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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Prepare mortgage data by fixing the prediction question first, then matching it to an appropriate source, time window, label, and observation unit. Keep an immutable raw copy, map every field to the specification for its reporting year or dataset release, distinguish missing from not-applicable codes, investigate tails and impossible combinations, and ensure every feature would have been available when the prediction is made.
For U.S. work, HMDA is suited to application and lending-pattern analysis, while Freddie Mac’s Single-Family Loan-Level Dataset is designed for acquisition and loan-performance studies. They are not interchangeable: they cover different populations, fields, privacy treatments, and outcomes.
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Start with the prediction you actually need
“Mortgage data” can describe an application, an originated loan, or a sequence of monthly loan-performance records. Those units support different questions. Write a short prediction contract before downloading or cleaning anything:
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- Unit of prediction: application, originated loan, or loan-month.
- Outcome: approval, denial, origination, default, delinquency, prepayment, or another explicitly defined event.
- Prediction time: the point at which the model is supposed to score the record.
- Eligible population: which applications or loans enter the modeling set.
- Observation window: the period used to create the label and the period reserved for evaluation.
- Permitted inputs: fields known at the prediction time, with a documented treatment for each field that arrives later.
For example, a default model scored at origination can use information available in the application and closing process, but not a later monthly delinquency status. A prepayment model needs a follow-up window and a clear rule for censoring loans that remain active. A lending-pattern analysis may use HMDA application outcomes but should not be described as a servicing-performance model.
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Keep identifiers for joins, deduplication, and audit trails. Exclude them from model features unless their use is specifically justified, legally permitted, and tested for leakage or memorization.
Choose a source that matches the target
Two major public or broadly accessible sources answer different questions.
HMDA for applications and lending activity
The Consumer Financial Protection Bureau describes the Home Mortgage Disclosure Act (HMDA) as the most comprehensive publicly available source of information about the U.S. mortgage market. Its loan/application register fields support analysis of applications, action taken, loan purpose, property and applicant characteristics, and reported lending patterns. Start with the relevant year’s documentation at the CFPB HMDA Data page.
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HMDA records are public-use records, not unmodified operational files. The CFPB states that “The public data are modified to protect applicant and borrower privacy.” Field availability and allowed values can change by reporting year, so save the year and the exact data dictionary used.
Freddie Mac for acquisition and loan performance
For default, delinquency, prepayment, or other servicing outcomes, Freddie Mac’s Single-Family Loan-Level Dataset provides acquisition records and monthly performance disclosures. The current page describes approximately 56 million mortgages originated from January 1, 1999 through March 31, 2026, with monthly performance information disclosed through March 31, 2026. The figures are tied to that current release and should not be treated as a permanently fixed count.
Access requires registration. The dataset is available for noncommercial academic, research, and limited uses under Freddie Mac’s terms; commercial redistribution requires a licensing agreement. The historical dataset is unaudited and subject to change, so record the download vintage and retain the user guide and file layout used for each build. The General User Guide explains important field and privacy treatments.
Do not merge the two sources casually
HMDA represents reported applications and lending activity across reporting institutions. Freddie Mac’s described file represents loans purchased or guaranteed by Freddie Mac and their subsequent performance. A model trained on one population should not be presented as representative of the other without a documented sampling and linkage design. A shared geographic or loan-purpose field does not make the records interchangeable.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute| Decision factor | HMDA | Freddie Mac Single-Family Loan-Level Dataset |
|---|---|---|
| Primary unit | Reported mortgage application or loan record | Acquired or guaranteed single-family loan, with monthly performance records |
| Best fit | Application outcomes, lending activity, and market-pattern analysis | Credit performance, delinquency, default, and prepayment modeling |
| Time coverage | Choose a reporting year and extract vintage | Originations from January 1, 1999 through March 31, 2026 in the current description |
| Updates | Dynamic national files are updated weekly; snapshot files preserve a dated view | Historical files and monthly performance disclosures have release vintages |
| Privacy and precision | Public records are privacy-modified | Dates, balances, ZIP codes, and other values may be coarsened |
| Access terms | Public data with year-specific documentation | Registration; commercial redistribution requires a licensing agreement |
| Quality caveat | Dynamic extracts can include late submissions and resubmissions | Historical dataset is unaudited and subject to change |
When using HMDA, record whether you used a dynamic or snapshot extract. The Dynamic National Loan-Level Dataset incorporates received submissions and resubmissions, so the same reporting year can produce different extracts on different dates.
