Reducing uncertainty in where we choose to live

January 17, 2026

I moved to the UK without a natural place to call home. I didn’t have a hometown to return to, or a family history that anchored me to a particular area.

At first, we rented. That was intentional. We wanted to try different places before committing. But that process takes time. Years, really. You settle somewhere that works, life fills in around it, and exploration slows down.

Eventually, we bought the house we were renting. It was a sensible decision. We liked the area and the house. It fit our needs at the time. Almost as soon as the deal closed, I started wondering whether this was the best place for us. Or just the best place we happened to find. Were there areas with better schools, better travel links, better access to the outdoors? Almost certainly. But I didn’t know where they were, or how to compare them properly. My uncertainty came from not knowing what the options were or how to explore them in the first place.

Uncertainty doesn’t disappear just because there’s more information. What matters is whether the trade-offs are clear and you can compare them consistently.

I could have built this city by city, relying on local knowledge. But that would be slow, hard to scale, and inconsistent.

To be genuinely data-led, the approach had to apply equally everywhere. The goal was to make the moving trade-offs visible, to show what you gain and what you give up, in a way that could work for many different people, not just a narrow set.

So I decided to try applying that way of thinking to this problem. To use data to create a consistent way of understanding neighbourhoods. Comparable. Editorial. Scalable.

That decision led to building Neighbourhood Finder.

What exists today is a set of detailed neighbourhood reports designed to support comparison. Each report presents data useful when choosing where to live, such as schools, safety, transport, and access to local amenities and outdoor space. There’s more data than most people will care about but enough that everyone should see what they need.

The focus is confidence through understanding different areas on the same terms and consciously making trade-offs to reduce uncertainty and doubt. Some areas may look stronger than you might expect. Others reveal compromises that aren’t obvious at first glance. The reports succeed if those differences are visible.

This is still a work in progress.

If you know an area well, I’m interested in whether the picture feels accurate and complete. Where it’s wrong or incomplete, that feedback is how this improves.

This is my attempt to reduce uncertainty in a decision that most of us only get to make a few times, and usually without a clear way to explore the full set of options. I’m looking forward to seeing if it holds up.

Here’s a link to Neighbourhood Finder