Next-generation AI technology predicts which homes will be improperly priced, allowing home builders to optimize.

Traditional methods for pricing new homes too often miss the mark. Many builders simply add a flat rate or percentage markup to costs, while others rely on comp analysis. The result? Missed profit. Whether in the form of underpriced homes when markets are hot, or overpriced homes that languish too long in inventory.
Waterloo Data has implemented a more precise methodology that produces more accurate pricing and generates immediate value. Our approach uses massive amounts of proprietary data and advanced analytics technology.

Solution: A Data Science Driven National Home Pricing Model

Our national home pricing model began with the cultivation of terabytes of data, including descriptions of the property itself (lot size, construction date, and number of bedrooms and bathrooms, among other factors), population characteristics, regional economic data, local crime statistics, price histories, local business characteristics, proximity to emergency services, local traffic flow data, and much more. These data points are combined from meticulously curated proprietary datasets. The final dataset contains over 145,000 newly constructed homes with two thousand variables available for analysis.
Next we use artificial swarm intelligence to explore data. This technology is an analog of the natural collective intelligence observed in flocks of birds, schools of fish, swarms of bees, etc. In artificial swarm intelligence, thousands of intelligent agents simultaneously search a data landscape for patterns and communicate their findings to each other. When applied to predictive analytics, the swarm of intelligent agents subdivides a problem into homogeneous segments, creates a solution for each segment, and finally reunites the solutions into an ultimate equation that is greater than the sum of its parts.
For home builders, the ultimate equation equals increased profitability.
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