Datasets:
Tasks:
Tabular Classification
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
economics
quantitative-finance
causal-inference
macroeconomics
housing-economics
market-microstructure
License:
| """Report generation. | |
| Every file written here begins with a ``GENERATED`` header and must not be | |
| hand-edited. Reports are built **only** from result artifacts in ``outputs/``, so | |
| every number in a report is traceable to a reproducible artifact with provenance. | |
| Two hard rules, enforced in code with no override flag: | |
| 1. If any input artifact has ``data_class == "SYNTHETIC"``, the synthetic banner is | |
| rendered. :func:`_assert_banner` raises otherwise. | |
| 2. Causal language is only emitted for artifacts whose ``evidence_tier`` is | |
| ``quasi_experimental`` **and** whose pre-trend test passed. Everything else gets | |
| "is associated with" / "under the model". :func:`verb_for` is the single place | |
| that decision is made. | |
| """ | |
| from __future__ import annotations | |
| from datetime import UTC, datetime | |
| from pathlib import Path | |
| from typing import Any | |
| import polars as pl | |
| from lockin.artifacts import list_artifacts, try_read_artifact | |
| from lockin.config import Config | |
| SYNTHETIC_BANNER = """> ⚠️ **SYNTHETIC DATA.** The loan-level numbers in this report were computed from | |
| > synthetic fixtures generated for engineering tests (`lockin.fixtures`, seed | |
| > recorded in the manifest). They are **not** empirical findings about U.S. | |
| > mortgages — they recover the parameters of this repository's own | |
| > data-generating process. The public aggregate series in this report (Freddie Mac | |
| > PMMS, FHFA HPI, HMDA, Census Building Permits Survey) **are** real. | |
| > | |
| > To produce empirical loan-level results, complete the registration in | |
| > `data/DATA_ACCESS.md` §R1 and re-run. See `AGENTS.md` §1 and | |
| > `data/LICENSE_AND_REDISTRIBUTION.md` §4.""" | |
| ATTRIBUTION = """**Data sources.** Freddie Mac Single-Family Loan-Level Dataset (registered; subject | |
| to Freddie Mac terms of use; not redistributed here) — or labeled synthetic fixtures | |
| where indicated. Freddie Mac Primary Mortgage Market Survey. Federal Housing Finance | |
| Agency House Price Index. Home Mortgage Disclosure Act data via the Consumer | |
| Financial Protection Bureau Data Browser. U.S. Census Bureau Building Permits Survey. | |
| Retrieval timestamps and file checksums are recorded in the manifests accompanying | |
| each result artifact.""" | |
| VOCAB_NOTE = """**Outcome vocabulary.** `prepayment` means Freddie Mac Zero Balance Code 01, | |
| *"Prepaid or Matured (Voluntary Payoff)"*. It conflates voluntary payoff with | |
| scheduled maturity and does **not** distinguish a refinance from a sale-related | |
| payoff. It is **not** a home sale and **not** a household move. No field in the | |
| source data supports either of those events, and this project never constructs one.""" | |
| def verb_for(art: dict[str, Any] | None) -> str: | |
| """The only permitted verb phrase for an artifact, given its tier and diagnostics.""" | |
| if art is None: | |
| return "could not be estimated" | |
| tier = art.get("evidence_tier") | |
| if tier == "simulation": | |
| return "under the model, changes" | |
| if tier == "quasi_experimental": | |
| es = art.get("result", {}).get("event_study", {}) | |
| pt = es.get("pretrend_test", {}) if isinstance(es, dict) else {} | |
| if pt.get("passes_at_alpha_0.10"): | |
| return "reduced" if _post_sign(art) < 0 else "increased" | |
| return "is associated with a change in" | |
| if tier == "hazard_association": | |
| return "is associated with" | |
| return "describes" | |
| def _post_sign(art: dict[str, Any]) -> float: | |
| es = art.get("result", {}).get("event_study", {}) | |
| v = es.get("mean_post_effect") if isinstance(es, dict) else None | |
| return -1.0 if (v is not None and v < 0) else 1.0 | |
| def _fmt(x: Any, nd: int = 4) -> str: | |
| if x is None: | |
| return "—" | |
| if isinstance(x, bool): | |
| return "yes" if x else "no" | |
| if isinstance(x, (int,)): | |
| return f"{x:,}" | |
| if isinstance(x, float): | |
| if x != x: | |
| return "—" | |
| return f"{x:,.{nd}f}" | |
| return str(x) | |
| class ReportContext: | |
| """Collects the artifacts a report needs and tracks whether any input is synthetic.""" | |
| def __init__(self, cfg: Config) -> None: | |
| self.cfg = cfg | |
| self.used: list[dict[str, Any]] = [] | |
| self.any_synthetic = False | |
| def get(self, group: str, name: str) -> dict[str, Any] | None: | |
| art = try_read_artifact(self.cfg, group, name) | |
| if art is not None: | |
| self.used.append(art) | |
| if art["provenance"]["data_class"] == "SYNTHETIC": | |
| self.any_synthetic = True | |
| return art | |
| def header(self, title: str, subtitle: str) -> str: | |
| ts = datetime.now(UTC).isoformat(timespec="seconds") | |
| prov = self.used[0]["provenance"] if self.used else {} | |
| lines = [ | |
| f"# {title}", | |
| "", | |
| "<!-- GENERATED by `make report` (lockin.reporting.render). DO NOT HAND-EDIT. -->", | |
| "", | |
| f"*{subtitle}*", | |
| "", | |
| "| | |", | |
| "|---|---|", | |
| f"| Generated | `{ts}` |", | |
| f"| Git commit | `{prov.get('git_commit', 'unknown')}` |", | |
| f"| Config | `{prov.get('config_name', '?')}` (digest `{prov.get('config_digest', '?')}`) |", | |
| f"| Data class | **{prov.get('data_class', '?')}** |", | |
| f"| Data period | `{prov.get('data_period', '?')}` |", | |
| f"| Artifacts used | {len(self.used)} |", | |
| "", | |
| ] | |
| if self.any_synthetic: | |
| lines += [SYNTHETIC_BANNER, ""] | |
| return "\n".join(lines) | |
| def sources_table(self) -> str: | |
| prov = self.used[0]["provenance"] if self.used else {} | |
| sv = prov.get("source_versions", {}) | |
| if not sv: | |
| return "" | |
| rows = [ | |
| "", | |
| "### Source versions", | |
| "", | |
| "| dataset | schema@retrieved#checksum |", | |
| "|---|---|", | |
| ] | |
| for k, v in sorted(sv.items()): | |
| rows.append(f"| `{k}` | `{v}` |") | |
| return "\n".join(rows) + "\n" | |
| def artifact_index(self) -> str: | |
| rows = [ | |
| "", | |
| "### Artifacts this report was built from", | |
| "", | |
| "| artifact | tier | population | weight |", | |
| "|---|---|---|---|", | |
| ] | |
| for a in self.used: | |
| rows.append( | |
| f"| `{a['group']}/{a['artifact']}` | `{a['evidence_tier']}` | " | |
| f"{a['population'][:70]}… | {a['weight'][:40]} |" | |
| ) | |
| return "\n".join(rows) + "\n" | |
| def _assert_banner(ctx: ReportContext, text: str) -> None: | |
| """Refuse to write a report that consumed synthetic data without the banner.""" | |
| if ctx.any_synthetic and "SYNTHETIC DATA" not in text: | |
| raise RuntimeError( | |
| "a report consumed SYNTHETIC artifacts but does not render the synthetic " | |
| "banner. There is no flag to disable this check." | |
| ) | |
| def _write(cfg: Config, name: str, ctx: ReportContext, body: str) -> Path: | |
| text = ( | |
| ctx.header(*_TITLES[name]) | |
| + body | |
| + "\n" | |
| + ctx.artifact_index() | |
| + ctx.sources_table() | |
| + "\n---\n\n" | |
| + ATTRIBUTION | |
| + "\n" | |
| ) | |
| _assert_banner(ctx, text) | |
| out = cfg.path("reports", f"{name}.md") | |
| out.parent.mkdir(parents=True, exist_ok=True) | |
| out.write_text(text) | |
| return out | |
| _TITLES: dict[str, tuple[str, str]] = { | |
| "technical_report": ( | |
| "Technical Report: Mortgage Rate Lock-In, Housing Liquidity, and Local Market Dynamics", | |
| "Full technical synthesis. Every section states its evidence tier.", | |
| ), | |
| "executive_housing_policy_memo": ( | |
| "Executive Memo: Mortgage Rate Lock-In", | |
| "For a policy audience. Answers ten questions and states what is not known.", | |
| ), | |
| "loan_hazard_analysis": ( | |
| "Loan-Level Duration Analysis", | |
| "Kaplan–Meier, cumulative incidence, discrete-time hazards, competing risks, Cox, " | |
| "and a predictive benchmark. Tier: descriptive and hazard-association only.", | |
| ), | |
| "local_market_event_study": ( | |
| "Local-Market Event Study", | |
| "Continuous-treatment event study on predetermined lock-in exposure, with " | |
| "pre-trends and placebos.", | |
| ), | |
| "demand_supply_decomposition": ( | |
| "Demand versus Supply Decomposition", | |
| "Why lock-in reduces transactions unambiguously but has an ambiguous price effect.", | |
| ), | |
| "policy_counterfactuals": ( | |
| "Policy Counterfactuals", | |
| "Model-dependent scenario projections. Not forecasts.", | |
| ), | |
| "methodology_and_limitations": ( | |
| "Methodology and Limitations", | |
| "What was built, what the data can and cannot support, and where the design fails.", | |
| ), | |
| "failed_hypotheses": ( | |
| "Failed and Fragile Specifications", | |
| "Specifications that did not survive. Recorded, not buried.", | |
| ), | |
| "replication_protocol": ( | |
| "Replication Protocol", | |
| "How to reproduce every number, and what cannot be reproduced without registered data.", | |
| ), | |
| "benchmark_comparison": ( | |
| "Benchmark Comparison", | |
| "Our estimands against published lock-in research. Nothing here is an exact replication.", | |
| ), | |
| } | |
| # --------------------------------------------------------------------------- | |
| # individual reports | |
| # --------------------------------------------------------------------------- | |
| def render_loan_hazard(cfg: Config) -> Path: | |
| ctx = ReportContext(cfg) | |
| ds = ctx.get("hazards", "survival_dataset") | |
| km = ctx.get("hazards", "km_prepayment") | |
| cif = ctx.get("hazards", "cif_competing_risks") | |
| logit = ctx.get("hazards", "dt_logit_prepayment") | |
| cloglog = ctx.get("hazards", "dt_cloglog_prepayment") | |
| credit = ctx.get("hazards", "dt_logit_credit_event") | |
| base = ctx.get("hazards", "baseline_hazard") | |
| gap = ctx.get("hazards", "gap_profile_nonlinear") | |
| het = ctx.get("hazards", "heterogeneity") | |
| cox = ctx.get("hazards", "cox_ph_prepayment") | |
| gbm = ctx.get("hazards", "predictive_benchmark") | |
| b: list[str] = [VOCAB_NOTE, ""] | |
| b += ["## 1. Population and event construction", ""] | |
| if ds: | |
| r = ds["result"] | |
| ll = r["loan_level"] | |
| b += [ | |
| f"- **{_fmt(ll['n_loans'])}** loans; " | |
| f"**{_fmt(ll['n_prepayments'])}** prepayments, " | |
| f"**{_fmt(ll['n_credit_events'])}** credit events, " | |
| f"**{_fmt(ll['n_censored'])}** censored.", | |
| f"- **{_fmt(ll['n_left_truncated'])}** loans are left truncated " | |
| f"(median entry loan age {_fmt(ll['median_entry_age'], 1)} months). Risk sets " | |
| "exclude loans not yet observed at each age.", | |
| f"- Estimation sample: {_fmt(r['estimation_sample']['n_rows'])} loan-months over " | |
| f"`{r['estimation_sample']['period']}`; out-of-time sample " | |
| f"{_fmt(r['out_of_time_sample']['n_rows'])} loan-months over " | |
| f"`{r['out_of_time_sample']['period']}` (split at " | |
| f"`{r['out_of_time_split']}`).", | |
| f"- Sampling design: `{r['estimation_sample']['sampling_design']['scheme']}`.", | |
| "", | |
| "**Event taxonomy** (from the official Zero Balance Code priority table):", | |
| "", | |
| "| class | codes | treatment |", | |
| "|---|---|---|", | |
| "| `prepayment` | 01 | event — pools refinance, sale-related payoff, and maturity |", | |
| "| `credit_event` | 02, 03, 09 | competing event |", | |
| "| `censored` | none, or 15, 16, 96 | right censoring — 15/16/96 are Freddie Mac " | |
| "portfolio and representation-and-warranty actions, not borrower decisions |", | |
| "", | |
| ] | |
| else: | |
| b += ["*Artifact `hazards/survival_dataset` unavailable.*", ""] | |
| b += [ | |
