Deal Screener

Paste a CSV of LoopNet links and whatever numbers you have next to them. Every row is run through the full underwriting model — sized debt, an LP/GP waterfall, an exit — and scored on a 100-point rubric. Rows are ranked best first, and every input is tagged so you can see what came from the listing and what the model assumed.

Listings

A url column is enough to identify a row, but a price is required to underwrite one.

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Recognized columns (any common alias works): url, name, type, address, city, state, price, units, sf, rent per unit, rent psf, annual rent, noi, cap rate, occupancy, walt, notes. Numbers can be written as $1,250,000, 1.25M or 6.25%.

Run Assumptions

Applied to every row

Hold periodyrsInterest rate%Max LTV%Min DSCRVacancy%Rent growth%Expense growth%Exit cap spreadⓘ%LP share of equity%Preferred return%

How this works The screener reads listing URLs, it does not fetch them. LoopNet’s terms prohibit scraping and the site is behind bot protection, so a scraper would be both a compliance problem and unreliable. What the URL already encodes — asset type, address, market, listing id — is parsed from the link; the numbers come from the columns beside it or from listing text pasted into a notes column. Anything still missing is filled with class defaults and flagged as an assumption.