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Prepare mortgage data in this order: choose a dataset that matches the prediction target, freeze the prediction-time cutoff, preserve the source files and definitions, profile missing and extreme values, account for privacy masking, then build time-aware labels, features and validation splits. HMDA is suited to application and lending-pattern analysis; Freddie Mac’s Single-Family Loan-Level Dataset is designed for acquisition and monthly performance analysis. They are not interchangeable, and a model can be invalid if it uses fields that were unavailable when the prediction would have been made.
Start with the target, not the spreadsheet
Mortgage records can represent an application, an originated loan, or a loan observed repeatedly over its life. Those units answer different questions. An application model might estimate approval or pricing patterns. An origination model might predict early payment default. A loan-month model might forecast delinquency, prepayment or loss using information available at each monthly observation.
Before downloading or cleaning data, write a one-page problem definition containing:
- Unit of prediction: application, originated loan, or loan-month.
- Outcome: the exact event, such as approval, first-payment default, 90-day delinquency, payoff or prepayment.
- Prediction date: the timestamp at which the score is supposed to be produced.
- Eligible population: which applications or loans enter the model and which are excluded.
- Observation and follow-up windows: the period used to construct predictors and the later period used to determine the label.
- Evaluation period: a later period that represents deployment conditions.
This definition prevents a common error: building a label from later performance fields and then accidentally leaving those same fields in the feature table. Keep identifiers for joins and audit trails, but exclude them from model inputs unless their use is explicitly justified and permitted.
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Choose a source that matches the question
Two official sources are commonly useful, but they describe different populations and data-generating processes.
| Source | Best fit | Unit and coverage | Time and outcomes | Access and cautions |
|---|---|---|---|---|
| HMDA public data | Application, approval, pricing, lender, geographic and market-pattern analysis | Reported mortgage applications and lending activity; fields and allowed values vary by reporting year | Reporting-year files; dynamic national files incorporate received submissions and resubmissions, while snapshot files preserve a dated view | Public records are modified to protect applicant and borrower privacy; record the reporting year and extract type |
| Freddie Mac Single-Family Loan-Level Dataset | Credit performance, delinquency, prepayment and servicing-outcome modeling | Freddie Mac-purchased or guaranteed single-family mortgages; approximately 56 million mortgages originated from January 1, 1999 through March 31, 2026 | Acquisition records plus monthly performance disclosures through March 31, 2026 | Registration is required. It is free for specified noncommercial academic/research and limited uses under its terms; commercial redistribution requires a licensing agreement. The historical dataset is unaudited and subject to change. |
HMDA is described by the Consumer Financial Protection Bureau as the most comprehensive publicly available source of information on the U.S. mortgage market. Its records concern reported applications and lending activity, not a complete performance history for every mortgage. Freddie Mac’s file concerns loans in Freddie Mac’s own acquisition and performance population, so it should not be treated as a national census.
For HMDA, capture whether you used a dynamic or snapshot extract. Dynamic national files are updated weekly to include received submissions and resubmissions; a snapshot is a dated view. Save the extract date and reporting vintage so another analyst can reproduce the population later.
For Freddie Mac data, download the current layout and General User Guide, save the vintage, and record the access and redistribution terms. Historical files can change, and Freddie Mac explicitly warns that they may be incomplete or contain errors.
Freeze the source version and build an audit trail
Keep an immutable copy of every raw file. Create a metadata record with the source name, URL, download timestamp, reporting year, file or specification version, extract type, license terms and checksum. Perform cleaning in a separate analytical layer rather than overwriting the source.
Field meanings and permitted values are specification-dependent. For GSE single-family deliveries, the Uniform Loan Delivery Dataset (ULDD) is the common set of data elements required by Fannie Mae and Freddie Mac. The ULDD page lists Phase 5 release 5.2.0 dated May 26, 2026. If a field is present in a delivery file, map it against the specification that applied to that file; do not assume a current label describes an older vintage.
The Uniform Mortgage Data Program standardizes data across appraisal, application, closing-disclosure and loan-delivery processes. That improves consistency for GSE delivery data, but it does not automatically define every public HMDA field or make every field appropriate as a model feature.
Establish a reproducible schema
Create a data dictionary for the analytical layer. For each column, store its source name, normalized name, data type, unit, valid values, reporting-year availability, missing-value meanings, privacy treatment and transformation history.
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- Keep the original categorical code and a separate documented label. Never replace a code with an undocumented number.
- Store dates as dates only when the source provides that precision. Keep month-only or year-month values at their stated granularity.
- Store monetary amounts with an explicit currency and unit. Do not infer dollars, thousands or rounded values from a column name alone.
- Retain source identifiers in a restricted join and audit table, while excluding them from features by default.
