PARAMOUNT SKYLINE LLC

Buy and hold active in Phoenix-Mesa-Chandler, AZ. 7 acquisitions, $2.85M tracked volume, $406,714 average price, latest observed Nov 2025.

Phoenix-Mesa-Chandler, AZbuy-and-holdorganization
0% cash2 observed lenders$406,714 avgLatest Nov 2025

Investor profile snapshot

PARAMOUNT SKYLINE LLC is a buy-and-hold real estate investor active in Phoenix-Mesa-Chandler, AZ with 7 recorded acquisitions totaling approximately $2,847,000. Recorded purchases appear concentrated in a tighter local market footprint. The most recent recorded purchase was in November 2025, 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 $406,714, which reads as mid-market acquisition pricing. Behavioral signals point to frequent private-lender financing within the recorded deed activity.

Observed window
July 2025 to November 2025

A relatively short observed acquisition window so far

Latest recorded purchase
November 2025

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

7
Transactions
7
Properties
$2.85M
Total Volume
$406,714
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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Renovo FinancialSKGNU LLC

Who else does Renovo Financial fund?

This investor uses 2 observed lenders. See borrower activity, lender market share, and verified contacts in Phoenix-Mesa-Chandler, AZ.

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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 PARAMOUNT SKYLINE LLC focus on?

Public records associated with PARAMOUNT SKYLINE LLC show activity in Single Family Residential across Phoenix-Mesa-Chandler, AZ.

How many properties are associated with PARAMOUNT SKYLINE LLC?

Public records show approximately 7 properties and 7 recorded transactions associated with PARAMOUNT SKYLINE LLC in this dataset.

Which lenders has PARAMOUNT SKYLINE LLC worked with?

Recent recorded mortgage activity references Renovo Financial, SKGNU LLC. Full lender history is available in SFR Analytics.

How recent is PARAMOUNT SKYLINE LLC's activity?

Records show activity in 2025, with the most recent observed purchase recorded in November 2025.

Where is PARAMOUNT SKYLINE LLC active?

Recorded activity is concentrated in Phoenix-Mesa-Chandler, AZ, 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.