Summary:
A machine learning model at UNSW’s Housing Analytics Lab predicts the impact of zoning law changes on housing development feasibility using real-time housing datasets. The “precinct feasibility explorer” model, a joint venture between Value Australia, e-permitting firm Archistar and the research centre, has so far analyzed the 1200-meter area around Hornsby train station and its 2555 land parcels. The results show the scope for capturing some of the value developers gain through rezoning policy.
What This Means for You:
- Developers and urban planners can leverage this model to predict how zoning law changes will influence the feasibility of developments, allowing them to make informed decisions about potential projects.
- Governments can use the insights generated by the model to fine-tune transport-oriented development and low- and medium-rise housing policies, capturing more value from rezoning and creating more affordable housing.
- Residents in areas undergoing rezoning can benefit from increased housing supply and more affordable housing options.
- Future outlook: With more systematic implementation and expansion of similar machine learning models, urban planning and development can become more predictive, efficient, and data-driven, ultimately benefiting all stakeholders involved.
Original Post:
Tucked away on the first floor of the Commonwealth Bank’s Eveleigh office, a crystal ball provides a glimpse into Sydney’s future.
Two men pore over a map generating dozens of green buildings over a map of Hornsby, a northern Sydney suburb named as one of the NSW government’s eight accelerated transport-oriented development precincts.
UNSW professor Chris Pettit (right, with Value Australia CEO Mark Nassif) argues the case study demonstrates the scope for capturing some of the value developers gain through rezoning policy.Credit: Steven Siewert
But there’s no wizardry at play. Here at UNSW’s Housing Analytics Lab, a machine learning model is being fed real-time housing datasets to predict how zoning law changes will influence the feasibility of developments.
The proof-of-concept model provides insight into how rezoning the 1200 metres around Hornsby train station will affect the 2555 land parcels within the area.
Extra Information:
Related resources:
These resources offer more context and information on the participants in this joint venture and their expertise in machine learning, urban planning, and development.
People Also Ask About:
- What is the precinct feasibility explorer? A machine learning model that predicts how zoning law changes will influence the feasibility of developments.
- What is its application? It can help developers, urban planners, and governments make informed decisions about potential projects and policies.
- How does it benefit residents? It can lead to increased housing supply and more affordable housing options in areas undergoing rezoning.
- What is the future outlook? The model has the potential to be systematically implemented across NSW and other regions, making urban planning and development more predictive, efficient, and data-driven.
Expert Opinion:
The successful implementation of the precinct feasibility explorer model showcases the power of machine learning in urban planning and development. By providing a more accurate and data-driven approach, policymakers can create more informed and balanced zoning regulations, ensuring equitable access to housing across different communities.
Key Terms:
- Machine learning model
- Zoning law changes
- Development feasibility
- Real-time housing datasets
- Urban planning and development
- Affordable housing
- Transport-oriented development policies
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