FWD #266 • 790 words

A map can tell you where the problem is. It can’t tell you what to do about it — that part’s still on us.

That was the honest, occasionally funny, and genuinely useful thread running through From Data to Decision: Mapping Housing & Climate Equity, our July 1 webinar with two researchers who spend their careers turning raw numbers into policy that actually moves. Here’s what we took away.

Data Can Do a Lot — Just Not Everything

Claudia Aiken, Director of New Research Partnerships at the NYU Furman Center’s Housing Solutions Lab, opened with a reality check dressed up as a pep talk. Good data, she explained, can reveal disparities hiding in plain sight, target interventions where they’ll matter most, and evaluate whether a program is actually working — not just whether it launched with a nice press release.

It can also do something subtler: give people who don’t normally agree on much a shared vocabulary. A city council member, a nonprofit director, and a state agency staffer can argue about a lot of things, but a well-built dashboard tends to shrink the argument down to something everyone can see. Data can also help bridge disciplines that don’t always work together into more productive joint ventures.

Then came the “but.” Datasets are messy. They’re often incomplete, expensive to collect, and stubbornly resistant to talking to each other. And even the cleanest dataset still needs a human to interpret it, which means data doesn’t eliminate judgment calls — it just makes them more informed. Claudia walked through examples from Richmond, Cleveland, Philadelphia, and Denver showing both sides of that coin: dashboards that tracked homelessness reductions and production goals with real precision, alongside the very real capacity it took to build them.

Zoning’s Fingerprints Are Still on the Climate Map

If Claudia’s half of the session was about what’s possible, Alex Fella’s was about what’s already happening in Virginia. Alex is the Principal of CityWork, a Norfolk-based research consultancy that turns spatial and social data into tools planners can actually use — and he brought one of those tools with him.

Using his live zoning-and-climate model using the Virginia Zoning Atlas data, Alex showed how decades-old land use decisions are still writing themselves into today’s climate data. Layer zoning maps over vehicle miles traveled and carbon emissions, and the pattern isn’t subtle: places built around single-family zoning and long commutes are still generating outsized emissions today, regardless of what a jurisdiction’s current climate goals say on paper.

It’s a good reminder that housing policy and climate policy were never really separate conversations — they just got filed in different departments.

More Hands on the Data, More Noise in the System

Both speakers pointed to something bigger happening underneath all of this: data is getting more democratized. Free tools, public dashboards, and open datasets mean a planning commission member or a tenant organizer can now pull up numbers that used to require a research shop and a budget line. That’s a real win for policy makers who’ve had to argue for resources using not much more than a hunch.

But there’s a flip side. The same accessibility that lets more people work with data also lets more bad data circulate — and AI tools have sped that up considerably. A slick-looking chart or an AI-generated summary can carry all the visual authority of rigorous research while resting on a shaky or entirely fabricated foundation. Telling the difference still takes expertise: knowing where a number came from, whether the methodology holds up, and when a tidy-looking result is actually too tidy to trust. Democratized data is a genuine gain. It just raises the bar on discretion, not lowers it.

Building the Muscle, Not Just Buying the Tool

The last stretch of the session turned practical: how does a locality with a small staff and a smaller budget actually build this kind of capacity? A few ideas surfaced repeatedly:

• Partner with a university or research institution rather than building data expertise from scratch.
• Team up with neighboring jurisdictions to split the cost of shared tools and platforms.
• Build the data relationships between agencies before there’s a crisis that requires them.
• Embed data collection into work your staff is already doing, instead of treating it as an extra task nobody has time for.

None of this is glamorous. All of it is more realistic than waiting for a grant to fund a perfect, unified data system that may never arrive.

The Takeaway

Data won’t make a hard housing decision easy. What it can do is make sure the decision is being made with eyes open — about who’s cost-burdened, where the deteriorating housing stock is concentrated, and which policies from 1975 are still shaping emissions in 2026.