Yes. As advanced as our machine learning and Artificial Intelligence systems are, they can pick up poor habits which affect accuracy. The accuracy of a machine learning powered AVM depends on the quality of the data it receives and the processes of the machine learning system itself. Think of it like maintaining a race car – if it is given high quality consumables and is finely tuned by experts, it will perform. If it’s given cheap consumables and is neglected, it’s performance will suffer.
Once a machine learning AVM is perfected, we systematically train it on specific cities with clean, city-specific datasets. This allows Valeri to learn the patterns, values, and market aspects unique to every city. Because Valeri is always learning and always being trained, it will only get smarter.
PointData has developed its own proprietary training data layers that are contiguous to specific locations, from neighbourhoods to entire cities and regions. Under strict supervision, we train Valeri with location specific data in order for it to identify and learn the patterns and insights unique to each city. These proprietary data layers do not form part of a sales dataset, yet they are just as important in improving Valeri’s accuracy.
PointData is constantly researching new and evolving areas of the exciting world of Artificial Intelligence to ensure the accuracy of the algorithms behind Valeri.
There are many reasons why the underlying land value of a property may differ to neighbouring properties, including:
While a landowner cannot control the supply and demand aspect of land value, there are other avenues for increasing their land value. The biggest opportunity is through subdivision.
If a property can be subdivided under council zoning provisions, a successful subdivision can significantly increase the resultant land value, depending on the location.
A successful subdivision of a typical quarter-acre (or 750m2) suburban block can, on average, double the total land value.
Property developers are well versed in the process of creating land value uplift through subdivision. The building process provides some margin, but often it is the subdivision itself that provides the most value.