Mortgage Rate Lock-In and Housing Market Dynamics

A registered-data housing-finance research system that measures lock-in without confusing mortgage exits with home sales or household moves.

Housing economicsMortgage financeApplied econometrics

Research question and relevance

Question: 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?

Mortgage lock-in may remove the same owner from both sides of the market: fewer existing homes may be listed, while repeat-buyer demand may also fall. The project therefore treats lock-in as a measurable state, not an effect, and does not assume the sign of the price response.

Vocabulary boundary: Freddie Mac Zero Balance Code 01 identifies a voluntary payoff or maturity. It does not distinguish a refinance from a sale-related payoff and is not a measure of moving. The loan-level outcome is always described as prepayment or mortgage exit.

Data and publication boundary

The registered analysis uses a 5% loan sample from Freddie Mac cohorts originating from 2013Q1 through 2022Q4, together with PMMS rates, FHFA house-price indexes, HMDA aggregations, Census building permits, BLS labor-market data, and documented geographic crosswalks.

MaterialPublic release treatment
Source, tests, configuration, documentationPublished on GitHub and mirrored in the Dataset.
Synthetic fixturesPublished with explicit SYNTHETIC labeling.
Registered Freddie Mac loan recordsNever redistributed.
Loan-granular derivatives and local cachesExcluded from every public package.
Aggregate reports and coefficientsPublished with evidence tiers, population statements, and source attribution.

Methodology and evidence tiers

  1. Construct eight point-in-time lock-in measures without forward-looking rate alignment.
  2. Build mortgage-exit and competing-risk outcomes with explicit left truncation and censoring.
  3. Estimate survival summaries and discrete-time hazards at the loan-month level.
  4. Freeze pre-shock local coupon exposure and estimate state and metropolitan event studies.
  5. Automatically demote results when pre-trends or placebos fail.
  6. Keep model-dependent policy scenarios in a separate simulation tier and never call them forecasts.

Every result artifact is labeled descriptive, hazard_association, quasi_experimental, or simulation. A sentence that mixes these tiers is treated as a defect.

What the registered-data run supports

ResultEvidence tierInterpretation
Rate-gap coefficient: -0.2020; hazard ratio: 0.817 per percentage pointHazard associationGreater lock-in is strongly associated with lower monthly prepayment hazard after observed controls. This is not a causal elasticity and not a mobility estimate.
Purchase-originations estimates are negative at state and MSA levelsDescriptivePre-trends fail at both geographic levels, so greater precision does not earn causal interpretation.
Single-family permit event studyQuasi-experimental candidatePre-trends pass, but the estimate is statistically indistinguishable from zero.
Policy scenariosSimulationUseful for comparing modeled orderings, not for forecasting magnitudes.

The honest headline is the ordering of evidence: the loan-level association is strong, while the market-level designs do not establish a causal effect on purchase activity, prices, or permits.

Reproduction and citation

Run make setup followed by make reproduce-sample for the public synthetic vertical slice. A registered-data run requires the user to obtain licensed Freddie Mac archives independently and place them in the documented local path; the project never bypasses registration or redistributes the files.

Cite the Observatory release, Dataset revision, project repository, and every original data provider used in a run.

Limitations

  • Prepayment is not a home sale, refinance, or household move; the source does not separate those events.
  • The Freddie Mac population excludes major mortgage segments and all-cash transactions.
  • Predetermined exposure is not exogenous, and time effects absorb the common national rate shock.
  • The demand-versus-supply decomposition is framed but not identified without listings, transactions, or household mobility data.
  • Published scenario outputs rely on calibrated assumptions and are not forecasts.