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Vivmark Residential: financing cash flow

Financing cash flow for Vivmark Residential. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All Vivmark Residential financial histories

What this measure means

Net cash from financing activities, including borrowing, repayments and transactions with owners. A positive amount does not establish operating profitability.

Exact concept: us-gaap:NetCashProvidedByUsedInFinancingActivities. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2007-01-01 to 2025-12-31. The SEC response was captured on 2026-09-20.

Selected filing history

Financing cash flow in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-31-1,328,713,000USD2026-02-1310-K · 0001193125-26-051433
2024-01-012024-12-31-376,952,000USD2026-02-1310-K · 0001193125-26-051433
2023-01-012023-12-31-1,120,471,000USD2026-02-1310-K · 0001193125-26-051433
2022-01-012022-12-31-1,785,612,000USD2025-02-1310-K · 0000950170-25-019894
2021-01-012021-12-31-565,056,000USD2024-02-1510-K · 0000950170-24-015907
2020-01-012020-12-31-1,946,393,000USD2023-02-1610-K · 0000950170-23-003060
2019-01-012019-12-31-684,474,000USD2022-02-1710-K · 0001564590-22-005566
2018-01-012018-12-31-963,910,000USD2021-02-1810-K · 0001564590-21-006679
2017-01-012017-12-31-789,818,000USD2020-02-2010-K · 0001564590-20-005562
2016-01-012016-12-31-7,054,092,000USD2019-02-2110-K · 0001564590-19-003683
2015-01-012015-12-31-666,167,000USD2018-02-2210-K · 0001564590-18-002873
2014-01-012014-12-31-692,861,000USD2017-02-2310-K · 0000906107-17-000007
2013-01-012013-12-31-1,420,995,000USD2016-02-2510-K · 0000906107-16-000029
2012-01-012012-12-31-556,331,000USD2015-02-2610-K · 0000906107-15-000007
2011-01-012011-12-31-650,746,000USD2014-02-2710-K · 0000906107-14-000006
2010-01-012010-12-31151,541,000USD2013-02-2110-K · 0000906107-13-000005
2009-01-012009-12-31-1,473,547,000USD2012-02-2410-K · 0000906107-12-000004
2008-01-012008-12-31428,739,000USD2011-02-2410-K · 0000950123-11-017717
2007-01-012007-12-31-801,929,000USD2010-02-2510-K · 0001193125-10-040142

Related financial histories

Inspect the source

Entity
Vivmark Residential / CIK 0000906107
Captured
2026-09-20T05:05:29.936Z
SEC response SHA-256
37c83ad0c835de423704177ce4071e2c62fcc9349f98adb1e86b17762b97c600

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000906107.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))