Freeze the source and schema before transforming values
Keep raw and analytical layers separate
- Store an immutable copy of every downloaded file, including compressed archives and accompanying documentation.
- Record the source URL, download timestamp, reporting year or dataset vintage, file name, checksum if your pipeline supports it, and applicable terms of use.
- Create a separate analytical layer in which names, types, code labels, and derived fields are standardized.
- Preserve the original value alongside every recoded value. A raw categorical code and its documented label should remain traceable after transformation.
- Version the cleaning and feature code so a later release can be rebuilt without silently changing historical training data.
Use the dated field specification
Mortgage fields 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 page lists ULDD Phase 5 release 5.2.0, dated May 26, 2026. A field name that looks stable can have a changed definition, allowed value, or validation rule in a later release.
The Uniform Mortgage Data Program standardizes data across appraisal, application, closing-disclosure, and loan-delivery processes. It improves consistency for GSE delivery data, but it does not define every HMDA field or prove that a field is suitable as a model feature.
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Build a data dictionary for the model
For each column, document:
- Source name, analytical name, type, unit, and allowed values.
- Whether the value is known at prediction time or arrives later.
- Reporting-year or release-specific definition.
- Missing, not-applicable, not-provided, exempt, and unknown codes.
- Privacy masking or rounding that affects precision.
- Permitted transformations and the reason for each one.
- Whether the field is an identifier, a join key, a label component, or a candidate feature.
Profile quality before imputing or scaling
Do not start by filling nulls or standardizing every numeric column. First measure what the source contains.
Inspect types and distinct values
- Confirm that dates are parsed as dates, amounts as numeric quantities, and categorical codes remain categorical.
- List distinct values and their frequencies for every coded field.
- Compare value sets and missingness across reporting years or dataset vintages.
- Check whether a number is a true measurement or a code whose digits merely look quantitative.
A code such as “2” for an action category must not be averaged as if it represented twice the amount of category “1.” Keep the code and map it to the documented label.
Check the intended unit and duplicates
Count records at the unit used by the prediction contract. A loan-month table naturally contains multiple rows per loan; an application table may contain one record per reported application. Test duplicate keys, repeated submissions, and accidental row multiplication after joins. If you create a loan-level table from monthly records, document the aggregation rule and the cutoff date.
Test combinations, not just individual ranges
Look for contradictions such as an outcome that is populated when the application is marked not applicable, a performance record before origination, or a ratio whose denominator is missing. Source definitions should determine whether a combination is invalid, uncommon, or simply not applicable. Flag anomalies for review rather than deleting them automatically.
Handle missing, not-applicable, and unknown values explicitly
Missingness in mortgage files often carries more meaning than an empty cell. A data dictionary may distinguish not applicable, not provided, exempt, and unknown. Preserve those states until you understand them.
- Map source-specific codes to named states in a separate field or a documented categorical representation.
- Do not replace an exempt or not-applicable code with zero; zero is a measured quantity and can change the business meaning.
- For numeric fields, decide whether to use an imputation value, a model-supported missing category, or record exclusion based on the field’s role and missingness mechanism.
- Add a missingness indicator only when it represents a stable, permissible signal and is evaluated for fairness and drift.
- Fit imputation parameters on the training partition only, then apply the frozen parameters to validation and test data.
Report missingness by year, institution or population where appropriate, and outcome. A sudden change can indicate a specification revision or reporting process rather than a real change in borrowers.
Investigate extreme values instead of imposing simple bounds
The CFPB’s 2023 HMDA snapshot documentation warns that loan amount and income have no upper limit and may contain outliers. A very large value is therefore not automatically an invalid record.
For loan amount, income, property value, debt-to-income ratio, loan-to-value ratio, and performance measures:
- Verify the source definition, unit, and precision.
- Compare the value with related fields and the record’s reporting context.
- Check whether a legitimate high-value segment is being mistaken for a data error.
- Investigate impossible arithmetic, such as a ratio inconsistent with its numerator and denominator.
- If you cap, winsorize, log-transform, or remove observations, store the exact rule, threshold, and affected-record count.
