Listing Comp Research

skill

Pull comparable sales from public portals, normalize with a script, and produce a comp sheet.

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Listing Comp Research

Pull comparable sales (comps) from public real estate portals, normalize the raw listings into a structured data set, and produce a clean comp sheet an agent can use for pricing discussions.

When to use

  • A listing agent needs recent comparable sales for a subject property before setting or defending a list price.
  • A buyer's agent wants a quick comp sheet to support an offer strategy.
  • Someone asks for a refreshed comp set because prior research is more than a few weeks old.

Tools

  • browser-search
  • browser-navigate
  • browser-extract
  • shell-execute
  • write-file

Playbook

  1. Confirm the subject property's address, property type, bedroom/bathroom count, square footage, and the comparison radius or neighborhood boundary before starting research.
  2. Use browser-search to find public listing portal search results for recently sold and active comparable properties near the subject address (e.g. a query like "site:redfin.com sold homes [neighborhood] last 6 months").
  3. Use browser-navigate to open each relevant portal search results page or individual listing page returned by the search.
  4. Use browser-extract to pull the structured fields from each listing page: address, sale price or list price, sale date, square footage, bed/bath count, lot size, and days on market.
  5. Repeat steps 3-4 until you have at least 5-8 comparable properties, favoring the most recent sales and the closest geographic and physical matches to the subject property.
  6. Use shell-execute to run a normalization script that converts the extracted raw records into a consistent format (standardized units, price-per-square-foot calculation, de-duplication by address).
  7. Review the normalized data for outliers (e.g. distressed sales, non-arm's-length transfers) and flag any that should be excluded or weighted differently, noting the reason.
  8. Use write-file to save the final comp sheet as a document listing each comparable, its key stats, price-per-square-foot, and a summary range (low/median/high) for the subject property.
  9. If a portal blocks automated access or required data (such as MLS-restricted fields) is unavailable through public pages, stop gathering that field and note the gap in the comp sheet rather than guessing at a value.

Failure modes

  • Portal search results return listings outside the requested radius or property type; verify each comp actually matches the subject property's criteria before including it.
  • Extracted sale prices may reflect list price rather than closing price on some portals; confirm the field is a true closing price before using it in calculations.
  • Small comp sets (fewer than 5 properties) produce unreliable price-per-square-foot ranges; widen the radius or time window rather than reporting from too few data points.
  • This research is informational only — it is not appraisal, legal, or financial advice, and any pricing or offer terms drawn from it require human review and approval before being used with a client.

Done when

  • A comp sheet with at least 5 normalized, verified comparable properties and a price-per-square-foot summary range has been saved via write-file.
  • Any excluded outliers or data gaps are documented with the reason for exclusion.
  • The comp sheet is handed off for human review before any pricing or offer decision is made.