We don't just wrap ChatGPT around property data. CortexaProp uses institutional-grade quantitative methods—the same approaches used by BlackRock and pension funds—adapted for individual investors.
Traditional property calculators use static assumptions: "Assume 3% rental growth, 1 month void per year, 5% interest rate." They output a single yield number.
The problem: Real estate doesn't work that way. Rental growth varies year to year. Void periods cluster. Interest rates spike unexpectedly. Maintenance costs are lumpy, not smooth.
MIT research found that Monte Carlo models valued properties $500,000+ higher than static DCF models because they properly captured the optionality in real estate.
"Your expected yield is 4.2%"
(But what's the range? What's the downside?)
"Expected yield 4.2%, but 5% chance of 1.8% or lower, 5% chance of 6.8% or higher. 8% probability of negative returns."
We run 1,000 simulations of your property's 5-year performance, randomizing key variables within realistic ranges.
Based on UK historical data
Probability-weighted
Lumpy, not smooth
Mean-reverting model
Annual fluctuation
5-year range
Tests your property against interest rate scenarios (+1%, +2%, +3%) and calculates the exact rate at which cash flow turns negative.
Inspired by bank stress testsCalculates probability of forced sale over 3/5/10 years based on LTV, cash reserves, and market liquidity factors.
Survival analysis modelMulti-factor scoring across Location, Build Quality, Rental Appeal, Growth Potential, Liquidity, and Value.
Weighted composite indexTracks price growth, rental demand, days on market, and new developments to identify areas with positive/negative momentum.
Land Registry + planning dataIntegrates Environment Agency flood data, EPC ratings, and projected insurance cost increases.
Government open dataAutomated check of 12+ UK landlord requirements based on postcode, property type, and tenancy structure.
Rules-based systemWe prioritize authoritative, verifiable sources. Every data point is traceable.
CortexaProp works with free data. Premium integrations enhance accuracy but aren't required.
Cornell University Real Estate Review
Key finding: Static DCF models systematically undervalue properties by ignoring optionality
Read paperMIT Center for Real Estate
Key finding: REIT returns exhibit kurtosis of 10.3 vs 0 for normal distribution
Morgan Stanley Research
Key finding: $34B efficiency gains projected by 2030 from AI adoption
Read paperUBS Wealth Management
Key finding: Methodology for assessing city-level real estate risk
Read paper