Live MLS Data Automation

MLS Signals In.
Agent Calls Out.

Pull active MLS listings, find stale or price-reduced inventory, and email each agent about their exact property.

Built for investors, brokers, and wholesalers who want off-market deal flow without manual list building.

Source: MLS API Filter: 90+ DOM Trigger: Price cuts Outreach: 1:1 email
1Signal Engine

It doesn’t buy lists. It reads the market.

The workflow pulls active listings through licensed MLS data, then filters for real opportunities: long days on market, price cuts, and older homes.

app.mls-pipeline.io / houston / active-residential / filtered
MActive Residential Pull
Idle
⏱Days on market 90+ ↓Price reduced 🏠Year built pre 1995 Single family 0 listings matched
AddressList priceDOMPrice cutYearListing agentEmail
Press Pull active listings to query the MLS
2Why Signals Beat Lists

Generic lists go stale. Signals stay live.

Most investors blast the same agent lists. This machine only contacts agents when their listing creates a real reason to reach out.

📋The generic list
20,000
agents, no context, no reason to open
✗ Same email to everyone
✗ No idea who has a live opportunity
✗ Reads as a blast in two seconds
✗ Burns the sending domain fast
vs
📡The signal feed
✓ 4412 Oak Forest Dr sat 142 days, cut 6%
✓ 910 Sage Brook Ln cut twice, now 118 days
✓ 2207 Briar Hollow built 1981, sitting 97 days
✓ 6033 Beverly Hill St price cut 9% last week
✓ Each agent emailed about their own property
3Personalization

Every email uses listing data.

The machine adds the agent’s name, address, days on market, and price cut, so the email reads like it was written by hand.

app.saleshandy.com / sequences / houston-stale-listings / step-1
To{{agent_email}}
SubjectYour listing at {{address}}
Hi {{first_name}}, I saw your listing at {{address}} has been on the market {{days_on_market}} days, with a price adjustment of {{price_cut}}. I buy in this area and can close without financing contingencies. If the seller would consider an off market sale, I will pay your full buyer side commission on the deal. Worth a short call this week? Reply yes and I will send times.
Pick a listing above to fill the merge fields
4Workflow

MLS API to inbox.

n8n filters and enriches the data. Saleshandy sends the emails across healthy mailboxes. Replies route back to you.

Pull listings
Active residential, every few days
→
Filter and enrich
DOM, price cuts, year, agent contact
→
Personalize and send
Dynamic fields, distributed mailboxes
→
Agent replies
Routed back to you to work
5Activity Model

See what one pull can produce.

Move the sliders to estimate replies and booked conversations. Deals still depend on your offer, buy box, and follow-up.

Stale listings in the pull200
Open rate55%
Reply rate6%
Reply to conversation booked40%
7
Agent replies per pull
3
Conversations booked per pull
6Handoff

We fill the pipeline. You close.

We build the system and generate conversations. Your team takes the calls, negotiates, and closes.

⚙Omni delivers
  • MLS data pipeline and filtering
  • Agent contact enrichment
  • Email copy and personalization
  • Sending infrastructure and warmup
  • Reply routing back to you
👤Your side owns
  • Taking the calls that get booked
  • Your buy box and your numbers
  • Negotiating and closing
  • Following up after the first call
⚠We do not
  • Guarantee a deal volume
  • Project commissions or profit
  • Control how a seller responds
  • Close on your behalf
7Same Engine. New Offer.

Sell to agents too.

Lenders, title companies, photographers, staging companies, and SaaS teams can use the same MLS signals with a different offer.

Lenders
Reach agents with active listings and pending volume who need a reliable financing partner.
Title and escrow
Target agents closing deals in your county and pitch a faster, cleaner closing experience.
Photographers and staging
Find new and dated listings that need better media, and offer to refresh them.
Real estate SaaS
Reach the most active producers in a market with a product pitch tied to their listing activity.
Demo notice: Every listing, address, agent name, email, and number shown here is fictional and built for illustration. The activity model outputs are estimates based on the inputs you set, not guarantees. Omni Online Strategies delivers the data pipeline, infrastructure, and booked agent conversations. Whether a deal closes depends on your buy box, your offer, and your sales process. MLS data access requires a licensed agreement with the relevant MLS or data provider.
Josh Leavitt, Founder of Omni Online Strategies
From the Founder
“The agents already told you which doors to knock on. They put it in the MLS. We just read the signal and get you in front of the right agent.”
Most real estate outreach fails because it treats every agent the same. This machine waits for a reason, then reaches out about the listing that agent wants to move.
Josh Leavitt
Founder and CEO, Omni Online Strategies

Want this in your market?

