calculations were pulling from a completely different temporal source than I expected. Another thing nobody warns you about: the school district data is sourced from GreatSchools API and only covers grades K-8 and 9-12 separately. There is no unified "school district rating" field. If you need a single score for a property search filter, you have to join the two yourself or just pick one and be upfront about the limitation. I learned this the hard way when I shipped a front-end filter that displayed "9 out of 10 school rating" for a home and got hit with a support ticket because the rating was actually a K-8 score for a middle school three miles away.

Edge Cases and Where It Falls Apart

The dataset handles single-family detached homes well. It struggles with multi-unit properties, manufactured housing, and tribal lands. If your application serves markets with high shares of any of those categories—Puerto Rico, parts of Arizona and New Mexico, rural Alaska—the coverage gaps are significant. The API documentation acknowledges this but doesn't give you a field that tells you whether a given record is incomplete versus genuinely absent. You just have to know the geography. Pricing transparency is another honest note. The $29/month tier gives you 50,000 requests. If you are building a consumer-facing search tool with even modest traffic, you will burn through that in a day. The overage rate is $0.001 per request, which sounds small until you are hitting it at volume. I ended up caching results aggressively in Redis and only fetching fresh data on cache miss or once every six hours for any given ZIP code. This cut my monthly API spend from roughly $400 down to about $35 and kept latency acceptable.

Practical Tips for Working With Facts Fun About Houses

Use the APN as your primary join key. It is more stable than addresses because addresses get renumbered during municipal updates and the dataset sometimes has stale street names. The APN persists even when the physical address changes. Don't trust the "last sale price" field for current valuations. It reflects the most recent recorded transaction, which for some properties in stagnant markets might be from 2007. Pair it with the county assessed value field and apply a manual appreciation adjustment based on the local CAGR for that metro area. My script applies a simple rolling average adjustment that takes about 12 minutes to run across the full dataset on a decent machine. If you need historical price trends, the dataset includes them but only going back to 2012 for most metros. Before that, you are on your own or looking at a different data source entirely. I supplement with Redfin's historical charts for pre-2012 context when the use case demands it.

The FAQ section on the Facts Fun About Houses website is sparse but accurate. Most of the useful information comes from the GitHub issues tab where contributors document quirks like the meter-vs-feet problem I mentioned earlier. Subscribe to that repo if you are building anything non-trivial with it.