During the month of March 2025, the company with the largest share of the reverse mortgage market in the United States was Mutual Of Omaha Mortgage Inc. Its share of **** percent was around ***** percent greater than the market share of Finance Of America Reverse LLC. Reverse mortgage volume increases Mutual Of Omaha Mortgage Inc. was the top lender of Home Equity Conversion Mortgages (HECMs) in 2023, with the highest number of loan originations. In 2023, the company, which specializes in home equity retirement solutions, closed a total of over ***** HECMs and ended the year as the leading reverse mortgage company in the United States. Despite the overall number of HECMs in the United States dropping dramatically between 2009 and 2019, this trend reversed in the following years, with 2022 recording the highest 10-year figure. Banks withdraw from reverse mortgage market In the past, some of the largest banks in the United States featured in the list of leading reverse mortgage lenders; as of 2024, financial services firm Wells Fargo remained the all-time leading reverse mortgage company in the country. However, banks have exited the reverse mortgage business, and the rankings now feature companies that focus primarily on HECMs. In 2011, Wells Fargo and Bank of America – the two largest providers of HECMs at the time – stopped offering the service because of an unpredictable housing market and the creditworthiness of borrowers.

The Duty to Serve regulation defines “small financial institution” as a financial institution with less than $304 million in assets. Below is a link to the list of small financial institutions that meet this definition. The list consists primarily of depository institutions, credit unions and Community Development Financial Institutions. The list does not include non-depository mortgage banks. The list also includes several Agricultural Credit Associations (ACAs).

Mortgage lending information comes from the Federal Financial Institutions Examination Council's (FFIEC) Home Mortgage Disclosure Act (HMDA) data. Loan originations are the creation of a loan after bank approval. Loan origination rates are calculated from the number of loan applications that were either approved or denied—what is termed as decisioned applications. For all charts, the loan’s purpose can be selected via a dropdown list. Trends are summarized by all loan purposes and by Loans for home purchase, home improvement, or refinancing.

In 2024, mortgage lending by the 10 largest lenders in the United Kingdom (UK) amounted to over ****billion British pounds. Lloyds Banking Group topped the list for mortgage lending, with approximately ** billion British pounds in gross lending. Nationwide BS and NatWest Group completed the top three mortgage lenders with roughly ***billion and ** billion British pounds in gross lending, respectively.

snapshot-tab-pane About Housing Mortgage Mortgage is a debt instrument that the borrower is obliged to pay back with a fixed set of payments and is secured by the collateral of a specified real estate property. Mortgages enable individuals and businesses to make large real estate purchases without paying the entire value of the purchase in one go. Borrowers repay the loan along with interest over a period of many years until they eventually own the property free and clear. However, if borrowers stop paying the mortgage, the lender can foreclose and may evict the property’s owner and sell it, using the income from the sale to clear the mortgage debt. In a fixed-rate mortgage system, borrowers pay the same interest rate for the life of the loan. Most fixed-rate mortgages have a 15 or 30-year term. There is no influence on borrowers’ payment if market interest rates rise. However, if market interest rates decline significantly, borrowers may be able to secure that lower rate by means of refinancing the mortgage. TechNavio's analysts forecast the Housing Mortgage market in the US to grow at a CAGR of 1.75 percent over the period 2013-2018.Covered in this Report This report covers the present scenario and the growth prospects of the Housing Mortgage market in the US for the period 2014-2018. To calculate the market size, the report considers the loan volume of primary housing mortgage banks, credit unions, and financial institutions. It takes into consideration the various product segments such as Home Purchase, Home Improvement, and Refinancing. The report mentions the role played by Federal Government by the way of government-sponsored enterprises operating in the system. TechNavio's report, the Housing Mortgage Market in the US 2014-2018, has been prepared based on an in-depth market analysis with inputs from industry experts. The report covers the US; it also covers the landscape of the Housing Mortgage market in the US and its growth prospects in the coming years. The report also includes a discussion of the key vendors operating in this market.Key Regions • USKey Vendors • Bank of America • Citigroup • JPMorgan Chase • U.S. Bancorp • Wells FargoOther Prominent Vendors • Ally Financial • Capital One Financial • Fifth Third Bancorp • Flagstar Bank, FSB • SunTrust Banks • Quicken Loans • Regions FinancialMarket Driver • Improved Demand for Home Loans • For a full, detailed list, view our reportMarket Challenge • Shrinking Lending Capacity • For a full, detailed list, view our reportMarket Trend • Less Incidence of Foreclosures • For a full, detailed list, view our reportKey Questions Answered in this Report • What will the market size be in 2018 and what will the growth rate be? • What are the key market trends? • What is driving this market? • What are the challenges to market growth? • Who are the key vendors in this market space? • What are the market opportunities and threats faced by the key vendors? • What are the strengths and weaknesses of the key vendors?