| "## 2. Descriptive survival and cumulative incidence", | |
| "", | |
| "*Evidence tier: `descriptive`.*", | |
| "", | |
| ] | |
| if km: | |
| r = km["result"] | |
| o = r["overall"] | |
| if o["survival"]: | |
| b += [ | |
| f"- Kaplan–Meier over {_fmt(len(o['times']))} distinct exit ages; " | |
| f"survival at the last observed age is **{_fmt(o['survival'][-1], 3)}**.", | |
| f"- {o['competing_risks_treatment']}", | |
| ] | |
| groups = list(r["by_entry_rate_gap_bucket"]["groups"]) | |
| if groups: | |
| b += ["", "Curves are stratified by the rate gap at first observation:", ""] | |
| b += [f" - {g}" for g in groups] | |
| b += [ | |
| "", | |
| "Stratifying controls for nothing: loans in different gap buckets were " | |
| "originated in different years to different borrowers at different LTVs.", | |
| "", | |
| ] | |
| if cif: | |
| f = cif["result"]["final_cif"] | |
| b += [ | |
| "", | |
| "**Cumulative incidence** (Aalen–Johansen, not 1−KM):", | |
| "", | |
| f"- prepayment: **{_fmt(f.get('cause_1'), 4)}**", | |
| f"- credit event: **{_fmt(f.get('cause_2'), 4)}**", | |
| "", | |
| f"> {cif['result']['why_not_one_minus_km']}", | |
| "", | |
| ] | |
| b += [ | |
| "## 3. Discrete-time hazard models", | |
| "", | |
| "*Evidence tier: `hazard_association`. These are conditional correlations.*", | |
| "", | |
| ] | |
| for art, label in ((logit, "Logit"), (cloglog, "Complementary log-log")): | |
| if not art: | |
| continue | |
| r = art["result"] | |
| b += [ | |
| f"### {label} — prepayment", | |
| "", | |
| f"{_fmt(r['n_obs'])} loan-months, {_fmt(r['n_events'])} events, " | |
| f"{_fmt(r['n_loans'])} loans. Standard errors: {r['standard_errors']}. " | |
| f"AIC {_fmt(r['aic'], 1)}.", | |
| "", | |
| "| term | coef | s.e. | hazard ratio |", | |
| "|---|---|---|---|", | |
| ] | |
| for c in r["coefficients"]: | |
| if c["term"].startswith("age_"): | |
| continue | |
| b.append( | |
| f"| `{c['term']}` | {_fmt(c['coef'])} | {_fmt(c['std_err'])} | " | |
| f"{_fmt(c['hazard_ratio'], 3)} |" | |
| ) | |
| ame = r.get("rate_gap_average_marginal_effect_monthly") | |
| if ame is not None: | |
| b += [ | |
| "", | |
| f"**Average marginal effect of the rate gap: {_fmt(ame, 6)}** — " | |
| f"{r['rate_gap_ame_interpretation']}.", | |
| ] | |
| b += ["", f"> {r['interpretation_warning']}", ""] | |
| if credit: | |
| r = credit["result"] | |
| rg = next((c for c in r["coefficients"] if c["term"] == "rate_gap"), None) | |
| b += [ | |
| "### Competing risk — credit events (cause-specific hazard)", | |
| "", | |
| f"{_fmt(r['n_obs'])} loan-months, {_fmt(r['n_events'])} credit events.", | |
| ] | |
| if rg: | |
| b.append(f"Rate-gap coefficient {_fmt(rg['coef'])} (s.e. {_fmt(rg['std_err'])}).") | |
| b += [ | |
| "", | |
| "A cause-specific coefficient does not translate directly into an effect on " | |
| "cumulative incidence: a covariate can raise one cause-specific hazard while " | |
| "lowering the other cause's CIF.", | |
| "", | |
| ] | |
| b += ["## 4. Duration dependence and the nonlinear rate-gap profile", ""] | |
| if base: | |
| rows = base["result"]["prepayment"]["rows"] | |
| b += [ | |
| "**Empirical monthly prepayment hazard by loan-age bin** (no covariates):", | |
| "", | |
| "| age bin | loan-months | events | hazard |", | |
| "|---|---|---|---|", | |
| ] | |
| for r0 in rows: | |
| b.append( | |
| f"| `{r0['age_bin']}` | {_fmt(r0['n_at_risk'])} | {_fmt(r0['n_events'])} | " | |
| f"{_fmt(r0['hazard'], 5)} |" | |
| ) | |
| b += [ | |
| "", | |
| "This age profile mixes genuine duration dependence with cohort and " | |
| "calendar-time composition and should not be read as pure seasoning.", | |
| "", | |
| ] | |
| if gap: | |
| emp = gap["result"]["prepayment"]["empirical"] | |
| b += [ | |
| "**Empirical monthly prepayment hazard by rate-gap bucket:**", | |
| "", | |
| "| rate-gap bucket | loan-months | events | hazard | mean gap (pp) |", | |
| "|---|---|---|---|---|", | |
| ] | |
| for r0 in emp: | |
| b.append( | |
| f"| {r0['label']} | {_fmt(r0['n_at_risk'])} | {_fmt(r0['n_events'])} | " | |
| f"{_fmt(r0['hazard'], 5)} | {_fmt(r0['mean_rate_gap'], 2)} |" | |
| ) | |
| b += [ | |
| "", | |
| "The binned specification uses *0 to +100 bp* as the reference bucket, so " | |
| "each coefficient reads as 'relative to a barely locked-in loan'.", | |
| "", | |
| ] | |
| b += ["## 5. Heterogeneity", ""] | |
| if het: | |
| r = het["result"] | |
| b += [ | |
| "Pre-specified subgroups: initial note rate, loan age, current LTV, credit " | |
| "score, loan balance, occupancy, loan purpose. Exploratory subgroups are " | |
| "labeled separately in the artifact and carry no multiplicity correction.", | |
| "", | |
| ] | |
| for name, rows in (r.get("prespecified") or {}).items(): | |
| if not isinstance(rows, list) or not rows or "error" in rows[0]: | |
| continue | |
| b += [ | |
| f"**{name}**", | |
| "", | |
| "| group | loans | prepayments | monthly hazard | mean gap |", | |
| "|---|---|---|---|---|", | |
| ] | |
| for r0 in rows: | |
| b.append( | |
| f"| {r0.get('group')} | {_fmt(r0.get('n_loans'))} | " | |
| f"{_fmt(r0.get('n_prepayments'))} | " | |
| f"{_fmt(r0.get('monthly_prepay_hazard'), 5)} | " | |
| f"{_fmt(r0.get('mean_rate_gap'), 2)} |" | |
| ) | |
| b.append("") | |
| b += [f"> {r.get('note', '')}", ""] | |
| b += ["## 6. Cox proportional hazards and PH diagnostics", ""] | |
| if cox: | |
| r = cox["result"] | |
| if r.get("status") in ("skipped", "failed"): | |
| b += [f"*Not estimated: {r.get('reason')}*", ""] | |
| else: | |
| b += [ | |
| f"{_fmt(r['n_obs'])} loans, {_fmt(r['n_events'])} events, concordance " | |
| f"{_fmt(r['concordance'], 4)}.", | |
| "", | |
| f"> {r['covariate_note']}", | |
| "", | |
| "| term | coef | s.e. | hazard ratio | p |", | |
| "|---|---|---|---|---|", | |
| ] | |
| for c in r["coefficients"]: | |
| b.append( | |
| f"| `{c['term']}` | {_fmt(c['coef'])} | {_fmt(c['std_err'])} | " | |
| f"{_fmt(c['hazard_ratio'], 3)} | {_fmt(c['p'], 4)} |" | |
| ) | |
| ph = r.get("proportional_hazards_test", {}) | |
| if ph.get("status") == "run": | |
| b += [ | |
| "", | |
| "**Proportional-hazards test** (Schoenfeld, rank transform):", | |
| "", | |
| "| covariate | statistic | p |", | |
| "|---|---|---|", | |
| ] | |
| for k, v in ph["per_covariate"].items(): | |
| b.append(f"| `{k}` | {_fmt(v['test_statistic'], 2)} | {_fmt(v['p'], 4)} |") | |
| b += ["", f"> {ph['interpretation']}", ""] | |
| b += ["## 7. Predictive benchmark (out of time)", ""] | |
| if gbm: | |
| r = gbm["result"] | |
| if r.get("status") == "skipped": | |
| b += [f"*Not run: {r.get('reason')}*", ""] | |
| else: | |
| b += [ | |
| "| metric | gradient boosting | discrete-time logit |", | |
| "|---|---|---|", | |
| f"| out-of-time AUC | {_fmt(r['out_of_time_auc_gbm'], 4)} | " | |
| f"{_fmt(r['out_of_time_auc_discrete_time_logit'], 4)} |", | |
| f"| out-of-time Brier | {_fmt(r['out_of_time_brier_gbm'], 6)} | " | |
| f"{_fmt(r['out_of_time_brier_logit'], 6)} |", | |
| "", | |
| f"Train `{r['train_period']}`, test `{r['test_period']}`, test base rate " | |
| f"{_fmt(r['base_rate_test'], 5)}.", | |
| "", | |
| f"> {r['interpretation_warning']}", | |
| "", | |
| ] | |
| b += [ | |
| "## 8. Refinancing versus mobility — the decisive limitation", | |
| "", | |
| "This section exists because it is the most important thing in this report.", | |
| "", | |
| 'Zero Balance Code 01 is officially *"Prepaid or Matured (Voluntary Payoff)"*. ' | |
| "It pools three economically distinct events:", | |
| "", | |
| "1. a **refinance** — the household stays put and replaces the loan;", | |
| "2. a **sale-related payoff** — the household may or may not have moved;", | |
| "3. a **scheduled maturity** — no decision at all.", | |
| "", | |
| "The dataset contains no field that separates them, no property identifier, and a " | |
| "postal code truncated to three digits plus `00`. Field 27 " | |
| "(`Pre-Relief-Refinance Loan Sequence Number`) links only Relief Refinance / HARP " | |
| "chains — a policy-program subset, not ordinary refinancing " | |
| "(`docs/DECISION_LOG.md` D005).", | |
| "", | |
| "**Consequences, applied throughout this project:**", | |
| "", | |
| "- The loan-level outcome is called `prepayment`, never *sale* and never *move*.", | |
| "- Refinancing behaviour is characterised through the **refinance incentive** " | |
| "measure (note rate minus market rate), not through any individual event label.", | |
| "- Mobility-adjacent market activity is approached only through **independent** " | |
| "local measures — HMDA purchase originations — at the market level, never by " | |
| "assigning an individual prepayment to a move.", | |
| "- The policy module reports every transaction-denominated quantity across a " | |
| "**range** of assumed prepayment-to-transaction shares, because that share is " | |
| "not identified from these data.", | |
| "", | |
| "A reader who wants the effect of lock-in on *moving* needs linked " | |
| "mortgage-and-property records or a credit-bureau address panel. This project " | |
| "does not have them and does not pretend to.", | |
| "", | |
| ] | |
| return _write(cfg, "loan_hazard_analysis", ctx, "\n".join(b)) | |
| def render_event_study(cfg: Config) -> Path: | |
| ctx = ReportContext(cfg) | |
| exp = ctx.get("eventstudy", "exposure_distribution") | |
| placebo = ctx.get("eventstudy", "placebos") | |
| outcomes = [ | |
| a.stem.replace("es_", "") | |
| for a in list_artifacts(cfg, "eventstudy") | |
| if a.stem.startswith("es_") | |
| ] | |
| arts = {o: ctx.get("eventstudy", f"es_{o}") for o in sorted(outcomes)} | |
| b = [ | |
| "## 1. Design", | |
| "", | |
| "$$y_{gt} = \\alpha_g + \\gamma_t + \\sum_{k \\neq k_0} \\beta_k " | |
| "\\left(E_g \\times \\mathbf 1\\{t=k\\}\\right) + X_{gt}'\\theta + \\varepsilon_{gt}$$", | |
| "", | |
| "$E_g$ is **predetermined** lock-in exposure: the frozen pre-shock local coupon " | |
| "distribution evaluated at the later national mortgage-rate path,", | |
| "", | |
| "$$E_g = \\sum_k \\omega_{gk}^{\\text{pre}} \\cdot " | |
| "\\mathbf 1\\{\\bar R^{\\text{post}} - r_k > \\tau\\}.$$", | |
| "", | |
| "All cross-sectional variation comes from the frozen shares " | |
| "$\\omega_{gk}^{\\text{pre}}$; $\\bar R^{\\text{post}}$ is a national scalar.", | |
| "", | |
| "**What is not identified.** The national rate path is common to every geography " | |
| "and is absorbed by $\\gamma_t$. Only *relative* effects across exposure are " | |
| 'identified. No statement of the form "the rate increase reduced national ' | |
| 'transactions by X%" is available from this design.', | |
| "", | |
| "**No instrumental-variable interpretation is claimed.** Predetermined is not " | |
| "exogenous — see `docs/IDENTIFICATION_STRATEGY.md` §A4.", | |
| "", | |
| ] | |
| if exp: | |
| p = exp["result"]["primary"] | |
| if p.get("status") == "ok": | |
| b += [ | |
| "## 2. Exposure distribution and balance", | |
| "", | |
| f"Exposure measure: `{p['exposure']}`, frozen at " | |
| f"`{exp['result']['pre_shock_date']}`, {_fmt(p['n_geographies'])} geographies.", | |
| "", | |
| "| statistic | value |", | |
| "|---|---|", | |
| f"| mean | {_fmt(p['mean'])} |", | |
| f"| s.d. | {_fmt(p['sd'])} |", | |
| f"| min | {_fmt(p['min'])} |", | |
| f"| p25 | {_fmt(p['p25'])} |", | |
| f"| median | {_fmt(p['median'])} |", | |
| f"| p75 | {_fmt(p['p75'])} |", | |
| f"| max | {_fmt(p['max'])} |", | |
| "", | |
| "Most exposed: " | |
| + ", ".join(f"`{t['geography']}` ({_fmt(t['exposure'], 3)})" for t in p["top_5"]) | |
| + ".", | |
| "", | |
| "Least exposed: " | |
| + ", ".join(f"`{t['geography']}` ({_fmt(t['exposure'], 3)})" for t in p["bottom_5"]) | |
| + ".", | |
| "", | |
| ] | |
| if p.get("balance_table"): | |
| b += [ | |
