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State markets/Puerto Rico/Trujillo Alto Municipio

COUNTY MORTGAGE MARKET · FIPS 72139

Trujillo Alto Municipio, Puerto Rico

Who lends in Trujillo Alto Municipio, how much, and to whom — built from HMDA public mortgage disclosure data, U.S. Census ACS housing estimates and FEMA's National Risk Index.

Origination volume · 2025

$63.5M

Origination volumeSum of loan amount over HMDA records with action taken = 1.

Originations · 2025

317

OriginationsCount of HMDA records with action taken = 1 (loan originated) in the activity year.

Denial rate · 2025

15.2%

Denial rateApplications denied (action taken = 3) divided by applications acted upon (actions 1-5).

Lenders active · 2025

38

Lenders activeDistinct LEIs with at least one application acted upon (HMDA action taken 1-5, 7 or 8) in the county and year.

Market structure

How spread out lending is across the 38 lenders that originated in Trujillo Alto Municipio in 2025. HHI is the Herfindahl-Hirschman index over lender shares.

Concentration (HHI)

761

unconcentrated

Top-5 lender share

54.6%

of originations

Purchase

85.3%

261 loans

Refinance

14.7%

45 loans

Origination trend

Annual mortgage origination volume and loan count in Trujillo Alto Municipio, 2023–2025. Pick a window, export the graph or its data, or click a year to open every record behind it.

Lenders in Trujillo Alto Municipio

Sort, filter, add columns, export, or open a row for the lender's card. Each row links to the lender's company page and to its Trujillo Alto Municipio loan records. The panel states which source answered and exactly which lenders that source covers.

ORIGINATION FLOOR
Hide the long tail of lenders with only a handful of loans here.
FULL RECORD LIST FOR TRUJILLO ALTO MUNICIPIO

TOP TEN LENDERS IN TRUJILLO ALTO MUNICIPIO · 10

The ten largest lenders by origination volume — the precomputed county leaderboard, not the whole market.

1–10 OF 10 LENDERS

Loading every lender that acted on an application in this county. The rows below are the precomputed county leaderboard — the ten largest by volume, not the whole market.

32$8.9M$8.9M originated in Trujillo Alto Municipio, 2025Share needs a county-wide denominator, which the narrower fallback source does not carry.
36$7.9M$7.9M originated in Trujillo Alto Municipio, 2025Share needs a county-wide denominator, which the narrower fallback source does not carry.
47$7.7M$7.7M originated in Trujillo Alto Municipio, 2025Share needs a county-wide denominator, which the narrower fallback source does not carry.
34$6.4M$6.4M originated in Trujillo Alto Municipio, 2025Share needs a county-wide denominator, which the narrower fallback source does not carry.
19$3.7M$3.7M originated in Trujillo Alto Municipio, 2025Share needs a county-wide denominator, which the narrower fallback source does not carry.
18$3.2M$3.2M originated in Trujillo Alto Municipio, 2025Share needs a county-wide denominator, which the narrower fallback source does not carry.
9$2.8M$2.8M originated in Trujillo Alto Municipio, 2025Share needs a county-wide denominator, which the narrower fallback source does not carry.
12$2.4M$2.4M originated in Trujillo Alto Municipio, 2025Share needs a county-wide denominator, which the narrower fallback source does not carry.
10$2.1M$2.1M originated in Trujillo Alto Municipio, 2025Share needs a county-wide denominator, which the narrower fallback source does not carry.
15$2.1M$2.1M originated in Trujillo Alto Municipio, 2025Share needs a county-wide denominator, which the narrower fallback source does not carry.

Source: the precomputed county lender leaderboard (lender_county_year) — its ten largest lenders by origination volume, not the county. As of HMDA activity year 2025. Denial rate = denials / decisioned applications (originated + approved-but-not-accepted + denied).

