NOVEL PROPERTIESKC LLC

Buy and hold active in Kansas City, MO-KS. 5 acquisitions, $2.09M tracked volume, $418,498 average price, latest observed Apr 2026.

Kansas City, MO-KSbuy-and-holdorganization
0% cash2 observed lenders$418,498 avgLatest Apr 2026

Investor profile snapshot

NOVEL PROPERTIESKC LLC is a buy-and-hold real estate investor active in Kansas City, MO-KS with 5 recorded acquisitions totaling approximately $2,092,489. Recorded purchases appear concentrated in a tighter local market footprint. The most recent recorded purchase was in April 2026, so the visible activity window still looks fairly recent from a deed-record standpoint.

Recorded purchases skew toward Single Family Residential. Average observed purchase price sits near $418,498, which reads as mid-market acquisition pricing. Behavioral signals point to a noticeable resale or short-hold component and frequent private-lender financing within the recorded deed activity.

Observed window
February 2025 to April 2026

Roughly 14 months of recorded buying activity

Latest recorded purchase
April 2026

Recent enough to suggest the profile is still active

Market footprint
1 state / 1 metro

Activity appears concentrated in a narrower local market footprint

Strategy read
Private-lender active

Derived from observed cash, financing, and flip-like transaction patterns

About this profile

SFR Analytics builds investor profiles from observed deed, mortgage, and assessor records. Use these metrics to compare acquisition patterns, market footprint, and financing signals across investors. Public-record coverage and entity resolution vary by county, so counts represent observed activity in our dataset rather than a complete legal ownership record.

Transaction Summary

5
Transactions
5
Properties
$2.09M
Total Volume
$418,498
Average Price

Property Types

Single Family ResidentialObserved type
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Financing Signal

Recorded mortgage data links this profile to 2 observed lenders. The public page shows a sample; Private Lender Radar is where lender teams can review the full lender history, borrower activity, lender share, and contact workflow.

Sample observed lenders

Showing 2 of 2 observed lender relationships.

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KiaviUnited Lending Fund 1 LLC

Who else does Kiavi fund?

This investor uses 2 observed lenders. See borrower activity, lender market share, and verified contacts in Kansas City, MO-KS.

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Lenders

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Wholesalers & flippers

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Data teams

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Frequently asked questions

What type of real estate does NOVEL PROPERTIESKC LLC focus on?

Public records associated with NOVEL PROPERTIESKC LLC show activity in Single Family Residential across Kansas City, MO-KS.

How many properties are associated with NOVEL PROPERTIESKC LLC?

Public records show approximately 5 properties and 5 recorded transactions associated with NOVEL PROPERTIESKC LLC in this dataset.

Which lenders has NOVEL PROPERTIESKC LLC worked with?

Recent recorded mortgage activity references Kiavi, United Lending Fund 1 LLC. Full lender history is available in SFR Analytics.

How recent is NOVEL PROPERTIESKC LLC's activity?

Records show activity from 2025 through 2026, with the most recent observed purchase recorded in April 2026.

Where is NOVEL PROPERTIESKC LLC active?

Recorded activity is concentrated in Kansas City, MO-KS, with footprint across 1 state and 1 metro in this dataset.

How is associated activity determined?

SFR Analytics matches recorded deeds, mortgages, and assessor records across counties to assemble investor-level activity profiles. Matches use name normalization, address co-occurrence, and entity signals from public records. Profiles are directional - they reflect what is observable in deed-backed data and may not capture every off-record transaction or affiliated entity.

Data sources & methodology

SFR Analytics builds these profiles from recorded deeds, mortgages, assessor records, and entity matching. They are designed for market research: comparing acquisition activity, property focus, market footprint, and financing signals across investors. Coverage varies by county and record type, so counts reflect observed activity in our dataset.