| "**Balance table — exposure is not randomly assigned.**", | |
| "", | |
| "| pre-period variable | correlation with exposure |", | |
| "|---|---|", | |
| ] | |
| for r0 in p["balance_table"]: | |
| b.append(f"| `{r0['variable']}` | {_fmt(r0['correlation_with_exposure'], 3)} |") | |
| b += ["", f"> {p['balance_interpretation']}", ""] | |
| b += ["## 3. Results by outcome", ""] | |
| for name, art in arts.items(): | |
| if art is None: | |
| continue | |
| es = art["result"].get("event_study", {}) | |
| did = art["result"].get("did_two_period", {}) | |
| b += [ | |
| f"### `{name}`", | |
| "", | |
| f"- **Evidence tier: `{art['evidence_tier']}`**", | |
| f"- Outcome definition: {art['outcome_definition']}", | |
| ] | |
| if es.get("status") != "ok": | |
| b += [f"- *Not estimable: {es.get('reason')}*", ""] | |
| continue | |
| pt = es["pretrend_test"] | |
| b += [ | |
| f"- {_fmt(es['n_obs'])} observations, {_fmt(es['n_geographies'])} geographies, " | |
| f"{_fmt(es['n_periods'])} periods, {es['standard_errors']}.", | |
| f"- Coefficient units: {es['coefficient_units']}.", | |
| f"- Controls: {', '.join(f'`{c}`' for c in es['controls']) or 'none'}. " | |
| f"Fixed effects: {', '.join(f'`{c}`' for c in es['fixed_effects'])}.", | |
| "", | |
| "**Pre-trend test** (joint Wald that all pre-period interactions are zero): " | |
| + ( | |
| f"p = {_fmt(pt.get('pvalue'), 3)}" | |
| if pt.get("pvalue") is not None | |
| else f"*{pt.get('test', 'not testable')}*" | |
| ) | |
| + ( | |
| f"; wild-cluster-bootstrap p = {_fmt(pt.get('wild_cluster_bootstrap_pvalue'), 3)}" | |
| if pt.get("wild_cluster_bootstrap_pvalue") is not None | |
| else "" | |
| ) | |
| + f" → **{'PASSES' if pt.get('passes_at_alpha_0.10') else 'FAILS'}** at α = 0.10.", | |
| "", | |
| ] | |
| if not pt.get("passes_at_alpha_0.10"): | |
| b += [ | |
| "> This outcome is **demoted to `descriptive`** and carries no causal " | |
| "language. It is recorded in `reports/failed_hypotheses.md`.", | |
| "", | |
| ] | |
| b += ["| period | coef | s.e. | 95% CI | |", "|---|---|---|---|---|"] | |
| for d in es["dynamic_effects"]: | |
| tag = "reference" if d["is_reference"] else ("pre" if d["is_pre"] else "post") | |
| b.append( | |
| f"| {d['time']} | {_fmt(d['coef'])} | {_fmt(d['std_err'])} | " | |
| f"[{_fmt(d['ci_low'], 3)}, {_fmt(d['ci_high'], 3)}] | {tag} |" | |
| ) | |
| b += ["", f"Mean post-shock effect: **{_fmt(es.get('mean_post_effect'))}**."] | |
| if did.get("status") == "ok": | |
| b += [ | |
| f" Collapsed pre/post DiD: **{_fmt(did['coef'])}** " | |
| f"(s.e. {_fmt(did['std_err'])}, t = {_fmt(did.get('t'), 2)}, " | |
| f"{_fmt(did['n_clusters'])} clusters).", | |
| ] | |
| if art.get("caveats"): | |
| b += ["", "Caveats:"] + [f"- {c}" for c in art["caveats"]] | |
| b.append("") | |
| if placebo: | |
| r = placebo["result"] | |
| b += ["## 4. Falsification", "", f"Headline outcome: `{r.get('headline_outcome')}`.", ""] | |
| if r.get("placebo_shock_dates"): | |
| b += [ | |
| "**Placebo shock dates** — a placebo passes when it is insignificant.", | |
| "", | |
| "| placebo date | coef | s.e. | t |", | |
| "|---|---|---|---|", | |
| ] | |
| for k, v in r["placebo_shock_dates"].items(): | |
| d = v["did"] | |
| if d.get("status") != "ok": | |
| b.append(f"| {k} | *{d.get('reason')}* | | |") | |
| else: | |
| b.append( | |
| f"| {k} | {_fmt(d['coef'])} | {_fmt(d['std_err'])} | " | |
| f"{_fmt(d.get('t'), 2)} |" | |
| ) | |
| b.append("") | |
| if r.get("placebo_outcomes"): | |
| b += ["**Placebo outcomes**", "", "| outcome | coef | s.e. | t |", "|---|---|---|---|"] | |
| for k, d in r["placebo_outcomes"].items(): | |
| if d.get("status") != "ok": | |
| b.append(f"| `{k}` | *{d.get('reason')}* | | |") | |
| else: | |
| b.append( | |
| f"| `{k}` | {_fmt(d['coef'])} | {_fmt(d['std_err'])} | " | |
| f"{_fmt(d.get('t'), 2)} |" | |
| ) | |
| b.append("") | |
| b += [f"> {r.get('interpretation', '')}", ""] | |
| b += [ | |
| "## 5. Threats this design does not resolve", | |
| "", | |
| "| threat | why it matters | what we did |", | |
| "|---|---|---|", | |
| "| Pandemic demand reallocation | boom markets refinanced most, so they have the " | |
| "highest exposure *and* mean-reverted for unrelated reasons | control for 2019–21 " | |
| "price growth; exclude top-decile boom markets as a robustness cell |", | |
| "| Differential refinancing booms | a market that already refinanced has an " | |
| "exhausted pipeline, mechanically depressing later refi counts | refi outcomes " | |
| "labeled contaminated; purchase originations are the headline |", | |
| "| Remote-work exposure | drives migration and construction independently | " | |
| "**unresolved** in this slice; no teleworkable-share control is wired in |", | |
| "| Local labour shocks | move both exits and originations | **unresolved** in this " | |
| "slice; the optional unemployment adapter is not in the critical path |", | |
| "| Supply constraints | determine whether a demand shift shows up in price or " | |
| "quantity | part of the mechanism, not a nuisance; discussed in the decomposition |", | |
| "| Coverage error in exposure | exposure is measured on Freddie-acquired loans only | " | |
| "loan counts per geography are carried as a coverage variable |", | |
| "| Spillovers | a locked-in household who does not move also does not buy elsewhere | " | |
| "biases estimates toward zero; not corrected |", | |
| "", | |
| "Full treatment: `docs/IDENTIFICATION_STRATEGY.md` §3.", | |
| "", | |
| ] | |
| return _write(cfg, "local_market_event_study", ctx, "\n".join(b)) | |
| def render_decomposition(cfg: Config) -> Path: | |
| ctx = ReportContext(cfg) | |
| purchase = ctx.get("eventstudy", "es_log_purchase_originations") | |
| refi = ctx.get("eventstudy", "es_log_refi_originations") | |
| hpi = ctx.get("eventstudy", "es_hpi_growth") | |
| p1 = ctx.get("eventstudy", "es_log_permits_1unit") | |
| p5 = ctx.get("eventstudy", "es_log_permits_5plus") | |
| gap = ctx.get("hazards", "gap_profile_nonlinear") | |
| b = [ | |
| "## 1. The central asymmetry", | |
| "", | |
| "A locked-in owner is **both** a potential seller and a potential buyer. When the " | |
| "rate gap makes moving expensive, that household withdraws from *both* sides of " | |
| "the market at once.", | |
| "", | |
| "```mermaid", | |
| "flowchart TD", | |
| " R[Market rate rises above existing note rates] --> G[Rate gap opens]", | |
| " G --> L[Locked-in owner does not move]", | |
| " L --> S[Fewer existing homes listed<br/>EXISTING-HOME SUPPLY FALLS]", | |
| " L --> D[Same owner does not buy a replacement<br/>REPEAT-BUYER DEMAND FALLS]", | |
| " S --> Q[Transaction volume falls]", | |
| " D --> Q", | |
| " S --> PU[Upward pressure on price]", | |
| " D --> PD[Downward pressure on price]", | |
| " PU --> N[NET PRICE EFFECT: AMBIGUOUS]", | |
| " PD --> N", | |
| " G --> FTB[First-time buyers are NOT locked in<br/>their demand is unaffected by lock-in<br/>but is hit by the higher rate itself]", | |
| " FTB --> N", | |
| " G --> INV[Investors and all-cash buyers are NOT locked in]", | |
| " INV --> N", | |
| " N --> C{Local supply elasticity}", | |
| " C -->|elastic| BUILD[More new construction,<br/>less price response]", | |
| " C -->|inelastic| PRICE[More price response,<br/>less construction]", | |
| "```", | |
| "", | |
| "**Quantities are unambiguous; prices are not.** Both channels reduce transaction " | |
| "volume, so purchase-mortgage originations should fall with exposure. The price " | |
| "effect depends on which side is more inelastic, on the share of demand from " | |
| "first-time buyers and investors (who are not locked in), and on how readily new " | |
| "construction substitutes for existing homes.", | |
| "", | |
| "> **A fall in transactions is not evidence of a supply-only mechanism.** This is " | |
| "the single most common inferential error in casual accounts of lock-in. The same " | |
| "aggregate decline in sales is consistent with a pure listing-side contraction, a " | |
| "pure repeat-buyer-demand contraction, or any mixture.", | |
| "", | |
| ] | |
| b += [ | |
| "## 2. What the evidence in this repository can and cannot separate", | |
| "", | |
| "| channel | measurable here? | with what |", | |
| "|---|---|---|", | |
| "| Locked-in owners listing fewer homes | **no, not separately** | no listings " | |
| "data and no sale indicator |", | |
| "| Locked-in owners buying fewer homes | **no, not separately** | would need to " | |
| "link a payoff to a subsequent purchase by the same household |", | |
| "| Combined effect on transaction volume | **yes** | HMDA purchase originations " | |
| "by state-year |", | |
| "| First-time-buyer demand | partially | HMDA does not flag first-time status; " | |
| "the loan-level file does, but only for purchase loans it acquired |", | |
| "| Investor demand | partially | Freddie `Occupancy Status` = `I`, but investor " | |
| "activity is concentrated in cash and non-agency channels we never see |", | |
| "| New construction | **yes** | Census BPS units authorized |", | |
| "| Local migration | **no** | no migration adapter in the critical path |", | |
| "| Credit availability | partially | HMDA denial rate |", | |
| "", | |
| "The honest summary: this design identifies the **combined** withdrawal, not its " | |
| "decomposition. Claiming a decomposition would require listings data, " | |
| "transaction records, or a household panel.", | |
| "", | |
| ] | |
| b += [ | |
| "## 3. Sign table from the estimated results", | |
| "", | |
| "No sign is presumed. Each cell reports what the artifacts actually show, with " | |
| "the tier that governs how it may be read.", | |
| "", | |
| "| outcome | mean post effect (per 1 s.d. exposure) | tier | pre-trend | reading |", | |
| "|---|---|---|---|---|", | |
| ] | |
| for label, art in ( | |
| ("purchase originations (log)", purchase), | |
| ("refinance originations (log)", refi), | |
| ("house price growth", hpi), | |
| ("single-family permits (log)", p1), | |
| ("multifamily 5+ permits (log)", p5), | |
| ): | |
| if art is None: | |
| b.append(f"| {label} | — | — | — | artifact unavailable |") | |
| continue | |
| es = art["result"].get("event_study", {}) | |
| if es.get("status") != "ok": | |
| b.append(f"| {label} | — | `{art['evidence_tier']}` | — | not estimable |") | |
| continue | |
| pt = es["pretrend_test"] | |
| passes = pt.get("passes_at_alpha_0.10") | |
| v = es.get("mean_post_effect") | |
| reading = ( | |
| f"{verb_for(art)}" if passes else "descriptive only — pre-trend fails or is untestable" | |
| ) | |
| b.append( | |
| f"| {label} | {_fmt(v)} | `{art['evidence_tier']}` | " | |
| f"{'pass' if passes else 'fail'} | {reading} |" | |
| ) | |
| b.append("") | |
| if gap: | |
| emp = gap["result"]["prepayment"]["empirical"] | |
| if emp: | |
| lo = emp[0]["hazard"] | |
| hi = emp[-1]["hazard"] | |
| b += [ | |
| "## 4. The loan-level gradient that motivates the market-level design", | |
| "", | |
| f"The monthly prepayment hazard falls from **{_fmt(lo, 5)}** in the " | |
| f"most-refinance-incentivised bucket to **{_fmt(hi, 5)}** in the " | |
| f"most-locked-in bucket — a ratio of roughly " | |
| f"**{_fmt(lo / hi if hi else float('nan'), 1)}×**.", | |
| "", | |
| "*Evidence tier: `hazard_association`.* This gradient is the borrower-level " | |
| "mechanism the market-level design is looking for. It is **not** itself " | |
| "evidence about listings, sales, moves, or prices.", | |
| "", | |
| ] | |
| b += [ | |
| "## 5. Why the price sign matters for policy", | |
| "", | |
| "If lock-in raises prices (listing channel dominates), policies that unlock " | |
| "existing owners improve affordability by adding supply. If lock-in lowers prices " | |