ZIP areas in Trujillo Alto Municipio

All 4 ZIP Code Tabulation Areas that touch Trujillo Alto Municipio, built by grouping the county's census tracts through the Census 2020 crosswalk. A ZCTA approximates a USPS ZIP code; HMDA itself reports no postal geography, so each row opens the tracts behind it, where the records are exact.

TRACT FLOOR
Hide the ZIP areas that only clip a corner of this county.
4 OF 4 ZIP AREASEVERY ZIP AREA TOUCHING THE COUNTY — NO ROWS WITHHELD
ZIP AREA (ZCTA)TRACTSPOPULATIONMINORITY %OWNER-OCC UNITSORIGINATIONSVOLUMETOP LENDERRECORDS
1515 of the 17 Trujillo Alto Municipio tracts this ZIP area touches sit mostly inside it.54,84499.4%13,309TRACTS →HMDA reports no ZIP. This opens the ZIP area's tract list, where each tract's records open exactly.
22 of the 5 Trujillo Alto Municipio tracts this ZIP area touches sit mostly inside it.12,89699.5%2,966TRACTS →HMDA reports no ZIP. This opens the ZIP area's tract list, where each tract's records open exactly.
0This ZIP area clips 1 tract of Trujillo Alto Municipio but is the dominant ZIP for none of them, so it owns no figures here.TRACTS →HMDA reports no ZIP. This opens the ZIP area's tract list, where each tract's records open exactly.
0This ZIP area clips 3 tracts of Trujillo Alto Municipio but is the dominant ZIP for none of them, so it owns no figures here.TRACTS →HMDA reports no ZIP. This opens the ZIP area's tract list, where each tract's records open exactly.

17 of Trujillo Alto Municipio’s 17 census tracts are grouped into the ZIP areas above, with none left over. The two add up to the county exactly, because each tract is counted once. A ZCTA — ZIP Code Tabulation Area — is the U.S. Census Bureau's area approximation of a USPS ZIP code, not the ZIP code itself. HMDA reports no postal geography at all, so these rows are census tracts grouped by the Census 2020 tract-to-ZCTA relationship file: each tract is counted once, in the single ZCTA covering the most of its land. Housing figures are HMDA tract attributes for activity year 2025, summed over the tracts each ZIP area owns — a ZIP area that owns no tract shows an em-dash, never a zero.

Census tracts in Trujillo Alto Municipio

All 17 tracts, with the FFIEC income character and HMDA housing attributes of each. Open a tract for its own lender ranking, or open its records directly.

17 OF 17 TRACTSEVERY TRACT IN THE COUNTY — NO ROWS WITHHELD
TOP LENDERRECORDS
Upper149.3%3,23299.1%OPEN →
Middle92.6%2,70199.7%OPEN →
Upper145.3%7,13799.5%OPEN →
Upper132.7%4,51199.3%OPEN →
Upper208.8%2,54898.7%OPEN →
Upper147.3%4,46099.4%OPEN →
Upper122.4%3,68699.6%OPEN →
Middle95.4%2,49299.8%OPEN →
Upper127.6%2,46999.6%OPEN →
Upper136.9%1,05999.8%OPEN →
Upper161.2%5,11199.6%OPEN →
Upper140.0%7,78599.5%OPEN →
Middle85.9%3,89199.8%OPEN →
Upper220.8%3,97198.9%OPEN →
Upper267.8%3,98898.3%OPEN →
Middle85.4%4,88299.6%OPEN →
Middle107.6%3,81799.6%OPEN →

Source: FFIEC census-tract file (income level, MSA, urban/rural) and HMDA tract attributes (population, minority share, housing units), latest loaded activity year. The whole county is listed — the table is filtered and paged in the browser over the complete set, never a server-side slice of it.

Housing & households

U.S. Census American Community Survey 2024 estimates — the demand side behind the lending figures above.

Population

67,088

Median household income

$40,055

Median home value

$164,500

Median gross rent

$694

per month

Owner-occupied

74.5%

of occupied units

Housing units

30,711

11.3% vacant

Poverty rate

27.7%

Unemployment

7.9%

Natural-hazard exposure

FEMA National Risk Index ratings for this county — expected annual loss, social vulnerability and community resilience, plus the individual perils rated Relatively High or above.

