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Built for a Midwest manufactured-housing investor

Off-Market Deal Sourcing Software for CRE: 197 Counties, Scored Weekly

We built off-market deal sourcing software for a Midwest manufactured-housing investor that refreshes 197 counties every week, scores each candidate property on 14 signals, resolves and skip-traces owners against a maintained registry, and generates the broker opinion of value and offering memo on demand. The investor owns and governs the entire system on their own infrastructure.

Clients are anonymized by agreement. The system mechanics and first-party numbers below are real.

197
counties refreshed weekly
14
signals scored per property
weekly
refresh cycle
on demand
BOV and OM generation

Walkthrough

Watch the system run end to end

Demo shown with sample data and a fictional deal.

01 · The problem

The challenge

Manufactured-housing parks rarely trade on the open market, and the ones worth acquiring are spread thin across hundreds of rural counties. The investor was hunting them by hand: pulling county lists, guessing which parks were viable, and chasing owners whose contact details were stale or wrong. Coverage was inconsistent, and the team had no reliable way to rank where to spend its limited outreach hours.

02 · The architecture

The system

  1. Multi-county sourcing refresh

    A weekly refresh across 197 counties pulls candidate properties into one normalized pipeline, replacing ad hoc county-by-county lookups.

  2. 14-signal scoring

    Each property is scored on fourteen signals so the highest-potential acquisitions rank first and outreach hours go where they matter.

  3. Owner resolution and skip-trace

    Owners are resolved and skip-traced, then verified against a maintained known-properties registry, so outreach reaches the right decision-maker the first time.

  4. BOV, OM, and market-report generation

    A broker opinion of value, offering memo, and market report generate on demand from the same pipeline data, turning a day of assembly into minutes.

03 · The workflow

How it runs

  1. Refresh 197 counties
  2. Score on 14 signals
  3. Resolve + skip-trace owners
  4. Verify against registry
  5. Generate BOV / OM
  6. Weekly call batch

04 · The outcomes

Results

First-party results of the system built for a Midwest manufactured-housing investor.
DimensionBeforeAfter
County coveragead hoc, county by county197 counties refreshed weekly
Deal rankinggut feel14-signal scoring
Owner outreachstale, missing contactsresolved and verified
BOV / OM outputa day of manual assemblygenerated on demand

Questions

Frequently asked questions

What counts as off-market deal sourcing software?

Software that finds acquisition candidates before they are listed: it ingests public and licensed data at scale, scores properties against your buy box, resolves owners, and hands your team a ranked, contactable list. This system does all four on a weekly cycle across 197 counties.

Who owns the system and the data?

The client does. The engine was delivered on the client’s own infrastructure, with full ownership and governance of the code, the pipeline, and every record in it. That was a deliberate design requirement, not an afterthought.

How is the owner contact information kept accurate?

Owners are resolved from the source records, skip-traced, and then verified against a maintained registry of known properties. Contacts that fail verification are flagged rather than dialed, so the call list stays clean.

Does this approach work outside manufactured housing?

Yes. The scoring signals and data sources are configured per asset class. The same architecture, ingest, score, resolve, generate, applies to multifamily, industrial, and land. See our multifamily off-market signals case study for a county-records variant.

Related

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Work with us

Want a system like this?

We start with a paid AI audit: we map your workflows, identify where a system like this pays back, and scope the build before any larger commitment. If enablement fits better than software, the AI Team Program trains your team to run AI-native workflows in-house.