Here is the comprehensive, expanded product listing for Monda.ai.

This version digs deeper into specific industry applications—particularly for Lenders, Note Buyers, and Solar/Home Service companies—and highlights the technical advantages of BatchData’s normalization engine.

BatchData | Monda.ai Product Listing Product Title US Mortgage & Lien Data (155M+ Properties): Complete Debt, Equity & Transaction Intelligence

Short Description Comprehensive debt intelligence on 155M+ US properties. Access 140+ data points including live interest rates, loan terms, private lender details, open lien balances, and involuntary liens to power underwriting, lead gen, and risk analysis.

Overview Stop guessing at a property's financial health. BatchData’s Mortgage Transaction & Open Liens dataset transforms messy, fragmented public records into a clean, developer-ready stream of truth. Covering over 155 million U.S. properties (Nationwide), this dataset provides a deterministic view of the entire "Debt Stack"—allowing you to calculate exact equity, identify distress, and audit loan positions with precision.

Unlike legacy aggregators that simply dump raw county recorder data, our proprietary Smart Ingestion Engine normalizes lender names, categorizes loan types (e.g., distinguishing a "Construction Loan" from a "Reverse Mortgage"), and recalculates open balances daily. We track the full lifecycle of a lien—from filing to assignment to release—ensuring you never trigger workflows based on stale debt.

The BatchData Difference:

Private Lender Visibility: We don't just track big banks. We expose private party lenders, hard money loans, and seller carry-back notes—critical data for investors finding off-market opportunities.

Involuntary Lien Detection: Instantly flag properties encumbered by "silent" debt like HOA liens, Mechanic’s liens, or Tax liens that threaten typical first-position security.

Calculated Equity Engine: We combine our AVM (Automated Valuation Model) with the total open lien balance to deliver a live "Estimated Equity" figure, saving you the math.

Data Schema & Attributes Includes 140+ standardized fields across three primary categories:

Mortgage Intelligence:

Loan Specifics: Loan Amount, Interest Rate (Fixed/Adjustable/Variable), Rate Type Code, Loan Term (Months), Maturity Date.

Loan Classification: Conventional, FHA, VA, Construction, HELOC/Credit Line, Reverse Mortgage, Private Party/Seller Carry.

Lender Details: Normalized Lender Name (e.g., "Wells Fargo" vs. "Wells Fargo Bank NA"), Trustee Details, Servicer (where available).

Key Dates: Recording Date, Document Date, Due Date.

Lien & Encumbrance Details:

Position Tracking: 1st, 2nd, 3rd, and Junior positions identified.

Involuntary Liens: Federal/State Tax Liens, Mechanic’s/Contractor Liens, HOA/COA Assessment Liens, Municipal Liens, Child Support Liens.

Judgments: Lien Amount, Judgment Amount, Filing Date, Release Date.

Status Indicators: Active/Open, Released, Assigned.

Financial Calculations:

CLTV (Combined Loan-to-Value): Precise ratio of all open debt against current market value.

Equity: Estimated Numeric Equity ($) and Equity Percentage (%).

Distress Signals: Notice of Default (NOD), Lis Pendens, Notice of Trustee Sale (NOTS).

Expanded Use Cases by Industry 1. Mortgage Lending & Refinance Marketing

Rate Arbitrage Targeting: Programmatically identify homeowners with interest rates >7% or those stuck in Adjustable Rate Mortgages (ARMs) nearing their reset date.