- Represent booleans consistently and document whether blank, zero, “not applicable,” “not provided,” “exempt” and “unknown” are distinct states.
- Version the dictionary whenever a reporting year or specification changes.
Normalize names and types only after preserving the raw representation. A value that looks numeric may be an enumerated code; converting it to a continuous feature can create a false ordering. Conversely, a percentage stored as text should be parsed with its scale documented so that 5 percent is not confused with 0.05.
Profile quality before imputing or scaling
Run a field-level profile before deciding how to handle missing values. At minimum, calculate type, row count, distinct count, frequency of every documented code, missingness rate, first and last observed date, and distribution summaries for numeric values. Repeat the profile by reporting year and by major population groups so a change in a definition is not mistaken for a market trend.
Separate kinds of missingness
Use the source data dictionary to distinguish true missing data from not applicable, not provided, exempt and unknown codes. These states can carry different meanings. For example, a field that is not applicable to a loan product should not automatically receive the median value for borrowers who were eligible for that field.
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Check units, ranges and combinations
Test domain relationships rather than relying only on generic minimum and maximum checks. Examples include a date sequence that runs backward, a nonzero balance with a missing loan identifier, a ratio whose numerator and denominator cannot produce the reported value, or a performance record appearing before the loan’s origination. Keep an exception table containing the source row, violated rule, investigation result and final disposition.
The CFPB’s HMDA 2023 snapshot documentation warns that loan amount and income have no upper limit and may contain outliers. Do not delete or cap large values simply because they look unusual. Compare the value with the field definition, neighboring records and source documentation first. If you winsorize, cap, log-transform or otherwise alter a tail, record the exact rule and compare model behavior with and without that transformation.
Find duplicates at the correct grain
A duplicate application, a resubmission and two monthly records for the same loan are not the same problem. Define the intended key before deduplicating. For an application table, the key may combine the source application identifier and reporting vintage. For a performance table, the key generally includes the loan identifier and observation month. Preserve removed rows and the reason for removal in an audit table.
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Public mortgage data are not necessarily exact operational records. The CFPB states that “The public data are modified to protect applicant and borrower privacy.” Treat the released precision as the maximum precision you can defend.
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Freddie Mac’s user guide describes treatments including dates reduced to month and year, original unpaid balances rounded to the nearest $1,000, and ZIP codes with the last two digits replaced by zeroes. These changes affect temporal granularity, geographic analysis and joins. Do not reverse-engineer a masked date or imply a ZIP-level precision that the file does not provide.
Privacy masking also changes how you should validate joins. A coarsened geography can match multiple real locations, and a month-only date cannot establish an exact ordering within that month. Use stable, documented keys where available and report unmatched or many-to-many joins rather than forcing a match.
Construct labels without using future information
Define the label from source fields only after checking the relevant year’s data dictionary. For a default or prepayment model, choose a follow-up window and mark observations that do not have enough follow-up as censored or ineligible instead of silently treating them as non-events.
Freddie Mac performance files contain monthly information. If the intended prediction is made at origination, a later monthly delinquency, modification, balance or status field cannot be an input. If the intended prediction is made at month six, fields through month six may be used only if they would have been available at that scoring time; month-seven information belongs exclusively to the outcome window or a later model.
Write the label-building logic as a versioned transformation. Store the prediction date, outcome window, event date, censoring date and label version for every training row. This makes it possible to explain why a loan was labeled an event, a non-event or not eligible.
Engineer features that would exist at prediction time
Start with source fields that are both documented and available at the cutoff. Useful transformations can include ratios, log-scaled monetary values, calendar indicators, grouped categories and counts, but each one needs a stated denominator, unit and time window.
- Ratios: preserve the numerator, denominator and handling for zero or missing denominators. A ratio should not be calculated from a value observed after the prediction date.
- Categories: use one-hot, target-safe or other encoding fitted on training data. Keep an explicit category for documented unknown or not-applicable states when they are meaningful.
- Geography: respect ZIP-code coarsening and privacy limits. Avoid presenting a masked area as an exact location.
- Time: derive age, seasoning or calendar variables from dates available at the cutoff, retaining the source precision.
- Aggregates: calculate lender, borrower or property histories using only records that precede the prediction date and define how sparse groups are handled.
Do not use a loan identifier as a proxy for risk merely because it improves a validation score. Likewise, avoid post-origination servicing fields in an origination model. Feature review should ask, “Could an analyst have known this value at the exact scoring instant?” rather than merely, “Is the column present in the file?”
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Split data by time and entity
For future-outcome prediction, a chronological holdout is usually more realistic than a random row split. Train on earlier observation periods, tune on a later period and reserve the newest period for final evaluation. A time split exposes degradation caused by changing rates, underwriting, reporting practices or economic conditions.
