Researchers at the University of South Australia 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.
The technique was created and validated using over 30 years of historical sale information in metro Adelaide and utilises purpose-developed machine learning algorithms to process massive amounts of data about housing, urban structure, and amenities, making it possible to quantify the effects of urban planning policies on housing value.
Lead researcher, UniSA geospatial data analyst and urban planning expert Dr Ali Soltani, stated that the technique has implications for the property, urban planning, and infrastructure sectors. The team’s modelling technique and findings have the potential to help real estate investors, builders, property owners, house appraisers, and other stakeholders gain a more realistic view of the value of properties and the factors that affect that.
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 economies.
By capturing the complicated influence of infrastructure elements such as road and public transportation networks, commercial centres, and natural landscapes on home value, the team’s 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 dependent on human experience and limited data.”
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.
The technology could 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. The technique makes it simpler for stakeholders and the general public to apply the findings of sophisticated models to historical or real-time data from multiple sources, which have previously been almost black-box and expert-oriented.
The PropTech market is expected to experience a significant CAGR of 16.8% during the period 2022-2032. The market is expected to grow from US$18.2 billion in 2022 to US$86.5 billion in 2032. According to FMI, in 2021, the market was valued at US$67.5 billion.
Some of the most recent PropTech market trends include artificial intelligence and data automation in real estate, big data and digitalization of property data assets, sustainable technology in building and maintenance, and IoT and IIoT with drones for 360-view presentation. Demand for PropTech is high, as the technology lowers operating costs and helps agencies save money. Customers are provided with digital/virtual services, and agents are able to work on the go. As a result, profits, and productivity rise.
PropTech also helps to reduce transactional costs while enhancing consumer convenience. Due to the best match between property sellers and purchasers, it also helps to achieve higher unit sales and rental occupancies, leading to higher sales of PropTech.