- Evaluate model behavior with and without the treatment; do not claim that a chosen threshold is mandated by HMDA.
For ratio fields, preserve the source-provided ratio and, where permitted, calculate a separately named derived ratio from the underlying values. Differences can reveal rounding, masking, or denominator conventions.
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Public-use mortgage records may deliberately sacrifice precision. Freddie Mac’s guide describes dates reduced to month and year, original unpaid balance rounded to the nearest $1,000, and ZIP codes whose last two digits are replaced with zeroes, among other treatments. These are properties of the release, not errors to be reverse-engineered.
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Privacy coarsening affects three common modeling tasks:
- Time features: a month-and-year date cannot support day-level ordering or precise seasoning calculations.
- Geographic features: a coarsened ZIP code should not be treated as an exact neighborhood identifier or joined to a more precise geography as if it were exact.
- Record linkage: rounded balances and masked geography can create collisions and false matches. Use documented identifiers and conservative matching rules.
State the precision that remains in the feature description. Do not imply that a model knows an exact date, balance, or location when the release does not provide one.
Construct labels without leaking future information
Define the event and follow-up window
A label such as “default” is incomplete without an event definition, start date, observation horizon, and treatment of loans that leave the sample. Define whether the target is first serious delinquency, foreclosure, charge-off, prepayment, or another source-supported event. For a loan-month model, the label may be an event in the next month or next several months; for an origination model, it may be an event during a fixed follow-up period.
Create labels from source fields only after checking the relevant year’s data dictionary. Keep the label-generation code and the eligible-population filter versioned with the dataset vintage.
Use an availability cutoff
For every candidate predictor, ask: “Could this value have been known at the exact scoring time?” Monthly performance records are future information for an origination-time model. Including a later delinquency status, payment, modification, or balance can produce an apparently strong model that cannot operate in production.
Build a feature-availability table with columns for field name, source date, earliest availability, cutoff rule, and leakage risk. When aggregating history, use only records at or before the cutoff. If a field’s availability cannot be established, exclude it or label the uncertainty rather than assuming it was available.
Keep joins auditable
Join acquisition and performance data through documented loan keys and preserve the number of rows before and after every join. A many-to-many join can duplicate loans and inflate event counts. Assert the expected cardinality, inspect unmatched records, and retain an audit table of dropped or unmatched keys.
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Engineer features that respect mortgage timing
Useful derived features are usually simple, interpretable transformations of fields available at the cutoff:
- Seasoning in months, calculated only when the date precision supports that granularity.
- Loan-to-value or debt-to-income measures derived from documented components, with a flag when a denominator is unavailable.
- Application-to-origination intervals when both dates are present and privacy masking does not make the calculation misleading.
- Prior performance summaries for a loan-month model, calculated from earlier months only.
- Grouped geography or institution categories that match the precision of the release and avoid tiny, identifying groups.
Keep raw fields beside derived fields. Name transformations so a reviewer can tell whether a value is source-provided, imputed, aggregated, rounded, or encoded. Avoid high-cardinality identifiers as ordinary categorical features; they can memorize institutions, borrowers, or geographic cells rather than learn a transferable relationship.
Split data by time and by entity
Use a chronological holdout for future prediction
When the model is intended to predict future loans or future performance, reserve a later period as the final test set. Fit preprocessing, imputation, encoding, and feature-selection decisions on the training period only. Evaluate on the later period using the same frozen pipeline.
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Keep repeated records together
If a dataset contains multiple loan-month rows, split at the loan level so months from one loan cannot appear in both training and test partitions. If the same borrower can appear in multiple records and the modeling objective requires borrower-level independence, split at the borrower level instead.
Measure stability across populations
Report performance and error patterns across meaningful borrower, property, geographic, institution, and time groups that are legally and operationally appropriate to examine. A single overall score can conceal deterioration in a subgroup or a later vintage. Do not invent a benchmark or prevalence rate when the source material does not establish one.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A reproducible preparation workflow
- Write the prediction contract. Specify unit, outcome, cutoff, eligible population, follow-up, and evaluation period.
- Select the source. Use HMDA for application and lending-pattern questions; use the Freddie Mac file for its covered acquisition and performance population.
