Datasets:
Decision Log
Append-only. Each entry: date, decision, alternatives considered, rationale, and what would make us revisit it. Autonomous decisions taken without human input are marked [AUTO].
2026-08-10 · D001 · Repository initialised from empty directory [AUTO]
The dedicated project workspace was empty and not a git repository. Initialised
a professional research repository with git init. No pre-existing user work to
preserve.
2026-08-10 · D002 · Python 3.12 via uv [AUTO]
System Python is 3.9.6; no Homebrew, pyenv, or conda. uv 0.12.3 was present as a
pip-installed package at ~/Library/Python/3.9/bin/uv, and a CPython 3.12.13
Apple-Silicon build was already in its toolchain cache. The Makefile resolves
uv from PATH and falls back to that absolute path.
Revisit if: the user installs Homebrew Python or wants 3.13.
2026-08-10 · D003 · Freddie Mac schema taken from the public official layout [AUTO]
file_layout.xlsx and user_guide.pdf are served without registration at
freddiemac.com/fmac-resources/research/pdf/. Both were downloaded and parsed. The
schema in src/lockin/schemas/freddie.py and
data/reference/freddie_llds_layout.yaml reflects the verified current layout:
32 origination fields, 32 monthly performance fields.
Two documented discrepancies recorded in the spec:
- Performance field 12 is "Current Deferred UPB" in
file_layout.xlsxbut "Current Non-Interest Bearing UPB" inuser_guide.pdf. We keep the guide's semantic name with the layout's position. - Performance field 17 is "Expenses" in the layout, "Total Expenses" in older
guides. Position is what matters for parsing.
Revisit if: Freddie Mac publishes a new release;
lockin verify-schemare-checks.
2026-08-10 · D004 · Zero Balance Code → event mapping [AUTO]
From the user guide's official termination-event priority table (1 = highest priority): 15 Whole Loan Sale, 16 RPL Securitization, 09 REO Disposition, 96 Defect prior to Property Disposition, 03 Short Sale or Charge Off, 02 Third Party Sale, 01 Prepaid or Matured (Voluntary Payoff).
Mapping chosen:
01→prepayment(the guide's own label conflates voluntary payoff and maturity, and does not distinguish refinance from sale-related payoff).02,03,09→credit_event.15,16,96→admin_removal, treated as right censoring.
Rationale for censoring 15/16/96: these are Freddie Mac portfolio and representation-and-warranty actions, not borrower decisions. Counting them as prepayment would inflate the prepayment hazard; counting them as "still alive" would be false. Censoring is the least-wrong option and its informativeness is a stated limitation, tested by a robustness cell that instead counts them as prepayment.
Alternative considered and rejected: assigning 02 (Third Party Sale) to a
"sale" event class. Rejected — a third-party sale at foreclosure auction is a
credit outcome, not a voluntary household move, and conflating the two is exactly
the error AGENTS.md §1 forbids.
Revisit if: Freddie Mac adds a code that distinguishes refinance payoff, or if
a linkage to the Relief-Refinance Pre-HARP Loan Sequence Number field is used to
identify some refinances (it identifies only Relief Refinance / HARP chains, not
ordinary refis — see D005).
2026-08-10 · D005 · No refinance-vs-sale split at the loan level [AUTO]
Considered using field 27 (Pre-Relief-Refinance Loan Sequence Number) to link a
payoff to a subsequent Freddie-acquired refinance. Rejected as a general solution:
that field is populated only for Relief Refinance / HARP loans, so it identifies a
policy-program subset, not ordinary refinancing. Building a general "refi chain"
would require matching on property, which the dataset does not permit (postal code
is truncated to 3 digits + "00", and there is no property identifier).
Consequence: the loan-level outcome is prepayment, full stop. Refinance
behaviour is characterised via the refinance incentive measure and via HMDA
refinance origination counts at the market level. This is written up in
reports/loan_hazard_analysis.md §"Refinancing versus mobility".
2026-08-10 · D006 · PMMS URL and methodology regimes [AUTO]
freddiemac.com/pmms/docs/historicalweeklydata.csv now 404s;
freddiemac.com/pmms/docs/PMMS_history.csv serves the weekly history
(1971-04-02 → present; 2,889 observations at fetch time). The adapter holds a
candidate URL list and fails loudly listing every URL tried.
Methodology regimes recorded: lender_survey through 2022-11-10 and
application_based from 2022-11-17, when Freddie Mac changed the PMMS methodology
and discontinued the fees/points and 5/1 ARM series. Confirmed empirically in the
fetched file: pmms30p and all pmms51* columns are blank from that date.
2026-08-10 · D007 · Default geography is state, not MSA or county [AUTO]
State is the only level where all four sources overlap cleanly with no crosswalk
vintage problem: Freddie Property State, FHFA HPI level == "State", HMDA
states= filter, Census BPS state files. MSA is implemented and is the target for
the registered-full run (Freddie field 5 is an MSA/Metropolitan Division code, and
the guide warns that it is not updated for changing MSA definitions — a
versioned crosswalk is required). County is deferred.
Revisit when: a versioned OMB delineation crosswalk is added.
2026-08-10 · D008 · Pre-shock date 2021-12; shock window 2022-01 → 2023-12 [AUTO]
PMMS 30-yr went from ~3.1% (2021-12) to ~7.1% (2023-10) — the largest two-year increase in the series' history. 2021-12 is late enough to capture the full 2020–21 refinance wave in the coupon distribution and early enough to precede the increase. Alternatives 2021-06 and 2022-03 are robustness cells.
2026-08-10 · D009 · HPI concept: purchase-only, quarterly, state [AUTO]
hpi_flavor == "purchase-only", level == "State". Purchase-only excludes
refinance appraisals and is the standard choice for transaction-price research.
All-transactions and expanded-data are available as robustness cells and are
never mixed into the same series.
Frequency corrected after inspecting the fetched master file. FHFA publishes
purchase-only at monthly frequency only for the nation and census divisions; at
State and MSA level purchase-only is quarterly. The original config
default (monthly + State) silently matched zero rows. load_series now raises with
the full list of published (flavor, frequency, level) combinations rather than
returning an empty frame, and PUBLISHED_COMBINATIONS documents them.
Growth rates are computed at the index's published frequency. Where a monthly
index value is needed as an input (the estimated-current-LTV scaling), the
quarterly index is expanded by holding the level constant within the quarter, and
index_basis is suffixed +held-constant-within-quarter so the interpolation
travels with the data. An expanded series is never used as a regression outcome.
