Predicting Housing Affordability Trends

Data Analytics Project : **Problem Statement:** Mounting housing costs in major Australian cities signal potential future crises in affordability. By forecasting trends, researchers can offer vital inputs for policy interventions that may alleviate housing stress. **Data Sources:** Projects can tap into datasets such as the AIHW Housing Data Dashboard, National Housing Data Exchange, and Rental Affordability Index, which provide metrics on property values, rental rates, and demographic changes. **Methodology:** - Compilation of longitudinal housing market and socio-economic data. - Employing time-series forecasting models (e.g., ARIMA or LSTM networks) to predict housing affordability dynamics. - Clustering and segmentation techniques to identify vulnerable neighborhoods and predict regions at risk of exacerbated affordability issues. **Expected Outcomes:** A predictive analytics framework that forecasts changes in housing affordability and visualizes high-risk areas for future intervention. **Impact:** By offering policymakers and urban planners actionable intelligence, this project can drive measures to stabilize housing markets and improve the quality of life for residents facing affordability challenges.

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