Overall risk

Insufficient Data

Social vulnerability

Data Unavailable

Community resilience

Data Unavailable

Federal disaster history

Months in which a FEMA disaster declaration was active in this county, from the declarations series.

Months with an active declaration

59

Declarations recorded

23

Incident types on record: Biological, Earthquake, Flood, Hurricane, Severe Storm.

Everything we hold on Trujillo Alto Municipio

8 data areas have rows for this county. Each one is one click away below; the 22 we checked and did not find at county grain are named at the end with the reason.

LOAN-LEVEL

The individual HMDA application and origination records behind every figure on this page, pre-filtered to Trujillo Alto Municipio.

HMDA loan-level (hmda_loan_wide, county_code)

Open the loan explorer for Trujillo Alto MunicipioEvery reported application and origination in FIPS 72139, filterable on rate, amount, income, purpose and action taken. Loan-level tier.

VA

VA-guaranteed lending in Trujillo Alto Municipio by fiscal year — the veteran channel, which HMDA counts but does not separate from the rest of the government book at this grain.

VA Loan Guaranty geographic loan volume (va_geo_loan_volume, county_fips), folded to fiscal year

VA loans · FY2026

17

Purchase share

76.5%

13 loans

IRRRL refinances

0

Cash-out refinances

4

2026171304
2025191108
2024111001
2023131201
20221412
2021191126
2020151131
2019141112
2018221624
2017301479
20163015141
20154516236

The most recent fiscal year is partial until VA closes it. This file carries no lender key, so VA volume cannot be attributed to a company.

UAD APPRAISALS

FHFA Uniform Appraisal Dataset aggregates for Trujillo Alto Municipio.

FHFA UAD aggregate statistics (uad_aggregate_county, geoid) and appraisal-level public use file (uad_puf, county_fips)