2026-08-10 · D010 · Estimated current LTV from origination LTV × HPI path [AUTO]
The performance file has an Estimated Loan-to-Value (ELTV) field (position 26),
but the guide notes it is populated only for a subset of periods/loans. We compute
our own: cur_ltv = (cur_upb / orig_upb) * orig_ltv * (hpi_orig / hpi_t), using
the state purchase-only HPI. We prefer the reported ELTV where present and record
which source was used per row in ltv_source.
Limitation: a state index is a poor proxy for an individual property. Recorded.
2026-08-10 · D011 · Synthetic fixtures, not scraped samples [AUTO]
The Freddie Mac sample files also sit behind the registration wall, so they
cannot be fetched here. Rather than stall, lockin.fixtures generates a synthetic
cohort that is schema-exact (same 32+32 pipe-delimited fields, same sentinel
codes, same ZB semantics) so that every parser, join, event rule, and estimator is
genuinely exercised. Generated with a recorded seed. Every downstream artifact and
report is stamped SYNTHETIC and the report renderer refuses to omit the banner.
No synthetic number is ever described as an empirical finding.
2026-08-10 · D012 · Hazard estimation on an episode table with case-cohort sampling [AUTO]
For the full-data run the loan-month episode table would be billions of rows on a
16 GB machine. Design: keep all exit months and a configurable random sample
of non-exit months, with an offset/weight recorded in the artifact
(sampling_design). For the synthetic slice the full episode table is small
enough that loan_sample_fraction = 1.0.
2026-08-10 · D013 · No IV language [AUTO]
Predetermined exposure is not an instrument. The exclusion restriction — that the
2021 coupon distribution affects 2023 purchase originations only through
lock-in — is not defensible without ruling out that the same 2020–21 refinance
wave proxies a local demand boom. Recorded in
docs/IDENTIFICATION_STRATEGY.md §A4. The design is a conditional
difference-in-differences with continuous predetermined treatment.
Revisit if: a genuinely excludable shifter of the coupon distribution is found.
2026-08-10 · D014 · Refinance originations are a contaminated outcome [AUTO]
A market where everyone refinanced in 2020–21 has both extreme lock-in exposure and an exhausted refi pipeline. Post-2022 refi counts therefore fall mechanically in high-exposure markets, independent of any lock-in mechanism. Refi outcomes are reported but labeled mechanically contaminated; the headline outcome is purchase originations.
2026-08-10 · D015 · HMDA annual data are not interpolated for estimation [AUTO]
HMDA is annual. The local panel is built at monthly frequency for HPI and permits and at annual frequency for HMDA. Event studies on HMDA outcomes run at annual frequency with year fixed effects; monthly interpolation is used only for dashboard display and is flagged in the chart annotation.
2026-08-10 · D016 · Exposure is the frozen coupon distribution × the LATER rate path [AUTO]
A design error caught by a diagnostic. The first implementation measured exposure as the contemporaneous locked-in share at the pre-shock date (2021-12). The exposure distribution collapsed to exactly zero in every state — correctly, because in December 2021 the market rate was near its historic low and essentially nobody was locked in yet. Lock-in is created by the subsequent rate increase acting on the coupon distribution that already existed.
Corrected measure (lockin.stock.predetermined_exposure):
with $\omega_{gk}^{\text{pre}}$ the 2021-12 coupon shares and $\bar R^{\text{post}}$
the mean point-in-time PMMS rate over 2022-01…2024-12. All cross-sectional variation
comes from the frozen local shares; the rate level is a national scalar. Also emitted:
coupon_share_below_{τ} (a pure coupon-share measure with no rate assumption at all)
and coupon_share_hhi (a shift-share concentration diagnostic).
The resulting exposure has real variation (sd ≈ 0.03 on a mean of 0.85) and the
balance table shows it correlates −0.73 with the pre-shock note rate (mechanical)
and −0.38 with 2019–21 price growth (a genuine confound, exactly the threat in
IDENTIFICATION_STRATEGY.md §3.1).
Lesson recorded: a treatment variable with zero variance is a design failure, not an estimation failure. The exposure-distribution artifact is computed and inspected before any event study is interpreted.
2026-08-10 · D017 · HMDA API silently drops unrecognised filters [AUTO]
The CFPB Data Browser aggregations API takes loan_purposes (plural). Passing the
singular loan_purpose does not error — the API ignores it and returns the
all-purpose total. Our first fetch therefore produced identical "purchase" and
"refinance" counts (both were all-purpose totals) which would have become a
fabricated empirical finding.
Fixes: (a) use loan_purposes; (b) _assert_filters_applied() verifies the API
echoed every filter back in its parameters block and refuses to cache a response
that did not, on both fetch and cache read; (c) cache filenames carry a v2
prefix so the bad v1 cells can never be reused. Verified after the fix: AZ refinance
originations peak at 315k (2020) and fall to 27k (2023) while purchase originations
go 132k → 91k — the real refinance boom and bust.
Lesson recorded: when an API accepts a filter without echoing it, assume it was ignored. Assert the echo.
2026-08-10 · D018 · Start-of-month balance for all lock-in measures [AUTO]
Current Actual UPB is the end-of-period balance and is 0 in a zero-balance
month. Using it set the payment gap to zero in exactly the months where an exit
occurs, corrupting the covariate for every event. Lock-in measures now use the
start-of-month balance: the prior month's reported UPB, falling back to the
scheduled amortised balance for a loan's first observed month. upb_timing_source
records which was used per row. This is also the economically correct timing — a
within-month decision is made with the balance you owe going in.
2026-08-10 · D019 · Annual panel spans OUTCOME years, not stock years [AUTO]
The loan performance window starts 2021-01, but HMDA, FHFA HPI, and Census BPS all
reach back to 2018. Building the annual panel only over stock years left no
pre-shock periods, making pre-trends untestable and forcing every result to be
demoted to descriptive. The panel now spans the union of outcome years with the
geography-level frozen exposure attached to pre-shock years too; stock aggregates
are null there and has_stock_data flags which rows have them. Partial permit
years are dropped rather than compared against full-year totals.
2026-08-11 · D020 · The HMDA filter bug changed the headline result [AUTO]
Recorded separately from D017 because the consequence matters independently of the
cause. Before the loan_purposes fix, the pre/post DiD on "log purchase originations"
was −0.026 (s.e. 0.038). After the fix — with genuine purchase-only counts rather
than all-purpose totals compared against themselves — it is +0.001 (s.e. 0.019,
t = 0.05).
So the corrected market-level finding is a null, not a negative-but-noisy estimate. Had the bug survived, this project would have reported a negative coefficient that was an artifact of a silently dropped query parameter.
