Great River Federal CU Mortgage Lending & HMDA Data, 2012

In 2012, Great River Federal CU of St Cloud, MN reported 303 mortgage applications totaling $33.2 million, down 4% from 2011. The average application was for $109,495, and applicants reported an average income of $60,633. 30% of its applications resulted in an originated loan and 22% were denied. Its largest markets were St. Cloud, MN (148) and Minneapolis-St. Paul-Bloomington, MN-WI (31).

Mortgage Company & Address Great River Federal CU
1532 W St Germain Street
St Cloud, MN 56301
Tel #: available with data download
Federal Regulator * available with data download
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Total Number of Mortgage Applications 303
Total Mortgage Loan Application Amount $33,177,000
Average Mortgage Loan Application Amount $109,495
Highest Mortgage Loan Application Amount $360,000
Average Applicant Income $60,633
Highest Applicant Income $340,000

About this data: figures come from the Home Mortgage Disclosure Act (HMDA) records Great River Federal CU filed with the FFIEC and count every application it reported (approved, denied, withdrawn or purchased), not just closed loans. Applications reported at $100 million or more are left out of these totals.

Mortgage Loan Application Totals for Great River Federal CU by County in 2012

County Total Number of Mortgage Loan Applications Total Dollar Amount of Mortgage Loans Applications Average Mortgage Loan Application Amount Average Applicant Income
Stearns, Minnesota 83 $ 9,053,000 $ 109,072 $ 74,000
Benton, Minnesota 65 $ 6,255,000 $ 96,230 $ 57,000
Sherburne, Minnesota 23 $ 2,180,000 $ 94,782 $ 62,000
Morrison, Minnesota 12 $ 984,000 $ 82,000 $ 52,000
Wright, Minnesota 6 $ 388,000 $ 64,666 $ 50,000
Mille Lacs, Minnesota 3 $ 234,000 $ 78,000 $ 59,000
Todd, Minnesota 2 $ 149,000 $ 74,500 $ 33,000
Anoka, Minnesota 1 $ 45,000 $ 45,000 $ 53,000
Dakota, Minnesota 1 $ 190,000 $ 190,000 $ 111,000
Douglas, Minnesota 1 $ 30,000 $ 30,000 $ 35,000
Hubbard, Minnesota 1 $ 103,000 $ 103,000 $ 50,000
Itasca, Minnesota 1 $ 84,000 $ 84,000 $ 101,000
Kandiyohi, Minnesota 1 $ 183,000 $ 183,000 $ 80,000
Otter Tail, Minnesota 1 $ 123,000 $ 123,000 $ 116,000
Pope, Minnesota 1 $ 88,000 $ 88,000 $ 77,000

Mortgage Loan Application Detail Totals for Great River Federal CU in 2012

Loan Purpose

Total Number of Mortgage Applications Total Dollar Amount of Mortgage Loan Applications Average Mortgage Loan Amount Highest Mortgage Loan Amount Average Applicant Income Highest Applicant Income
Refinancing 174 $17,199,000 $98,000 $325,000 $64,000 $340,000
Home Purchase 127 $15,915,000 $125,000 $360,000 $56,000 $252,000
Home Improvement 2 $63,000 $31,000 $50,000 $62,000 $70,000

Applicant Race

Total Number of Mortgage Applications Total Dollar Amount of Mortgage Loan Applications Average Mortgage Loan Amount Highest Mortgage Loan Amount Average Applicant Income Highest Applicant Income
White 301 $32,972,000 $109,000 $360,000 $60,000 $340,000
American Indian or Alaskan Native 1 $87,000 $87,000 $87,000 $168,000 $168,000
Black or African American 1 $118,000 $118,000 $118,000 $116,000 $116,000
Hispanic 0 $0 $0 $0 $0 $0
Native Hawaiian or Other Pacific Islander 0 $0 $0 $0 $0 $0
Not applicable 0 $0 $0 $0 $0 $0
Not Provided 0 $0 $0 $0 $0 $0
Asian 0 $0 $0 $0 $0 $0

Loan Type

Total Number of Mortgage Applications Total Dollar Amount of Mortgage Loan Applications Average Mortgage Loan Amount Highest Mortgage Loan Amount Average Applicant Income Highest Applicant Income
Conventional (any loan other than FHA, VA, FSA, or RHS loans) 235 $24,314,000 $103,000 $360,000 $65,000 $340,000
FHA-insured (Federal Housing Administration) 52 $6,403,000 $123,000 $315,000 $44,000 $116,000
FSA/RHS (Farm Service Agency or Rural Housing Service) 9 $1,334,000 $148,000 $316,000 $44,000 $81,000
VA-guaranteed (Veterans Administration) 7 $1,126,000 $160,000 $179,000 $46,000 $65,000

Outcome

Total Number of Mortgage Applications Total Dollar Amount of Mortgage Loan Applications Average Mortgage Loan Amount Highest Mortgage Loan Amount Average Applicant Income Highest Applicant Income
Loan Originated 92 $6,621,000 $71,000 $315,000 $68,000 $340,000
Application Withdrawn By Applicant 73 $8,903,000 $121,000 $360,000 $66,000 $173,000
Application Denied By Financial Institution 68 $8,047,000 $118,000 $257,000 $49,000 $156,000
Preapproval Denied by Financial Institution 33 $4,036,000 $122,000 $244,000 $46,000 $116,000
Application Approved But Not Accepted 13 $1,679,000 $129,000 $230,000 $71,000 $130,000
File Closed For Incompleteness 13 $2,250,000 $173,000 $337,000 $65,000 $116,000
Preapproval Approved but not Accepted 11 $1,641,000 $149,000 $240,000 $62,000 $111,000
Loan Purchased By The Institution 0 $0 $0 $0 $0 $0

Denial Reason

Total Number of Mortgage Applications Total Dollar Amount of Mortgage Loan Applications Average Mortgage Loan Amount Highest Mortgage Loan Amount Average Applicant Income Highest Applicant Income
Credit History 48 $5,485,000 $114,000 $220,000 $51,000 $155,000
Debt-To-Income Ratio 30 $3,902,000 $130,000 $257,000 $38,000 $75,000
Employment History 10 $1,074,000 $107,000 $167,000 $32,000 $59,000
Collateral 7 $982,000 $140,000 $246,000 $75,000 $106,000
Other 4 $229,000 $57,000 $99,000 $50,000 $109,000
Unverifiable Information 1 $250,000 $250,000 $250,000 $156,000 $156,000
Insufficient Cash (Downpayment, Closing Cost) 1 $161,000 $161,000 $161,000 $77,000 $77,000
Mortgage Insurance Denied 0 $0 $0 $0 $0 $0
Credit Application Incomplete 0 $0 $0 $0 $0 $0

Our comprehensive HMDA mortgage statistics provide invaluable insights into Great River Federal CU's 2012 lending activity. By analyzing detailed data on mortgage applications, total loan amounts, applicant incomes, loan purposes, and approval rates, industry professionals, researchers, and analysts can gain a clear understanding of this lender's role in the mortgage market.

This in-depth HMDA data allows real estate experts, financial analysts, and policymakers to track lending trends, evaluate borrower demographics, and assess Great River Federal CU's mortgage approval patterns. Utilize this powerful dataset to analyze market performance, compare lending practices, and make data-driven decisions in the ever-changing mortgage industry.
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