| "(repeat-buyer channel dominates), the same policies add demand and could raise " | |
| "prices while raising transaction volume. **The two cases imply opposite " | |
| "affordability consequences from the same intervention**, which is why " | |
| "`reports/policy_counterfactuals.md` reports quantity and price responses " | |
| "separately and never nets them into a single welfare claim.", | |
| "", | |
| "The supply-elasticity scenario in the policy module exists to make this concrete: " | |
| "the *same* modelled demand shift produces mostly-quantity or mostly-price " | |
| "responses depending on a calibrated elasticity that this project does not " | |
| "estimate.", | |
| "", | |
| ] | |
| return _write(cfg, "demand_supply_decomposition", ctx, "\n".join(b)) | |
| def render_policy(cfg: Config) -> Path: | |
| ctx = ReportContext(cfg) | |
| comp = ctx.get("scenarios", "scenario_comparison") | |
| names = [a.stem for a in list_artifacts(cfg, "scenarios") if a.stem != "scenario_comparison"] | |
| arts = [ctx.get("scenarios", n) for n in sorted(names)] | |
| b = [ | |
| "> **These are model-dependent projections, not forecasts.**", | |
| "", | |
| "## 1. How the simulator works", | |
| "", | |
| "1. Take the estimated discrete-time prepayment hazard from " | |
| "`outputs/hazards/dt_logit_prepayment.json`.", | |
| "2. Perturb the **rate gap** (or the effective gap under a policy) for every loan " | |
| "in the active stock at a baseline month.", | |
| "3. Re-predict each loan's monthly prepayment probability and sum the difference.", | |
| "4. Map the modelled change in prepayments into transaction, price, and permit " | |
| "responses through **calibrated** elasticities.", | |
| "", | |
| "**The honesty boundary.** Step 1 is *estimated*. Steps 2–3 assume a hazard " | |
| "*association* behaves as a structural response function — which the " | |
| "identification strategy does not establish. Step 4 is *entirely calibrated*.", | |
| "", | |
| "**The largest single source of uncertainty** is that a prepayment is not a move. " | |
| "Converting modelled prepayments into modelled transactions requires the share of " | |
| "prepayments that correspond to a property transaction, and that share is **not " | |
| "identified** from these data. Every transaction-denominated quantity is therefore " | |
| "reported across a range of assumed shares, and no point value is preferred.", | |
| "", | |
| ] | |
| if comp: | |
| r = comp["result"] | |
| b += [ | |
| "## 2. Scenario ranking", | |
| "", | |
| f"Baseline month `{r['baseline_month']}`, " | |
| f"{_fmt(r['n_loans_in_baseline_stock'])} active loans.", | |
| "", | |
| "| scenario | policy | additional monthly prepayments | % change | " | |
| "additional monthly prepaid UPB |", | |
| "|---|---|---|---|---|", | |
| ] | |
| for row in r["ranking"]: | |
| b.append( | |
| f"| `{row['scenario']}` | {row['policy_type']} | " | |
| f"{_fmt(row['additional_monthly_prepayments'], 1)} | " | |
| f"{_fmt((row['pct_change_in_prepayments'] or 0) * 100, 1)}% | " | |
| f"${_fmt(row['additional_monthly_prepaid_upb'], 0)} |" | |
| ) | |
| b += [ | |
| "", | |
| f"> {r['how_to_read_this']}", | |
| "", | |
| "### Calibrated inputs (no error bars, chosen not estimated)", | |
| "", | |
| "| parameter | value |", | |
| "|---|---|", | |
| ] | |
| for k, v in r["calibrated_inputs"].items(): | |
| b.append(f"| `{k}` | {_fmt(v)} |") | |
| b += ["", "### Estimated inputs", "", "| field | value |", "|---|---|"] | |
| for k, v in r["estimated_inputs"].items(): | |
| if isinstance(v, list): | |
| continue | |
| b.append(f"| `{k}` | {_fmt(v)} |") | |
| b.append("") | |
| b += ["## 3. Scenario detail", ""] | |
| for art in arts: | |
| if art is None: | |
| continue | |
| r = art["result"] | |
| pol = r.get("policy", {}) | |
| b += [ | |
| f"### `{r.get('scenario')}` — {pol.get('type', '')}", | |
| "", | |
| f"- Implementation: {pol.get('implementation', '—')}", | |
| f"- Baseline expected monthly prepayments: " | |
| f"**{_fmt(r.get('baseline_expected_monthly_prepayments'), 1)}**", | |
| f"- Scenario expected monthly prepayments: " | |
| f"**{_fmt(r.get('scenario_expected_monthly_prepayments'), 1)}**", | |
| f"- Modelled additional monthly prepayments: " | |
| f"**{_fmt(r.get('modelled_additional_monthly_prepayments'), 1)}** " | |
| f"({_fmt((r.get('modelled_pct_change_in_prepayments') or 0) * 100, 1)}%)", | |
| ] | |
| for k, v in pol.items(): | |
| if k in ("type", "implementation"): | |
| continue | |
| b.append(f"- `{k}`: {_fmt(v)}") | |
| mapping = r.get("transaction_price_construction_mapping", {}) | |
| if mapping: | |
| b += [ | |
| "", | |
| "Transaction / price / construction mapping across the unidentified " | |
| "prepayment-to-transaction share:", | |
| "", | |
| "| assumed move share | additional transactions/month | % change in flow | " | |
| "log price change | % change in permits |", | |
| "|---|---|---|---|---|", | |
| ] | |
| for _k, m in mapping.items(): | |
| b.append( | |
| f"| {_fmt(m['assumed_share_of_prepayments_that_are_property_transactions'])} | " | |
| f"{_fmt(m['modelled_additional_transactions_per_month'], 1)} | " | |
| f"{_fmt(m['modelled_pct_change_in_transaction_flow'] * 100, 2)}% | " | |
| f"{_fmt(m['modelled_log_price_change'], 5)} | " | |
| f"{_fmt(m['modelled_pct_change_in_permits'] * 100, 2)}% |" | |
| ) | |
| if art.get("caveats"): | |
| b += ["", "Caveats:"] + [f"- {c}" for c in art["caveats"]] | |
| b.append("") | |
| b += [ | |
| "## 4. What none of these scenarios do", | |
| "", | |
| "- They do not forecast. They hold the loan population, housing-stock composition, " | |
| "credit conditions, income, and migration fixed.", | |
| "- They do not attach confidence intervals. The hazard coefficients have sampling " | |
| "error; the calibrated elasticities have none at all; the " | |
| "prepayment-to-transaction share is unidentified.", | |
| "- They do not net quantity and price responses into a welfare claim.", | |
| "- They do not model who bears the cost of portability or assumability. Those " | |
| "policies **transfer** a below-market-coupon loss rather than eliminating it.", | |
| "- They do not model capitalisation of demand-side subsidies into prices, which in " | |
| "an inelastic-supply market can absorb much of the intended benefit.", | |
| "- They are not additive. Running two scenarios together is not the sum of the two.", | |
| "", | |
| "The **ordering** of scenarios is the useful output. Any single magnitude should be " | |
| "read as an order of magnitude at best.", | |
| "", | |
| ] | |
| return _write(cfg, "policy_counterfactuals", ctx, "\n".join(b)) | |
| def render_failed(cfg: Config) -> Path: | |
| ctx = ReportContext(cfg) | |
| grid_art = ctx.get("robustness", "robustness_grid") | |
| es_arts = [ | |
| ctx.get("eventstudy", a.stem) | |
| for a in list_artifacts(cfg, "eventstudy") | |
| if a.stem.startswith("es_") | |
| ] | |
| b = [ | |
| "This file exists so that specifications which did not survive are **recorded, " | |
| "not buried**. A research system that only reports what worked is not a research " | |
| "system.", | |
| "", | |
| "## 1. Outcomes demoted from `quasi_experimental` to `descriptive`", | |
| "", | |
| "The event-study runner assigns the tier from the data: a pre-trend test that " | |
| "fails or cannot be run **automatically** demotes the artifact. There is no manual " | |
| "override.", | |
| "", | |
| "| outcome | tier assigned | pre-trend p | reason |", | |
| "|---|---|---|---|", | |
| ] | |
| demoted = 0 | |
| for art in es_arts: | |
| if art is None: | |
| continue | |
| es = art["result"].get("event_study", {}) | |
| pt = es.get("pretrend_test", {}) if es.get("status") == "ok" else {} | |
| if art["evidence_tier"] == "quasi_experimental": | |
| continue | |
| demoted += 1 | |
| reason = ( | |
| es.get("reason") | |
| if es.get("status") != "ok" | |
| else ( | |
| "pre-trend test failed" | |
| if pt.get("pvalue") is not None | |
| else pt.get("test", "pre-trends not testable") | |
| ) | |
| ) | |
| b.append( | |
| f"| `{art['artifact']}` | `{art['evidence_tier']}` | " | |
| f"{_fmt(pt.get('pvalue'), 3)} | {reason} |" | |
| ) | |
| if demoted == 0: | |
| b.append("| *(none)* | | | every estimated outcome passed its pre-trend test |") | |
| b.append("") | |
| if grid_art: | |
| r = grid_art["result"] | |
| if r.get("status") == "skipped": | |
| b += ["## 2. Robustness grid", "", f"*Not run: {r.get('reason')}*", ""] | |
| else: | |
| b += [ | |
| "## 2. Robustness grid", | |
| "", | |
| f"Baseline coefficient **{_fmt(r.get('baseline_coefficient'))}** " | |
| f"(s.e. {_fmt(r.get('baseline_std_err'))}) across {_fmt(r['n_cells'])} cells.", | |
| "", | |
| "| verdict | cells |", | |
| "|---|---|", | |
| ] | |
| for v in r["verdict_counts"]: | |
| b.append(f"| `{v['verdict']}` | {_fmt(v['n'])} |") | |
| b += ["", "**Verdict definitions**", ""] | |
| for k, v in r["verdict_definitions"].items(): | |
| b.append(f"- `{k}`: {v}") | |
| b += ["", f"### Flagged cells ({_fmt(r['n_flagged'])})", ""] | |
| if r["n_flagged"] == 0: | |
| b += [ | |
| "*No cell produced a sign flip, an inestimable specification, or a " | |
| "failing placebo.*", | |
| "", | |
| ] | |
| else: | |
| b += [ | |
| "| axis | variant | outcome | coef | s.e. | t | verdict | rationale |", | |
| "|---|---|---|---|---|---|---|---|", | |
| ] | |
| for c in r["flagged_cells"]: | |
| b.append( | |
| f"| {c['axis']} | {c['variant']} | `{c['outcome']}` | " | |
| f"{_fmt(c['coef'])} | {_fmt(c['std_err'])} | {_fmt(c['t'], 2)} | " | |
| f"**{c['verdict']}** | {c['rationale']} |" | |
| ) | |
| b.append("") | |
| b += [ | |
| "### Every cell", | |
| "", | |
| "| axis | variant | outcome | coef | s.e. | t | n | clusters | verdict |", | |
| "|---|---|---|---|---|---|---|---|---|", | |
| ] | |
| for c in r["cells"]: | |
| b.append( | |
| f"| {c['axis']} | {c['variant']} | `{c['outcome']}` | {_fmt(c['coef'])} | " | |
| f"{_fmt(c['std_err'])} | {_fmt(c['t'], 2)} | {_fmt(c['n_obs'])} | " | |
| f"{_fmt(c['n_clusters'])} | `{c['verdict']}` |" | |
| ) | |
| b.append("") | |
| sens = ctx.get("hazards", "sensitivity_cells") | |
| if sens: | |
| rs = sens["result"] | |
| b += [ | |
| "## 3. Loan-level sensitivity to modelling choices", | |
| "", | |
| "Three checks re-estimate the headline discrete-time prepayment hazard under " | |
| "alternatives the baseline deliberately does **not** use. Each corresponds to " | |
| "a choice documented elsewhere as an *assumption* rather than a fact.", | |
| "", | |
| f"Baseline rate-gap coefficient: **{_fmt(rs['baseline_rate_gap_coef'])}** " | |
| f"(s.e. {_fmt(rs['baseline_std_err'])}).", | |
| "", | |
| "| cell | coefficient | s.e. | verdict |", | |
| "|---|---|---|---|", | |
| ] | |
| for c in rs["cells"]: | |
| if c["cell"] == "baseline": | |
| continue | |
| b.append( | |
| f"| `{c['cell']}` | {_fmt(c.get('coef'))} | {_fmt(c.get('std_err'))} | " | |
| f"**{c.get('verdict')}** |" | |
| ) | |
| b += ["", "Verdicts are measured in **baseline standard errors**:", ""] | |
| for k, v in rs["verdict_definitions"].items(): | |
| b.append(f"- `{k}`: {v}") | |
| b += [""] | |
| for c in rs["cells"]: | |
| if c["cell"] == "baseline" or not c.get("interpretation"): | |
| continue | |
| b += [ | |
| f"**`{c['cell']}`** — {c.get('description', '')}", | |
| "", | |
| f"> {c['interpretation']}", | |
| "", | |
| ] | |
| movers = [ | |
| c | |
| for c in rs["cells"] | |
| if c.get("verdict") in ("large_shift", "sign_flip", "moderate_shift") | |
| ] | |
| if movers: | |
| b += [ | |
| "**These are fragilities, not bugs.** " | |
| + ", ".join(f"`{c['cell']}`" for c in movers) | |
| + " move the coefficient by more than one baseline standard error. That " | |
| "means the corresponding modelling choice is doing real work, and it " | |