2023PurchaseNo CharacteristicAll Appraisals46.0
2023PurchaseNo CharacteristicAll Appraisals0.2
2023RefinanceNo CharacteristicAll Appraisals11.0
2023PurchaseNo CharacteristicAll Appraisals1.0
2023BothNo CharacteristicAll Appraisals57.0
2023PurchaseNo CharacteristicAll Appraisals0.5
2023PurchaseNo CharacteristicAll Appraisals1.0
2023RefinanceNo CharacteristicAll Appraisals166,000.0
2023BothNo CharacteristicAll Appraisals166,000.0
2023PurchaseNo CharacteristicAll Appraisals168,000.0
2023PurchaseNo CharacteristicAll Appraisals0.3
2022PurchaseNo CharacteristicAll Appraisals1.0
2022BothNo CharacteristicAll Appraisals193,000.0
2022PurchaseNo CharacteristicAll Appraisals0.1
2022PurchaseNo CharacteristicAll Appraisals215,000.0
2022PurchaseNo CharacteristicAll Appraisals1.0
2022RefinanceNo CharacteristicAll Appraisals40.0
2022PurchaseNo CharacteristicAll Appraisals0.3
2022PurchaseNo CharacteristicAll Appraisals0.5
2022RefinanceNo CharacteristicAll Appraisals173,000.0
2022BothNo CharacteristicAll Appraisals93.0
2022PurchaseNo CharacteristicAll Appraisals53.0
2021PurchaseNo CharacteristicAll Appraisals1.0
2021RefinanceNo CharacteristicAll Appraisals227,500.0
2021BothNo CharacteristicAll Appraisals149.0
2021PurchaseNo CharacteristicAll Appraisals1.0
2021PurchaseNo CharacteristicAll Appraisals55.0
2021PurchaseNo CharacteristicAll Appraisals0.1
2021RefinanceNo CharacteristicAll Appraisals94.0
2021PurchaseNo CharacteristicAll Appraisals0.4
2021BothNo CharacteristicAll Appraisals212,000.0
2021PurchaseNo CharacteristicAll Appraisals0.5
2021PurchaseNo CharacteristicAll Appraisals188,000.0
2020PurchaseNo CharacteristicAll Appraisals0.3
2020PurchaseNo CharacteristicAll Appraisals1.1
2020BothNo CharacteristicAll Appraisals129.0
2020PurchaseNo CharacteristicAll Appraisals41.0
2020PurchaseNo CharacteristicAll Appraisals0.6
2020BothNo CharacteristicAll Appraisals179,000.0
2020RefinanceNo CharacteristicAll Appraisals189,500.0
2020RefinanceNo CharacteristicAll Appraisals88.0
2020PurchaseNo CharacteristicAll Appraisals170,000.0
2020PurchaseNo CharacteristicAll Appraisals1.0
2020PurchaseNo CharacteristicAll Appraisals0.1
2019PurchaseNo CharacteristicAll Appraisals0.1
2019PurchaseNo CharacteristicAll Appraisals0.9
2019BothNo CharacteristicAll Appraisals135,000.0
2019PurchaseNo CharacteristicAll Appraisals49.0
2019PurchaseNo CharacteristicAll Appraisals0.2
2019PurchaseNo CharacteristicAll Appraisals1.1
2019RefinanceNo CharacteristicAll Appraisals131,500.0
2019BothNo CharacteristicAll Appraisals77.0
2019RefinanceNo CharacteristicAll Appraisals28.0
2019PurchaseNo CharacteristicAll Appraisals148,000.0
2019PurchaseNo CharacteristicAll Appraisals0.8
2018RefinanceNo CharacteristicAll Appraisals147,000.0
2018BothNo CharacteristicAll Appraisals85.0
2018PurchaseNo CharacteristicAll Appraisals47.0
2018RefinanceNo CharacteristicAll Appraisals38.0
2018BothNo CharacteristicAll Appraisals167,300.0
2018PurchaseNo CharacteristicAll Appraisals190,000.0
2018PurchaseNo CharacteristicAll Appraisals0.7
2018PurchaseNo CharacteristicAll Appraisals0.1
2018PurchaseNo CharacteristicAll Appraisals0.2
2018PurchaseNo CharacteristicAll Appraisals0.9
2018PurchaseNo CharacteristicAll Appraisals1.1
2017RefinanceNo CharacteristicAll Appraisals148,600.0
2017PurchaseNo CharacteristicAll Appraisals0.9
2017PurchaseNo CharacteristicAll Appraisals1.1
2017PurchaseNo CharacteristicAll Appraisals0.1
2017PurchaseNo CharacteristicAll Appraisals0.2
2017BothNo CharacteristicAll Appraisals95.0
2017PurchaseNo CharacteristicAll Appraisals32.0
2017PurchaseNo CharacteristicAll Appraisals194,000.0
2017RefinanceNo CharacteristicAll Appraisals63.0
2017PurchaseNo CharacteristicAll Appraisals0.8
2017BothNo CharacteristicAll Appraisals166,000.0
2016PurchaseNo CharacteristicAll Appraisals19.0
2016RefinanceNo CharacteristicAll Appraisals159,000.0
2016PurchaseNo CharacteristicAll Appraisals0.9
2016PurchaseNo CharacteristicAll Appraisals212,000.0
2016PurchaseNo CharacteristicAll Appraisals0.9
2016PurchaseNo CharacteristicAll Appraisals0.1
2016BothNo CharacteristicAll Appraisals161,000.0
2016PurchaseNo CharacteristicAll Appraisals1.1
2016PurchaseNo CharacteristicAll Appraisals0.0
2016BothNo CharacteristicAll Appraisals128.0
2016RefinanceNo CharacteristicAll Appraisals109.0
2015PurchaseNo CharacteristicAll Appraisals1.1
2015PurchaseNo CharacteristicAll Appraisals155,000.0
2015PurchaseNo CharacteristicAll Appraisals0.0
2015RefinanceNo CharacteristicAll Appraisals155,000.0
2015BothNo CharacteristicAll Appraisals176.0
2015RefinanceNo CharacteristicAll Appraisals133.0
2015PurchaseNo CharacteristicAll Appraisals0.1
2015PurchaseNo CharacteristicAll Appraisals0.8
2015PurchaseNo CharacteristicAll Appraisals43.0
2015BothNo CharacteristicAll Appraisals155,000.0
2015PurchaseNo CharacteristicAll Appraisals1.1
2014BothNo CharacteristicAll Appraisals167,500.0