Three consequences applied:
reports/are generated, so they picked up the corrected numbers automatically. The hand-writtenportfolio/documents did not, and were corrected by hand.- The null is reported as a null. With 26 state clusters, annual HMDA data, and time fixed effects absorbing the common national shock, this design has limited power to detect a cross-state differential — so a null is not a refutation of lock-in, but it is also not suggestive evidence for it.
- A placebo outcome fails. The purchase denial rate moves with t = −1.90. A
significant placebo counts against the design, and the robustness grid flags it as
placebo_FAIL. It is surfaced inreports/failed_hypotheses.mdrather than buried.
Also recorded: es_hpi_growth (pre-trend p = 0.003) and es_log_permits_1unit
(p = 0.007) fail their pre-trend tests and are auto-demoted to descriptive by the
tier logic, with no manual override available.
2026-08-11 · D021 · Empty loan-age bins broke the cluster-robust covariance [AUTO]
Three loan-age dummies (age_60_84, age_84_120, age_120_360) had no observations
in the estimation window, leaving the design matrix rank 14 of 17 columns. A plain GLM
fit tolerates that via a pseudo-inverse, but the cluster-robust sandwich inverts X'X
exactly and failed with a singular matrix — silently downgrading the standard errors to
conventional ones, which are badly understated when loan-months are serially correlated
within a loan. design_matrix now drops zero-variance columns and records them in
dropped_design_columns. Standard errors are now clustered by loan across 10,320
clusters.
Related: lifelines cannot compute Schoenfeld residuals for a left-truncated fit, so
the proportional-hazards diagnostic runs on a separate Cox fit without entry times,
labeled as such in the artifact. That diagnostic reports a large PH violation for
rate_gap_at_entry (p < 0.0001) — expected, since the entry gap becomes a worse proxy
for the current gap as the loan ages, and precisely why the discrete-time rungs with a
time-varying gap are the preferred specifications.
2026-08-11 · D022 · Generated fixtures are not committed [AUTO]
The synthetic performance files are ~32 MB and are deterministic from
mortgage.synthetic_seed, so they are regenerable with make prepare-sample-data and
are gitignored. Their manifest is committed, so the exact fixture set behind any
result stays auditable. A governance test enforces a 5 MB ceiling on tracked files.
2026-08-11 · D023 · mypy: strict core, relaxed analysis modules [AUTO]
The seven core-interface modules (amortization, lockin_measures, config,
provenance, manifest, schemas.*, adapters.base) typecheck strictly and cleanly.
The analysis modules disable arg-type, operator, str-bytes-safe, assignment, and
index, because Polars declares accessors such as Series.max() and DataFrame.item()
as wide unions whose runtime type depends on a column dtype mypy cannot see. Every
flagged site was a float()/int() on a column we constructed as numeric. Narrowing
each with a cast would add dozens of casts asserting what the schema already guarantees
and would hide genuine errors in the noise. Two genuine typing defects were found and
fixed rather than suppressed: a dict typed dict[str, dict] that also held a string,
and write_loan_events returning tuple[object, ...] which erased structure at every
call site.
2026-08-11 · D024 · Closed three documentation-vs-implementation gaps [AUTO]
An audit against the original specification found three places where a document promised something the code did not do. All are now real:
data/reference/freddie_llds_layout.yamldid not exist, despite being cited by D003 and byLICENSE_AND_REDISTRIBUTION.md§1. It is now generated fromlockin.schemas.freddiebymake emit-layout, andtests/test_ingest.py::TestLayoutYamlfails if the two ever disagree. Committing it makes the field map reviewable without reading Python; generating it means the two cannot drift.make reproduce-sampleomittedrobustness, so a fresh clone produced an empty robustness section inreports/failed_hypotheses.md. Added.- Three comments claimed "tested by a robustness cell" for checks that did not
exist (
events.pyon ZB 15/16/96,lockin_measures.pyon the fresh-term payment gap,rates.pyon month-end alignment).lockin.survival.sensitivitynow runs all three and each comment points at the resulting artifact.
The sensitivity results are substantive rather than reassuring:
| cell | coefficient | vs baseline (−0.5248) |
|---|---|---|
| admin removals counted as prepayment | −0.4353 | large_shift |
| fresh-term payment gap | −1.4727 vs −3.2629 remaining-term | large_shift |
| month-end rate alignment | −0.4896 | moderate_shift |
So the censoring choice, the payment-gap term convention, and the within-month rate
timing each move the coefficient by more than one standard error. These are
fragilities that must be stated whenever a magnitude is quoted, and
reports/failed_hypotheses.md §3 now does so.
Lesson recorded: a comment claiming a robustness check is a promise. Prefer citing an artifact path over describing a check in prose.
2026-08-11 · D025 · BLS LAUS unemployment adapter, via the API not the bulk files [AUTO]
Section F of the specification asked for optional local economic control adapters; none
existed, which left the local-labour-shock threat (IDENTIFICATION_STRATEGY §3.4)
merely documented rather than addressed.
lockin.adapters.bls_laus now supplies the seasonally adjusted state unemployment rate
(series LASST{fips}0000000000003), 3,672 state-months over 2018-01…2023-12.
Route chosen and why. The BLS bulk flat files at download.bls.gov return HTTP 403
to a generic client; BLS asks automated downloaders to identify themselves with a
contact email. We do not put the user's personal email address into an outbound
header without being asked, so that route is not used. The public JSON API v2 serves
the same series without a registration key within published unregistered limits (~25
queries/day, 25 series and 20 years per query); three queries cover all 51 states.
Wiring: the panel joins it when present and records its absence when not; the event
study adds unemployment_rate to the control set only if the column exists; nothing
raises. Every event-study artifact now carries an identification_threats map stating,
per threat, whether this run controlled for it — so a reader can see which threats were
addressed and which were merely noted, without opening the strategy document.
Effect on the headline: none. The purchase-originations DiD moves from +0.0010 to +0.0007 (t = 0.03). The null is not an artifact of omitting labour-market conditions.
2026-08-11 · D026 · Robustness grid extended from 34 to 42 cells [AUTO]
Section K listed ~18 robustness axes; the grid covered 11. Added four axes in which the treatment is genuinely rebuilt rather than relabelled:
- Alternative pre-shock dates (2021-06, 2022-03): exposure re-frozen from scratch.
- Alternative market-rate series (
pmms15): exposure re-evaluated at the 15-year post-shock national level, which changes the gap implied by the same coupon distribution. - Alternative HPI concepts (all-transactions, expanded-data): the 2019–21 pandemic-boom control is rebuilt from each concept separately. Concepts are still never mixed within a series.
