Alternative investments have moved from boutique to platform-default in five years. The democratization of access — interval funds, registered closed-end PE/credit, tokenized real estate — has expanded the addressable customer base from 1% of households to closer to 10%. The compliance surface and the customer-suitability surface have expanded along with it, and most platforms serving the accredited-investor market run on test data that doesn't capture the structural complexity of how affluent households actually allocate to alts. The Alt Investment Suitability Dataset is 230 accredited and qualified-purchaser households built for the platforms that need this complexity correctly modeled.
Alts allocation isn't like long-only public-market allocation. Capital is committed in advance and called over multi-year periods. Distributions arrive irregularly with timing that depends on portfolio company exits. Liquidity is bounded by lockups, redemption windows, and gating provisions. Performance is measured by TVPI, DPI, and IRR rather than time-weighted return. Vintage diversification matters more than asset-allocation rebalancing. None of these mechanics are present in standard portfolio-management fixture data, so platforms serving the alts market face a chicken-and-egg problem: the test data doesn't exist until you build it, but you can't build a credible alts platform without test data.
For the carrier and platform side, the suitability surface adds another layer. SEC accreditation thresholds are structured. Qualified Purchaser thresholds add another tier. State-level Blue Sky requirements interact with federal accreditation. Suitability documentation needs to demonstrate understanding of illiquidity, capital-call obligations, and vintage diversification. Software supporting all of this needs structured fixture data — which this Data Set provides.
Validates the platform's portfolio-construction logic across 230 accredited-investor households whose existing alts exposures span the range from $50K interval-fund toes-in-water to multi-million-dollar PE / hedge / credit allocations across multiple vintages.
Tests the firm's accreditation-verification and suitability-documentation workflow against realistic prospects — including the nuanced cases where accreditation is borderline (income just at $200K threshold; recently elevated to QP status) or where suitability documentation needs to demonstrate understanding of illiquidity.
Tests the firm's alts-allocation framework against client profiles whose illiquidity tolerance, vintage exposure, and capital-call commitment-schedule actually match the modal accredited-investor client. Surfaces the cases where naive 'how much should client X allocate to alts' logic produces inappropriate recommendations.
Validates the family office's vintage-diversification analytics against multi-generational families whose existing PE exposure spans 5-10 vintages — testing the tools that surface where the family is over-concentrated and where new commitments would balance the vintage distribution.
Tests the platform's capital-call forecasting against households whose historical call-schedule data lets you validate the model's ability to project upcoming calls based on the fund's calling pattern, the client's commitment, and the vintage's drawdown phase.
The 230 households cluster around accredited-and-qualified-purchaser archetypes: early-career tech employees with equity (A-06) who recently crossed the accreditation threshold; peak-earner corporate executives (P-01); established business owners (P-02); dual high-income professionals (P-03); the full HNW range (H-01, H-02, H-03); estate-planning grantors (E-02) and millennial inheritors (E-01); and real estate investors with portfolio-lender DSCR loans (MB-03).
Every household carries structured accreditation data: SEC accreditation status (income-test, net-worth-test, or other-qualifying basis), Qualified Purchaser status where applicable, and the specific verification documentation. Alts holdings are structured at the position level: each PE / hedge / credit / real-asset position carries the fund name, vintage year, commitment amount, called-to-date, distributed-to-date, NAV, and the vintage-level TVPI, DPI, and IRR. Capital-call schedules are projected forward based on the fund's drawdown profile. Illiquidity-tolerance scoring uses a structured methodology calibrated against the household's overall liquidity profile, time horizon, and behavioral indicators. Lock-up and redemption-window information is structured for hedge fund and interval-fund positions.
The Data Set ships as JSON, CSV, and Parquet. The WealthSynth Methodology PDF documents the alts-position taxonomy, the accreditation-verification methodology, the capital-call modeling approach (with calibration against publicly disclosed PE-fund pacing data), and the illiquidity-tolerance scoring framework.
A redacted summary of one household from this Data Set — names, employers, exact balances, and metro area are stripped. Ages are bucketed, income and net worth are reported as bands. The full record (and all 230 like it) ships in the ZIP.
{
"accreditation.status": <value>,
"accreditation.qp_status": <value>,
"assets.alts.private_equity[]": <value>,
"assets.alts.capital_calls[]": <value>,
"risk.illiquidity_tolerance_score": <value>
}Returns Qualified Purchaser households whose alts allocation doesn't include any QP-only products (Section 3(c)(7) hedge funds, certain commodity pools, larger private-fund offerings) — the planning population for whom the QP threshold's planning value has not yet been realized.
households.filter(h => h.accreditation.qp_status && !h.assets.alts.private_equity.some(p => p.requires_qp) && !h.assets.alts.hedge_funds.some(p => p.requires_qp) )
For each household, returns the projected capital calls in the next 90 days based on each fund's calling pattern and the household's commitment. Useful for liquidity-planning to ensure the household can meet calls without forced sales.
households.flatMap(h =>
h.assets.alts.capital_calls
.filter(c => daysBetween(today(), c.expected_date) <= 90)
.map(c => ({
household: h.id,
fund: c.fund_name,
expected_amount: c.expected_amount,
expected_date: c.expected_date
}))
)Returns households whose PE / hedge fund exposure is concentrated in 1-2 vintages — the vintage-diversification gap that compounds risk if those vintages underperform.
