City of Melbourne
Dynamic residential, demographic, and employment land use forecasts help the City of Melbourne understand changing trends and preferences induced by the COVID-19 pandemic and confidently plan future projects and strategies.
During 2020, population and employment levels, and trends, in the City of Melbourne were heavily impacted by the effects of COVID-19 and the associated restrictions introduced to stop the spread of the virus. These restrictions stopped overseas migration (a key driver of population growth) and resulted in many people either working from home, working reduced hours, or being stood down.
Universities and other tertiary institutions were also heavily impacted, with international students not allowed into Australia and coursing having to be largely delivered via remote learning. International tourism also ceased during the pandemic, and many major events were cancelled or heavily restricted. These direct impacts have had flow on impacts across the economy, particularly for the services sectors.
SGS was engaged by the City of Melbourne to develop a dynamic forecasting model that could estimate future land use needs over a 20-year horizon, which included:
- Dwellings, households, population, and demographic characteristics
- Employment (total jobs) by industry and space use such as office, retail, industrial
- Residential and employment floorspace demand required to accommodate the population and jobs
- Thirteen Small Areas and five Urban Renewal Areas within the City of Melbourne.
Necessitated by the uncertainty of planning in a post-COVID context, the forecasts also included three alternate scenarios by testing a range of outcomes for significant land use drivers from the macroeconomic scale, such as migration, to preferences, such as working patterns, and policies, such as shifting infrastructure investment timing. We generated these forecasts through a model which simultaneously considers both residential and employment uses along with bottom-up capacity constraints, macroeconomic drivers, strategic policy across all levels of government, localised data, and development patterns.