Experiments
Yield
Designed a system to evaluate real estate investments across cash flow, risk, and long-term return scenarios.
Why this experiment matters
Real estate decisions are rarely about one number.
Most tools focus on isolated metrics like cash flow or cap rate.
I built Yield to model full scenarios including financing, risk, and long-term outcomes so decisions can be made with context instead of guesswork.
Experiment
Context
Static calculators produce weak decisions.
Real estate investors often rely on spreadsheets, static calculators, and fragmented tools.
This leads to inconsistent analysis, poor comparisons, and over-simplified decisions.
What I will build
The goal is a real decision support system.
Build a deal input system that captures purchase price, financing structure, rent assumptions, and expenses.
Build scenario modeling across cash flow, appreciation, loan dynamics, and risk exposure.
Build a comparison layer for side-by-side deal comparison, scenario testing, and sensitivity analysis.
Build decision framing outputs including best-case vs worst-case, break-even timelines, and long-term ROI scenarios.
Expected outcome(s)
Expected outcomes.
More consistent decision-making.
Reduced reliance on guesswork.
Clear comparison between opportunities.
Why it will matter
This shows financial systems thinking.
It applies decision frameworks to a real-world domain outside software.
It shows the same pattern: structure inputs, model scenarios, compare tradeoffs, then decide.
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