With the continuous development of technology, artificial intelligence (AI) is profoundly changing our lives and work, and property valuation is no exception. Along with technological advancements, the integration of big data with AI technology is becoming increasingly prevalent and important in the field of property valuation.
The two most important aspects of property valuation are data collection and analysis. Traditional methods of property valuation typically rely on experienced valuers who analyze market data (such as transaction or rental data) and property characteristics (such as location, surrounding environment, construction quality, age of the building, etc.) to estimate the value of the properties. However, there are inherent limitations for this approach. Firstly, the valuation may become subjective, producing different valuation results from different valuers. For example, the value or rental value of a shop located in a densely populated area in a shopping mall is often higher than that of a shop in a sparsely populated area. However, valuers or landlords often have to make subjective judgements about the reasonable difference based on experience and observation. Secondly, traditional valuation methods require a significant input in time and manpower. Given the countless factors that can impact the value of a property, relying solely on human effort often leads to an inability to consider all factors comprehensively and accurately in each valuation case, particularly in a fast-paced commercial society.
Big data refers to large sets of data, which includes structured data (such as numbers and tables) and unstructured data (such as text and images). The importance of big data lies in its ability to provide more comprehensive information and uncover hidden patterns and trends. In property valuation, big data refers to historical property price, land use planning, demographic data, geographic information system (GIS) data, etc. These data can be used to improve the accuracy and predictive ability of valuation models. The application of AI technology can process large amounts of data quickly and extract relevant and useful key information. It can predict future property values or cash flow income by analyzing historical sales or rental data, as well as market economic data, land use planning, and demographic information. This data-driven approach is faster and more accurate than traditional valuation methods as it considers a wider range of factors. To get the aforementioned shopping mall as an example, a number of malls nowadays incorporate various systems to collect data on customer flow patterns and consumption behaviour. By leveraging AI analysis, it becomes possible to more accurately determine reasonable rental levels and even design tenant compositions based on the customer flow and consumption behaviour.
Although big data and AI technology can effectively improve the efficiency of property valuation, there are still needs of valuers’ personal observation and professional judgment, especially in some aspects where big data may not cover. For example, the author once inspected a residential unit where there were deficiencies in building design and management, this resulted in an accumulation of sewage on the flat roof outside the window, posing hygiene problem and adverse impact on the property value. These specific issues may not be captured by big data and AI technology and require the judgment of the valuers. Additionally, big data and AI technology highly rely on high-quality raw data, and over-reliance on technology providers can pose potential risks and privacy concerns, which are issues that need to be addressed in the current trend of AI and big data.