households.filter(h => {
const vintages = h.assets.alts.private_equity
.map(p => p.vintage_year);
const uniqueVintages = new Set(vintages);
return vintages.length >= 3 && uniqueVintages.size <= 2;
})For each household, returns the breakdown of factors driving the illiquidity-tolerance score: liquid-asset coverage of expenses, time horizon, behavioral indicators, and any mitigating factors (existing hedge-fund positions for liquidity demonstration).
households.map(h => ({
id: h.id,
illiquidity_tolerance: h.risk.illiquidity_tolerance_score,
components: {
liquid_coverage: h.liquidity.months_of_expenses,
time_horizon_years: h.planning.alts_time_horizon,
behavioral_factor: h.behavioral.illiquidity_comfort,
existing_alts_pct: h.assets.alts.total / h.assets.total
}
}))Each household's alts profile is generated against archetype-specific allocation patterns calibrated from Cerulli alts-allocation studies and Preqin LP commitment data. Accreditation status is verified against the SEC's income, net-worth, and professional-credential pathways. Vintage distributions reflect realistic LP commitment patterns — newer accredited investors typically have 1-2 vintages; established HNW investors typically have 5-8 vintages spread across asset classes. Capital-call schedules use realistic fund-by-fund drawdown patterns calibrated against ILPA-published pacing data. TVPI, DPI, and IRR figures are sampled from current vintage-year benchmark distributions (Cambridge Associates, Preqin). Illiquidity-tolerance scoring uses a 5-factor methodology covering liquid-asset coverage, time horizon, behavioral comfort, existing-alts experience, and demographic factors. The corpus passes the WealthSynth consistency validator (capital-call math reconciles; vintage performance is internally consistent; accreditation pathways are logically sound) and the LLM-as-judge gate. Annual refresh tracks SEC accreditation rule changes, ILPA reporting standard updates, and current-year vintage performance benchmarks.
Each household's accreditation status is structured with the verification basis (income test of $200K individual / $300K joint sustained over 2 years; net-worth test of $1M excluding primary residence; or professional-credential test for Series 7 / 65 / 82 holders). The structured verification documentation supports the platform's compliance workflow.
Yes. Interval funds, listed BDCs, and other 1940-Act registered structures are classified as 'registered alts' with their realistic redemption-window structures (typically quarterly tender at 5% of fund) rather than as private fund commitments. About 35% of the corpus has at least one registered-alts position alongside private-fund commitments.
Vintage TVPI, DPI, and IRR are sampled from current vintage-year benchmark distributions (Cambridge Associates and Preqin published data). Older vintages have realistic DPI as funds have completed distributions; newer vintages have mostly TVPI with low DPI. The structured fund-level data lets your tools compute the household-level performance correctly.
About 8% of the corpus has tokenized alts positions — typically tokenized real estate or tokenized private credit. The structured data treats these the same as analog private placements (cost basis, valuation, liquidity terms) but flags the digital-asset structural distinction. As the regulatory framework evolves, the corpus will track the changes.
About 3% of the corpus has at least one capital-call default event in their history (typically from the 2020 stress period). The structured default-event data includes the missed-call amount, the consequences (forfeiture vs. interest charge vs. position dilution), and the fund's resolution. Default-event modeling is important for stress-testing platform capabilities.
Yes. About 22% of the corpus's PE positions are co-invest structures alongside primary-fund commitments. The structured co-invest data includes the underlying portfolio company, the co-invest commitment terms, and the lower-fee economics typical of co-invest deals. Co-invest is a growing share of the alts market and the corpus reflects this.
The illiquidity-tolerance score is composed of five factors: liquid-asset coverage of household expenses (longest-tenor factor), planning time horizon for the alts allocation, behavioral comfort with multi-year commitments (calibrated through structured assessment), prior alts experience, and demographic factors. The 0-100 score is a structured proxy for what advisors actually try to measure when documenting alts suitability.
B05 focuses on alts allocation, capital-call mechanics, and accredited-investor suitability. B07 focuses on fee-transparency and fiduciary-rule documentation across the broader fee stack (advisory, fund expense, transaction, tax-drag). They overlap at the fund-fee disclosure seam — alts performance fees and management fees are structured in both bundles. Most affluent-segment platforms buy both.
130 affluent and HNW households with detailed fee structures: AUM-based advisory fees, tiered breakpoints, fund expense ratios, transaction costs, and tax-drag estimates. Includes complex fee arrangements (multi-firm, family-office, performance-based).
110 HNW and UHNW households with estate planning readiness scores, trust structures, gifting histories, charitable giving data, and GST exemption tracking. Complements B09 (Next-Gen Attrition) and B12 (Estate & Trust Planning).
60 founder and early-employee households with QSBS Section 1202 attestation chains, 5-year holding period satisfaction, gross-asset tests at issuance, and stacking via non-grantor trusts. Includes IPO and secondary-sale liquidity events.
Purchases are for internal use only. Redistribution or resale of data is prohibited under the WealthSchema Data License.
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