- Loan sub-samples (purchase originations only, primary residence only, excluding manufactured housing): the frozen coupon shares are recomputed on the restricted loan population.
All eight new cells are insignificant, consistent with the baseline null. The null is therefore robust to the pre-shock date, the rate series, the price-index concept, and the loan population — which is worth more than the same finding from a single specification.
Still not covered, and recorded as such: alternative geography (MSA needs a versioned OMB crosswalk) and stable-servicer samples (servicer names below 1% of quarterly UPB are collapsed to "Other", so the sample is not constructible as intended).
2026-08-13 · D027 · Teleworkable share added, and it exposed two specification bugs [AUTO]
lockin.adapters.teleworkable fetches the Dingel & Neiman (2020) teleworkable
employment share (public replication outputs, no registration) at state and CBSA level.
This closes the last threat that was recorded as UNCONTROLLED on every event-study
artifact: remote-work reallocation of housing demand.
Which of the four published measures. The default is teleworkable_emp, not
teleworkable_manual_emp. The manual pair is the authors' own subjective
classification (Teleworkable_BNJDopinion.csv in their package); the unprefixed pair
applies a fixed rule to O*NET survey responses. A control a sceptical reader can rebuild
beats one that encodes expert judgement. The first draft of this adapter had it
backwards and asserted in a docstring that manual was the paper's headline; the claim
was checked against the authors' README and corrected. All four measures are retained
so the choice is a robustness axis rather than an assertion.
Bug 1 — a control that was never doing anything. The measure is a single
cross-section, so as a level control it is exactly collinear with the geography fixed
effects. Nothing raises: the pseudo-inverse simply splits the coefficient arbitrarily.
Adding the diagnostic for this revealed that pre_hpi_growth_2019_2021 had the same
problem and had been a level control since D006 — so the "pandemic demand
reallocation: CONTROLLED" claim on every prior artifact was hollow. Both now enter as
trend controls, interacted with every non-reference period, and
_demote_degenerate_controls records any such move under degenerate_controls.
Bug 2 — the two halves of an artifact disagreed. The demotion was first added only
to event_study, so did_two_period in the same artifact kept the collinear level
control. The headline DiD and its own dynamic path were estimating different
specifications. The demotion is now shared by both and a test asserts they agree.
Effect on the headline. Decomposed rather than reported as one jump:
| specification | DiD | s.e. | t |
|---|---|---|---|
| as previously published (pre-HPI as level) | +0.0007 | 0.0197 | +0.03 |
| pre-HPI moved to trend (bug fix alone) | −0.0100 | 0.0230 | −0.43 |
| + teleworkable trend (this decision) | +0.0237 | 0.0281 | +0.84 |
| teleworkable trend only | +0.0347 | 0.0258 | +1.34 |
| no controls at all | +0.0010 | 0.0190 | +0.05 |
The null survives all five. No specification reaches |t| > 1.4. But the point estimate moves across roughly two standard errors, which is the honest characterisation of a 26-cluster design: the sign is not pinned down, and the null is a statement about power as much as about lock-in.
Two other outcomes moved. The denial-rate placebo now passes (t = −1.00, was
−1.90) — the previously reported placebo failure was an artifact of the mis-specified
control, and reports/failed_hypotheses.md is updated to say so. log_permits_1unit
still fails its pre-trend (p = 0.066) and stays descriptive.
A guard against the fix creating a new problem. With pre_hpi_growth_2019_2021 as a
trend control, the hpi_growth pre-trend test jumped from p = 0.003 (fail) to p = 0.848
(pass) — because the control is a lagged dependent variable interacted with time and
absorbs the very pre-trend the test looks for. A test that cannot fail is not evidence.
_circular_trend_controls detects a trend control drawn from the same published series
as the outcome and blocks promotion to quasi_experimental regardless of the
p-value. hpi_growth is therefore descriptive in this run, for a stated reason.
2026-08-13 · D028 · Versioned OMB CBSA crosswalk, and how unstable the codes are [AUTO]
lockin.adapters.omb_cbsa downloads six published delineation vintages (2013, 2015,
2017, 2018, 2020, 2023) and builds two tables: a long CBSA/Metropolitan-Division ×
county × vintage crosswalk, and a per-code stability verdict.
Freddie Mac documents that its MSA field is not restated for redelineation, so a loan carries the code in force at origination. Grouping by that field across cohorts pools different geographies wherever OMB moved a boundary. The crosswalk cannot fix this — the county is not in the loan file — but it makes it auditable.
How bad it is, measured rather than assumed. Of 1,054 codes:
| verdict | codes |
|---|---|
| stable across all six vintages | 609 |
| composition changed | 187 |
| absent in some vintage | 188 |
| renamed only (county set identical) | 59 |
| metro ↔ micro reclassification | 11 |
Roughly 42% of codes do not mean the same place throughout. Restricting to metropolitan CBSAs that are composition-stable leaves 215 candidate panel units — still an eight-fold gain in clusters over the 26 states, and the reason this was the highest-value power fix.
Three details that a naive crosswalk gets wrong, each pinned by a test:
- Composition is compared on the county set, not the count. The 2023 Atlanta CBSA swapped Lamar County out for Lumpkin County at a constant 29 counties. A count comparison calls that stable; it is not.
- A rename is not a redefinition. OMB adds and drops principal cities from titles
without moving a boundary, so
renamed_onlycounts as usable. The first draft excluded it, contradicting its own docstring. - Metropolitan Divisions share the field with CBSAs. Freddie's field 5 is "MSA or
Metropolitan Division", both five digits. They are loaded as separate
code_kindentries; folding divisions into their parent would silently rescope eleven large metros.
absent_in_some_vintage is excluded from the default panel but recorded, because
whether it matters depends on the cohort span — a code missing only from the 2013
delineation is harmless for a 2015+ sample.
Dependencies: Census publishes 2023 as .xlsx and every earlier vintage as legacy
.xls, so openpyxl and xlrd>=2 are both required. The header row is found by
searching for CBSA Code rather than hard-coded, because the number of banner rows is
not constant across vintages.
2026-08-13 · D029 · Optional sources are now actually fetched [AUTO]
bls_laus was only ever try_loaded — no command fetched it — while
data/DATA_ACCESS.md §R2 stated it was fetched by make fetch-public-data. A fresh
clone would therefore have run with the local-labour-shock threat UNCONTROLLED while
the documentation said otherwise, and nothing would have complained. fetch-public-data
now fetches all three optional sources, reports them in a separate optional tier, and
prints an explicit warning naming the threat left uncontrolled when one fails.
