Valeri is a ground-breaking, Artificial Intelligence powered property value and development intelligence platform that draws on a wide array of locational, market and property data sets to identify patterns that influence and quantify property values.
Using proprietary Artificial Intelligence powered systems, Valeri draws on up to 2,000 surrounding sales to accurately value both property and land price.
Its powerful systems process enormous amounts of relevant property data, quantifying off-site factors such as distance to the beach, access to schools & local amenities as well as on-site factors like land size, structural elements, and property features.
Our machine learning Land Value Algorithm has taken years to perfect. It determines a unique value for the selected property based on its size, shape, and the location of that property in relation to other attributes:
A house, or any other built form, will depreciate in value over time as the building degrades, lifestyles change, and society expectations evolve. Independently, land increases in value where population growth places pressure on space.
We understand and evaluate the economics of land and quantify the value of location
Land values, while more stable than overall property values, are subject to market forces and our analysis shows that in some areas there can be divergent land value trends in different market segments. Valeri draws on city specific data sets, including sales data and unique proprietary layers to learn patterns and accurately predict land and property values as well as market trends.
Dynamic Neighbourhood Scaling is a process PointData has developed to define the characteristics of a neighbourhood to predict property values with a higher degree of accuracy. Because property values are defined by a wide range of attributes and not static suburb boundaries, defining a neighbourhood by its geographic, socioeconomic and market attributes yields more accurate property values.
We customise neighbourhood statistics to be representative of a property’s physical location and create custom boundaries which draw from real world features.
This delivers more accuracy in our calculated property value outputs (such as the property price estimate, estimated land value and neighbourhood price trends) as well as providing a truer representation of local properties and the features that define them.