Agent Selection Guide

lens-quantitative

PhD-trained quantitative analyst who rejects qualitative claims without numerical support. Produces distributions of outcomes rather than point estimates, builds explicit assumptions tables with sources, runs DCF and sensitivity grids, and defines the Monte Carlo framework for any deal it touches.

What it does for you
  • Build assumptions tables with source, historical basis, and base/upside/downside values for every model input
  • Produce DCF cash flows, NPV, and IRR across three scenarios with stated probabilities
  • Generate two-variable sensitivity grids and breakeven analysis at hurdle and target IRR
  • Describe a Monte Carlo simulation structure with input distributions and correlation assumptions
  • Identify the top variables by IRR impact via tornado chart ranking
  • Stress-test qualitative arguments by demanding the number behind every claim
When to use it

Best for

Any moment where a single-scenario return is being passed off as analysis, where assumptions need explicit sourcing, or where you need a structured numerical stress-test before an IC presentation or capital commitment.

Not the right lens

Situations that call for relationship judgment, qualitative market positioning, narrative investor communications, or legal and structural deal work where numbers alone do not resolve the question.

Equipped skills

Skills this persona reaches for

Sensitivity & Stress Test EngineMonte Carlo Return SimulatorAcquisition Underwriting EngineDeal Underwriting AssistantDeal QuickScreenOM Reverse PricingRefinancing Decision AnalyzerPerformance AttributionIC Memo GeneratorIC Red Team ChallengerSubmarket Truth SerumT-12 Operating Statement NormalizerAMOS IC Demo OrchestratorCapEx PrioritizerCapital Stack OptimizerComp SnapshotConstruction Budget & GC Contract AnalyzerConstruction Cost EstimatorCost Segregation AnalyzerLease Trade-Out AnalyzerOperating Statement to DatabaseOpportunity Zone UnderwriterProperty Tax Appeal AnalyzerREIT Profile BuilderRent Roll AnalyzerRent Roll to T-12 Tie-OutSupply-Demand ForecastT-12 to DatabaseWarehouse-to-Exhibit Mapper
Example prompts
  • Build a full DCF assumptions table with base, upside, and downside cases and tell me what breaks the model
  • Show me a two-variable sensitivity grid on exit cap rate vs rent growth and the breakeven on each
  • Describe the Monte Carlo structure for this deal with input distributions and correlation assumptions
  • The sponsor is projecting 5% annual rent growth -- what does the historical distribution actually look like and what confidence interval supports that?
What a human still signs off

A human investment committee owns every go/no-go and return-target decision. Legal counsel reviews deal terms. The lender underwrites debt independently. This persona produces the quantitative framework and surfaces parameter sensitivity; it does not substitute for any of those sign-offs.

Limitations

Produces quantitative frameworks and analysis from data the user provides. Has no live access to CoStar, Bloomberg, or market data feeds. Point estimates still require human judgment on distribution shape and correlation structure. All model outputs carry the limitations of their inputs; a human owns every capital decision.