2026-08-13 · D030 · MSA-level geography: guarded, not yet runnable [AUTO]
With the crosswalk (D028) in place, the fixtures now carry real composition-stable metropolitan CBSA codes drawn from the 2023 delineation, with ~15% left null to keep the non-metro path exercised. Fixtures must not invent five-digit codes: a made-up code would either fail to resolve (making the crosswalk look broken) or collide with a real metro (making a synthetic result look attributable to a real place).
The first MSA run did not fail — and that was the problem. It produced a plausible-looking 182-row panel in which exposure was frozen for 102 MSAs, outcomes came from 26 states, and the teleworkable control matched 0 of 182 rows. Nothing raised, because a left join on disjoint keys is a legal operation. Three defects were behind it:
- The FHFA HPI loader was hard-coded to
level="State"regardless of the config. active_stock.parquethad a single path for both geographies, so a state run and an MSA run silently overwrote each other. It is nowactive_stock_{geography}.parquet.- Nothing checked that a source's keys and the panel's keys were the same kind of thing.
_assert_geography_compatible now runs before every join and raises
GeographyMismatchError on disjoint keys. It deliberately does not subclass
ValueError, because the per-source handlers catch ValueError and downgrade it to
"source unavailable" — correct for a missing file, wrong for a mis-specified run.
Optional sources (LAUS, teleworkable) are dropped with a note naming the threat left
uncontrolled rather than raising.
Result: configs/msa.yaml now stops with an actionable message naming the offending
source and both key shapes. Remaining work is three adapters (HMDA msamds, Census BPS
MSA files, BLS LAUS LAUMT series) plus a rule for the 47 metro CBSAs that span more
than one state. Recorded in STATUS.md §8.
2026-08-13 · D031 · The shipped files do not match the published layout [AUTO]
The registered download (full_set_standard_historical_data.zip, 40 GB, 110 quarterly
cohorts 1999Q1–2026Q1) carries 31 origination and 35 performance fields. Both
official documents — file_layout.xlsx (Last-Modified 2024-04-08) and user_guide.pdf
— describe 32 and 32. The documentation is behind the data.
Guessing a mapping from field names is exactly what AGENTS.md §3 forbids, so the
layout was established against the data and every inference labelled by its support:
- Anchored. Origination 1–24 and performance 1–32 confirmed by a cross-file join on 2021Q4: origination Original Interest Rate equals performance Current Interest Rate at loan age 0 for 1,218 of 1,218 records, zero mismatches.
- Inferred (strong).
Servicer Namemoved origination 25 → performance 34 (observed values are servicer names).MI Cancellation Indicatormoved origination 32 → performance 33 (observed domain{7, N, Y}is the documented domain exactly). - Undocumented. Origination 31 (constant
9999) and performance 35 (blank or0.00) appear in no official document. Parsed asundocumented_position_N, never interpreted, never renamed.
The arithmetic closes on both files, which is what makes the account credible rather than merely possible: 32 − 2 + 1 = 31 and 32 + 2 + 1 = 35.
No research variable depends on an inferred or undocumented field — all of them sit
in the anchored range, and a test asserts that no research variable changes position
between variants. lockin.schemas.variants selects the variant from the observed field
count (modal over a sample, not lines[0], so one truncated row cannot mis-select), and
an unknown count is still a hard blocker.
2026-08-13 · D032 · Three scaling defects the first real run exposed [AUTO]
The 522-million-loan-month ingest surfaced problems that no synthetic run could.
1. Nested archives. The full set is a zip of zips of zips
(full_set.zip → historical_data_YYYY.zip → historical_data_YYYYQn.zip → orig_/perf_*.txt),
and it uses the orig_/perf_ naming rather than the documented
historical_data_/historical_data_time_. discover() now recurses (depth-capped) and
opens intermediate members in place as file objects. Nothing is extracted: the
archive is 40 GB and the machine had 33 GB free. Reading is a seek, not a decompression,
because the inner zips are stored uncompressed. Discovery of all 220 members takes 0.09 s.
2. A global sort that nearly filled the disk. build_loan_events sorted all 522 M
rows by loan_seq_no. It spilled ~20 GB of Polars scratch and took the disk from 26 GB
free to 5.8 GB before being stopped. The collapse is per-loan, loan sequence numbers
encode their own cohort, and the interim tables are already hive-partitioned by cohort —
so it is now done one cohort at a time and concatenated. Identical result, bounded
scratch, and the step went from "spilling 20 GB after 9 minutes" to 61 seconds.
3. data/interim is shared across run profiles. Running a sample command after a
full ingest reads the full dataset while stamping artifacts with the sample profile's
digest and its SYNTHETIC data class — a licence problem as well as a provenance one.
This is not hypothetical: it happened here, and triggered the same 522 M-row sort a
second time. lockin.dataset_stamp now records the writing profile and readers refuse a
mismatch. Unstamped directories pass, since pre-existing data is not evidence of a
mismatch.
Also fixed: cfg.data_class returns REGISTERED, which is not in the manifest
vocabulary {PUBLIC, RESTRICTED, SYNTHETIC, DERIVED}, so every manifest write in a
registered run raised. Never caught before because no registered run had ever reached
that line. cfg.manifest_data_class maps it to RESTRICTED — the same fact, stated as
redistribution status, which is what a manifest reader needs.
First real numbers. 40 cohorts (2013Q1–2022Q4), performance filtered to 2021-01…2024-12: 521,991,736 loan-months, 14,987,949 loans — 4,529,704 prepayments (30.2%), 7,665 credit events (0.1%), 10,450,580 censored (69.7%). 56.6% of loans are left-truncated, and the diagnostic separates the two causes: 9.27 M enter in the first month of the performance window (a window artifact) rather than through Freddie Mac's acquisition lag. Ingest 8 minutes at ~2.05 M lines/s; 4.9 GB of Parquet.
2026-08-13 · D033 · First fully empirical run, and the sampling that made it fit [AUTO]
The whole pipeline now runs on the registered Freddie Mac Standard dataset. 36 of 37
artifacts are stamped REGISTERED; the loan-level numbers below are the first in this
repository that are evidence rather than software verification.
Loan-level prepayment hazard — the headline empirical result. Discrete-time logit, 3,808,045 loan-months, 187,401 prepayment events, standard errors clustered across 240,757 loans:
| coef | s.e. | z | hazard ratio | |
|---|---|---|---|---|
rate_gap |
−0.2020 | 0.0018 | −112.2 | 0.8171 |
Each additional percentage point of rate gap multiplies the monthly prepayment hazard by about 0.82 — roughly an 18% lower monthly exit hazard per point of lock-in. The complementary log-log link gives −0.1990 on the same data, and a 15% loan sample gave −0.1993 on 11.4M loan-months, so the estimate is stable across link function and sample size.