ECONOMY & INCOME

Local economy indicators — BLS unemployment rates and BEA personal income for Trujillo Alto Municipio.

BLS Local Area Unemployment Statistics (bls_laus_county_month, geoid) and BEA personal income (bea_county_income, geo_fips)

BLS Local Area Unemployment

2026-0531,77431,0197552.4%
2026-0431,78231,0307522.4%
2026-0331,89331,1237702.4%
2026-0231,99431,2817132.2%
2026-0131,96031,2547062.2%
2025-1232,09331,4066872.1%
2025-1132,00131,2857162.2%
2025-1031,57830,8627162.3%
2025-0931,51530,6718442.7%
2025-0831,82331,0238002.5%
2025-0731,59930,8297702.4%
2025-0632,05731,2937642.4%
2025-0532,08131,3507312.3%
2025-0431,81631,1486682.1%
2025-0331,85431,1666882.2%
2025-0231,73831,0966422.0%
2025-0131,81231,2425701.8%
2024-1231,88231,2256572.1%
2024-1132,24531,5996462.0%
2024-1031,66930,9896802.1%
2024-0931,51930,8296902.2%
2024-0831,30030,6046962.2%
2024-0731,16730,3388292.7%
2024-0631,29830,5337652.4%
2024-0531,23030,5297012.2%
2024-0431,16330,4457182.3%
2024-0331,43730,6527852.5%
2024-0231,43530,7566792.2%
2024-0131,44930,8126372.0%
2023-1231,60530,7998062.6%
2023-1131,87331,0867872.5%
2023-1031,61830,8397792.5%
2023-0931,20630,4227842.5%
2023-0831,04230,1878552.8%
2023-0730,67229,7119613.1%
2023-0630,17129,2748973.0%
2023-0530,26529,4158502.8%
2023-0430,57129,8037682.5%
2023-0330,99930,1228772.8%
2023-0231,23930,4338062.6%
2023-0131,44930,6827672.4%
2022-1231,61230,6639493.0%
2022-1131,11730,1219963.2%
2022-1030,42429,3071,1173.7%
2022-0930,23629,0741,1623.8%
2022-0830,17829,0881,0903.6%
2022-0729,94428,8851,0593.5%
2022-0630,48629,3801,1063.6%
2022-0530,89329,8161,0773.5%
2022-0431,21730,1441,0733.4%
2022-0331,56130,4561,1053.5%
2022-0231,70230,5771,1253.5%
2022-0131,35230,1111,2414.0%
2021-1232,16030,7851,3754.3%
2021-1131,50329,9911,5124.8%
2021-1030,82129,3221,4994.9%
2021-0930,72629,0681,6585.4%
2021-0830,58828,6071,9816.5%
2021-0730,23228,2312,0016.6%
2021-0630,12528,3511,7745.9%
2021-0530,56128,8541,7075.6%
2021-0430,36828,8761,4924.9%
2021-0329,90028,3551,5455.2%
2021-0229,11527,5101,6055.5%
2021-0128,63826,7821,8566.5%
2020-1228,65626,4282,2287.8%
2020-1128,73726,7252,0127.0%
2020-1028,99927,1931,8066.2%
2020-0929,30627,2942,0126.9%
2020-0829,35827,5521,8066.2%
2020-0729,29227,7341,5585.3%
2020-0629,69127,8621,8296.2%
2020-0528,16326,2471,9166.8%
2020-0227,27826,1121,1664.3%
2020-0126,83025,3451,4855.5%
2019-1227,30825,8991,4095.2%
2019-1127,43526,1281,3074.8%
2019-1027,91826,4431,4755.3%
2019-0927,94526,6161,3294.8%
2019-0828,44727,2951,1524.0%
2019-0729,10527,7341,3714.7%
2019-0629,10527,7601,3454.6%
2019-0528,43027,1861,2444.4%
2019-0428,11426,8701,2444.4%
2019-0327,58526,1411,4445.2%
2019-0227,71226,3131,3995.0%
2019-0127,53326,3001,2334.5%
2018-1227,85826,5311,3274.8%
2018-1127,52526,3411,1844.3%
2018-1027,66326,2951,3684.9%
2018-0927,73926,3411,3985.0%
2018-0827,48326,0251,4585.3%
2018-0727,75726,2491,5085.4%
2018-0627,83826,4621,3764.9%
2018-0527,52526,1841,3414.9%
2018-0427,54626,1391,4075.1%
2018-0327,37625,9631,4135.2%
2018-0227,43425,9451,4895.4%
2018-0127,39725,8881,5095.5%
2017-1227,79826,4451,3534.9%