HELOC Origination: Filter for "High Equity" (>60%) properties with low CLTV to identify prime candidates for Home Equity Lines of Credit marketing.

Non-QM Lead Gen: Spot self-employed borrowers with private money loans or balloon payments coming due—ideal candidates for Non-QM refinance products.

1. Note Investing & Debt Buying

Distress Scouting: Identify "Zombie Mortgages" or non-performing notes by correlating "Pre-Foreclosure" flags with "Open Lien" balances.

Junior Lien Strategy: Find properties where the 2nd lien is active but the 1st is distressed, allowing strategic buyouts of junior positions.

Due Diligence: Verify the priority of liens before purchasing a note to ensure you aren't buying into a position wiped out by superior tax or municipal liens.

1. Real Estate Investment (Creative Finance)

"Subject-To" Acquisition: Locate sellers with high-equity, low-interest fixed-rate mortgages (e.g., <4%) who are facing distress (Tax Liens/Divorce). These are prime targets for taking over payments ("Subject-To" deals).

Private Money Lending: Analyze local private lending activity to see who is funding flips in your target zip codes (Contractor Vetting).

1. Solar & Home Improvement Services

Credit Qualification: Stop wasting ad spend on homeowners who can’t get financing. Use our LTV and Equity data to pre-qualify leads before the sales call.

Lien Risk Check: Ensure a property doesn't have a "Mechanic’s Lien" or "Tax Lien" that would prevent your solar fin...

This data set represents thousands of loans made through the Lending Club platform, which is a platform that allows individuals to lend to other individuals. Of course, not all loans are created equal. Someone who is a essentially a sure bet to pay back a loan will have an easier time getting a loan with a low interest rate than someone who appears to be riskier. And for people who are very risky? They may not even get a loan offer, or they may not have accepted the loan offer due to a high interest rate. It is important to keep that last part in mind, since this data set only represents loans actually made, i.e. do not mistake this data for loan applications!

A data frame with 10,000 observations on the following 55 variables.

emp_title

Job title.

emp_length

Number of years in the job, rounded down. If longer than 10 years, then this is represented by the value 10.

state

Two-letter state code.

homeownership

The ownership status of the applicant's residence.

annual_income

Annual income.

verified_income

Type of verification of the applicant's income.

debt_to_income

Debt-to-income ratio.

annual_income_joint

If this is a joint application, then the annual income of the two parties applying.

verification_income_joint

Type of verification of the joint income.

debt_to_income_joint

Debt-to-income ratio for the two parties.

delinq_2y

Delinquencies on lines of credit in the last 2 years.

months_since_last_delinq

Months since the last delinquency.

earliest_credit_line

Year of the applicant's earliest line of credit

inquiries_last_12m

Inquiries into the applicant's credit during the last 12 months.

total_credit_lines

Total number of credit lines in this applicant's credit history.

open_credit_lines

Number of currently open lines of credit.

total_credit_limit

Total available credit, e.g. if only credit cards, then the total of all the credit limits. This excludes a mortgage.

total_credit_utilized

Total credit balance, excluding a mortgage.

num_collections_last_12m

Number of collections in the last 12 months. This excludes medical collections.

num_historical_failed_to_pay

The number of derogatory public records, which roughly means the number of times the applicant failed to pay.

months_since_90d_late

Months since the last time the applicant was 90 days late on a payment.

current_accounts_delinq

Number of accounts where the applicant is currently delinquent.

total_collection_amount_ever

The total amount that the applicant has had against them in collections.

current_installment_accounts

Number of installment accounts, which are (roughly) accounts with a fixed payment amount and period. A typical example might be a 36-month car loan.

accounts_opened_24m

Number of new lines of credit opened in the last 24 months.

months_since_last_credit_inquiry

Number of months since the last credit inquiry on this applicant.

num_satisfactory_accounts

Number of satisfactory accounts.

num_accounts_120d_past_due

Number of current accounts that are 120 days past due.

num_accounts_30d_past_due

Number of current accounts that are 30 days past due.

num_active_debit_accounts

Number of currently active bank cards.

total_debit_limit

Total of all bank card limits.

num_total_cc_accounts

Total number of credit card accounts in the applicant's history.

