About Repit

Repit is a ZIP-level housing analytics platform — landlord activity signals, rent burden pressure, delinquency trends, and investment ratings for 37,369+ U.S. ZIP codes. It exists because most real estate platforms sell to consumers what consumers want to hear, and most investor tools cite the same public datasets without doing the work to interpret them. Repit is the data-publisher half of the equation. We surface the signal investors need before they commit capital — including the bad markets dressed as good ones.

Why Repit was built

Real estate investors — including the rapidly growing population of high-income physicians entering real estate — typically make acquisition decisions on incomplete market data: listing platform estimates, gut feel, and stories from other investors. ZIP-level dynamics like landlord exodus, rent burden pressure, delinquency trends, and housing stress concentration are invisible in standard platforms. Investors overpay for deteriorating markets and miss genuine opportunity because the data infrastructure to see the signal doesn't exist in the tools they're using.

Repit closes that gap with research-grade analytics, an AI copilot on every page, and a calculator suite (BRRRR, fix-and-flip, rent-vs-buy, affordability, salary equivalency, college ROI, student-loan-and-affordable-cities) that lets investors and homebuyers underwrite decisions before they make them.

What Repit looks at before a real estate decision

Investors don't lose money on the markets they understand — they lose money on the markets they thought they understood. The questions that distinguish a defensible real estate decision from a speculative one are largely the same across every market, but the answers vary dramatically by ZIP code. Repit surfaces those answers in structured form so the same analytical framework gets applied consistently, market to market, rather than ad hoc.

For any market you're evaluating, Repit makes the following data discoverable in one place:

• Cash-flow math. What does the rent-to-price ratio look like at the local rent point? Does the cash-on-cash math actually work, or only the appreciation gamble? What's the cap rate the data supports, not the cap rate a listing claims?
• Landlord economics & legal posture. Eviction-friendliness. Security-deposit norms. Rent-control posture. How delinquency trends are moving year over year. The operational reality of being a landlord in this specific jurisdiction.
• Demand fundamentals. Population trend, employment composition, household income trajectory, school quality (because tenant pools follow schools), commute patterns. What actually generates demand for housing here, and is that demand growing or thinning?
• Supply pressure. Permit pipeline, vacancy rates, housing-stock year-over-year change. The early-warning indicators of oversupply that show up in the data months before they show up in prices.
• Stress signals. Landlord exodus indicators, foreclosure starts, mortgage delinquency, rent-burden concentration. The same dataset that produced Repit's 2026 Landlord Exodus white paper — applied to every ZIP, not just the ones in the headlines.
• Comparable markets. Side-by-side comparisons against statistically similar ZIPs so you can see whether your target is an outlier in a good way or a bad way before you commit capital.

Repit doesn't tell you whether to buy. It surfaces what the data says about the market you're considering and provides the structured framework to evaluate it. The investor makes the call — but with the actual numbers, not the listing platform's gut feel.

How Repit's data is produced

Repit's analytics are built on a combination of public housing datasets (county recorder data, U.S. Census ACS releases, BLS employment data, FRED economic indicators) and proprietary ZIP-level synthesis we run on top of them. The Housing Stress Index, landlord activity signals, and ZIP-level investment ratings are calculated using published methodology — every claim on the platform is traceable to its source dataset. Investor decisions are too consequential to run on black-box models.

See the full methodology documentation for source datasets, refresh cadences, and how each metric is calculated.

Original research

Real estate publishing & press coverage

Original housing data and commentary by Pouyan Golshani and Repit has been featured in:

• Yahoo Finance 2026 Landlord Exodus & Housing Stress Index Reveals Extreme Divergence in U.S. Housing Markets
• Benzinga 2026 Landlord Exodus & Housing Stress Index Reveals Extreme Divergence in U.S. Housing Markets
• PR Newswire 2026 Landlord Exodus & Housing Stress Index Reveals Extreme Divergence in U.S. Housing Markets
• BiggerPockets America\\\'s Most Underwater Housing Markets Present a Golden Opportunity for Investors
• ConnectCRE A Sharp Divide — February 2, 2026

Founder

Pouyan Golshani, MD
Founder, Repit · Physician-Investor

Repit was founded by Pouyan Golshani — an investor across asset classes for 30+ years (equities first, then futures and options, then REITs and direct real estate) and a practicing physician. The diligence framework physicians apply in the clinic — gather the evidence, apply known frameworks, stress-test the assumptions — turns out to be the same framework that distinguishes a defensible investment decision from a speculative one. Repit is the data infrastructure for applying that framework systematically to housing markets.

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What Repit isn't

Contact

Questions, research collaboration, or press? Reach out via the contact form.