ALSO CHECKED

22 more data areas were checked for Trujillo Alto Municipio and did not earn a tab. A missing tab and an unasked question look the same on screen, so each one is named with the reason it is missing.

  • WRONG GRAIN
    FHA

    FHA Neighborhood Watch DOES publish county rows, but keys them on a free-text county NAME (“CHITTENDEN|VT”), not a FIPS code, and the single-family endorsement snapshot's property_county is likewise free text. Joining either to a county FIPS by name would attribute one county's delinquency to another wherever a name repeats, so FHA is a state-grain tab only until a curated name→FIPS crosswalk exists

  • WRONG GRAIN
    Fannie / Freddie

    the FHFA public-use database publishes single-family acquisitions at STATE and CENSUS-TRACT grain; no county rollup is published or loaded, and rolling tracts up to counties would double-count the tracts that straddle a county line

  • WRONG GRAIN
    GSE credit-risk transfer

    CAS and STACR loan-level disclosures publish PROPERTY STATE and a 3-digit zip; neither carries a county code, so credit-risk-transfer collateral cannot be attributed to a county

  • WRONG GRAIN
    Ginnie Mae

    Ginnie Mae's loan-level disclosure publishes PROPERTY STATE and nothing finer — there is no county, ZIP or tract field in the file, so agency-pool collateral has no county grain at all. The same is true of the UMBS pool file (fhfa_mbs_pool), which carries only CA/NY/FL/TX/PR concentration percentages

  • WRONG GRAIN
    Housing finance agency

    a housing finance agency is a STATE-chartered body — county is not a grain it has. Its programmes reach counties, but the agency itself is one row per state: hfa_agency is keyed on state_code and carries no county column, and there is no county-level agency underneath it to aggregate from

  • WRONG GRAIN
    CUSO network

    the NCUA CUSO Registry publishes a CUSO's city and STATE and no county at all (cuso_registry, migration 0195). A county FIPS does exist on the member-credit-union side (cuso_credit_union_link.cu_county_fips), but it is the CREDIT UNION'S CHARTER ADDRESS — where the institution is registered, not where the mortgage service it buys is delivered — so a county tab built on it would pin a statewide, often multi-state, service relationship onto the one county a charter document happens to name

  • WRONG GRAIN
    Subservicing map

    the same absence, one grain finer: subservicing_relationships holds no property or collateral column at all, so there is nothing to aggregate to a county. Where a client is a state HFA, the state housing finance agency area on this hub carries that agency and its named master servicer at its own state grain

  • WRONG GRAIN
    Co-issue MSR

    the same absence, and worse at county grain: Ginnie's loan-level disclosure publishes property STATE and nothing finer — no county, ZIP or tract field exists in the file — so even the collateral split that is rejected above at state grain is not available here at all