num_open_cc_accounts

Total number of currently open credit card accounts.

num_cc_carrying_balance

Number of credit cards that are carrying a balance.

num_mort_accounts

Number of mortgage accounts.

account_never_delinq_percent

Percent of all lines of credit where the applicant was never delinquent.

tax_liens

a numeric vector

public_record_bankrupt

Number of bankruptcies listed in the public record for this applicant.

loan_purpose

The category for the purpose of the loan.

application_type

The type of application: either individual or joint.

loan_amount

The amount of the loan the applicant received.

term

The number of months of the loan the applicant received.

interest_rate

Interest rate of the loan the applicant received.

installment

Monthly payment for the loan the applicant received.

grade

Grade associated with the loan.

sub_grade

Detailed grade associated with the loan.

issue_month

Month the loan was issued.

loan_status

Status of the loan.

initial_listing_status

Initial listing status of the loan. (I think this has to do with whether the lender provided the entire loan or if the loan is across multiple lenders.)

disbursement_method

Dispersement method of the loan.

balance

Current balance on the loan.

paid_total

Total that has been paid on the loan by the applicant.

paid_principal

The difference between the original loan amount and the curre...

Following the drastic increase directly after the COVID-19 pandemic, the delinquency rate started to gradually decline, falling below *** percent in the second quarter of 2023. In the second half of 2023, the delinquency rate picked up but remained stable throughout 2024. In the second quarter of 2025, **** percent of mortgage loans were delinquent. That was significantly lower than the **** percent during the onset of the COVID-19 pandemic in 2020 or the peak of *** percent during the subprime mortgage crisis of 2007-2010. What does the mortgage delinquency rate tell us? The mortgage delinquency rate is the share of the total number of mortgaged home loans in the U.S. where payment is overdue by 30 days or more. Many borrowers eventually manage to service their loan, though, as indicated by the markedly lower foreclosure rates. Total home mortgage debt in the U.S. stood at almost ** trillion U.S. dollars in 2024. Not all mortgage loans are made equal ‘Subprime’ loans, being targeted at high-risk borrowers and generally coupled with higher interest rates to compensate for the risk. These loans have far higher delinquency rates than conventional loans. Defaulting on such loans was one of the triggers for the 2007-2010 financial crisis, with subprime delinquency rates reaching almost ** percent around this time. These higher delinquency rates translate into higher foreclosure rates, which peaked at just under ** percent of all subprime mortgages in 2011.

Lending Club

Lending Club is a peer-to-peer Lending company based in the US. They match people looking to invest money with people looking to borrow money. When investors invest their money through Lending Club, this money is passed onto borrowers, and when borrowers pay their loans back, the capital plus the interest passes on back to the investors. It is a win for everybody as they can get typically lower loan rates and higher investor returns.

The Lending Club dataset contains complete loan data for all loans issued through the 2007-2015, including the current loan status (Current, Late, Fully Paid, etc.) and latest payment information. Features (aka variables) include credit scores, number of finance inquiries, address including zip codes and state, and collections among others. Collections indicates whether the customer has missed one or more payments and the team is trying to recover their money. The file is a matrix of about 890 thousand observations and 75 variables.

