Develop an in-depth understanding of housing market economics, analytics and modelling to gain a competitive edge as a real estate professional, investor, developer, policy maker, fund manager, or home buyer in the Australian Context
Materials included in each module: description of the problem, analytical method to be used, market trend charts, data files (Excel), step by step implementation of the method by Excel and a quiz to test your skills and knowledge
MODULE 1: The Australian housing market: an overview, prices, growth, rent, volatility and performance
Understand the economic theories that underpin the formation and evolution of cities; review the role of the housing market in the economy; examine historical data such as trends and patterns of the key indicators including prices, growth, rent, volatility and returns.
MODULE 2: Supply and demand: the market fundamentals
Understand the forces that drive supply and demand in housing market and how these forces determine prices and trends; frame a solid explanation about the differences in prices and growth paths between cities. Examine the characteristics of property market cycles and identify the phase of the housing market at any point of time for informed decisioning.
MODULE 3: Forecasting: theory and practice
Learn how to use historical data and forecasting methods to predict the future housing market trends.
MODULE 4: Property valuation methods: theory and practice of automated home valuation modelling and cash flow modelling
Understand the theory of the hedonic modelling approach that underpins the modern automated home valuation models currently offered by the leading companies such as Corelogic/RP Data; use real property data to build your own automated home valuation model for a suburb; and carry out a valuation for a specific property or a building.
MODULE 5: Simulation modelling for risk management in property investment
Learn to use analytical approaches to manage uncertainties in property investment; carry our the Monte Carlo simulation method using real world data set and interpret the results for decision making.
