Featured case · Real estate · Private equity
A database becamea probabilistic pricing engine.
A Brazilian private equity firm with large-scale real estate exposure held an extensive proprietary database, but no structure to turn it into pricing intelligence. Grand Thera deployed an econometric decision layer on TheraOS.
- Client
- Real estate private equity firm
- Sector
- Real estate · Private equity
- Engine
- Econometric · probabilistic
- Horizon
- Asset level, by region
The pricing engine
Is this asset mispriced?How likely is the target?
Each asset carries a fair-value distribution that widens with time and volatility. Move the target and the volatility, switch assets, then open the portfolio view to see mispricing across the book.
Demonstration data · drag to orbit
Asset
P(target in 24m)
3%
Asking vs fair value
Overpriced
fair 88 · ask 100
From raw data depth to return precision.
- The challenge01
Data without a valuation structure.
- A large dataset without structured valuation models
- No clear view of whether assets were over-, fairly or under-priced
- No probabilistic framework for target-price achievement
- Limited simulation of volatility and return scenarios
- The deployment02
TheraOS as the intelligence layer; econometric AITs on top.
- Transactional, regional and macroeconomic data integrated
- Standardized valuation and pricing indicators
- Asset-level dashboards and scenario views by geography
- Continuous pricing recalibration
- Econometric pricing models
- Probability of reaching target price
- Volatility and return simulation
- The results03
Pricing signals with probabilities attached.
- Dynamic identification of mispriced assets
- Probabilistic assessment of target-price achievement
- Structured volatility and return simulations
- Clearer view of risk-adjusted return scenarios
Scenario visualization
Return and volatility, simulated across scenarios.
Structured volatility and return simulations by geography: the fan of outcomes, not a single forecast.
Loading scenario fan
“What initially seemed like a complex data challenge quickly became a structured, probabilistic pricing engine.”