acceptD The date which the borrower accepted the offer accNowDelinq The number of accounts on which the borrower is now delinquent. accOpenPast24Mths Number of trades opened in past 24 months. addrState The state provided by the borrower in the loan application all_util Balance to credit limit on all trades annual_inc_joint The combined self-reported annual income provided by the co-borrowers during registration annualInc The self-reported annual income provided by the borrower during registration. application_type Indicates whether the loan is an individual application or a joint application with two co-borrowers avg_cur_bal Average current balance of all accounts bcOpenToBuy Total open to buy on revolving bankcards. bcUtil Ratio of total current balance to high credit/credit limit for all bankcard accounts. chargeoff_within_12_mths Number of charge-offs within 12 months collections_12_mths_ex_med Number of collections in 12 months excluding medical collections creditPullD The date LC pulled credit for this loan delinq2Yrs The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years delinqAmnt The past-due amount owed for the accounts on which the borrower is now delinquent. desc Loan description provided by the borrower dti A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income. dti_joint A ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, excluding mortgages and the requested LC loan, divided by the co-borrowers' combined self-reported monthly income earliestCrLine The date the borrower's earliest reported credit line was opened effective_int_rate The effective interest rate is equal to the interest rate on a Note reduced by Lending Club's estimate of the impact of uncollected interest prior to charge off. emp_title The job title supplied by the Borrower when applying for the loan.* empLength Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. expD The date the listing will expire expDefaultRate The expected default rate of the loan. ficoRangeHigh The upper boundary range the borrower’s FICO at loan origination belongs to. ficoRangeLow The lower boundary range the borrower’s FICO at loan origination belongs to. fundedAmnt The total amount committed to that loan at that point in time. grade LC assigned loan grade homeOwnership The home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER. id A unique LC assigned ID for the loan listing. il_util Ratio of total current balance to high credit/credit limit on all install acct ils_exp_d wholeloan platform expiration date initialListStatus The initial listing status of the loan. Possible values are – W, F inq_fi Number of personal finance inquiries inq_last_12m Number of credit inquiries in past 12 months inqLast6Mths The number of inquiries in past 6 months (excluding auto and mortgage inquiries) installment The monthly payment owed by the borrower if the loan originates. intRate Interest Rate on the loan isIncV Indicates if income was verified by LC, not verified, or if the income source was verified listD The date which the borrower's application was listed on the platform. loanAmnt The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value. max_bal_bc Maximum current balance owed on all r...

The mortgage credit interest rate is the average interest rate on mortgage loan products offered to individuals and households by the commercial banks in the country. The mortgage credit is a loan used to finance the purchase of real estate. The table shows the latest available data from the national authorities as well as the values from three months ago and one year ago. The data are updated continuously.

The 10 largest mortgage lenders in the United Kingdom accounted for approximately 83 percent of the total market, with the top three alone accounting for 48 percent in 2024. Lloyds Banking Group had the largest market share of gross mortgage lending, with nearly 47 billion British pounds in lending in 2024. HSBC, which is the largest UK bank by total assets, ranked fifth. Development of the mortgage market In 2024, the value of outstanding in mortgage lending to individuals amounted to 1.6 trillion British pounds. Although this figure has continuously increased in the past, the UK mortgage market declined dramatically in 2024, registering the lowest value of mortgage lending since 2015. In 2020, the COVID-19 pandemic caused the market to contract for the first time since 2012. The next two years saw mortgage lending soar due to pent-up demand, but as interest rates soared, the housing market cooled, leading to a decrease in new loans of about 100 billion British pounds. The end of low interest rates In 2021, mortgage rates saw some of their lowest levels since recording began by the Bank of England. For a long time, this was particularly good news for first-time homebuyers and those remortgaging their property. Nevertheless, due to the rising inflation, mortgage rates started to rise in the second half of the year, resulting in the 10-year rate doubling in 2022.

Lending Club offers peer-to-peer (P2P) loans through a technological platform for various personal finance purposes and is today one of the companies that dominate the US P2P lending market. The original dataset is publicly available on Kaggle and corresponds to all the loans issued by Lending Club between 2007 and 2018. The present version of the dataset is for constructing a granting model, that is, a model designed to make decisions on whether to grant a loan based on information available at the time of the loan application. Consequently, our dataset only has a selection of variables from the original one, which are the variables known at the moment the loan request is made. Furthermore, the target variable of a granting model represents the final status of the loan, that are "default" or "fully paid". Thus, we filtered out from the original dataset all the loans in transitory states. Our dataset comprises 1,347,681 records or obligations (approximately 60% of the original) and it was also cleaned for completeness and consistency (less than 1% of our dataset was filtered out). TARGET VARIABLE The dataset includes a target variable based on the final resolution of the credit: the default category corresponds to the event charged off and the non-default category to the event fully paid. It does not consider other values in the loan status variable since this variable represents the state of the loan at the end of the considered time window. Thus, there are no loans in transitory states. The original dataset includes the target variable loan status, which contains several categories ('Fully Paid', 'Current', 'Charged Off', 'In Grace Period', 'Late (31-120 days)', 'Late (16-30 days)', 'Default'). However, in our dataset, we just consider loans that are either Fully Paid or Default and transform this variable into a binary variable called Default, with a 0 for fully paid loans and a 1 for defaulted loans. EXPLANATORY VARIABLES The explanatory variables that we use correspond only to the information available at the time of the application. Variables such as the interest rate, grade, or subgrade are generated by the company as a result of a credit risk assessment process, so they were filtered out from the dataset as they must not be considered in risk models to predict the default in granting of credit. FULL LIST OF VARIABLES Loan identification variables:

id: Loan id (unique identifier).

issue_d: Month and year in which the loan was approved.