| "should be stated whenever the magnitude is quoted.", | |
| "", | |
| ] | |
| b += [ | |
| "## 4. Design errors caught during construction", | |
| "", | |
| "These were genuine mistakes in this project, found by diagnostics rather than by " | |
| "inspection. They are recorded because the diagnostic that caught each one is now " | |
| "part of the pipeline.", | |
| "", | |
| "| # | error | how it was caught | fix |", | |
| "|---|---|---|---|", | |
| "| D016 | Exposure was measured as the *contemporaneous* locked-in share at the " | |
| "pre-shock date. In 2021-12 the market rate was near its historic low, so " | |
| "essentially nobody was locked in yet and the treatment variable had **exactly " | |
| "zero variance in every state**. | The exposure-distribution artifact reported " | |
| "sd = 0.000. | Exposure is now the frozen pre-shock coupon distribution evaluated " | |
| "at the **later** national rate path — the actual shift-share design. |", | |
| "| D017 | The HMDA Data Browser API **silently ignores** unrecognised query " | |
| "parameters. Passing the singular `loan_purpose` (correct name: `loan_purposes`) " | |
| "returned **all-purpose totals** that looked like a clean purchase-only series, " | |
| "making purchase and refinance originations numerically identical. | Purchase and " | |
| "refi counts were byte-identical in the panel. | Use `loan_purposes`; assert the " | |
| "API echoes every filter back in its `parameters` block, on both fetch and cache " | |
| "read; version the cache filenames so bad cells cannot be reused. |", | |
| "| D018 | `Current Actual UPB` is the **end**-of-period balance and is 0 in a " | |
| "zero-balance month, so the payment-gap covariate was zero in exactly the months " | |
| "where an exit occurred — corrupting the covariate for **every event**. | An " | |
| "episode validation check found 2,927 rows where the payment gap and the rate gap " | |
| "disagreed in sign. | All lock-in measures now use the **start-of-month** balance, " | |
| "with `upb_timing_source` recording its provenance per row. |", | |
| "| D019 | The annual panel was built only over years with an active mortgage stock " | |
| "(2021+), leaving **no pre-shock periods**, so pre-trends were untestable and every " | |
| "result was auto-demoted. | Every event study reported `n_pre_coefficients = 0`. | " | |
| "The panel now spans the union of outcome years (2018+); exposure is a " | |
| "geography-level constant legitimately attached to pre-shock years. |", | |
| "| D009 | The config asked FHFA for **monthly** purchase-only HPI at **state** " | |
| "level, a combination FHFA does not publish (monthly purchase-only is national and " | |
| "census-division only). The filter matched zero rows. | `load_series` returned an " | |
| "empty frame and the LTV scaling silently degraded. | Default is now quarterly; " | |
| "`load_series` raises with the full list of published combinations; " | |
| "quarterly→monthly expansion is labeled in `index_basis`. |", | |
| "", | |
| ] | |
| b += [ | |
| "## 5. Hypotheses this project cannot test at all", | |
| "", | |
| "Not failures of execution — failures of data availability. Recording them " | |
| "prevents a future reader from assuming they were tested and passed.", | |
| "", | |
| "- **Does lock-in reduce household mobility?** No mobility measure exists in these " | |
| "data. Not tested. Not testable here.", | |
| "- **Does lock-in reduce home *listings*?** No listings data. Not tested.", | |
| "- **What fraction of prepayments are sales rather than refinances?** Zero Balance " | |
| "Code 01 pools them. Not identified.", | |
| "- **Do locked-in owners trade down instead of moving?** Requires linking a payoff " | |
| "to a subsequent purchase by the same household. No property or household " | |
| "identifier exists.", | |
| "- **Is the exposure measure a valid instrument?** No. See " | |
| "`docs/IDENTIFICATION_STRATEGY.md` §A4. We do not use IV language.", | |
| "- **Do FHA/VA, jumbo, non-QM, or all-cash segments behave the same way?** They are " | |
| "entirely outside the loan-level population.", | |
| "", | |
| ] | |
| return _write(cfg, "failed_hypotheses", ctx, "\n".join(b)) | |
| def render_methodology(cfg: Config) -> Path: | |
| ctx = ReportContext(cfg) | |
| val = ctx.get("validation", "validation_report") | |
| ds = ctx.get("hazards", "survival_dataset") | |
| exp = ctx.get("eventstudy", "exposure_distribution") | |
| b = [ | |
| VOCAB_NOTE, | |
| "", | |
| "## 1. Pipeline", | |
| "", | |
| "```", | |
| "official public docs -> verified schema (32 + 32 fields)", | |
| "registered or SYNTHETIC loan files", | |
| " -> streaming origination parser -> partitioned Parquet (cohort=)", | |
| " -> streaming performance parser -> partitioned Parquet (cohort=, period_year=)", | |
| " -> loan-event table (exits, censoring, left truncation)", | |
| " -> loan-month episode table (point-in-time rates + 8 lock-in measures)", | |
| " -> geography-month active stock (count- and UPB-weighted)", | |
| " -> predetermined exposure (frozen pre-shock coupon shares x later rate path)", | |
| " -> local market panel (+ FHFA HPI, HMDA, Census BPS)", | |
| " -> hazard ladder | event studies | robustness grid | scenarios", | |
| " -> generated reports", | |
| "```", | |
| "", | |
| "Memory discipline: the loan-by-month panel is never materialised as a Python " | |
| "object. Parsers stream line chunks; the episode builder is a Polars lazy plan " | |
| "collected in streaming mode; aggregation prunes partitions. A configurable row " | |
| "budget (`survival.max_episode_rows`) fails the run rather than swapping.", | |
| "", | |
| ] | |
| b += [ | |
| "## 2. The eight lock-in measures", | |
| "", | |
| "| # | measure | definition |", | |
| "|---|---|---|", | |
| "| 1 | `rate_gap` | market rate − note rate. Positive ⇒ locked in |", | |
| "| 2 | `lockin_gap` | max(rate_gap, 0) |", | |
| "| 3 | `refi_incentive` | note rate − market rate. Positive ⇒ refinancing pays |", | |
| "| 4 | `payment_gap` | monthly P&I change if the **start-of-month** balance were " | |
| "refinanced at the market rate over the remaining term |", | |
| "| 5 | `pv_financing_gap` | PV of measure 4 over a **calibrated** holding period " | |
| "at a **calibrated** discount rate |", | |
| "| 6 | `locked_share_*` | share of active loans above a bp threshold |", | |
| "| 7 | `locked_share_upb_*` | measure 6, UPB-weighted |", | |
| "| 8 | `locked_share_count_*` | measure 6, loan-count-weighted |", | |
| "", | |
| "All are **point-in-time**: the market rate attached to month *m* is the last " | |
| "PMMS observation available on or before the first day of *m*. The alignment is " | |
| "enforced by a backward as-of join and asserted by " | |
| "`lockin.rates.assert_no_look_ahead`, which is called by `make validate-data` and " | |
| "by a unit test.", | |
| "", | |
| ] | |
| b += [ | |
| "## 3. Conforming-mortgage selection — the population is not the market", | |
| "", | |
| "The loan-level population is **Freddie Mac acquisitions**: conventional, " | |
| "conforming, single-family. What that excludes, and why each exclusion matters " | |
| "for lock-in specifically:", | |
| "", | |
| "| excluded | why it matters |", | |
| "|---|---|", | |
| "| **FHA / VA** | disproportionately first-time, lower-income, and lower-wealth " | |
| "buyers. FHA and VA loans are also **assumable**, so the lock-in mechanism " | |
| "operates differently — the excluded segment is the one where the policy " | |
| "counterfactual already partly exists |", | |
| "| **Jumbo** | high-price metros are systematically under-represented, biasing " | |
| "any geographic heterogeneity |", | |
| "| **Non-QM, portfolio, credit-union** | different borrower risk profiles and " | |
| "different refinance frictions |", | |
| "| **Fannie Mae** | roughly half the conforming conventional universe is absent, " | |
| "so `n_active_loans` is a coverage variable, not a market size |", | |
| "| **All-cash purchases** | a large and cyclically varying share of transactions " | |
| "involves no mortgage at all and cannot be locked in |", | |
| "| **Mortgage-free owners** | roughly a third of owner-occupied U.S. homes carry " | |
| "no mortgage. These households are **structurally immune** to lock-in and are " | |
| "entirely absent from any share we compute |", | |
| "", | |
| 'Consequence for interpretation: a statement like "X% of loans are locked in ' | |
| 'above 200 bp" is a statement about **Freddie-acquired loans**, and the ' | |
| "corresponding share of *U.S. households* is necessarily smaller. Every artifact " | |
| "carries this in its `population` field.", | |
| "", | |
| ] | |
| b += [ | |
| "## 4. Censoring, truncation, and competing risks", | |
| "", | |
| "- **Left truncation.** Performance records begin at Freddie Mac *acquisition*, " | |
| "not origination, and the configured performance window truncates earlier " | |
| "cohorts further. Risk sets exclude loans not yet observed at each loan age. The " | |
| "two causes are reported separately in validation because only the first is a " | |
| "property of the data.", | |
| "- **Right censoring.** At the performance cutoff, or at an administrative " | |
| "removal (ZB 15/16/96).", | |
| "- **Administrative removals as censoring.** ZB 15 (whole-loan sale), 16 (RPL " | |
| "securitization), and 96 (defect prior to other termination) are Freddie Mac " | |
| "portfolio and representation-and-warranty actions. Counting them as prepayment " | |
| "would inflate the hazard; counting them as still-alive would be false. " | |
| "Censoring is the least-wrong option **and it is an assumption**: it requires " | |
| "the removal to be uninformative about the borrower's latent exit time, which is " | |
| "not guaranteed.", | |
| "- **Competing risks.** Cause-specific hazards for prepayment and credit events, " | |
| "with Aalen–Johansen cumulative incidence rather than 1−KM.", | |
| "- **Missing performance months** contribute no risk time and are counted in " | |
| "`n_month_gaps`.", | |
| "- **Modifications** reset loan age per the official guide; the validator " | |
| "tolerates the reset and flags affected loans.", | |
| "- **Reappearing loans** (performance months after an exit) are truncated at the " | |
| "exit and flagged.", | |
| "", | |
| ] | |
| if ds: | |
| ll = ds["result"]["loan_level"] | |
| b += [ | |
| f"In this run: {_fmt(ll['n_left_truncated'])} of {_fmt(ll['n_loans'])} loans " | |
| f"left truncated; {_fmt(ll['n_censored'])} censored.", | |
| "", | |
| ] | |
| b += [ | |
| "## 5. Frequency and index-concept discipline", | |
| "", | |
| "- **HMDA is annual** and is never interpolated for estimation. HMDA event " | |
| "studies run at annual frequency.", | |
| "- **FHFA purchase-only HPI is quarterly at state level** (monthly exists only " | |
| "nationally). Growth is computed at the published frequency; where a monthly " | |
| "value is needed as an input to the LTV scaling, the quarterly level is held " | |
| "constant within the quarter and `index_basis` is suffixed " | |
| "`+held-constant-within-quarter`. An expanded series is never a regression " | |
| "outcome.", | |
| "- **Index concepts are never mixed.** purchase-only, all-transactions, and " | |
| "expanded-data are different objects; `load_series` requires the flavor " | |
| "explicitly.", | |
| "- **Census BPS measures permits authorized**, not starts and not completions. " | |
| "The monthly `c` vintage is preliminary. Partial years are dropped rather than " | |
| "compared against full-year totals.", | |
| "- **PMMS methodology regimes** are labeled, not silently spliced: the survey " | |
| "changed to an application-based method on 2022-11-17, and the fees/points and " | |
