Researchers develop simplified property valuation method
Researchers at the University of South Australia have developed a new technique they say will help the property sector gain a more realistic, reliable and practical view of the value of property.
Property valuation has long been something of an artform based on professional experience but researchers at the University of South Australia are transforming it into pure science.
They have developed a machine learning technique that makes property valuation more transparent, reliable, and practical, with the ability to accurately model the impact of urban development decisions on property prices.
Lead researcher, UniSA geospatial data analyst and urban planning expert Dr Ali Soltani, says the technique, has implications for the property, urban planning, and infrastructure sectors.
While it is only a research paper at the moment, Dr Soltani told API Magazine that the next step will be to formalise it into a management dashboard and a home value estimator in the form of a software package that can be utilised by a wide variety of industry stakeholders.
“It requires further work to make it more generally applicable, and then more work to make it a useful software tool with prediction and visualisation capabilities,” he said.
“Our modelling technique and findings may help real estate investors, builders, property owners, house appraisers, and other stakeholders gain a more realistic view of the value of property and the factors that affect that,” Dr Soltani says.
The technique was created and validated using more than 30 years of historical sale information in metropolitan Adelaide and uses purpose-developed machine learning algorithms to process huge amounts of data about housing, urban structure and amenities, making it possible to quantify the effects of urban planning policies on housing value.
“This research has implications for policymakers by providing insights into the potential impacts of urban planning – such as infill regeneration, master-planned communities, gentrification, and population displacement – and infrastructure provision policies on the housing market and subsequent local and regional economy.
“By capturing the complicated influence of infrastructure elements, such as road and public transportation networks, commercial centres, and natural landscapes on home value, our model is especially valuable for enhancing the accuracy of current land value predictions and lowering the risks associated with traditional property valuation methodologies, which are largely dependent on human experience and limited data.”
Expanding the model
Dr Soltani says the model – developed in conjunction with Professor Chris Pettit from UNSW’s City Futures Research Centre – may also be extended to include other economic features at both the macro and micro levels, such as changes in interest rates, employment rates, and the influence of COVID-19, by harnessing the benefits of big data technologies.
He told API Magazine that the team is presently working on regional housing transaction data and would welcome the opportunity to collaborate with additional partners to develop this model.
“This model has the potential to be used as a decision-support platform for a variety of stakeholders, including home buyers and sellers, banks and financial agents, investors, the government, and insurance or loan agents,” Dr Soltani said.
“Our technique makes it simpler for stakeholders and the general public to apply the findings of sophisticated models on historical or real-time data from multiple sources, which have previously been almost black-box and expert-oriented.”
He added that the model is applicable to different states as long as they have access to transactional history.
“This may be combined with other databases such as the Australian Housing Data Analytics Platform (AHDAP), which was built by UNSW in collaboration with other partners, or those used by the private sector such as PointData, OMNILINK, CoreLogic, and so on.”