Quantitative variables:

revenue: Borrower's self-declared annual income during registration.

dti_n: Indebtedness ratio for obligations excluding mortgage. Monthly information. This ratio has been calculated considering the indebtedness of the whole group of applicants. It is estimated as the ratio calculated using the co-borrowers total payments on the total debt obligations divided by the co-borrowers combined monthly income.

loan_amnt: Amount of credit requested by the borrower.

fico_n: Defined between 300 and 850, reported by Fair Isaac Corporation as a risk measure based on historical credit information reported at the time of application. This value has been calculated as the average of the variables fico_range_low and fico_range_high in the original dataset.

experience_c: Binary variable that indicates whether the borrower is new to the entity. This variable is constructed from the credit date of the previous obligation in LC and the credit date of the current obligation; if the difference between dates is positive, it is not considered as a new experience with LC.

Categorical variables:

emp_length: Categorical variable with the employment length of the borrower (includes the no information category)

purpose: Credit purpose category for the loan request.

home_ownership_n: Homeownership status provided by the borrower in the registration process. Categories defined by LC: mortgage, rent, own, other, any, none. We merged the categories other, any and none as other.

addr_state: Borrower's residence state from the USA.

zip_code: Zip code of the borrower's residence.

Textual variables

title: Title of the credit request description provided by the borrower.

desc: Description of the credit request provided by the borrower.

We cleaned the textual variables. First, we removed all those descriptions that contained the default description provided by Lending Club on its web form (Tell your story. What is your loan for?). Moreover, we removed the prefix Borrower added on DD/MM/YYYY from the descriptions to avoid any temporal background on them. Finally, as these descriptions came from a web form, we substituted all the HTML elements by their character (e.g. amp; was substituted by , lt; was substituted by , etc.). RELATED WORKS This dataset has been used in the following academic articles:

Sanz-Guerrero, M. Arroyo, J. (2024). Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending. arXiv preprint arXiv:2401.16458. https://doi.org/10.48550/arXiv.2401.16458 Ariza-Garzn, M.J., Arroyo, J., Caparrini, A., Segovia-Vargas, M.J. (2020). Explainability of a machine learning granting scoring model in peer-to-peer lending. IEEE Access 8, 64873 - 64890. https://doi.org/10.1109/ACCESS.2020.2984412

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Beginning in data year 2020, categories were added to Mortgage Status to account for the variety of mortgage arrangements that may exist. See "American Community Survey Subject Definitions" for more information on Mortgage Status..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

The number of mortgage cases received and disposed in the High Court. Datasets are produced on an annual year basis. The dataset is entered onto ICOS, the Integrated Courts Operations System. The data are then extracted and merged with the Central Postcode Directory, and aggregated information uploaded to this portal.

Northern Ireland Courts and Tribunals Service collects information on writs and originating summonses issued in respect of mortgages in Chancery Division of the Northern Ireland High Court. This covers both Northern Ireland Housing Executive and private mortgages, and relates to both domestic and commercial properties.

A mortgage case may involve more than one address or a land property. In such cases, the first postcode address entered onto ICOS is used. Not all writs and originating summonses lead to eviction. A plaintiff begins an action for an order for possession of property. The court, following a judicial hearing, may grant an order for possession. This entitles the plaintiff to apply for an order to have the defendant evicted. However, even where an order for eviction is issued the parties can still negotiate a compromise to prevent eviction.

Users of this data may have been able to self-identify themselves due to the low values in some cells. Primary and secondary disclosure control methods have been applied to this data, denoted by cells with missing data in the tables. Values of less than four participants, but not zero participants, were initially suppressed, but some of these values could have been calculated using some row and column totals and thus secondary suppression was applied to the next lowest value in the row and column.

