Real estate is the largest asset class in the world. It is also, quietly, one of the worst-connected. The data that describes the built environment — every parcel, address, permit, sale, school catchment, flood line and demographic shift — exists in enormous volume. What has never really existed is a way to make all of it speak the same language. For a century the industry has run on fragments: a listing here, a valuation there, a spreadsheet stitched together by hand the night before a decision. The next decade will belong to the companies that stop treating property data as a pile of files and start treating it as connected infrastructure. That connective layer has a name. We call it the intelligence layer — and this is what it means, and why it matters now more than ever.
What we mean by 'the intelligence layer'
The phrase is deliberate. A data vendor sells you a file. A dashboard shows you a chart. An intelligence layer does something different: it sits underneath everything and turns raw location into understanding. Ask it about a single address and it does not just return a record — it returns context. What is here, what surrounds it, how it compares to everywhere else, and how it is changing over time. It is the difference between knowing a home's square footage and knowing what that home will cost to run, how its neighbourhood is trending, what is being built next door, and what risk it carries a decade out. The intelligence layer is the part of the stack that makes a coordinate mean something.
It is not a feed. It is connective tissue.
The core idea is almost embarrassingly simple, and it is where most of the hard engineering hides. Every piece of data about the built world can be anchored to a place, and every place can be anchored to one shared hierarchy — country, province, city, neighbourhood, street, parcel, coordinate. We call that hierarchy the location graph. When a permit, a school boundary, a business registration or a census figure enters the platform, the first thing we do is resolve it to its exact position in that graph. A permit stops being a row in a municipal spreadsheet and becomes a fact attached to a parcel, inside a neighbourhood, inside a market. Once everything shares the same coordinates and the same hierarchy, data that used to be incompatible becomes comparable — and comparable data is where insight begins. The layer is not a bigger pile of data. It is the tissue that connects the piles.
Why this matters now
Three forces have turned a nice-to-have into a necessity. The first is the cost of money. Over the past decade the single variable that moved Canadian real estate more than any headline price was the Bank of Canada's policy rate — from an emergency low of 0.25% in 2020 to 5.00% by mid-2023, then easing again through 2024. Read the market of almost any month in that span and you are, in large part, reading this one line. When conditions swing that hard and that fast, decisions made on stale, disconnected data are decisions made half-blind.
Bank of Canada policy rate
Bank of Canada overnight target rate, year-end. Source: Bank of Canada.
The market got more regional, and more complex
The second force is the fragmentation of the market itself. 'The Canadian housing market' is no longer a useful phrase — Calgary, Toronto, Vancouver, Montreal and Halifax now behave like different countries, diverging on price, supply and migration. Understanding any one of them requires granular, address-level data rather than national averages. The third force is artificial intelligence. Automated valuation, real-time property data and AI-assisted analysis have moved from experiment to mainstream in the space of a few years. But AI is only as trustworthy as the data beneath it, and that raises the stakes on getting the data layer right. Cheap, disconnected data produces confident, wrong answers. Connected, verified data produces answers you can act on.
The hidden tax of fragmentation
Every proptech founder, lender and brokerage has paid this tax, usually without naming it. To build almost anything data-driven in real estate, you first have to assemble the data: source it from dozens of incompatible providers and public records, license it, clean it, standardize the addresses, reconcile the identifiers, map it to boundaries, and keep all of it fresh. That is months of work and a permanent maintenance burden before you have written a single line of the product you actually set out to build. The intelligence layer exists to erase that tax. The unglamorous, expensive work of connecting the built world is done once, centrally, so everyone else can start from a working foundation instead of a blank page.
From data to decisions
Connected data changes what different teams can do, in concrete ways. For a lender, an address resolves to risk and value in real time — collateral, flood and environmental exposure, market momentum — so credit decisions get faster and better-priced. For an insurer, the same address prices risk with confidence instead of guesswork. For a brokerage or portal, location context is what turns a listing into a decision, and a curious visitor into a client. For a proptech builder, it is the difference between shipping in weeks and stitching sources for months. Whatever the seat, the pattern holds: better decisions require better, connected data — and the teams building on it are quietly pulling ahead of those still working from spreadsheets and instinct.
Trust is the real product
There is a temptation, in a field this data-hungry, to chase scale at the expense of trust. We think that is exactly backwards. An intelligence layer is only worth building if people can rely on it, which is why we hold a hard line between what is measured and what is modelled. Verified public facts — Bank of Canada rates, dated policy events, Statistics Canada figures — are stated as fact. Anything estimated is labelled as an estimate, every time. And in the age of AI, grounding matters more than ever: an answer is only as good as the data it reasons over. Our commitment on that front is simple and non-negotiable.
Our standing commitment
Client datasets used to train AI models shared with other clients. Insight is aggregated, never raw exposure; accountability stays human; and grounded AI reasons over verified data rather than guesswork.
The data types that will matter most
Most property data answers where and how much. The more valuable questions are what a place is actually like to live in, what it truly costs, and where it is heading — and those require data types the industry historically ignored because they were hard to collect and harder to connect. We are investing in exactly those. Verified energy data reveals the true cost of a home, the number buyers never saw until after they moved in. Building permits are one of the earliest visible signals of where growth and supply are heading, before it shows up in prices. Environmental and climate exposure — flood, heat, natural risk — resolved to the property makes long-term risk part of the decision rather than a surprise later. School catchments, resolved at the coordinate level, remain one of the strongest and least-standardized drivers of residential value. Each is meaningful on its own; connected through one graph, they become far more than the sum of their parts.
Closing real estate's information gap
There is a public dimension to this too. Real estate has always run on information asymmetry — professionals and institutions holding data that buyers, sellers and owners never see, while those people make the biggest financial decisions of their lives with a fraction of the picture. We think that is backwards, and the same platform that serves the industry is the one that can level the field. Free tools, open neighbourhood data and consumer products put clear, trustworthy information directly into the hands of the people who need it most. Better data leads to better decisions, and better decisions are good for everyone — not only the market.
How Homicity is leading the charge
We did not arrive at this thesis recently. Homicity was founded in 2015 on a single conviction: the advantage in real estate would eventually belong not to whoever held the most data, but to whoever could connect it. We spent our early years building consumer property search, pivoted to serving the industry directly in 2020, built Homeprint in 2022, and commercialized Neighbourly — the real estate data API for Canada — in 2024. Today that platform spans ten connected data layers, hundreds of millions of data points and a single location graph covering the country, and in 2026 we are building its next generation. Every advance becomes infrastructure other teams can build on.
The Neighbourly.io foundation — one API, Canada-wide.
What the next decade rewards
If there is one idea to take from all of this, it is that the future of real estate intelligence is not about collecting more data. Data is already abundant to the point of noise. The advantage — the durable, compounding advantage — belongs to those who can connect the data that already exists, responsibly, into something people and machines can actually trust. That is the intelligence layer. It is the connective tissue beneath the modern property industry, and it is quietly becoming the thing the whole market runs on.
The invitation
We have been building this layer since 2015, and we intend to keep building it — deeper layers, richer connections, and grounded AI that works from verified data rather than guesswork. If you are a lender, insurer, brokerage, portal or proptech team, the fastest way to understand what an intelligence layer makes possible is to put an address into it and watch the picture assemble. That is what Neighbourly.io is for. And if you want the full methodology behind how we build it — our principles, our approach to responsible data, and where we are taking meaningful location intelligence next — we have written it down.
Explore the data
This analysis is built on Neighbourly.io — the real estate data API for Canada. Standardized addresses, boundaries, demographics, permits and market signals through a single interface.
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