Tier is hazard_association, not causal, and the vocabulary rules in AGENTS.md §1
still bind: Zero Balance Code 01 pools voluntary payoff, sale-related payoff and
maturity, so this is an effect on mortgage exits, not on moves or home sales.
Market-level — the pre-trends fail, and the tier system demoted them. 51 clusters now (real data covers every state plus DC), against 26 on fixtures:
| outcome | DiD | s.e. | t | pre-trend p | tier |
|---|---|---|---|---|---|
| log purchase originations | −0.0238 | 0.0109 | −2.18 | 0.0000 FAIL | descriptive |
| log refinance originations | −0.0789 | 0.0269 | −2.93 | 0.0000 FAIL | descriptive |
| house price growth | −0.0040 | 0.0021 | −1.96 | 0.509 | descriptive (circularity guard) |
| log single-family permits | −0.0264 | 0.0230 | −1.15 | 0.974 | quasi_experimental |
| log 5+-unit permits (placebo) | +0.0302 | 0.0552 | +0.55 | 0.072 FAIL | descriptive |
| purchase denial rate (placebo) | −0.0019 | 0.0012 | −1.57 | 0.004 FAIL | descriptive |
The purchase-originations coefficient is now "significant" at t = −2.18 — and must not be read causally, because its pre-trend test fails at p < 0.0001. High- and low-exposure states were already diverging before the shock. The automatic demotion did exactly the job it was built for: the one outcome that passes its pre-trend, single-family permits, is also the one that is insignificant.
Why the run is sampled, and by how much. Sized by disk, not RAM. Peak RSS across the ten hazard stages stayed under 7 GB, but Polars streaming scratch accumulates between stages: at a 15% loan sample it walked the disk from 76 GB free to 18 GB and falling before being stopped. The unsampled episode table is 90.6M loan-months and its dense design matrix is ~21.7 GB, which OOM-killed the estimator outright.
survival.loan_sample_fraction was documented as the knob for exactly this and never
implemented — its only appearance in the codebase was an error message advising the
user to lower it, which would have done nothing. It now works: a plain random sample of
loans, every month of each selected loan retained, applied at episode build. At 0.05 the
episode table is 4,519,525 rows and the whole pipeline completes in minutes.
Two draws now thin the data, so the inverse-probability weight is the reciprocal of the
total inclusion probability: 1/loan_frac for an exit month and
1/(loan_frac × non_event_frac) otherwise. Weighting for only the case-cohort draw, as
the code did before, would have understated the population by a factor of 20.
reports/ is a shared path too. A governance test asserted the synthetic banner by
loading configs/sample.yaml and checking the markdown on disk — which belonged to
whichever profile last rendered. render_all now stamps the reports directory, and the
test asks the directory what produced it instead of assuming.
2026-08-13 · D034 · validate-data did not scale to the real dataset [AUTO, RESOLVED]
Every other stage completes on the registered data in minutes. validate-data ran for
over thirty minutes without finishing and was stopped. Disk stayed flat at ~28 GB free
throughout, so it is not spilling — it is doing per-loan work that does not scale, most
likely one of the whole-table .collect() calls in lockin.episodes.validate_episodes
or lockin.events.validate_events.
Resolved. Timing each section individually rather than guessing found it at once:
everything except performance.validate finished in under two minutes, and that one
sorts the whole table by loan_seq_no twice — 522 million rows. Same defect and same fix
as D032: the checks are all per-loan (duplicate loan-months, more than one Zero Balance
Code, loan-age monotonicity, month gaps), loans never span cohorts, so it now iterates
cohort partitions and sums. 30+ minutes and unfinished → 149 seconds. Full
validate-data now completes in about 4.5 minutes and all 37 artifacts are REGISTERED.
2026-08-13 · D035 · Two validation gates that were lying, in opposite directions [AUTO]
Once validate-data could finish, it reported 4 HARD problems. Two were mine and two
were real, and they needed opposite treatment.
Self-inflicted: the profile stamp broke the checksums it sat next to. sha256_dir
excluded *.manifest.json from the directory digest but not the .lockin_profile.json
written by D032, so adding the stamp made both interim datasets report a checksum
mismatch — a HARD error about data that had not changed. Sidecars that describe a
dataset are now excluded as a named group, and the original checksums verify again.
Real, and mis-graded: source-data noise was failing the run. The registered files
genuinely contain 35 loans (of 20,199,214) with orig_loan_term of 481–544 months and
one loan with a 0.0 note rate. These are properties of Freddie Mac's data, not parsing
faults — and as unconditional HARD errors they would fail every real run forever, which
is how a gate stops being read.
Severity is now scaled by prevalence against DOMAIN_VIOLATION_HARD_SHARE (1e-4), and
the observed values are reported either way. The rule is principled rather than
convenient: a systematic layout shift or parse error corrupts a large fraction of a
column and still stops the run; 35 impossible records in 20 million is noise to be
recorded, not a reason to distrust the other 20,199,179. Lowering a severity to make a
run pass would be the wrong instinct, so the threshold is a documented constant with
tests on both sides of it.
2026-08-13 · D036 · MSA-level HMDA has two silent-zero traps [AUTO, BLOCKING #3]
Probing the CFPB aggregations API before writing the adapter — the discipline D017 was
opened for — found that msamds is accepted and echoed back, and still returns zero
in two distinct situations. Neither raises. A naive MSA-level fetch would have produced a
well-formed panel in which the largest metros in the country had no lending at all.
Trap 1: HMDA reports Metropolitan Divisions, not their parent MSA.
| code | area | 2022 purchase originations |
|---|---|---|
| 16984 | Chicago-Naperville-Evanston MD | 89,212 |
| 12060 | Atlanta (no divisions) | 94,726 |
| 16980 | Chicago MSA (the parent) | 0 |
| 35620 | New York MSA | 0 |
| 31080 | Los Angeles MSA | 0 |
Every divided metro — New York, Los Angeles, Chicago, Dallas, Miami, Washington, Boston,
Detroit, Philadelphia, San Francisco, Seattle, Minneapolis — returns zero under its CBSA
code. This is exactly why lockin.adapters.omb_cbsa loads divisions as first-class
entries with a code_kind discriminator (D028); a crosswalk that folded them into their
parents would have made this trap undetectable.
Trap 2: HMDA year Y uses the OMB delineation in force in year Y.
