Every investor has a buy box — the markets, price range, property type, and condition they're willing to buy. Manually re-running the same MLS filters every morning, or scrolling county record sites looking for matches, wastes hours that could go toward calls and offers. Offr AI's buy-box matching turns your criteria into a standing filter that works continuously across off-market data and the buyer marketplace.
What buy-box matching outputs, field by field
Once you submit a buy box, each matching property or buyer record comes back with:
- Match source — whether the record came from a fresh buy box search, a re-run of a saved buy box, or the cash-buyer marketplace.
- Structured criteria used — the parsed version of your plain-English description (location, price range, property type, bed/bath minimums, motivation filters), so you can confirm the platform understood your intent correctly.
- Matched property list — typically 25+ off-market properties per search, each carrying its own ARV, rehab range, and Offr Score.
- Motivation tags per match — pre-foreclosure, vacant, probate, tax-delinquent, absentee-owner, code-violation, FSBO, or stale-MLS, layered from seller intelligence.
- Buyer-side matches — if you're a cash buyer with a posted buy box, matching wholesale deals surfaced from the marketplace, with the same core deal numbers attached.
Inputs required
You need a plain-English description of what you're looking for — location (state, county, city, or ZIP), property type, price range, and any motivation filters that matter to your strategy (e.g., "only vacant or pre-foreclosure"). Cash buyers additionally provide their strategy (fix-and-flip, buy-and-hold, wholesale-to-buyer) so the platform can prioritize deal types that fit. No property address is required up front — the whole point of buy-box matching is that you describe criteria once and let the search engine find candidates, rather than starting from a specific address.
How the matching works
- You type your buy box in natural language, e.g., "single-family, 3+ bed, under $150,000, pre-foreclosure or vacant, in Cook County."
- Offr AI's language parser converts that description into structured filters: property type = single-family, beds ≥ 3, price ≤ $150,000, county = Cook, IL, motivation ∈ {pre-foreclosure, vacant}.
- Those structured filters query public and county record data sources across all 50 states, looking for properties that satisfy every hard filter (price, location, property type) and at least one motivation signal if specified.
- Each candidate that clears the filters is passed through ARV and rehab analysis, then scored with the Offr Score.
- On the buyer side, the same structured-filter logic runs in reverse: a wholesaler's contracted deal is compared against every buyer's saved buy box, and any buyer whose criteria the deal satisfies gets surfaced as a match in the cash buyer marketplace.
- You can re-run a saved buy box at any time, or tighten/loosen filters (e.g., raise the price ceiling, add a county) and re-search instantly.
Worked example
Suppose you enter: "3+ bed single-family, $100k–$175k, absentee-owner or tax-delinquent, in Maricopa County, AZ."
- The parser sets: property type = single-family, beds ≥ 3, price range $100,000–$175,000, county = Maricopa, motivation ∈ {absentee-owner, tax-delinquent}.
- The search returns, say, 31 candidate properties meeting those hard filters.
- Of those, 12 carry an absentee-owner tag, 9 carry tax-delinquent, and 4 carry both — those 4 double-signal properties are worth calling first.
- One example: a 3-bed/1-bath home listed with ARV $168,000 and rehab estimate $22,000–$29,000, tagged absentee-owner + tax-delinquent, lands with an Offr Score in the top decile of this batch of 31.
- You save this buy box so it automatically re-runs weekly, surfacing new tax-delinquent filings or ownership changes in the same county without you re-typing the search.
Where it's uncertain, and how to verify by hand
- Parsing ambiguity: a vague buy box ("good deals in Texas") returns a broad, low-precision list. Tighten location to county-level and add at least one motivation filter to get a usable match count.
- Stale records: county and public data sources update on different schedules — a property may have sold or gone under contract before the record reflects that. Spot-check a sample of matches against a current MLS pull or county assessor lookup before investing calling time across the full list.
- Buyer-side matches depend entirely on how current and specific a buyer's posted buy box is; an outdated buy box (stale price range, old market focus) will produce technically-correct but practically-useless matches. Encourage buyers you work with to update their buy box after every closed deal.
- Overlapping motivation signals (e.g., a property flagged both pre-foreclosure and vacant) generally indicate stronger motivation but are not a guarantee the owner wants to sell — always confirm by phone.
How it fits the rest of the workflow
Buy-box matching is the front door to the platform: it's what generates the property list that deal analysis, ARV, and rehab estimates run against, and what seller intelligence tags with motivation signals before lead scoring ranks the results. On the buyer side, the same matching logic powers the cash buyer finder, connecting your contracted deals to buyers whose stated criteria already fit. Start a search in the app or check pricing.