DIAZ GUADALUPE ARIELA

Cash-heavy investor in Homosassa Springs, FL. 8 acquisitions, $320,000 tracked volume, $40,000 average price, latest observed Aug 2025.

Homosassa Springs, FLbuy-and-holdperson
100% cashNo observed lender$40,000 avgLatest Aug 2025

DIAZ GUADALUPE ARIELA is a buy-and-hold real estate investor active in Homosassa Springs, FL with 8 recorded acquisitions totaling approximately $320,000. Recorded purchases appear concentrated in a tighter local market footprint. The most recent recorded purchase was in August 2025, so the visible activity window still looks fairly recent from a deed-record standpoint.

Recorded purchases skew toward Vacant Land. Average observed purchase price sits near $40,000, which reads as entry-level to lower-middle price points. Behavioral signals point to cash-heavy execution within the recorded deed activity.

Observed window
August 2025 to August 2025

A relatively short observed acquisition window so far

Latest recorded purchase
August 2025

Still fairly recent, even if buying pace may be uneven

Market footprint
1 state / 1 metro

Activity appears concentrated in a narrower local market footprint

Strategy read
Cash-heavy

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.

Financing Signal

This profile shows cash-tagged activity without an observed lender in the recorded mortgage data. For lenders, that is not just a missing field; it can point to an active borrower prospect whose financing relationship is not visible in the public-record trail.

Cash-tagged purchases
100%
Observed lenders
0

Find cash-heavy borrower prospects in Homosassa Springs, FL

No lender relationship is visible in this profile's recorded mortgage data. Cash-heavy investors like this can be borrower-prospecting signals for lenders in Homosassa Springs, FL.

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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 DIAZ GUADALUPE ARIELA focus on?

Public records associated with DIAZ GUADALUPE ARIELA show activity in Vacant Land across Homosassa Springs, FL.

How many properties are associated with DIAZ GUADALUPE ARIELA?

Public records show approximately 8 properties and 8 recorded transactions associated with DIAZ GUADALUPE ARIELA in this dataset.

Which lenders has DIAZ GUADALUPE ARIELA worked with?

Recorded mortgage data linking DIAZ GUADALUPE ARIELA to specific lenders is limited in this dataset. Cash purchases or out-of-record financing may explain gaps.

How recent is DIAZ GUADALUPE ARIELA's activity?

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

What entities is DIAZ GUADALUPE ARIELA associated with?

DIAZ GUADALUPE ARIELA is associated with corporate or trust entities used to acquire property. Entity-resolution depth varies by state and recording office; SFR Analytics surfaces matched entities where the underlying public records make the relationship inferable.

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.