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When random sampling is necessary, split at the borrower or loan level so repeated loan-month records cannot appear in both training and test sets. Otherwise, the model may recognize the same loan’s history rather than learn a pattern that generalizes to unseen loans.
Fit preprocessing, imputation, scaling, encoding, feature selection and threshold selection inside the training folds. Apply the resulting transformations to validation and test data without refitting. Report performance by time period and by meaningful borrower, product and geographic groups. The available material does not establish a universal accuracy, default-rate or lift benchmark; publish measured results only for your own defined population and evaluation design.
Validate population coverage and drift
Compare the training population with the intended deployment population before interpreting a score. Examine changes in application mix, product categories, geography, income, loan amount, missingness, reporting institution and observation timing. A model trained on Freddie Mac acquisitions should not be described as representative of all U.S. applications, and a model trained on one HMDA reporting year may not transfer unchanged to another.
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Monitor both feature drift and label drift after deployment. A rise in missing “income” values, for example, may indicate a reporting change rather than a borrower change. Keep the original source vintage in monitoring records so a sudden shift can be traced to a revised file, late submission or specification change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build governance into preparation
Mortgage models can affect access to credit and pricing. Preparation alone does not establish that a model is lawful, fair or suitable for a consequential decision. Include fair-lending, privacy, explainability, model-risk and human-review expertise in the governance process.
- Document the intended use, prohibited uses, population, cutoff and known limitations.
- Keep an approval record for each source, transformation, label definition and specification version.
- Evaluate error and performance across relevant groups and time periods, not only in aggregate.
- Preserve enough provenance to reproduce a score without exposing restricted applicant information.
- Define escalation and human-review procedures for uncertain, anomalous or out-of-distribution cases.
- Reassess the model when a source changes its fields, privacy treatment, reporting process or access terms.
A practical preparation workflow
- State the task: record unit, outcome, cutoff, eligible population, follow-up and evaluation periods.
- Select the source: use HMDA for application and lending-pattern questions; use the Freddie Mac loan-level files for the covered acquisition and performance population.
- Freeze provenance: save raw files, URLs, timestamps, reporting vintage, file layout, terms and checksums.
- Map the schema: retain raw codes, documented labels, units, precision and year-specific definitions.
- Profile quality: measure missingness, distinct values, distributions, duplicates, invalid combinations and outliers before changing anything.
- Represent privacy limits: preserve month-only dates, rounded balances and coarsened geography as released.
- Build labels: define event and censoring rules from source fields, then store label versions and dates.
- Engineer cutoff-safe features: fit all learned transformations only on training data.
- Split and test: prefer chronological evaluation; otherwise group repeated records by borrower or loan.
- Review governance: assess group performance, explainability, privacy, fair-lending concerns and human oversight before deployment.
Common failure modes and fixes
Using the wrong population
Symptom: a performance model is trained on application records with no reliable follow-up, or an application model is treated as if it covered every originated loan.
Fix: return to the unit and outcome definition, then select the source whose population and observation window actually contain them.
Converting codes into fake measurements
Symptom: an encoded category is treated as a continuous number, making the model assume that code 4 is twice code 2.
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Fix: preserve the raw code, map its documented label and use an encoding appropriate for a nominal or ordinal variable only when the specification supports that interpretation.
Dropping every unusual value
Symptom: high-income or high-loan records are removed because they exceed an arbitrary bound.
Fix: investigate against the source definition; HMDA documentation does not impose an upper limit on loan amount or income. Apply a documented transformation only after measuring its effect.
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Symptom: an origination model reports implausibly strong validation because later delinquency or balance fields remain in the feature table.
Fix: enforce the prediction cutoff in the feature-building query and test every feature for availability at that instant.
Letting repeated loans cross a split
Symptom: loan-month rows from one mortgage appear in both training and test data.
Fix: split by loan or borrower, or use a chronological split that keeps the future evaluation period separate.
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Assuming public precision is operational precision
Symptom: a model or dashboard claims exact location or day-level timing from masked public fields.
Fix: retain the released granularity and describe the privacy treatment in the data dictionary and model documentation.
What a defensible handoff contains
A finished training dataset should be accompanied by the raw-file manifest, versioned data dictionary, code and configuration for every transformation, exception and duplicate logs, label definition, cutoff and censoring rules, split assignments, privacy notes, source terms, and validation results by time and population group. A reviewer should be able to trace any model row back to its source record and explain why each feature was available when the prediction was made.
That discipline matters more than a particular algorithm. Correct population selection, temporal validity, transparent field definitions and reproducible quality rules determine whether a mortgage model measures a real, deployable signal or merely exploits artifacts in a released file.
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