  • WRONG GRAIN
    MSR transfer graph

    the same absence at a finer grain, with no additional route to one: ginnie_servicing_transfers has no county column and the Ginnie loan-level disclosure it would have to be joined to publishes property state and nothing finer — no county, ZIP or tract field exists in either file

  • WRONG GRAIN
    Builder & realtor JV

    the same absence, one grain finer: the affiliation corpus has no geographic column at all, so nothing can be aggregated to a county. The affiliate lender's actual county-level originations are on the HMDA area of the county hub, keyed on the lender rather than on its parent

  • WRONG GRAIN
    Non-QM deal calendar

    the same structural gap, one grain finer: a 144A non-QM deal publishes no asset-level file at all, so there is no loan, no property and therefore no county to attribute anything to. This is the one absence on this hub that no future load can close

  • WRONG GRAIN
    Agency-MBS demand

    the same absence, and Ginnie's disclosure has no county field at any grain, so even the collateral allocation that is rejected above at state grain cannot be computed here

  • WRONG GRAIN
    Complaints-rate stack

    the same broken denominator, plus a numerator that does not reach county either: the CFPB publishes a complaint's state and a partially redacted ZIP and no county, so neither half of this ratio exists at county grain

  • WRONG GRAIN
    NMLS licensing

    NMLS licences are issued by a STATE regulator and the industry report is published by state agency and quarter; there is no county in the licensing system to aggregate to

  • WRONG GRAIN
    CFPB complaints

    the CFPB publishes a complaint's STATE and a partially redacted ZIP; it publishes no county, and deriving one from a redacted ZIP would put complaints in the wrong county wherever a ZIP straddles a county line

  • WRONG GRAIN
    State enforcement

    enforcement orders are issued by a STATE regulator against a licensee; they carry no property or county

  • WRONG GRAIN
    WARN notices

    a WARN notice names an employer, a CITY and a region — no county code — and matching city to county by name is ambiguous in most states

  • WRONG GRAIN
    Mortgage employment

    the QCEW extract loaded here holds STATE and METRO area types only — the county-level QCEW file is a separate, far larger download that is not loaded, so county mortgage employment cannot be shown honestly

  • NONE HERE
    Building permits

    the Census permits survey publishes no county series for this FIPS

    Census Building Permits Survey county series (census_building_permits, geo_id at geo_level 'county')

  • NONE HERE
    Valuations & Market

    neither Zillow nor Redfin holds published market data for this county FIPS

    Zillow and Redfin county-month metrics (zillow_county_month, redfin_county_month, geoid)

  • NONE HERE
    Migration & Wealth

    no IRS migration data is available for this county FIPS

    IRS county-to-county migration flows (irs_county_migration, self_geoid)

  • NONE HERE
    NFIP Flood Claims

    no FEMA NFIP claims are recorded for this county FIPS

    FEMA NFIP redacted claims (nfip_claim, county_code)

Several of the areas above are published only at state grain. They are all on the PR state hub, which carries the same registry at the grain those sources actually have.

Go deeper

EVERY HMDA RECORD IN TRUJILLO ALTO MUNICIPIOOpens the loan explorer already filtered to county FIPS 72139.

See the Puerto Rico state market profile for the statewide picture, the Puerto Rico state hub for every dataset we hold on the state in one place, or the national lender leaderboard for who is largest across every market.

Sources: HMDA public loan-level disclosure (CFPB/FFIEC) aggregated to county-year; U.S. Census Bureau American Community Survey; FEMA National Risk Index and disaster declarations. Figures are as loaded — see data attribution.

Fair-lending context: denial rates and demographic figures shown here are descriptive statistics derived from public HMDA and U.S. Census data. HMDA does not contain the credit, income, or underwriting variables that explain most lending outcomes, so a difference between lenders or areas is a statistical observation about published data — not evidence of, and not a determination of, discrimination or redlining by any institution.