Abstract
Real estate market is a significant for economy development in Australia. There are several factors affecting real estate markets, such as marketing demands, land development plans, land taxes, bank interest rates, peoples’ incomes, and so on. Predicting the real estate market is a challenge task due to these complex and interconnected factors. Over the past decade, there has been a significant progressive in Artificial Intelligence (AI) and Machine Learning (ML) technologies, which helps many real estate companies to enhance their day-to-day operations, for example using ML for property valuation. It is possible to analyse vast amounts of data from different perspectives by using ML technologies for real estate price predictions. ML might also assist real estate professionals by providing a trading strategy for investment purposes. Traditionally, real estate experts have relied on their personal experience and insights to analyse various factors like past sales data, home loan rates, and auction clearance rates. They would then provide investment recommendations to clients and customers, guiding them on how to maximize returns. This might involve suggestions on which suburbs to invest in or to identify the optimal time for selling a property. However, this manual analysis process can be time-consuming and challenging, as it heavily depends on the individual real estate agent's work experience. The ML-based decision support systems could make the process easier for real estate professionals to understand of the market from different aspects, and help their customers to navigate real sales, purchases and investments.| Date of Award | 2024 |
|---|---|
| Original language | English |
| Supervisor | Girija CHETTY (Supervisor) & Dat TRAN (Supervisor) |