The data contain the number of cases received and the number of cases disposed by each Local Government District and have the following proportions of postcode coverage: 2012, 97.2%; 2013, 95.9%; 2014, 96.0%; 2015, 94.5%; 2016, 95.4%; 2017, 95.2%; 2018, 94.5%; 2019, 95.5%; 2020, 96.7%; 2021, 95.7%; 2022, 95.6%; 2023, 95.2%; 2024, 95.8%.

Our Home Ownership Mortgage Database is rebuilt every two months and contains information on over 50+ million US Homeowners. The data is collected from county recorder and assessor offices.

The file is processed via National Change of Address (NCOA) to ensure deliverability. Additionally, the data is passed against suppression files to eliminate consumers or telephone numbers as appropriate, such as the Deceased File, State Attorney General (SAG) data, the Direct Marketing Association's (DMA) do-not-mail and do-not-call lists, and the national FTC do-not-call file.

Selections include mortgage loan and property attributes along with household, individual and neighborhood demographics.

Description

This data set represents thousands of loans made through the Lending Club platform, which is a platform that allows individuals to lend to other individuals. Of course, not all loans are created equal. Someone who is a essentially a sure bet to pay back a loan will have an easier time getting a loan with a low interest rate than someone who appears to be riskier. And for people who are very risky? They may not even get a loan offer, or they may not have accepted the loan offer due to a high interest rate. It is important to keep that last part in mind, since this data set only represents loans actually made, i.e. do not mistake this data for loan applications!

Format

A data frame with 10,000 observations on the following 55 variables.

emp_title

Job title.

emp_length

Number of years in the job, rounded down. If longer than 10 years, then this is represented by the value 10.

state

Two-letter state code.

homeownership

The ownership status of the applicant's residence.

annual_income

Annual income.

verified_income

Type of verification of the applicant's income.

debt_to_income

Debt-to-income ratio.

annual_income_joint

If this is a joint application, then the annual income of the two parties applying.

verification_income_joint

Type of verification of the joint income.

debt_to_income_joint

Debt-to-income ratio for the two parties.

delinq_2y

Delinquencies on lines of credit in the last 2 years.

months_since_last_delinq

Months since the last delinquency.

earliest_credit_line

Year of the applicant's earliest line of credit

inquiries_last_12m

Inquiries into the applicant's credit during the last 12 months.

total_credit_lines

Total number of credit lines in this applicant's credit history.

open_credit_lines

Number of currently open lines of credit.

total_credit_limit

Total available credit, e.g. if only credit cards, then the total of all the credit limits. This excludes a mortgage.

total_credit_utilized

Total credit balance, excluding a mortgage.

num_collections_last_12m

Number of collections in the last 12 months. This excludes medical collections.

num_historical_failed_to_pay

The number of derogatory public records, which roughly means the number of times the applicant failed to pay.

months_since_90d_late

Months since the last time the applicant was 90 days late on a payment.

current_accounts_delinq

Number of accounts where the applicant is currently delinquent.

total_collection_amount_ever

The total amount that the applicant has had against them in collections.

current_installment_accounts

Number of installment accounts, which are (roughly) accounts with a fixed payment amount and period. A typical example might be a 36-month car loan.

accounts_opened_24m

Number of new lines of credit opened in the last 24 months.

months_since_last_credit_inquiry

Number of months since the last credit inquiry on this applicant.

num_satisfactory_accounts

Number of satisfactory accounts.

num_accounts_120d_past_due

Number of current accounts that are 120 days past due.

num_accounts_30d_past_due

Number of current accounts that are 30 days past due.

num_active_debit_accounts

Number of currently active bank cards.

total_debit_limit

Total of all bank card limits.

num_total_cc_accounts

Total number of credit card accounts in the applicant's history.

num_open_cc_accounts

Total number of currently open credit card accounts.

num_cc_carrying_balance

Number of credit cards that are carrying a balance.

num_mort_accounts

Number of mortgage accounts.

account_never_delinq_percent

Percent of all lines of credit where the applicant was never delinquent.