- Capture provenance. Save raw files, documentation, release or reporting year, extract type, download date, and access terms.
- Freeze the schema. Map fields to the dated specification, preserve raw codes, and record changes between releases.
- Profile the raw data. Measure types, frequencies, missingness, duplicates, invalid combinations, and distributions before transformation.
- Model privacy precision. Record date, balance, ZIP, and other coarsening; limit features and joins to the precision supplied.
- Define labels. Generate outcomes from source fields and the stated follow-up window; document censoring and exclusions.
- Build cutoff-safe features. Prove that every input was available at prediction time and retain raw values next to derived values.
- Apply quality rules. Investigate tails and contradictions, then document any cap, transform, exclusion, or imputation.
- Split correctly. Prefer chronological validation for future prediction and keep repeated loan or borrower records in one partition.
- Validate the pipeline. Recheck row counts, joins, missingness, distributions, and feature availability after each major transformation.
- Publish a model-ready manifest. List dataset vintage, feature definitions, label rule, cutoff, split dates, and known limitations.
Common failure modes and fixes
Using the wrong population
Symptom: An application model is trained on a performance file, or a Freddie Mac result is described as the whole U.S. market.
Fix: Re-state the unit and selection process, then choose the source whose population matches the intended claim.
Treating codes as measurements
Symptom: Category codes are averaged, scaled, or interpreted as ordered quantities.
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Fix: Consult the year-specific dictionary, retain the code, and encode it as categorical unless an ordering is explicitly defined.
Turning “not applicable” into zero
Symptom: A model learns that an unavailable field means a borrower has no debt or no property value.
Fix: Preserve the source state and use a documented missing or not-applicable treatment.
Deleting every extreme value
Symptom: High-income or high-loan records disappear because they exceed a hand-picked limit.
Fix: Verify definitions and related fields first. If a robust transformation is needed, keep the rule auditable and compare alternatives.
Leaking monthly performance
Symptom: An origination model uses a later delinquency, payment, or status field.
Fix: Enforce a source-date cutoff and generate all historical aggregates using records available no later than that cutoff.
Joining masked geography as if it were exact
Symptom: Coarsened ZIP codes are joined to precise neighborhood data, creating false matches or overstated geographic precision.
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Randomly splitting loan-month rows
Symptom: Rows from the same loan occur in both training and test sets.
Fix: Group the split by loan or borrower and use a later time period for the final test when forecasting future outcomes.
Ignoring changing extracts
Symptom: A later HMDA run cannot reproduce an earlier row count or model input.
Fix: Record dynamic-versus-snapshot status and extract date; preserve the original files and checksums where possible.
Governance matters after cleaning
Good preparation does not by itself make a mortgage model lawful, fair, or suitable for a credit decision. For consequential use, involve fair-lending, privacy, compliance, explainability, and human-review expertise. Document why each feature is used, how privacy modifications limit interpretation, how missingness and outliers are handled, and what monitoring will detect drift or subgroup harm.
Use public privacy-modified data only for purposes supported by its precision and terms. Do not attempt to reconstruct masked dates, balances, ZIP codes, or applicant identities. Keep access controls, retention rules, and redistribution restrictions aligned with the source agreement.
Final preparation checklist
- The prediction unit, label, cutoff, population, and follow-up window are written down.
- The source and its selection limitations match the question.
- Raw files, reporting year or release vintage, extract type, and documentation are preserved.
- Every field has a dated definition, type, allowed values, and missingness treatment.
- Privacy masking is reflected in feature precision and joins.
- Outliers and impossible combinations were investigated rather than removed by habit.
- Labels use only the specified follow-up period.
- No predictor uses information unavailable at scoring time.
- Preprocessing is fitted on training data and applied unchanged to validation and test data.
- Repeated loan or borrower records stay in one split.
- Later time periods and relevant groups are checked for stability.
- Terms of use, licensing, privacy, and governance decisions are recorded.
Bottom line
The reliable path is not “clean every column and train a model.” It is to define the prediction moment, choose a dataset whose population and outcomes fit that moment, preserve the source’s dated schema and privacy limits, treat missingness and extremes as evidence to investigate, and validate with time- and entity-aware splits. That process produces mortgage features that are reproducible, interpretable, and much less likely to be contaminated by leakage or false precision.
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