We build the MLS pipeline, filtering, copy, and sending system. You take the calls.

Build my outreach machine →
MLS access requires a licensed agreement with your MLS or data provider.
About This System
Real Estate Agent Outreach Machine — MLS Data to Agent Inbox
This system pulls active MLS listings, filters for stale inventory and price-reduced properties, and automatically emails each listing agent about their exact property — referencing the address, days on market, and price reduction history pulled directly from the listing. Built for investors, brokers, wholesalers, and B2B service providers targeting real estate agents who need deal flow or agent relationships without manual prospecting. Every email references the specific property the agent has listed, making it the highest-relevance outreach possible in the real estate vertical.
System Facts
CategoryDetail
Outcome DeliveredAutomated personalized outreach to listing agents about their specific active properties — generating deal flow, agent relationships, or service conversations
Who BenefitsReal estate investors seeking off-market deals, brokers recruiting agents, wholesalers building pipelines, mortgage brokers targeting listing agents, B2B service providers selling to real estate professionals
Target AudienceActive listing agents with properties matching defined criteria — stale listings (60+ days on market), price reductions, specific property types or price ranges
Data SourceMLS API — real listing data updated daily with agent contact information
Filter CriteriaDays on market threshold, price reduction percentage, property type, list price range, zip code or radius, number of previous price cuts
Outreach MethodPersonalized cold email referencing the specific property — address, price, days on market, reduction history — deployed via Saleshandy
Personalization LevelEach email generated specifically for that agent's listing — not a template, a property-specific message
Tools InvolvedMLS API, n8n, OpenAI, Saleshandy, Google Sheets, Hunter.io
Sources & Research
NAR: The average US home spends 56 days on market before going under contract — stale listings represent agents under pressure who are receptive to new conversations
National Association of Realtors Market Data · 2024
NAR: There are approximately 1.5 million active Realtors in the US — the largest professionally licensed database addressable via MLS listing data
National Association of Realtors Membership Statistics · 2024
RealTrends: Real estate agents consistently respond better to outreach that references their specific listing rather than generic prospecting messages
RealTrends Agent Engagement Research · 2024
Frequently Asked Questions

An automated system that pulls active MLS listing data, identifies listings matching specific criteria — stale inventory, price reductions, specific property types — finds each listing agent's contact information, and sends a personalized cold email about their exact property. The system runs on a schedule so new listings matching the criteria trigger outreach automatically without any manual prospecting.

MLS listing data is accessed through official MLS API integrations or third-party MLS data providers. The data includes property address, list price, days on market, price history, property details, and the listing agent's name and contact information. This is public listing data — agents submit listings to the MLS specifically to generate exposure.

The most productive targets are listings with 60 or more days on market (agent is under pressure with a motivated seller), listings with one or more price reductions (signals flexibility), expired listings that came back on market (previous deal fell through), and listings in specific price ranges or zip codes matching an investor's acquisition criteria.

The email references the specific property — address, list price, current days on market, and price reduction history. The message demonstrates the sender looked at that exact property, making it the highest-relevance cold email possible. Content varies by use case: investors offer cash conversations, mortgage brokers offer buyer pre-qualification, B2B service providers reference the agent's listing volume.

Agent contact information from MLS data includes the professional email registered with the agent's MLS board. Hunter.io provides an additional verification layer to confirm deliverability before any email is sent.

The system processes 500 to 2,000 listings per day depending on MLS coverage and filter criteria. In major metropolitan markets, hundreds of listings matching typical filter criteria are available daily.

Yes. Many MLS APIs provide access to recently expired and withdrawn listings. Expired listings represent motivated sellers and agents open to new approaches — and are targeted with outreach calibrated to acknowledge the expired status.

How It Works
STEP 01

MLS API queried with filter criteria

n8n queries the MLS API daily with the configured criteria — days on market, price reduction flag, property type, price range, and geographic area.

STEP 02

Listings filtered and agent contact info extracted

Matching listings extracted with the listing agent's name, email, and contact information. Properties already contacted within 90 days excluded via Supabase deduplication.

STEP 03

Email verified via Hunter.io

Each agent's email verified for deliverability before being queued for outreach.

STEP 04

OpenAI generates property-specific email

GPT-4o receives the property details and generates a unique email for that specific listing, referencing the actual property data — address, price, market time, reduction history.

STEP 05

Email loaded into Saleshandy sequence

The personalized email loaded into the configured Saleshandy sequence. Sequence includes 2 to 3 follow-up touches over 10 days if no reply.

STEP 06

Positive replies routed to CRM

Agent replies indicating interest automatically routed to GoHighLevel with property details attached to the contact record.