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Where Can I Buy Real Estate Leads

DATABASE ID: LOCAL-WHER | RECORDS: 50,000 | LAST VERIFIED: TODAY
IDCompanyWebsiteKey ContactTitleVerified EmailIntent Score
00001Company 186 LLCwww.company1.comExec Name 1CEOexec1@company1.com91
00002Company 221 LLCwww.company2.comExec Name 2Founderexec2@company2.com86
00003Company 377 LLCwww.company3.comExec Name 3VP of Marketingexec3@company3.com97
00004Company 369 LLCwww.company4.comExec Name 4Founderexec4@company4.com88
00005Company 301 LLCwww.company5.comExec Name 5CEOexec5@company5.com92
00006Company 11 LLCwww.company6.comExec Name 6VP of Marketing••••••••••••@••••.com93
00007Company 49 LLCwww.company7.comExec Name 7CEO••••••••••••@••••.com92
00008Company 771 LLCwww.company8.comExec Name 8Founder••••••••••••@••••.com81
00009Company 273 LLCwww.company9.comExec Name 9VP of Marketing••••••••••••@••••.com95
00010Company 805 LLCwww.company10.comExec Name 10Founder••••••••••••@••••.com91
00011Company 865 LLCwww.company11.comExec Name 11CEO••••••••••••@••••.com99
00012Company 711 LLCwww.company12.comExec Name 12VP of Marketing••••••••••••@••••.com80
00013Company 892 LLCwww.company13.comExec Name 13CEO••••••••••••@••••.com96
00014Company 227 LLCwww.company14.comExec Name 14Founder••••••••••••@••••.com89
00015Company 23 LLCwww.company15.comExec Name 15VP of Marketing••••••••••••@••••.com97
00016Company 643 LLCwww.company16.comExec Name 16Founder••••••••••••@••••.com80
00017Company 610 LLCwww.company17.comExec Name 17CEO••••••••••••@••••.com86
00018Company 370 LLCwww.company18.comExec Name 18VP of Marketing••••••••••••@••••.com94
00019Company 220 LLCwww.company19.comExec Name 19CEO••••••••••••@••••.com93
00020Company 87 LLCwww.company20.comExec Name 20Founder••••••••••••@••••.com83
00021Company 986 LLCwww.company21.comExec Name 21VP of Marketing••••••••••••@••••.com93
00022Company 782 LLCwww.company22.comExec Name 22Founder••••••••••••@••••.com87
00023Company 993 LLCwww.company23.comExec Name 23CEO••••••••••••@••••.com99
00024Company 447 LLCwww.company24.comExec Name 24VP of Marketing••••••••••••@••••.com86
00025Company 546 LLCwww.company25.comExec Name 25CEO••••••••••••@••••.com82
00026Company 126 LLCwww.company26.comExec Name 26Founder••••••••••••@••••.com96
00027Company 290 LLCwww.company27.comExec Name 27VP of Marketing••••••••••••@••••.com98
00028Company 987 LLCwww.company28.comExec Name 28Founder••••••••••••@••••.com93
00029Company 724 LLCwww.company29.comExec Name 29CEO••••••••••••@••••.com85
00030Company 416 LLCwww.company30.comExec Name 30VP of Marketing••••••••••••@••••.com98
00031Company 698 LLCwww.company31.comExec Name 31CEO••••••••••••@••••.com93
00032Company 415 LLCwww.company32.comExec Name 32Founder••••••••••••@••••.com82
00033Company 814 LLCwww.company33.comExec Name 33VP of Marketing••••••••••••@••••.com96
00034Company 623 LLCwww.company34.comExec Name 34Founder••••••••••••@••••.com95
00035Company 127 LLCwww.company35.comExec Name 35CEO••••••••••••@••••.com80
00036Company 82 LLCwww.company36.comExec Name 36VP of Marketing••••••••••••@••••.com92
00037Company 26 LLCwww.company37.comExec Name 37CEO••••••••••••@••••.com88
00038Company 386 LLCwww.company38.comExec Name 38Founder••••••••••••@••••.com99
00039Company 501 LLCwww.company39.comExec Name 39VP of Marketing••••••••••••@••••.com87
00040Company 141 LLCwww.company40.comExec Name 40Founder••••••••••••@••••.com89
00041Company 508 LLCwww.company41.comExec Name 41CEO••••••••••••@••••.com93
00042Company 78 LLCwww.company42.comExec Name 42VP of Marketing••••••••••••@••••.com95
00043Company 615 LLCwww.company43.comExec Name 43CEO••••••••••••@••••.com80
00044Company 558 LLCwww.company44.comExec Name 44Founder••••••••••••@••••.com97
00045Company 946 LLCwww.company45.comExec Name 45VP of Marketing••••••••••••@••••.com92
00046Company 294 LLCwww.company46.comExec Name 46Founder••••••••••••@••••.com96
00047Company 929 LLCwww.company47.comExec Name 47CEO••••••••••••@••••.com98
00048Company 682 LLCwww.company48.comExec Name 48VP of Marketing••••••••••••@••••.com99
00049Company 20 LLCwww.company49.comExec Name 49CEO••••••••••••@••••.com81
00050Company 644 LLCwww.company50.comExec Name 50Founder••••••••••••@••••.com80
[ 49,950 ROWS LOCKED ]
>_ SYSTEM.ANALYSIS.LOG_

