Year-in-preview: 12 signals shipping in 2026

ausdata.io shipped 23 endpoints in its first six months. The next 12 months are about depth, not breadth, adding cross-source signals that answer the questions our existing customers keep asking variants of.

This post lays out 12 candidate endpoints we're scoping for the rest of 2026. Treat this as a roadmap, not a commitment. Order, naming, and exact scope will move based on what early users actually integrate vs. wave at.

The pattern that earns a spot on this list:

1. The component data exists in our nine LIVE sister MCPs
2. No source agency publishes the composed version
3. We've heard the question from ≥3 customers or evidenced prospects

Anything that fails (1) is out of scope (see post #23 on what we don't have). Anything that fails (2) is a wrapper, not a signal, and we won't bother. Anything that fails (3) is a vanity feature.

<!-- IMG: roadmap timeline, 12 endpoints arranged across Q2/Q3/Q4 2026 with dependency arrows -->

The 12

1. `mortgage-affordability-by-postcode (planned)`

Composes: ABS household income (5206.0) × RBA F5 lending rates × ABS dwelling price index. Returns the % of median local income consumed by a P&I repayment on the median-priced dwelling in the postcode, quarterly.

Why we'd ship it: brokers, fintechs, and policy shops ask for this constantly. The hard part is the postcode-level dwelling price approximation, we'd use the published SA4 price index scaled by ABS Census median dwelling value, with a precision_warning field flagging that postcode-resolution price is an estimate, not a measurement. Tier: Q3 2026.

2. `disposable-income-real (planned)`

Composes: ABS household disposable income × ABS CPI All-groups. Returns quarterly real household disposable income per capita, in 2026 dollars.

Why we'd ship it: the most-requested "real X" signal after /v1/real-rate-regime and /v1/real-wages. Trivially derivable but everyone re-derives it themselves with subtly different deflator choices. We'd standardise on All-groups CPI and document the choice. Tier: Q2 2026.

3. `inflation-vs-expectations (planned)`

Composes: ABS CPI YoY × Melbourne Institute consumer inflation expectations × RBA market-implied breakeven inflation (where derivable from bond yields). Returns the gap between realised, surveyed, and market-pricing inflation.

Why we'd ship it: monetary-policy commentary lives on this triangle. The signal abstraction is meaningful because the three series come from three different conventions and lining them up is genuinely fiddly. Tier: Q3 2026.

4. `business-confidence-decomposed (planned)`

Composes: NAB business survey conditions (where derivable from RBA F-tables that reproduce NAB headline) × ABS business indicators × ABS retail trade. Returns a confidence index alongside its component drivers, so commentary can say "the headline rose 4 points, driven entirely by profitability, not employment."

Why we'd ship it: AU finance journalism re-decomposes this every survey release. Tier: Q4 2026.

# what calling this would look like
from ausdata import Ausdata
api = Ausdata()
bcd = api.business_confidence_decomposed(period="2026-Q1")
print(bcd["data"]["headline"], bcd["data"]["drivers"])
# {'employment': 0.2, 'profitability': 3.1, 'forward_orders': 0.7}

5. `retail-spend-by-category (planned)`

Composes: ABS Retail Trade (8501.0) sliced by industry × ABS CPI deflated to real. Returns nominal and real retail spend per capita by category, with rolling-12-month seasonality strip.

Why we'd ship it: every consumer-facing brand wants this for board decks. Tier: Q3 2026.

6. `labour-market-tightness (planned)`

Composes: ABS labour force (employment, unemployment, participation, hours) × ABS job vacancies × ABS WPI growth. Returns a single tightness score plus the four-way decomposition.

Why we'd ship it: "the labour market is tight" is the most-repeated phrase in AU economic commentary and the operational definition is never given. We'd publish ours and let users disagree. Tier: Q3 2026.

7. `state-export-mix (planned)`

Composes: ABS international trade (5368.0) sliced by state of origin × ABS Australian-industry codes. Returns each state's top-N export categories with YoY change.

Why we'd ship it: state-government policy shops repeatedly rebuild this from scratch. The dataset exists; the slicing is annoying. Tier: Q2 2026.

8. `super-tax-impact (planned)`

Composes: ATO taxation statistics × ABS earnings × policy parameters (concessional cap, Div 293 threshold, etc.). Returns marginal vs effective tax rate including super for a stated income, with the underlying assumptions exposed.

