DATA CURATION - UNIQUE TO EVERY CITY
People value features in different ways in every city and location. Valeri uses Artificial Intelligence and is trained using curated data learning sets specific to each city.
Our different technological approach and the use of artificial intelligence improves the valuation process and helps business to translate difficult to value, qualitative factors such as neighbourhood and location characteristics, planning, property and market attributes into an equivalent dollar value. This is how we do it:
People value features in different ways in every city and location. Valeri uses Artificial Intelligence and is trained using curated data learning sets specific to each city.
PointData carefully cleanses its proprietary data sets for spurious outliers, mis-entered values and poor or missing features to ensure its machine learning algorithms are trained on good quality data.
PointData has developed a unique process to dynamically grow the area of influence surrounding each property irrespective of boundary or suburb constraints. Valeri uses up to 2000 sales to achieve a statistically significant sample and only “like for like” properties and sales.
Valeri’s growth indices are calculated separately for property and land, in both space (spatially) and time (temporally), at a detailed neighbourhood level.
PointData’s AI-AVMs draw on multiple variables and factors, including property sales, through applying its proprietary data learning sets. Valeri uses machine learning to relate “like for like” properties and location features in multi-dimensional space.
PointData has developed a system that mimics best practice ‘out of sample’ testing, usually applied by banks to test the validity and accuracy of an AVM. PointData’s Forecast Standard Deviation (FSD) is therefore far more representative of the real error and price range compared to traditional ‘in-sample’ methods.