Scraping real estate leads from MLS is a nightmare. They block every datacenter IP range, require captcha solving, and the public records are scattered across thousands of county sites. We finally cracked the MLS via a loophole in their IDX feed—now we pull listings and agent info directly. It's not perfect, but it's the closest thing to raw truth.

The biggest issue was deduplication. Agents list the same property on multiple platforms, and we had to write a fuzzy matching algorithm to merge entries. It took three rewrites to get the recall down to 99%. Also, we had to parse hundreds of date formats and address quirks. Our codebase is held together by duct tape, but it works.

We also pull from tax assessor sites and county recorder offices. Those are a pain—some are PDFs, some are HTML tables, some are PDFs inside HTML tables. We automated the parsing with a mix of OCR and custom scrapers. The data is raw, messy, but it's the real deal. You won't find this in a polished CRM—this is the ground truth.

Data Schema & Dictionary

property_address

The full street address of the property. If you're skipping this column, you're not selling real estate leads. It's the key to geocoding and cross-referencing.

owner_name

The recorded owner's name from public records. Often it's a trust or LLC, so don't expect a friendly human name. But it's the first step to finding who to call.

owner_mail_address

The mailing address for the owner, which often differs from the property address. This is gold for direct mail campaigns. If it's a PO box, you know they're not living there.

property_type

Single-family, condo, multi-family, etc. Filters your list to what you actually care about. Nobody wants to pitch a duplex if they only buy single-families.

estimated_value

Our rough valuation based on comparable sales and tax assessment. It's not Zillow, but it's a ballpark. Use it to rank leads by potential profit.

mortgage_amount

The outstanding loan amount from public records. If the mortgage is high and the owner is late on payments, you've got a motivated seller. This column is the sneaky filter.

last_sale_date

The date the property last sold. If it's been 20 years, the owner might have huge equity and be ready to move. If it sold last month, they're probably not selling again.

listing_status

For-sale-by-owner, expired listing, or not listed at all. Expired listings are gold—they've already tried and failed to sell, so they're likely to listen to a different approach.

Frequently Asked Questions

How fresh is this data?
We scrape it on the 1st of every month. If it bounces, tell us and we swap the credit. Simple. But don't expect real-time updates—this is a CSV, not an API. If you need live feeds, you're barking up the wrong tree.
Can I get a sample before buying?
Sure, we'll send you 100 rows of raw data. But don't be a pain about it—we're not giving you the whole dataset for free. If you want to see the quality, we'll show you enough to make a decision.
How many rows are in the full CSV?
We've got about 1.2 million rows covering 15 major metros. It's a lot of data, but it's raw. You'll need to clean it up to match your own CRM. We're not doing that for you.

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