| "5/1 ARM series were discontinued at the same time.", | |
| "", | |
| ] | |
| if exp: | |
| p = exp["result"]["primary"] | |
| if p.get("status") == "ok": | |
| b += [ | |
| "## 6. Treatment definition", | |
| "", | |
| f"Exposure `{p['exposure']}`, frozen at " | |
| f"`{exp['result']['pre_shock_date']}`, mean {_fmt(p['mean'])}, " | |
| f"s.d. {_fmt(p['sd'])} across {_fmt(p['n_geographies'])} geographies. " | |
| "Standardised in every regression so coefficients read per standard " | |
| "deviation.", | |
| "", | |
| ] | |
| b += ["## 7. Validation", ""] | |
| if val: | |
| r = val["result"] | |
| b += [ | |
| f"`make validate-data`: **{_fmt(r['n_hard'])} hard**, " | |
| f"{_fmt(r['n_soft'])} soft, {_fmt(r['n_info'])} informational findings.", | |
| "", | |
| "| severity | meaning |", | |
| "|---|---|", | |
| ] | |
| for k, v in r["severity_meaning"].items(): | |
| b.append(f"| `{k}` | {v} |") | |
| b.append("") | |
| for section, problems in r["sections"].items(): | |
| if not problems: | |
| continue | |
| b += [f"**{section}**", ""] | |
| b += [f"- {p}" for p in problems] | |
| b.append("") | |
| b += [ | |
| "## 8. Limitations, ranked by how much they should change your reading", | |
| "", | |
| "1. **A prepayment is not a move, a sale, or a refinance.** Zero Balance Code 01 " | |
| "pools all three. Nothing in this project can separate them. This caps what the " | |
| "loan-level results can mean.", | |
| "2. **Only relative effects are identified at the market level.** The national rate " | |
| "path is absorbed by time fixed effects. There is no aggregate causal magnitude " | |
| "here.", | |
| "3. **Predetermined exposure is not exogenous.** It correlates with pandemic price " | |
| "growth and with refinance intensity, both of which independently predict " | |
| "post-2022 outcomes. No IV language is used.", | |
| "4. **The population is a selected slice of the mortgage market**, which is itself " | |
| "a selected slice of the housing market (§3).", | |
| "5. **The demand/supply decomposition is not achieved**, only framed. No listings " | |
| "data, no transaction records, no household panel.", | |
| "6. **Policy scenarios rest on an association used as a response function** plus " | |
| "calibrated elasticities with no error bars.", | |
| "7. **The rate gap is measured with error**: PMMS is national, while local offered " | |
| "rates differ by tens of basis points. This attenuates loan-level coefficients.", | |
| "8. **A state house price index is a poor proxy for an individual property's price " | |
| "path**, so estimated current LTV is noisy.", | |
| "9. **HMDA reporting-threshold changes** break comparability of counts across " | |
| "2017/2018 and across the closed-end threshold change.", | |
| "10. **Remote-work exposure and local labour shocks are unresolved threats** in " | |
| "this slice; the optional adapters are not in the critical path.", | |
| "", | |
| ] | |
| return _write(cfg, "methodology_and_limitations", ctx, "\n".join(b)) | |
| def render_replication_protocol(cfg: Config) -> Path: | |
| ctx = ReportContext(cfg) | |
| ctx.get("validation", "validation_report") | |
| b = [ | |
| "## 1. Reproducing this run exactly", | |
| "", | |
| "```bash", | |
| "make setup", | |
| "make fetch-public-data", | |
| "make reproduce-sample", | |
| "make test", | |
| "```", | |
| "", | |
| "`make reproduce-sample` executes, in order: `prepare-sample-data`, " | |
| "`ingest-mortgages`, `build-loan-events`, `build-lockin`, `build-local-panel`, " | |
| "`validate-data`, `estimate-hazards`, `estimate-local-effects`, `benchmark`, " | |
| "`simulate-policy`, `report`.", | |
| "", | |
| "Determinism:", | |
| "", | |
| "- Synthetic fixtures are generated from `mortgage.synthetic_seed` (recorded in " | |
| "the fixture manifest).", | |
| "- Model seeds come from `survival.seed`.", | |
| "- Every artifact records `git_commit`, `config_digest`, `data_period`, " | |
| "`source_versions` (schema@retrieved#checksum per dataset), and a UTC timestamp.", | |
| "- Every dataset on disk has a manifest with a SHA-256 checksum; " | |
| "`make validate-data` re-checksums and fails on mismatch.", | |
| "", | |
| "The one non-deterministic input is the **public data vintage**. PMMS, FHFA HPI, " | |
| "and Census BPS are revised; HMDA is re-released. A rerun weeks later will fetch " | |
| "newer vintages. The manifests record exactly which vintage was used, so a " | |
| "difference is diagnosable rather than mysterious.", | |
| "", | |
| ] | |
| b += [ | |
| "## 2. What cannot be reproduced without registered data", | |
| "", | |
| "Loan-level results in this run were computed from **synthetic fixtures** unless " | |
| "the artifact's `data_class` says `REGISTERED`. To reproduce them empirically:", | |
| "", | |
| "1. Register at the Freddie Mac Single-Family Loan-Level Dataset page and accept " | |
| "the terms of use **yourself**. This repository does not and will not bypass that " | |
| "wall, and the terms prohibit redistributing the records.", | |
| "2. Place the archives unmodified in `data/raw/freddie/`.", | |
| "3. Set `mortgage.mode: registered_sample` (or `registered_full`) and rerun.", | |
| "", | |
| "The adapter discovers `historical_data_YYYYQn.zip` / `sample_YYYY.zip` " | |
| "automatically, reads members without full extraction, and the `SYNTHETIC` stamps " | |
| "and report banners disappear on their own. **No code changes are needed** — that " | |
| "is the point of the mode switch.", | |
| "", | |
| "Public aggregate results (PMMS path, FHFA HPI growth, HMDA origination counts, " | |
| "Census permits) are **fully reproducible now**, because those sources need no " | |
| "registration.", | |
| "", | |
| ] | |
| b += [ | |
| "## 3. Artifact-to-claim traceability", | |
| "", | |
| "Every number in every generated report comes from a JSON artifact under " | |
| "`outputs/`. To trace one:", | |
| "", | |
| "```bash", | |
| "uv run lockin dump-artifact hazards dt_logit_prepayment", | |
| "```", | |
| "", | |
| "Each artifact carries `evidence_tier`, `population`, `geography`, " | |
| "`outcome_definition`, `weight`, `caveats`, and full `provenance`. A report " | |
| "sentence with no artifact behind it is a defect.", | |
| "", | |
| "Reports are regenerated by `make report` and begin with a `GENERATED` header. " | |
| "Hand-editing them is a defect: the edit is destroyed on the next run and breaks " | |
| "traceability in the meantime.", | |
| "", | |
| ] | |
| b += [ | |
| "## 4. Verification checklist for a reviewer", | |
| "", | |
| "| check | command |", | |
| "|---|---|", | |
| "| schema matches the official layout | `uv run lockin verify-schema` |", | |
| "| all manifests checksum-clean | `make validate-data` |", | |
| "| market rates have no look-ahead | `make validate-data` (rates section) |", | |
| "| payment and rate-gap math | `make test` (`tests/test_amortization.py`, " | |
| "`tests/test_lockin_measures.py`) |", | |
| "| prepayment is not called mobility | `make test` " | |
| "(`tests/test_governance.py::test_no_mobility_language`) |", | |
| "| no restricted data is tracked by git | `make validate-data` (governance section) |", | |
| "| pipeline stage status | `uv run lockin status` |", | |
| "", | |
| "## 5. Known non-reproducible or fragile steps", | |
| "", | |
| "- The FRED cross-check on PMMS is an optional network call and may time out; its " | |
| "failure is recorded and does not stop the pipeline.", | |
| "- Census BPS monthly files are fetched at the `c` (preliminary) vintage by " | |
| 'default. Passing `vintages_to_try=("r", "c")` prefers revised where it exists, ' | |
| "at the cost of one failed request per missing month.", | |
| "- The HMDA aggregations API is rate-sensitive; the adapter caches every cell and " | |
| "records which cells failed, so a partial fetch is visible rather than silently " | |
| "filled with zeros.", | |
| "", | |
| ] | |
| return _write(cfg, "replication_protocol", ctx, "\n".join(b)) | |
| def render_benchmark(cfg: Config) -> Path: | |
| ctx = ReportContext(cfg) | |
| art = ctx.get("benchmark", "benchmark_comparison") | |
| b: list[str] = [] | |
| if not art: | |
| b += ["*Artifact `benchmark/benchmark_comparison` unavailable. Run `make benchmark`.*"] | |
| return _write(cfg, "benchmark_comparison", ctx, "\n".join(b)) | |
| r = art["result"] | |
| b += [ | |
| f"> **{r['standing_rule']}**", | |
| "", | |
| "## Comparison types", | |
| "", | |
| "| type | meaning |", | |
| "|---|---|", | |
| ] | |
| for k, v in r["comparison_type_definitions"].items(): | |
| b.append(f"| `{k}` | {v} |") | |
| b += ["", f"> {r['verification_note']}", "", "---", ""] | |
| for bm in r["benchmarks"]: | |
| b += [ | |
| f"## {bm['id']}", | |
| "", | |
| f"**Comparison type: `{bm['comparison_type']}`**", | |
| "", | |
| f"- **Citation.** {bm['citation']}", | |
| f"- **Target estimand.** {bm['target_estimand']}", | |
| f"- **Original data.** {bm['original_data']}", | |
| f"- **Original identification.** {bm['original_identification']}", | |
| f"- **Our available data.** {bm['our_available_data']}", | |
| f"- **Population differences.** {bm['population_differences']}", | |
| f"- **Outcome-definition differences.** {bm['outcome_definition_differences']}", | |
| f"- **Published magnitude (reference).** {bm['published_magnitude_reference']}", | |
| f"- **Verification status.** {bm['verification_status']}", | |
| f"- **Why not exact.** {bm['why_not_exact']}", | |
| "", | |
| "**Our estimate.**", | |
| "", | |
| ] | |
| oe = bm.get("our_estimate", {}) | |
| if oe.get("status") == "unavailable": | |
| b += [f"*Unavailable: {oe.get('reason')}*", ""] | |
| else: | |
| for k, v in oe.items(): | |
| if k in ("rows", "dynamic_effects"): | |
| b.append( | |
| f"- `{k}`: {len(v) if isinstance(v, list) else '—'} rows in the artifact" | |
| ) | |
| continue | |
| b.append(f"- `{k}`: {_fmt(v)}") | |
| b.append("") | |
| if oe.get("comparison_blocked"): | |
| b += [f"> ⚠️ **Comparison blocked.** {oe['comparison_blocked']}", ""] | |
| b += ["---", ""] | |
| return _write(cfg, "benchmark_comparison", ctx, "\n".join(b)) | |
| def render_executive_memo(cfg: Config) -> Path: | |
| ctx = ReportContext(cfg) | |
| gap = ctx.get("hazards", "gap_profile_nonlinear") | |
| logit = ctx.get("hazards", "dt_logit_prepayment") | |
| het = ctx.get("hazards", "heterogeneity") | |
| purchase = ctx.get("eventstudy", "es_log_purchase_originations") | |
| hpi = ctx.get("eventstudy", "es_hpi_growth") | |
| p1 = ctx.get("eventstudy", "es_log_permits_1unit") | |
| exp = ctx.get("eventstudy", "exposure_distribution") | |
| comp = ctx.get("scenarios", "scenario_comparison") | |
| grid = ctx.get("robustness", "robustness_grid") | |
| def tier_line(art: dict[str, Any] | None) -> str: | |
| if art is None: | |
| return "*not available*" | |
| es = art["result"].get("event_study", {}) | |
| pt = es.get("pretrend_test", {}) if isinstance(es, dict) else {} | |
| v = es.get("mean_post_effect") if isinstance(es, dict) else None | |
| ok = pt.get("passes_at_alpha_0.10") | |
| return ( | |
| f"{_fmt(v)} per s.d. of exposure · tier `{art['evidence_tier']}` · " | |
| f"pre-trend {'passes' if ok else 'fails/untestable'}" | |
| ) | |
| b = [ | |
| "## The ten questions", | |
| "", | |
| "### 1. How is mortgage lock-in defined?", | |
| "", | |
| "Lock-in is a **state**, not an effect: the borrower's outstanding note rate sits " | |
| "below the rate a new mortgage would carry, so moving means giving up cheap " | |
| "financing. We measure it eight ways rather than one — a raw rate gap, its " | |
| "positive part, the mirror-image refinance incentive, a dollar-per-month " | |
| "payment-equivalent cost, a present-value financing gap, and three " | |
