Listino Methodology & Data Sources: How We Prepare Listings

What our analysis is based on

A Listino listing prep combines three things: (1) the property details you provide or import, (2) comparable properties identified through current publicly accessible web sources, and (3) established real estate research about what moves listings. It is research assistance, not an MLS feed or appraisal; verify time-sensitive facts before client use.

Our primary sources

When our guides reference market behavior, the underlying data generally comes from sources like these:

• NAR Profile of Home Buyers and Sellers and other National Association of Realtors research — buyer behavior, search habits, and seller outcomes.
• Zillow Research — listing engagement, pricing, and days-on-market trends.
• Remodeling's Cost vs. Value Report — return-on-investment ranges for common pre-sale improvements (these change yearly, so we date them).
• Publicly accessible listing, brokerage, assessor, and other web sources — used to identify candidate comparables, with source details retained where available.
• First-party data from listings analyzed on Listino — used only in clearly-labeled, aggregated form.

How we handle statistics in our guides

• We prefer qualitative, durable statements over precise figures that go stale.
• When we cite a number, we attribute it to a primary source and date it.
• Worked numeric examples are labeled as illustrative examples, not as data or guarantees.
• Figures that move year to year (such as remodeling ROI) are refreshed on a regular cadence.

How a listing prep is produced

1. You import a Zillow listing or enter property details.
2. We research candidate comparable sales and active competition using publicly accessible web sources.
3. We analyze market competitiveness, listing appeal, and price position.
4. We generate an optimized listing description, a photo sequencing plan, and AI photo touch-ups.
5. We assemble pricing guidance and projected outcomes into an editable first draft. You can verify facts, revise or restore sections, personalize branding, and publish the finished package.

We're also preparing an aggregate data study of the listings we've analyzed; it will be published here once finalized.