One of the many uses of AI and computer vision in real estate is to improve the accuracy of real estate appraisals.
The increased accuracy can improve the credibility of these numbers for insurance, purchasing decisions, green light loans and other areas.
Real estate valuation is often riddled with inconsistencies and inaccurate information, especially in underdeveloped and developing countries. In Zimbabwe, for example, valuation problems due to inflationary pressures have recently caused volatility in the insurance and pensions sectors. The calculation of property value is done by predicting the growth in demand for real estate assets and their future availability. Several variables should be considered when evaluating property, such as the current market rate of similar properties, the market positioning of the property builder, the proximity of a given property to places of utility such as shopping centers, stations, hospitals among others. In-depth analysis of real-time information can be achieved by involving computer vision-based tools and applications in real estate operations. Apart from real-time scanning, computer vision technology also captures data with precision and depth for clients to gain insights from property image metadata.
Estimated luxury level
Computer vision tools can be used to classify images of a home into different categories based on utility – bedroom, living room, dining room among others – build quality, space and ergonomics. All of this information can be displayed to clients located remotely. More importantly, AI and machine learning-based tools can assess whether a given house or property is worth paying a premium or not. To this end, properties can be listed independently in different “luxury level” categories. Computer vision models are trained with thousands of data sets to identify and perform these classifications of visuals without human intervention.
Future Price Prediction
The use of AI and computer vision in real estate functions involves predicting the price of a given property based on factors as varied as the weather conditions in the area where a given house or land is located, connectivity to national or state highways, cost of transportation, water supply and other factors. Machine learning and computer vision algorithms sift through all available information online or on various smart city platforms to gather this information. After that, factors such as inflation rate and apartment or bungalow prices are analyzed to arrive at a prospective future price. This figure is incredibly accurate because it is achieved after large amounts of data processing. While the use of computer vision in real estate functions is still in its infancy, the technology promises to be a game changer for remote real estate valuation.