QuantPS offers a predictive analytics platform that evaluates and ranks local real‑estate agents using peer‑to‑peer indexing, market comparison models, and full transaction histories. The API‑first solution delivers geospatially precise recommendations to help property owners and investors select agents likely to achieve above‑ or below‑market pricing for sales or leases.
Funding
Funding not disclosed
SAFounders
Product
Problem
Real estate sellers and landlords often lack reliable insight into which local agents can achieve pricing outcomes above or below market averages, leading to suboptimal sale or lease results and inefficient marketing efforts.
Solution
QuantPS provides a patented predictive analytics platform that evaluates local real estate agents using peer‑to‑peer indexing, agent‑versus‑market comparisons, and full transaction histories. The system generates data‑driven recommendations identifying agents most likely to secure above‑market or below‑market pricing for a given property. Results are contextualized by geospatial factors down to region, suburb, street, or individual property, enabling precise agent selection. The platform integrates via plug‑and‑play APIs with existing data feeds, allowing clients to incorporate the analytics into their current workflows without extensive re‑engineering. By leveraging hedonic and quantitative dynamic tools, QuantPS helps clients improve transaction outcomes and streamline their property marketing strategies.
Target Audience
Primary customers are property owners, developers, and real‑estate investment firms seeking to optimize agent selection for sales or lease transactions, as well as brokerage platforms that want to offer data‑driven agent recommendations to their clients.
Features
- Peer‑to‑peer agent indexing that benchmarks each agent against peers in the same market
- Agent‑versus‑market analysis combining hedonic pricing models with historical performance data
- Comprehensive transaction history database used to predict pricing deviations
- Geospatial and geospecific recognition delivering predictions at region, suburb, street, or property level
- API‑first architecture for seamless integration with existing real‑estate data pipelines
- Predictive and prescriptive attributes that enhance forecasting accuracy for sales and leases