Vacationland Federal Credit Un Mortgage Lending & HMDA Data, 2009

In 2009, Vacationland Federal Credit Un of Sandusky, OH reported 529 mortgage applications totaling $41.3 million, up 27% from 2008. The average application was for $78,030, and applicants reported an average income of $64,451. 56% of its applications resulted in an originated loan and 16% were denied. Its largest markets were Sandusky, OH (380), Toledo, OH (25) and Cleveland-Elyria-Mentor, OH (22) (applications in parentheses).

Mortgage Company & Address Vacationland Federal Credit Un
2911 South Hayes Avenue
Sandusky, OH 44870
Tel #: available with data download
Federal Regulator * available with data download
Years 2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  
Total Number of Mortgage Applications 529
Total Mortgage Loan Application Amount $41,278,000
Average Mortgage Loan Application Amount $78,030
Highest Mortgage Loan Application Amount $353,000
Average Applicant Income $64,451
Highest Applicant Income $330,000

About this data: figures come from the Home Mortgage Disclosure Act (HMDA) records Vacationland Federal Credit Un filed with the FFIEC and count every application it reported (approved, denied, withdrawn or purchased), not just closed loans.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 Vacationland Federal Credit Un by County in 2009

County Total Number of Mortgage Loan Applications Total Dollar Amount of Mortgage Loans Applications Average Mortgage Loan Application Amount Average Applicant Income
Erie, Ohio 380 $ 29,045,000 $ 76,434 $ 66,000
Huron, Ohio 39 $ 2,504,000 $ 64,205 $ 61,000
Ottawa, Ohio 24 $ 2,226,000 $ 92,750 $ 63,000
Sandusky, Ohio 20 $ 1,756,000 $ 87,800 $ 48,000
Lorain, Ohio 18 $ 1,248,000 $ 69,333 $ 76,000
Seneca, Ohio 5 $ 386,000 $ 77,200 $ 36,000
Cuyahoga, Ohio 3 $ 89,000 $ 29,666 $ 129,000
Franklin, Ohio 3 $ 519,000 $ 173,000 $ 106,000
Richland, Ohio 3 $ 193,000 $ 64,333 $ 36,000
Genesee, Michigan 1 $ 76,000 $ 76,000 $ 78,000
Butler, Ohio 1 $ 110,000 $ 110,000 $ 50,000
Clark, Ohio 1 $ 83,000 $ 83,000 $ 79,000
Delaware, Ohio 1 $ 142,000 $ 142,000 $ 73,000
Knox, Ohio 1 $ 135,000 $ 135,000 $ 60,000
Medina, Ohio 1 $ 173,000 $ 173,000 $ 55,000
Paulding, Ohio 1 $ 60,000 $ 60,000 $ 45,000
Wood, Ohio 1 $ 65,000 $ 65,000 $ 35,000

Mortgage Loan Application Detail Totals for Vacationland Federal Credit Un in 2009

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 290 $28,084,000 $96,000 $353,000 $70,000 $243,000
Home Purchase 137 $11,286,000 $82,000 $255,000 $59,000 $330,000
Home Improvement 102 $1,908,000 $18,000 $251,000 $56,000 $230,000

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 455 $37,869,000 $83,000 $350,000 $66,000 $330,000
Black or African American 38 $1,306,000 $34,000 $170,000 $51,000 $119,000
Not Provided 20 $1,049,000 $52,000 $353,000 $63,000 $230,000
Hispanic 13 $703,000 $54,000 $132,000 $58,000 $187,000
American Indian or Alaskan Native 3 $351,000 $117,000 $175,000 $104,000 $129,000
Asian 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

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) 529 $41,278,000 $78,000 $353,000 $65,000 $330,000
FHA-insured (Federal Housing Administration) 0 $0 $0 $0 $0 $0
FSA/RHS (Farm Service Agency or Rural Housing Service) 0 $0 $0 $0 $0 $0
VA-guaranteed (Veterans Administration) 0 $0 $0 $0 $0 $0

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 294 $21,645,000 $73,000 $353,000 $65,000 $216,000
Application Denied By Financial Institution 84 $6,595,000 $78,000 $279,000 $54,000 $130,000
Application Approved But Not Accepted 74 $8,085,000 $109,000 $350,000 $78,000 $330,000
Application Withdrawn By Applicant 32 $1,454,000 $45,000 $143,000 $59,000 $168,000
Preapproval Approved but not Accepted 23 $2,107,000 $91,000 $240,000 $56,000 $119,000
File Closed For Incompleteness 20 $1,220,000 $61,000 $300,000 $73,000 $243,000
Preapproval Denied by Financial Institution 2 $172,000 $86,000 $125,000 $35,000 $42,000
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
Debt-To-Income Ratio 21 $1,829,000 $87,000 $255,000 $40,000 $124,000
Credit History 15 $880,000 $58,000 $279,000 $48,000 $123,000
Collateral 7 $373,000 $53,000 $136,000 $70,000 $108,000
Other 2 $51,000 $25,000 $30,000 $78,000 $109,000
Employment History 2 $89,000 $44,000 $82,000 $29,000 $34,000
Insufficient Cash (Downpayment, Closing Cost) 0 $0 $0 $0 $0 $0
Mortgage Insurance Denied 0 $0 $0 $0 $0 $0
Unverifiable Information 0 $0 $0 $0 $0 $0
Credit Application Incomplete 0 $0 $0 $0 $0 $0

Our comprehensive HMDA mortgage statistics provide invaluable insights into Vacationland Federal Credit Un's 2009 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 Vacationland Federal Credit Un'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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