Why we'd ship it: every super reform cycle, commentary needs this and there's no canonical calculator. We'd ship the calculator with all knobs exposed and explicit policy-parameter dating so historical scenarios are reproducible. Tier: Q4 2026.

# rough shape
api.super_tax_impact(
    income=180_000,
    salary_sacrifice=15_000,
    div293_applies=True,
    policy_year=2026,
)

9. `welfare-payment-real-value (planned)`

Composes: Services Australia / DSS published payment rates (sourced via AIHW reporting) × ABS CPI. Returns the real value of JobSeeker, Age Pension, Family Tax Benefit, etc. over time in current-year dollars.

Why we'd ship it: every welfare-policy commentary cycle needs this and rebuilds it. The political sensitivity is exactly why a transparent, citation-clean version is useful. Tier: Q4 2026.

10. `skilled-migration-flow (planned)`

Composes: ABS overseas arrivals & departures × Department of Home Affairs published visa-grants aggregates (where data.gov.au exposes them) × ABS labour force by occupation. Returns net skilled-migration flow with occupation breakdown.

Why we'd ship it: workforce planners, state economic strategy units, sector lobby groups. Caveat: the occupation breakdown depends on a Home Affairs dataset whose machine-readability is patchy, we'd ship with whatever resolution that source actually publishes, with explicit occupation_resolution field. Tier: Q4 2026, soft.

11. `SME-credit-conditions (planned)`

Composes: APRA monthly ADI statistics (small-business lending stock) × RBA F-tables (small-business lending rates) × ABS business demography (new entries, exits). Returns a composite SME-credit-conditions index plus components.

Why we'd ship it: the SME-credit story is told constantly without a clean composite. APRA's MADIS is under-cited (see post #16); pairing it with rate and demography data is exactly the kind of thing a signal surface unlocks. Tier: Q3 2026.

12. `yield-curve-real (planned)`

Composes: RBA F2 Australian government bond yields × ABS trimmed-mean CPI. Returns the nominal AGS yield curve and the inflation-deflated real curve at standard tenors (2y, 5y, 10y, 30y).

Why we'd ship it: AU fixed-income commentary keeps re-deriving real yields with inconsistent deflator choices. Same logic as `disposable-income-real (planned)`, pick a standard and document it. Tier: Q2 2026.

api.yield_curve_real(date="2026-05-15")
# returns {"nominal": {...}, "real": {...}, "deflator": "trimmed-mean-cpi"}

Tiering and reality

By tier:

| Tier | Endpoints |

|--|--|

| Q2 2026 | disposable-income-real, state-export-mix, yield-curve-real |

| Q3 2026 | mortgage-affordability-by-postcode, inflation-vs-expectations, retail-spend-by-category, labour-market-tightness, SME-credit-conditions |

| Q4 2026 | business-confidence-decomposed, super-tax-impact, welfare-payment-real-value, skilled-migration-flow |

We'll ship Q2 endpoints with high confidence. Q3 are funded and scoped. Q4 are aspirational, if user demand redirects us, three of those four will get shuffled out and replaced.

What this isn't

This isn't a feature pre-sale. No endpoint here is purchasable yet and we don't take pre-orders. If something on this list is critical to your use case, tell us via [email protected], three customer emails for the same endpoint moves it up a tier.

Also, this list deliberately excludes things ausdata.io will never ship: commercial-data wrappers (CoreLogic, Kyckr), per-property data, real-time NEM trading feeds. Those gaps are covered in post #23 and we're not changing the scope.

How we pick the 13th

We won't extend the list speculatively. The 13th signal will be whichever of these earns three independent customer requests first:

• `regional-house-price-growth (planned)` (capital-city only, sub-state is CoreLogic territory)
• `childcare-cost-real (planned)`
• `aged-care-bed-supply (planned)`
• `agricultural-export-prices (planned)`

Vote with your support tickets.

Pricing

When these ship, they'll be available on every tier, Free, Analyst, Pro, Enterprise. We don't gate endpoints behind tiers; tiers gate volume.

• Free: 500 calls/month
• Analyst: $29/mo, 10k calls
• Pro: $99/mo, 100k calls

Grab a key at ausdata.io.

Sources

• ABS 5206.0, National Accounts
• RBA F5, Indicator Lending Rates
• APRA Monthly ADI Statistics (MADIS)
• ATO Taxation Statistics
• Melbourne Institute Consumer Inflation Expectations