The research and data behind
every decision our companies make.
Big data, OSINT, digital twin and AI, turned into priceable decisions.
FHIP (Future Housing Intelligence Programme) is not a research project. It's the infrastructure behind the group's own decisions. Ten research domains feed three kinds of output — decision intelligence, data foundations, and research models — built on first-party project data, public OSINT and live tracking, and put to work through the group's companies.
We don't forecast housing. We measure it.
Three Types Of Output From
One Infrastructure.
Different uses, one underlying measurement layer. Built for the group, shared with partners.
Decision Intelligence
Feasibility scoring, risk assessment, valuation, carbon and energy analysis — the evidence behind the group's own development and investment decisions, and the reports its companies stand behind for their clients.
How decisions are madeData Foundations
Datasets the group builds and maintains (OSINT, market, supply chain, construction) at suburb, LGA, state and national scale — opened to government and research partners through collaboration, not a storefront.
See the data foundationsResearch Models
HousingGPT, knowledge graphs, vision models for site analysis, demand models — the research frontier we're building with university partners, ahead of anything productised.
Explore foundation modelsTen Domains Across Five Clusters.
Every research domain answers a specific business question. Together, they answer the entire feasibility → risk → valuation → outcome chain.
Explore the full research programmeAMarket & Opportunity Intelligence
- Demand forecasting — where will housing demand be in 5 years?
- Supply analysis — how much can actually be built?
- Price & affordability — what rents and sale prices will the market support?
BReal Estate & Asset Intelligence
- Development feasibility — which sites can be built on, and why?
- Construction cost intelligence — material, labour, contingency estimates?
- Property condition analysis — what defects or opportunities exist?
CSustainability & Carbon Intelligence
- Embodied carbon — what is the lifecycle carbon cost of a project?
- Operational carbon — what is the post-handover footprint?
- Circular economy — what existing materials can be reused?
DSpatial & Digital Twin Intelligence
- Digital twin environments — virtual suburb models for planning
- Site analysis via computer vision — identify constraints
- Spatial accessibility & equity — neighbourhood quality metrics
EFoundation Models & Core Data
- Housing dataset — proprietary residential project data
- Multimodal models — vision + text + structured data fusion
- Knowledge graph — semantic relationships across properties
Here's What Real Intelligence Looks Like.
Illustrative assessment. Not a model. Not a forecast. This is the shape of the evidence the group's companies put in front of a decision — measured from real projects and backtested against historical outcomes.
Pooraka, SA
- Flood Risk: Low, but rising with climate models (mapped by zone).
- Material Lead Times: Specialty windows +12 weeks.
- Market Rank: Top quartile for feasibility, middle quartile for demand.
Read, structured, and routed by confidence.
Nothing is a black box. Each field a model reads is scored: high-confidence results flow straight to the system, medium-confidence goes to human review, and anything unclear is escalated. The machine drafts — a person decides where it matters.
We don't just use today's tools.
We're building tomorrow's.
Alongside the systems already in use, Cyberate Technologies runs an active research frontier — concepts in development that will define the next decade of residential intelligence. Here's where we're heading.
Digital Twin
Property-to-city scale digital models for planning, design, and investment.
Explore the research→HousingGPT
A residential foundation model — industry intelligence, built for the industry.
Explore the research→"The best proof of a research company isn't only what it ships — it's what it's building next."
A platform shows you the map.
A decision needs someone who signs it.
What a data platform gives you
- • Layers, dashboards and APIs
- • Everything you need to look it up yourself
- • No opinion, and no one on the hook for one
What a decision needs
- → The layers read for this site, with the reference each answer came from
- → A clear verdict — approvable, buildable, bankable
- → A person accountable for it
We build the data layer and use it ourselves. When one of our companies gives an answer, a named person in this group stands behind it.
Built For Our Decisions.
Shared Through Partnership.
Explore the research, or talk to us about working with it.