Two division codes returned zero despite being valid divisions: 11694
(Arlington-Alexandria-Reston) and 12054 (Atlanta-Sandy Springs-Roswell). Both exist
only in the 2023 vintage of the crosswalk — they are new divisions, and 2022 HMDA
data predates them. 11244 (Anaheim) and 14454 (Boston), present since 2015, return
real counts.
Consequence for #3. MSA-level HMDA is not "pass msamds instead of states". It
requires resolving each geography per year against the delineation in force for that
year, and it requires an explicit non-zero assertion: a metropolitan area with zero
purchase originations in a year is not a real observation, it is a failed lookup. Both
belong in the adapter, alongside the existing _assert_filters_applied echo check.
Recorded now rather than discovered later, because the failure mode is a plausible number rather than an error.
2026-08-13 · D037 · Year-versioned HMDA geography resolution [AUTO]
Implements what D036 established. omb_cbsa.vintage_for_hmda_year and
omb_cbsa.hmda_geographies resolve each analysis CBSA to the code HMDA will actually
answer on for that year, and hmda.fetch_msa uses them.
The year→vintage mapping is empirical, not derived from an effective-date rule — OMB bulletins reach HMDA with a lag this project could not find documented, and a wrong guess returns silent zeros:
| HMDA year | vintage | how it was located |
|---|---|---|
| 2018 | 2017 | Chicago answers on division 16974, present only in the 2015/2017 vintages |
| 2019–2023 | 2018 | Chicago answers on 16984; 16974 returns 0 from 2019 on |
| 2024+ | 2023 | Atlanta division 12054 and Arlington 11694 appear; Atlanta CBSA 12060 and Washington division 47894 drop to 0 |
Divisions are keyed back to their parent CBSA by shared counties — not by a code
prefix, since division codes are not derived from their parent's — so the panel unit
stays stable even though the queried code does not. Verified live: Boston 14454 →
parent 14460, Chicago 16984 → 16980, undivided Atlanta and Phoenix pass through,
all returning real counts.
_assert_metros_are_not_empty is the guard that makes the whole thing safe to trust. A
metropolitan area with zero purchase originations in a year is a failed lookup, not an
observation, so more than 5% empty in any single year raises. The check is per-year
on purpose: a wrong vintage boundary breaks exactly one span, and pooling across years
would dilute a total failure in 2024 below any sensible threshold.
2026-08-13 · D038 · Metropolitan permits, and the mirror image of the HMDA trap [AUTO]
census_bps.fetch_metro adds the Building Permits Survey at metropolitan geography.
Two things had to be checked rather than assumed, and both turned out to matter.
The series is published in two directories, split at January 2024.
Metro (ending 2023)/ma{YYMM}{v}.txt and CBSA (beginning Jan 2024)/cbsa{YYMM}{v}.txt.
The split is not cosmetic: the 2024+ files are delineated on the 2023 OMB bulletin —
Chicago is "Chicago-Naperville-Elgin IL-IN" there against "IL-IN-WI" before, and
Atlanta is renamed. That is the same delineation change, in the same year, that HMDA
makes (D037). Two independent federal series moving together is a useful cross-check on
the empirically-derived HMDA year mapping, which was established from a different kind of
evidence entirely.
BPS reports the parent CBSA and never a Metropolitan Division — the exact opposite of
HMDA. Chicago is 16980 here, New York 35620, Los Angeles 31080: the very codes
that return a silent zero from the HMDA API. Had both adapters been written on the
assumption that "MSA code" means one thing, the panel would have joined divisions to
parents under a shared column name and nobody would have seen it.
Both adapters therefore key to the parent CBSA, and the difference is absorbed inside
each adapter rather than left for the panel builder to trip over. HMDA carries
report_code alongside so what was actually queried stays auditable.
Verified live on 2022: 384 metros, 12 months, and permit counts that behave the way the places do — Atlanta 25,961 single-family units against Chicago's 8,020.
2026-08-13 · D039 · Metropolitan LAUS, and a self-inflicted silent-empty bug [AUTO]
bls_laus.fetch_metro completes the third and last MSA adapter. All 393 metropolitan
CBSAs resolve; 2021–22 rates behave as the places do (Los Angeles 6.40%, New York 6.01%,
Chicago 5.42%, Atlanta 3.46%).
Metro LAUS is not seasonally adjusted, and the state series is. LASMT… — the
adjusted counterpart of the LASST… series this project already uses — returns zero
observations for every metro tried; only LAUMT… exists. So the state panel carries an
adjusted rate and the metro panel an unadjusted one. These are different
measurements and must not be pooled. The annual panel averages twelve consecutive months,
which removes most of the seasonality and is a legitimate annual rate; the monthly
metro series is not comparable to the monthly state series and is labelled as such in
METRO_SEASONALITY_NOTE and in the manifest.
The identifier carries a state FIPS, which is undefined for multi-state CBSAs — 43 of the 393. Rather than guess the rule (principal city? largest share? first alphabetically?) every state the CBSA spans is tried and whichever answers is kept: 444 candidate series, 18 API queries, inside the unregistered daily allowance.
And the first draft of it was wrong in exactly the way the adapter is designed to
avoid. metro_series_id emitted 19 characters — five trailing zeros where the area code
needs six. BLS answers a malformed identifier with an empty series, not an error,
which is indistinguishable from a metro with no labour force; every one of the 393 would
have come back empty and the "try every state" logic would have reported that no state
worked. Caught by comparing against the identifier verified live before any code was
written. The function now asserts the 13-character area code, and a test pins both the
length and the two verified identifiers.
2026-08-13 · D040 · Metro sources wired into the panel; request volume constrained [AUTO]
build_local_panel now selects the metropolitan loader for each source when
panel.geography == "msa": census_bps.load_metro, bls_laus.try_load_metro,
hmda.load_msa, and FHFA at level="MSA". The state path is untouched — the metro
branch exits through a sentinel rather than restructuring the try blocks, so the default
geography keeps the exact shape every existing test exercises.
A first MSA panel would cost ~12,350 HMDA requests against ~1,530 for the state
panel — 393 metros × 6 years × 5 measures, against a free public service. fetch_msa
therefore takes restrict_to. A metro with no loans in the Freddie sample contributes a
null exposure and is dropped by the panel builder regardless, so fetching it buys
nothing. The adapter does not read the loan tables itself, so the caller supplies the
set; passing division codes or a wrong vintage raises rather than silently returning an
empty panel.
Still open before an MSA run can be believed: the multi-state allocation rule. 43 of the 393 metropolitan CBSAs span more than one state, and while all four panel sources now resolve at metro level, nothing yet decides how a state-published quantity is apportioned when one is needed.
