Live in Sydney

What’s on the site

The idea

Live in Brisbane worked, so the real test was whether the system could move to a new city without starting again. Sydney is bigger, pricier and has a different state data setup, which made it a good stress test.

How it’s built

The same pipeline and page templates as Live in Brisbane, pointed at NSW data and Sydney's suburb boundaries. The tone is warmer and more casual, written for people weighing up a move.

Stack Next.js · ABS Data API · Postgres · LLM API · Netlify

Skills it shows

• Analytics: benchmarking 336 suburbs against the state average and ranking them on what matters to movers.
• AI development: reusing an AI writing pipeline across a new dataset and a new voice.
• AI workflow: a second city launched by one person, because the work is automated rather than repeated.

What I learned

• Build the first version to be reused. The second city cost a fraction of the first.
• Every state publishes data differently, so the mapping layer matters more than the pages.

Behind the scenes

Why I built it

The Brisbane guide proved the format: published numbers, explained the way a mate would. Sydney was the obvious next test. It's the country's largest city, and the first stop for many people moving to Australia.

Most suburb information online is built to sell property. Listings lead with homes for sale, and the useful context is scattered across a dozen government portals. Few newcomers know what a SEIFA decile, an SA2 or a school intake zone means, and they shouldn't have to.

The primary audience is people weighing up a move to Sydney, especially newcomers to Australia who don't yet know the city's geography. The secondary audiences are families checking school catchments before they sign a lease, and Sydneysiders comparing suburbs before their next move.

What it deliberately does not do: list property, rank schools on results, or model and estimate figures. If a published dataset doesn't cover a suburb, that section simply doesn't appear.

The methodology

The rules below are published on the site's About and methodology page.

1. Published data only. Every figure comes from a published open dataset. Nothing is modelled or estimated.
2. Gaps stay gaps. If a figure isn't available for a suburb, the section is hidden instead of filled with a guess.
3. Dated and traceable. Each figure shows when it was published and where it came from.
4. Always a benchmark. Numbers sit beside the Greater Sydney or NSW average, because a figure without context helps nobody.
5. Plain English first. Terms like SEIFA deciles and catchments are explained where they appear, not hidden in a glossary.
6. Readable without the charts. Key charts come with a plain data table for screen readers and anyone who prefers the numbers.
7. Licences respected. Data is used under its published licence, and every page credits its sources.
8. Independent and correctable. The site isn't affiliated with any data publisher, and readers can report a wrong figure straight to me.

Where the data comes from

The site draws on 12 open datasets listed on its methodology page, plus Transport for NSW timetables for the getting around section. Most of the Sydney specific depth comes from NSW Government sources.

Not covered yet: parks detail, TAFE, NBN availability and development applications.

The hard problems

The layout carried over from Brisbane. Almost everything underneath it had to be solved again for NSW.

Suburb names nobody searches for. The ABS reports on statistical areas that often join two or three suburbs, so Manly is filed together with Fairlight. People search for the suburb they know, not the official area. I lead with the familiar name wherever I can, and this is still the problem I'm working on hardest.

Crime data drawn on a different map. NSW crime figures are reported by gazetted locality, which doesn't line up with ABS areas. I match localities by name and report rates per 100,000 people. Where a match isn't reliable, the suburb has no safety section rather than a misleading one.

One suburb, five school catchments. Catchment lines ignore suburb boundaries, and Surry Hills alone touches five public school zones. I work out the overlap for every suburb and label each school as the main catchment or part of the suburb. That tells families exactly when they need to check their street.

Choosing data I'm allowed to publish. Some of the richest NSW property sales data carries a non commercial, no derivatives licence. I used the NSW Communities and Justice rent and sales figures instead, which allow reuse with attribution. Trading a little detail for a clean licence was the right call for a site I want to grow.

Flood data for a quarter of the city. The flood database I use covers only the Georges River catchment, about a quarter of Sydney. The site shows flood risk where that data exists and says so plainly, instead of implying everywhere else is safe.

Transport measured from one point. Public transport access is calculated from each suburb's centre, not every address. A large suburb can look better or worse connected than a particular street. The page states this openly, and a resident weighted measure is on the way.

Technical SEO and organic traffic

A guide nobody finds helps nobody, so search was designed in from the first page template.

Matching pages to real searches. Suburb pages are titled the way people search, such as "Living in Surry Hills NSW 2010: Pros, Cons, Rent". Rankings answer questions like the cheapest suburbs to rent, and city pages answer "Melbourne vs Sydney" style searches.

Clusters and internal linking. Each suburb links to its zoned schools, its catchment map, its neighbours and a comparison. Rankings and cost of living pages feed back into suburb pages, so 336 suburbs and 1,000 schools work as one connected cluster.

Technical foundations. One canonical address per page, clean sitemaps, and titles and descriptions written from each suburb's own numbers. Pages are fast on mobile, and thin pages stay out of the index until they have real data.

Fast indexing. Data refreshes quarterly, so updated pages need to be picked up quickly. Google Search Console and Bing Webmaster Tools show what's indexed, and IndexNow tells participating search engines when pages change. I also check how AI search tools describe and cite the site, because many people now ask a chatbot before they search.

Trust signals. A named author, a public methodology page, a dated source on every figure and an open invitation to report errors. These matter even more for a site giving advice on where to live.

Things worth linking to. The catchment finder, rankings and city comparisons are useful on their own. They're the pages people share when a friend asks where they should live in Sydney.

What's next

• Resident weighted transport. Measuring public transport access from where people actually live, not just each suburb's centre.
• Rent rise rankings. The biggest rent rises ranking goes live once enough quarters of rental data have landed.
• Everyday detail. Parks, TAFE campuses, NBN availability and development applications, the gaps listed on the methodology page.