| "geography-level exposure shares (loan-count- and UPB-weighted). Every measure is " | |
| "computed **point-in-time**, using only the market rate observable on or before " | |
| "the date in question.", | |
| "", | |
| "The distinction that matters most: lock-in is a *state* we can measure; its " | |
| "*effect* has to be estimated, and the two are constantly conflated in public " | |
| "commentary.", | |
| "", | |
| "### 2. Which borrower groups are most exposed?", | |
| "", | |
| ] | |
| if het: | |
| pre = het["result"].get("prespecified", {}) | |
| rows = pre.get("note_rate_tercile") or [] | |
| if rows and "error" not in rows[0]: | |
| b += [ | |
| "By initial note rate (the dominant driver — a low coupon *is* exposure):", | |
| "", | |
| "| note-rate tercile | loans | mean rate gap (pp) | mean payment gap ($/mo) |", | |
| "|---|---|---|---|", | |
| ] | |
| for r0 in rows: | |
| b.append( | |
| f"| {r0.get('group')} | {_fmt(r0.get('n_loans'))} | " | |
| f"{_fmt(r0.get('mean_rate_gap'), 2)} | " | |
| f"{_fmt(r0.get('mean_payment_gap'), 0)} |" | |
| ) | |
| b.append("") | |
| b += [ | |
| "Exposure is mechanically concentrated in the 2020–21 origination and " | |
| "refinance cohorts. Borrowers who transacted at the rate trough hold the " | |
| "largest gaps; borrowers who transacted before 2019 or after mid-2022 hold " | |
| "small or negative gaps.", | |
| "", | |
| "Two groups are **structurally immune** and are invisible in any share we " | |
| "compute: mortgage-free owners (roughly a third of owner-occupied U.S. " | |
| "homes) and all-cash buyers.", | |
| "", | |
| ] | |
| b += ["### 3. Does higher lock-in predict lower mortgage exits?", ""] | |
| if gap: | |
| emp = gap["result"]["prepayment"]["empirical"] | |
| if emp: | |
| b += [ | |
| "**Yes, strongly and monotonically.** Empirical monthly prepayment " | |
| "hazard by rate-gap bucket:", | |
| "", | |
| "| rate-gap bucket | monthly prepayment hazard |", | |
| "|---|---|", | |
| ] | |
| for r0 in emp: | |
| b.append(f"| {r0['label']} | {_fmt(r0['hazard'], 5)} |") | |
| lo, hi = emp[0]["hazard"], emp[-1]["hazard"] | |
| b += [ | |
| "", | |
| f"The most-locked-in bucket prepays at roughly " | |
| f"**1/{_fmt(lo / hi if hi else float('nan'), 1)}** the rate of the " | |
| f"most-refinance-incentivised bucket.", | |
| "", | |
| ] | |
| if logit: | |
| rg = next((c for c in logit["result"]["coefficients"] if c["term"] == "rate_gap"), None) | |
| if rg: | |
| b += [ | |
| f"Conditional on loan age, credit score, DTI, LTV, balance, and local " | |
| f"price growth, the discrete-time logit coefficient on the rate gap is " | |
| f"**{_fmt(rg['coef'])}** (s.e. {_fmt(rg['std_err'])}), a hazard ratio of " | |
| f"**{_fmt(rg['hazard_ratio'], 3)}** per percentage point.", | |
| "", | |
| ] | |
| b += [ | |
| "**Tier: `hazard_association`.** This is a conditional correlation, not a causal " | |
| "elasticity. The rate gap is a deterministic function of the note rate the " | |
| "borrower chose and the national rate path, and borrowers with different note " | |
| "rates differ in cohort, credit, equity, and tenure.", | |
| "", | |
| "**And note what this is *not*.** These are prepayments — Zero Balance Code 01, " | |
| '*"Prepaid or Matured (Voluntary Payoff)"* — which pools refinancing, ' | |
| "sale-related payoff, and maturity. It is **not** a measure of moving.", | |
| "", | |
| ] | |
| b += [ | |
| "### 4. Does local lock-in exposure predict lower purchase-market activity?", | |
| "", | |
| f"Log HMDA purchase originations: **{tier_line(purchase)}**.", | |
| "", | |
| ] | |
| if purchase: | |
| es = purchase["result"].get("event_study", {}) | |
| did = purchase["result"].get("did_two_period", {}) | |
| if es.get("status") == "ok" and did.get("status") == "ok": | |
| t = did.get("t") | |
| sig = abs(t) >= 1.645 if t is not None else False | |
| b += [ | |
| f"The collapsed pre/post estimate is {_fmt(did['coef'])} " | |
| f"(s.e. {_fmt(did['std_err'])}, t = {_fmt(t, 2)}, " | |
| f"{_fmt(did['n_clusters'])} clusters) — " | |
| + ( | |
| "statistically distinguishable from zero at 10%." | |
| if sig | |
| else "**not statistically distinguishable from zero.**" | |
| ), | |
| "", | |
| "The sign is negative, consistent with the mechanism, but with " | |
| f"{_fmt(did['n_clusters'])} state clusters and a within-sample exposure " | |
| "spread of only a fraction of a standard deviation in economic terms, this " | |
| "design has limited power. **A negative point estimate that does not " | |
| "clear conventional significance is not evidence of an effect, and it is " | |
| "not evidence against one either.**", | |
| "", | |
| ] | |
| b += [ | |
| "### 5. What happens to local prices?", | |
| "", | |
| f"House price growth: **{tier_line(hpi)}**.", | |
| "", | |
| "**We do not assume a sign, and the theory does not give us one.** A locked-in " | |
| "owner withdraws from *both* sides of the market: they do not list, and they do " | |
| "not buy a replacement. The first raises prices, the second lowers them. The net " | |
| "effect depends on which side is more inelastic and on how much demand comes " | |
| "from first-time buyers and investors, who are not locked in at all.", | |
| "", | |
| "This is why the decomposition report exists and why any confident public claim " | |
| 'that lock-in "propped up prices" is running ahead of the identification.', | |
| "", | |
| ] | |
| b += [ | |
| "### 6. What happens to construction?", | |
| "", | |
| f"Log single-family permits authorized: **{tier_line(p1)}**.", | |
| "", | |
| "Two channels point in opposite directions. If lock-in makes existing homes " | |
| "scarce and expensive, builders substitute toward new construction. If it " | |
| "suppresses trade-up demand, permits fall. Census BPS measures permits " | |
| "**authorized**, not starts and not completions, so even a clean estimate would " | |
| "be an intention rather than an outcome.", | |
| "", | |
| ] | |
| b += [ | |
| "### 7. Which evidence is causal?", | |
| "", | |
| "**Candidate causal evidence:** the continuous-treatment event studies on " | |
| "predetermined exposure, and **only** those outcomes whose pre-trend test passes " | |
| "and whose placebos are clean. The tier is assigned by the code from the " | |
| "diagnostics, not by an author's judgement — a failed pre-trend automatically " | |
| "demotes the artifact to `descriptive` with no override.", | |
| "", | |
| ] | |
| if exp: | |
| p = exp["result"]["primary"] | |
| if p.get("balance_table"): | |
| worst = max(p["balance_table"], key=lambda x: abs(x["correlation_with_exposure"])) | |
| b += [ | |
| f"Even where pre-trends pass, exposure is **not randomly assigned**: its " | |
| f"correlation with `{worst['variable']}` is " | |
| f"{_fmt(worst['correlation_with_exposure'], 2)}. Predetermined is not " | |
| "exogenous, and we use **no instrumental-variable language** anywhere.", | |
| "", | |
| ] | |
| b += [ | |
| "What is *not* identified even in the best case: the **aggregate** effect of the " | |
| "national rate increase. The rate path is common to every geography and is " | |
| "absorbed by time fixed effects. This design can only speak to differences " | |
| "across exposure.", | |
| "", | |
| ] | |
| b += [ | |
| "### 8. Which evidence is correlational?", | |
| "", | |
| "- Every loan-level hazard result (`hazard_association`).", | |
| "- Every descriptive table, survival curve, and cumulative-incidence function " | |
| "(`descriptive`).", | |
| "- Every event-study outcome demoted by a failed or untestable pre-trend.", | |
| "- The predictive-benchmark comparison, which speaks to fit and nothing else.", | |
| "", | |
| "The loan-level and market-level results are deliberately reported in separate " | |
| "files so that a reader cannot accidentally borrow the credibility of one for " | |
| "the other.", | |
| "", | |
| ] | |
| b += ["### 9. Which policy scenarios appear most effective under the model?", ""] | |
| if comp: | |
| r = comp["result"] | |
| b += ["| scenario | modelled additional monthly prepayments | % change |", "|---|---|---|"] | |
| for row in r["ranking"][:8]: | |
| b.append( | |
| f"| {row['policy_type']} | " | |
| f"{_fmt(row['additional_monthly_prepayments'], 1)} | " | |
| f"{_fmt((row['pct_change_in_prepayments'] or 0) * 100, 1)}% |" | |
| ) | |
| b += [ | |
| "", | |
| "**Read the ordering, not the magnitudes.** These are " | |
| "`simulation`-tier projections and explicitly **not forecasts**. They apply " | |
| "a hazard *association* as if it were a structural response function, and " | |
| "the mapping into transactions, prices, and permits rests on calibrated " | |
| "elasticities with no error bars plus an **unidentified** " | |
| "prepayment-to-transaction share (reported across a range, never as a point " | |
| "value).", | |
| "", | |
| "Three points a policy reader should take from the scenario set:", | |
| "", | |
| "1. **Portability and assumability transfer the below-market-coupon loss, " | |
| "they do not eliminate it.** Someone holds the cheap coupon; the scenarios " | |
| "do not model who pays.", | |
| "2. **Cost per *additional* transaction is far above cost per assisted " | |
| "borrower**, because most assisted borrowers would have transacted anyway. " | |
| "The buydown scenario reports both.", | |
| "3. **Supply elasticity and lock-in policy are complements.** The same " | |
| "modelled demand shift converts into mostly-quantity or mostly-price " | |
| "depending on a calibrated supply elasticity — which is why a demand-side " | |
| "unlock in an inelastic market partly capitalises into prices.", | |
| "", | |
| ] | |
| b += [ | |
| "### 10. What are the largest limitations?", | |
| "", | |
| "1. **A prepayment is not a move.** Zero Balance Code 01 pools refinancing, " | |
| "sale-related payoff, and maturity. Nothing here separates them, so nothing here " | |
| "measures mobility.", | |
| "2. **Only relative effects are identified**, never the aggregate effect of the " | |
| "rate increase.", | |
| "3. **Predetermined exposure is not exogenous** — it correlates with pandemic " | |
| "price growth and refinance intensity, which independently predict post-2022 " | |
| "outcomes.", | |
| "4. **The population is doubly selected**: conforming conventional Freddie Mac " | |
| "acquisitions, within a mortgage market that itself excludes cash buyers and the " | |
| "roughly one third of owner-occupied homes with no mortgage.", | |
| "5. **The demand/supply decomposition is framed, not achieved.** No listings " | |
| "data, no transaction records, no household panel.", | |
| "6. **Low power at the state level.** 26 clusters and a narrow exposure spread.", | |
| ] | |
| if grid and grid["result"].get("n_flagged") is not None: | |
| b.append( | |
| f"7. **Robustness:** {_fmt(grid['result']['n_flagged'])} of " | |
| f"{_fmt(grid['result']['n_cells'])} specification cells were flagged as sign " | |
| "flips, inestimable, or failing placebos. See `reports/failed_hypotheses.md`." | |
| ) | |
| b.append("") | |
| b += [ | |
| "---", | |
| "", | |
| "## What would change these conclusions", | |
| "", | |
| "| would resolve | needs |", | |
| "|---|---|", | |
| "| lock-in and mobility | linked mortgage-and-property records, or a " | |
| "credit-bureau address panel |", | |
| "| refinance vs sale payoff | a property identifier, or a servicer panel |", | |
| "| listing vs repeat-buyer channel | MLS listings data |", | |
| "| statistical power | MSA-level analysis with a versioned crosswalk, plus both " | |
| "Enterprises' loan-level files |", | |
| "| exogenous exposure | a shifter of the local coupon distribution unrelated to " | |
| "local demand — we have not found one |", | |