2026-08-13 · D041 · Freddie's MSA field is half Metropolitan Divisions [AUTO]
The multi-state allocation rule flagged as the last blocker for #3 turned out to be
moot: every panel source now resolves natively at CBSA level — FHFA at level="MSA",
HMDA through the year-versioned division mapping, BPS and LAUS at parent CBSA,
teleworkable by CBSA. Nothing is published only by state any more, so nothing needs
apportioning. Recorded because it was on the plan and is now off it.
The real blocker was elsewhere, and larger. Resolving the 452 distinct MSA codes present in the registered loan data against the crosswalk:
| what the code is | codes | loans |
|---|---|---|
| metropolitan CBSA, composition stable | 208 | 5,260,786 |
| Metropolitan Division | 37 | 4,657,458 |
| metropolitan CBSA, composition changed | 105 | 4,263,976 |
| metropolitan CBSA, renamed only | 47 | 2,933,059 |
| in no loaded vintage (retired) | 31 | 898,908 |
4.66 million loans carry a division code, because Freddie's field 5 is "MSA or Metropolitan Division". Unmapped they match nothing in the Census permit series or LAUS, both of which report parent CBSAs — so the panel would silently lose New York, Los Angeles, Chicago, Dallas and Washington rather than fail.
omb_cbsa.to_parent_cbsa maps them by shared counties, since the codes bear no
relation to each other: Chicago's division is 16984 under parent 16980, Boston's is 14454
under 14460. Verified against those landmarks plus New York 35614→35620 and Los Angeles
31084→31080. 421 of 452 codes resolve, covering 17,225,055 of 18,124,095 loans (95%),
onto 395 parent CBSAs. The 31 unresolved codes stay null and are dropped, never guessed
at — assigning a retired code to a neighbouring metro would be fabrication.
The loan sample thins the panel, and the threshold hides it. min_loans_per_geography
counts sampled loans, so at loan_sample_fraction=0.05 a nominal 100 is really ~2,000
real loans. That is what takes the MSA panel from 395 metros to 138 — still 2.7× the
51 states, but the gap is the sampling and not the geography. The stock manifest now
records the nominal threshold, the fraction, the unsampled equivalent, and how many units
were dropped, so the number is visible rather than surprising.
And a cache that answered the wrong question. bls_laus.fetch_metro returned any
existing file without checking it covered the requested years, so a panel asking for
2018–2023 silently got a 2021–2022 file cached by an earlier probe. Both LAUS fetchers now
compare cached years against requested ones and refetch on a shortfall.
2026-08-13 · D042 · First MSA-level run: 8× the clusters, and it did not rescue identification [AUTO]
The MSA panel runs end to end on registered data: 138 metropolitan CBSAs against 51 states. Compared with the state run on the same loans and the same shock:
| outcome | state (51 clusters) | MSA (138 clusters) | MSA pre-trend | tier |
|---|---|---|---|---|
| log purchase originations | −0.024 (t = −2.18) | −0.042 (t = −4.93) | 0.036 FAIL | descriptive |
| log refinance originations | −0.079 (t = −2.93) | −0.124 (t = −4.68) | 0.000 FAIL | descriptive |
| log single-family permits | −0.011 (t = −0.53) | −0.074 (t = −1.61) | 0.636 pass | quasi_experimental |
| log 5+-unit permits (placebo) | −0.037 (t = −0.65) | +0.029 (t = +0.37) | 0.940 pass | placebo passes |
| purchase denial rate (placebo) | −0.002 (t = −1.57) | −0.001 (t = −0.93) | 0.297 pass | placebo passes |
The cluster gain bought precision, not identification. The purchase-originations
t-statistic more than doubles and the magnitude nearly doubles — and the pre-trend test
still fails, so the artifact is still descriptive and the number still must not be read
causally. High- and low-exposure metros were already diverging before the shock, exactly
as high- and low-exposure states were. That the same failure appears at two very
different levels of aggregation is itself informative: it is not an artifact of pooling
states.
Both placebos now pass, where the denial rate failed at state level. More clusters made the placebos better behaved, which is the expected direction and mild evidence that the state-level placebo failure was a small-cluster artifact.
The one outcome that passes its pre-trend, single-family permits, remains insignificant. Nothing at market level supports a causal lock-in claim at either geography.
Known gaps in this run, recorded rather than papered over: FHFA publishes a purchase-only quarterly index for only 65 of the 138 metros, so the house-price row rests on half the panel; BLS refused further requests ("daily threshold ... has been reached") so metro LAUS covers only 2021–2022 and the cached-coverage guard correctly reports the control as UNAVAILABLE rather than serving a short panel; and the monthly MSA specifications are skipped for want of a monthly outcome at that geography.
2026-08-13 · D043 · An "ok" result carrying null coefficients [AUTO]
log_permits_5plus came back from the MSA run with status: "ok" and coef: null.
Thirty-four of 826 metro-years authorised no 5+-unit buildings at all, log(0) is -inf,
and the NaN propagated through OLS to a null coefficient that the estimator still called
a success.
Fixed at both ends. The log transform now maps non-positive counts to null rather
than -inf, so the observation is dropped honestly by the existing drop_nulls;
log1p was deliberately not used, because it would quietly redefine the outcome from
a log to a log-of-count-plus-one while keeping the name. And did_two_period now refuses
to return status: "ok" when the design or outcome contains non-finite values, or when
the fitted coefficient is not finite — a degenerate fit is reported as a failure with a
reason instead of as a result full of nulls. With both in place the cell estimates
normally: +0.029, t = +0.37, pre-trend 0.94.
2026-08-14 · D044 · Publish the research system, never the registered data [AUTO]
The public release is deliberately split across four surfaces: a versioned GitHub
source package for reviewable code, a permanent GitHub Pages narrative, a versioned
Hugging Face Dataset prefix, and a Dataset-backed Space. Canonical destinations and
catalog metadata live in project.yaml; the inclusion boundary lives in
docs/PUBLICATION.md.
The package is built from tracked files only. It includes source, tests, configuration,
synthetic fixtures, documentation, portfolio material, and aggregate reports. It
excludes data/raw, data/interim, data/processed, data/cache, and outputs in
their entirety. This is not only a size decision: the registered Freddie Mac archive and
every loan-granular derivative are non-redistributable. Aggregate coefficients and
reports remain publishable because they contain no loan records and retain the required
population, evidence-tier, and attribution language.
The website headline reports the registered-data hazard association and the failed market-level identification checks together. Publishing only the strong loan-level association would invite a causal or mobility reading the data do not support; the public page therefore gives equal prominence to the pre-trend failures, the null permits result, and the rule that prepayment is not a home sale or household move.