Where Will Development Happen Next? Forecaz's Bradley Rasmussen on the Hustle & Bustle Podcast
- Team Forecaz
- Apr 22, 2025
- 3 min read
Updated: 4 hours ago
KEY TAKEAWAYS:
AI-driven urban growth modelling gives planners a property-level, spatially sequenced forecast of development that separates what zoning permits from what the market will actually deliver, producing more defensible evidence for infrastructure planning and policy decisions.
Forecaz validates the Forecaz platform by comparing predicted development sequences against actual Development Applications over time, giving councils and infrastructure planners confidence the model's propensity scores reflect real market behaviour.
It was a pleasure to join Nicole Bennetts RPIA on her Hustle & Bustle podcast to talk about something I’m deeply passionate about — how technology can help shape the future of our cities.
As the team behind Forecaz, we’re focused on empowering urban planners and policymakers to make more informed, data-backed decisions about growth and land use.
About the Podcast
The Hustle & Bustle podcast, hosted by Nicole Bennetts RPIA, is a resource for planning professionals across Australia. Bradley Rasmussen's appearance gave listeners a detailed look at how the Forecaz platform and why AI-driven urban growth modelling is becoming a practical tool for real planning decisions, not just a research exercise.
Urban Growth Modelling Fundamentals
The conversation started with the fundamentals. Urban growth modelling is the process of using spatial data, AI, and planning expertise to forecast where, when, and at what density cities will grow. For councils and utilities, this matters because infrastructure investment decisions depend on it. Building a sewer pump station, extending a water main, or planning a road network all require confidence in where population will actually land over the next 10 to 30 years. Modelling that is optimistic by design, or that treats all zoned land as equivalent in development potential, produces infrastructure plans that do not reflect what the market will actually deliver.
How the Forecaz Platform Works
Bradley walked through how Forecaz addresses this. The platform takes multiple inputs, including council planning schemes, GIS data, development approvals, and existing land use information, then applies spatial analysis and AI to produce a sequenced, property-level forecasts of development. At the centre of the approach is a Bayesian Network model that encodes urban planner expertise into a structured framework, weighting factors such as lot size, site coverage, street frontage, zoning, and proximity to services to produce a Development Desirability Index for each parcel. That index drives the development propensity score, which the model uses to sequence growth across five-year projection periods.
Scenario Testing for Planning Decisions
The discussion also covered scenario testing, one of the most practical applications of the Forecaz platform. Planners use scenario modelling to test policy decisions before they are formalised. A council considering upzoning a suburban precinct for Gentle Density housing, for example, needs to know how much of that zoned land will actually redevelop under market conditions. Applying higher zoning densities to established suburbs on paper produces large theoretical capacity numbers. The model shows which parcels are feasible for redevelopment based on site coverage, frontage, and yield, and which will remain as single dwellings regardless of zoning.
Model Accuracy and Validation
Testing model accuracy was another topic the conversation addressed directly. Forecaz validates the Forecaz platform by comparing model-predicted development against actual Development Applications lodged after the model was built. When the model's predicted development and actual DA locations align at the suburb and catchment level over time, planners gain confidence the sequencing and propensity scores are reflecting real market behaviour.
Modelling, Policy, and Funding
The episode also addressed the relationship between modelling, planning policy, and funding. Growth models produce outputs that councils and infrastructure agencies use to justify capital investment decisions and support funding applications such as Residential Activation Fund submissions. The model needs to be defensible, transparent, and grounded in spatial evidence rather than assumptions. Bradley discussed how the Forecaz platform is built to produce outputs that planners, engineers, and funding bodies can interrogate and trust.
Advice for Planning Professionals
For planning professionals looking to build skills in this area, Bradley's advice focused on understanding the data that drives growth models, including land use, development approvals, zoning, and infrastructure capacity, and on developing a working knowledge of how AI propensity and scenario testing tools operate in practice.
The episode also pointed listeners to the PlanTech Best Practice Guidelines and the Australian Housing Data Analytics Platform as practical resources for planners working in this space.
Listen to the full conversation with Nicole Bennetts on the Hustle & Bustle podcast: Where Will Development Happen Next?