| "", | |
| ] | |
| return _write(cfg, "executive_housing_policy_memo", ctx, "\n".join(b)) | |
| def render_technical(cfg: Config) -> Path: | |
| ctx = ReportContext(cfg) | |
| ds = ctx.get("hazards", "survival_dataset") | |
| logit = ctx.get("hazards", "dt_logit_prepayment") | |
| gap = ctx.get("hazards", "gap_profile_nonlinear") | |
| exp = ctx.get("eventstudy", "exposure_distribution") | |
| purchase = ctx.get("eventstudy", "es_log_purchase_originations") | |
| grid = ctx.get("robustness", "robustness_grid") | |
| comp = ctx.get("scenarios", "scenario_comparison") | |
| val = ctx.get("validation", "validation_report") | |
| b = [ | |
| VOCAB_NOTE, | |
| "", | |
| "## 0. How to read this document", | |
| "", | |
| "Every claim below is tagged with an **evidence tier**. The tiers are not " | |
| "decorative: they determine the verb the sentence is allowed to use.", | |
| "", | |
| "| tier | permitted language | what it means |", | |
| "|---|---|---|", | |
| '| `descriptive` | "describes", "among … the rate was" | means, rates, ' | |
| "distributions. No causal content |", | |
| '| `hazard_association` | "is associated with", "predicts" | conditional ' | |
| "correlation from a duration model |", | |
| '| `quasi_experimental` | "reduced", "increased" — **only** with passing ' | |
| "pre-trends and clean placebos | event study / DiD with a stated identification " | |
| "argument |", | |
| '| `simulation` | "under the model", "model-dependent" | counterfactual ' | |
| "projection. **Never a forecast** |", | |
| "", | |
| "A sentence that mixes tiers is a defect. The decision rule is in " | |
| "`docs/IDENTIFICATION_STRATEGY.md` §6 and is enforced by " | |
| "`lockin.reporting.render.verb_for`.", | |
| "", | |
| "---", | |
| "", | |
| "## 1. Research question and design", | |
| "", | |
| "> How does the gap between homeowners' existing mortgage rates and current market " | |
| "mortgage rates affect mortgage exits, housing-market activity, local prices, and " | |
| "new construction?", | |
| "", | |
| "Four layers:", | |
| "", | |
| "1. **Loan-level duration analysis** — does a larger rate gap predict lower " | |
| "prepayment? (`hazard_association`)", | |
| "2. **Local-market panel** — how do exposure, originations, prices, and permits " | |
| "co-move? (`descriptive`)", | |
| "3. **Quasi-experimental design** — continuous-treatment event study on " | |
| "predetermined exposure. (`quasi_experimental`, conditional on diagnostics)", | |
| "4. **Counterfactual module** — hazard-based policy scenarios. (`simulation`)", | |
| "", | |
| "The layers answer different questions and are reported in separate files " | |
| "precisely so their credibility does not leak into one another.", | |
| "", | |
| ] | |
| b += [ | |
| "## 2. Data and population", | |
| "", | |
| "| source | what it is | role | access |", | |
| "|---|---|---|---|", | |
| "| Freddie Mac Single-Family Loan-Level | origination + monthly performance | " | |
| "exits, coupon distribution | **registration required**; not redistributed |", | |
| "| Freddie Mac PMMS | weekly national average offered rate | the market rate in " | |
| "every gap measure | public |", | |
| "| FHFA HPI | repeat-sales index | price outcome, LTV scaling | public |", | |
| "| HMDA (CFPB Data Browser) | applications and originations | purchase/refi " | |
| "activity, denial rate | public API |", | |
| "| Census BPS | permits authorized | construction outcome | public |", | |
| "", | |
| ] | |
| if ds: | |
| ll = ds["result"]["loan_level"] | |
| r = ds["result"] | |
| b += [ | |
| f"This run: **{_fmt(ll['n_loans'])}** loans, " | |
| f"**{_fmt(r['estimation_sample']['n_rows'])}** estimation loan-months over " | |
| f"`{r['estimation_sample']['period']}`, " | |
| f"**{_fmt(ll['n_prepayments'])}** prepayments, " | |
| f"**{_fmt(ll['n_credit_events'])}** credit events, " | |
| f"**{_fmt(ll['n_censored'])}** censored, " | |
| f"**{_fmt(ll['n_left_truncated'])}** left truncated.", | |
| "", | |
| ] | |
| b += [ | |
| "**The population is not the market.** Conforming conventional Freddie Mac " | |
| "acquisitions only: no FHA/VA (which are *assumable*, so lock-in works " | |
| "differently there), no jumbo, no non-QM, no portfolio, no Fannie Mae, no " | |
| "all-cash purchases, and no mortgage-free owners. Full treatment in " | |
| "`reports/methodology_and_limitations.md` §3.", | |
| "", | |
| ] | |
| b += ["## 3. Loan-level results — `hazard_association`", ""] | |
| if gap: | |
| emp = gap["result"]["prepayment"]["empirical"] | |
| if emp: | |
| b += [ | |
| "Monthly prepayment hazard by rate-gap bucket:", | |
| "", | |
| "| bucket | loan-months | hazard |", | |
| "|---|---|---|", | |
| ] | |
| for r0 in emp: | |
| b.append(f"| {r0['label']} | {_fmt(r0['n_at_risk'])} | {_fmt(r0['hazard'], 5)} |") | |
| b.append("") | |
| if logit: | |
| r = logit["result"] | |
| rg = next((c for c in r["coefficients"] if c["term"] == "rate_gap"), None) | |
| if rg: | |
| b += [ | |
| f"Discrete-time logit, {_fmt(r['n_obs'])} loan-months, " | |
| f"{_fmt(r['n_events'])} events, {r['standard_errors']}: rate-gap " | |
| f"coefficient **{_fmt(rg['coef'])}** (s.e. {_fmt(rg['std_err'])}), hazard " | |
| f"ratio **{_fmt(rg['hazard_ratio'], 3)}** per pp, average marginal effect " | |
| f"**{_fmt(r.get('rate_gap_average_marginal_effect_monthly'), 6)}** per month.", | |
| "", | |
| ] | |
| b += [f"> {r['interpretation_warning']}", ""] | |
| b += [ | |
| "Full ladder — Kaplan–Meier, cumulative incidence, logit, cloglog, " | |
| "cause-specific competing risks, Cox with PH diagnostics, and a gradient-boosted " | |
| "predictive benchmark — in `reports/loan_hazard_analysis.md`.", | |
| "", | |
| ] | |
| b += [ | |
| "## 4. Identification for the market-level design", | |
| "", | |
| "$$E_g = \\sum_k \\omega_{gk}^{\\text{pre}} \\cdot " | |
| "\\mathbf 1\\{\\bar R^{\\text{post}} - r_k > \\tau\\}$$", | |
| "", | |
| "Frozen pre-shock coupon shares × the later national rate level. All " | |
| "cross-sectional variation is in the shares.", | |
| "", | |
| ] | |
| if exp: | |
| p = exp["result"]["primary"] | |
| if p.get("status") == "ok": | |
| b += [ | |
| f"Exposure `{p['exposure']}`: mean {_fmt(p['mean'])}, s.d. " | |
| f"{_fmt(p['sd'])}, range [{_fmt(p['min'])}, {_fmt(p['max'])}] across " | |
| f"{_fmt(p['n_geographies'])} states, standardised in every regression.", | |
| "", | |
| ] | |
| if p.get("balance_table"): | |
| b += ["| pre-period variable | correlation with exposure |", "|---|---|"] | |
| for r0 in p["balance_table"]: | |
| b.append(f"| `{r0['variable']}` | {_fmt(r0['correlation_with_exposure'], 3)} |") | |
| b += [ | |
| "", | |
| "These correlations are the reason **no IV interpretation is " | |
| "claimed**. Predetermined ≠ exogenous.", | |
| "", | |
| ] | |
| b += [ | |
| "Assumptions, threats, and the decision rule for causal language: " | |
| "`docs/IDENTIFICATION_STRATEGY.md`.", | |
| "", | |
| ] | |
| b += ["## 5. Market-level results", ""] | |
| if purchase: | |
| es = purchase["result"].get("event_study", {}) | |
| did = purchase["result"].get("did_two_period", {}) | |
| pt = es.get("pretrend_test", {}) if es.get("status") == "ok" else {} | |
| b += [ | |
| f"Headline outcome: log HMDA purchase originations. Tier " | |
| f"`{purchase['evidence_tier']}`; pre-trend p = {_fmt(pt.get('pvalue'), 3)} " | |
| f"({'passes' if pt.get('passes_at_alpha_0.10') else 'fails'} at α = 0.10).", | |
| "", | |
| ] | |
| if did.get("status") == "ok": | |
| b += [ | |
| f"Collapsed DiD: **{_fmt(did['coef'])}** (s.e. {_fmt(did['std_err'])}, " | |
| f"t = {_fmt(did.get('t'), 2)}, {_fmt(did['n_clusters'])} clusters).", | |
| "", | |
| ] | |
| b += [ | |
| "Every outcome, its dynamic path, pre-trends, and placebos: " | |
| "`reports/local_market_event_study.md`. Why quantities are unambiguous and prices " | |
| "are not: `reports/demand_supply_decomposition.md`.", | |
| "", | |
| ] | |
| b += ["## 6. Robustness", ""] | |
| if grid and grid["result"].get("n_cells"): | |
| r = grid["result"] | |
| b += [ | |
| f"{_fmt(r['n_cells'])} specification cells; {_fmt(r['n_flagged'])} flagged.", | |
| "", | |
| "| verdict | cells |", | |
| "|---|---|", | |
| ] | |
| for v in r["verdict_counts"]: | |
| b.append(f"| `{v['verdict']}` | {_fmt(v['n'])} |") | |
| b += [ | |
| "", | |
| "Cells vary the exposure definition, the bp threshold, the weighting " | |
| "(count vs UPB), the control set, sample exclusions (pandemic-boom and " | |
| "high-refi markets), the HMDA coverage regime, panel balance, placebo shock " | |
| "dates, and placebo outcomes.", | |
| "", | |
| "Failures are enumerated in `reports/failed_hypotheses.md`, together with " | |
| "five genuine **design errors** caught during construction and the " | |
| "diagnostics that caught them.", | |
| "", | |
| ] | |
| b += ["## 7. Counterfactuals — `simulation`", ""] | |
| if comp: | |
| r = comp["result"] | |
| b += ["| scenario | additional monthly prepayments | % change |", "|---|---|---|"] | |
| for row in r["ranking"][:6]: | |
| b.append( | |
| f"| {row['policy_type']} | " | |
| f"{_fmt(row['additional_monthly_prepayments'], 1)} | " | |
| f"{_fmt((row['pct_change_in_prepayments'] or 0) * 100, 1)}% |" | |
| ) | |
| b += ["", f"> {r['not_a_forecast']}", ""] | |
| b += [ | |
| "Detail, calibrated inputs, and the unidentified prepayment-to-transaction share: " | |
| "`reports/policy_counterfactuals.md`.", | |
| "", | |
| ] | |
| b += ["## 8. Validation and reproducibility", ""] | |
| if val: | |
| r = val["result"] | |
| b += [ | |
| f"`make validate-data`: **{_fmt(r['n_hard'])} hard**, {_fmt(r['n_soft'])} " | |
| f"soft, {_fmt(r['n_info'])} informational findings. A hard finding fails the " | |
| "run.", | |
| "", | |
| ] | |
| b += [ | |
| "Every artifact records git commit, config digest, data period, and per-dataset " | |
| "`schema@retrieved#checksum`. Every dataset on disk carries a manifest with a " | |
| "SHA-256 checksum that `make validate-data` re-verifies. Reproduction steps and " | |
| "the reviewer checklist: `reports/replication_protocol.md`.", | |
| "", | |
| "## 9. What this project does not establish", | |
| "", | |
| "1. Any effect of lock-in on household **mobility**. No mobility measure exists " | |
| "in these data.", | |
| "2. Any effect on home **sales** or **listings**. No sale indicator, no listings source.", | |
| "3. The **aggregate** effect of the 2022–23 rate increase. Absorbed by time fixed effects.", | |
| "4. A **decomposition** of the listing-side and repeat-buyer-side channels.", | |
| "5. Behaviour of FHA/VA, jumbo, non-QM, portfolio, or all-cash segments.", | |
| "6. Any **forecast**. The scenario module is explicitly not one.", | |
| "", | |
| ] | |
| return _write(cfg, "technical_report", ctx, "\n".join(b)) | |
| def render_all(cfg: Config) -> list[Path]: | |
| """Regenerate every report. Order matters only for readability of the log.""" | |
| # Stamp the reports directory with the profile that produced it. `reports/` is a | |
| # shared path, so without this a reader -- or a governance test -- has no way to tell | |
| # whether the markdown on disk came from a SYNTHETIC or a REGISTERED run, and would | |
| # have to guess from whichever config it happened to load. | |
| from lockin import dataset_stamp | |
| dataset_stamp.write(cfg, cfg.path("reports")) | |
| return [ | |
| render_loan_hazard(cfg), | |
| render_event_study(cfg), | |
| render_decomposition(cfg), | |
| render_policy(cfg), | |
| render_failed(cfg), | |
| render_methodology(cfg), | |
| render_replication_protocol(cfg), | |
| render_benchmark(cfg), | |
| render_executive_memo(cfg), | |
| render_technical(cfg), | |
| ] | |
| def _unused(x: pl.DataFrame) -> None: # pragma: no cover | |
| return None | |