tax_liens

a numeric vector

public_record_bankrupt

Number of bankruptcies listed in the public record for this applicant.

loan_purpose

The category for the purpose of the loan.

application_type

The type of application: either individual or joint.

loan_amount

The amount of the loan the applicant received.

term

The number of months of the loan the applicant received.

interest_rate

Interest rate of the loan the applicant received.

installment

Monthly payment for the loan the applicant received.

grade

Grade associated with the loan.

sub_grade

Detailed grade associated with the loan.

issue_month

Month the loan was issued.

loan_status

Status of the loan.

initial_listing_status

Initial listing status of the loan. (I think this has to do with whether the lender provided the entire loan or if the loan is across multiple lenders.)

disbursement_method

Dispersement method of the loan.

balance

Current...

Data Description

1 id : To uniquely identify every loan in the dataset.

2 member_id : To identify the borrower to who has applied for the loan. 3 loan_amnt : The listed amount of the loan applied for by the borrower. 4 funded_amnt : The amount that was sanctioned by the LC. 5 term : The number of payments on the loan. Values are in months and can be either 36 or 60. 6 int_rate : Interest Rate on the loan 7 installment : The monthly payment owed by the borrower if the loan originates. 8 grade : LC assigned loan grade which depends on the borrower’s credit score. 9 sub_grade : LC assigned loan subgrade 10 emp_title : The job title supplied by the Borrower when applying for the loan.* 11 emp_length : Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. 12 home_ownership : The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER 13 annual_inc : The self-reported annual income provided by the borrower during registration. 14 verification_status : Indicates if income was verified by LC, not verified, or if the income source was verified 15 issue_d : The month which the loan was funded 16 loan_status : Current status of the loan 17 purpose : A category provided in the form of a code to indicate the purpose for the loan. 18 title : Explaining the ‘purpose’ of the loan. 19 dti : The debt to income ratio is the ratio of how much the borrower owes every month to the borrower’s income every month. 20 delinq_2yrs : The number of delinquencies(late installment payment) by the borrower in the past 2 years. 21 earliest_cr_line : The month-year the borrower's earliest reported credit line was opened 22 inq_last_6mths : Inquiries for loans made by the borrower over the past 6 months. 23 mths_since_last_delinq : Months that have passed since the borrower last missed the timely payment of installment. 24 open_acc : The number of open credit lines in the borrower’s credit file. 25 pub_rec Number of derogatory public records 26 revol_bal : Total credit revolving balance 27 revol_util : Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit. 28 total_acc : The total number of credit lines currently in the borrower's credit file 29 initial_list_status : The initial listing status of the loan. Possible values are – W(whole), F(fractional) 30 out_prncp : Remaining outstanding principal for total amount funded 31 total_pymnt : Payments received to date for the total amount funded. 32 total_rec_prncp : Principal received till date. 33 total_rec_int Interest received till date. 34 total_rec_late_fee : Late fees received to date. 35 recoveries : Total recovery procedures initiated against the borrower. 36 collection_recovery_fee : The fees collected during the recovery procedures. 37 last_pymnt_d The last month when payment was received. 38 last_pymnt_amnt : The last payment amount received. 39 next_pymnt_d : Next scheduled payment date. 40 last_credit_pull_d : The most recent month LC pulled credit for this loan 41 collections_12_mths_ex_med : Number of collections in 12 months excluding medical collections 42 mths_since_last_major_derog : Months since most recent 90-day delinquency or worse rating 43 application_type Indicates whether the loan is an individual application or a joint application with two co-borrowers 44 annual_inc_joint : The combined self-reported annual income provided by the co-borrowers during registration 45 dti_joint : A ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, excluding mortgages and the requested LC loan, divided by the co-borrowers' combined self-reported monthly income 46 acc_now_delinq : The number of accounts on which the borrower is now delinquent 47 tot_coll_amt : Total collection amounts ever owed by the borrower 48 tot_cur_bal : Total current balance of all accounts owned by the borrower 49 total_rev_hi_lim : Total high credit/credit limit

30 Year Mortgage Rate in the United States increased to 6.11 percent in February 5 from 6.10 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.