Vo, Nguyen (2014) A New Conceptual Automated Property Valuation Model for Residential Housing Market. PhD thesis, Victoria University.

Abstract

Property market not only plays a major role in the Australian real estate economy but also holds a large portion of the country’s overall economic activities. In the state of Victoria, Australia alone, residential property values surpassed one trillion dollars in 2012. A typical weekend property auctions in Victoria could see tens of millions of dollars change hands. Residential property evaluation is important to banks or mortgage lenders, real-estates, policy-makers, home buyers and those involved in the housing industry. A tool which can predict prices is essential to the housing market. Residential properties in Victoria are re-valued manually every two years by the Department of Sustainability and Environment, Victoria, Australia (DSE) with up to 30%± uncertainty of the market values. Municipal councils use the values established by DSE to determine property rates and land tax liabilities. According to rpdata.com, there are currently five types of Automated Valuation Models (AVMs) used in residential property valuation in Australia: sales comparison approach, cost approach, hedonic, income capitalisation approach and price indexation. The calculation backbone for these AVMs is still based on traditional statistics approach. At the time of writing this thesis, only a handful of researchers in the world have used Artificial Neural Network (ANN) in AVM to estimate residential property prices. In this research work, a Conceptual Automated Property Valuation Model (CAPVM) using ANNs was proposed to evaluate residential property price. The ultimate goal was to produce long-term house price forecast for urban Victoria. The CAPVM was first optimised and then its residential property price forecast capability was investigated. Optimisation of CAPVM was achieved by determining the best number of the hidden layers, the hidden neurons and the input variables, and finding the best value of training error threshold. CAPVM was excellent in predicting 86.39% of residential property prices within the accuracy margin of 10%± error of the actual sale price, a better performance than DSE’s manual valuations and National Australia Bank’s published figures. It successfully modelled the annual changes in residential property prices for hard to predict periods 2007-2008 during the global financial crisis and 2010-2012 residential property boom when the interest rates were on a downwards trend. CAPVM also outperformed the prediction performance of multiple regression analysis.

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