Mortgage Application in the United States increased by 1.10 percent in the week ending June 20 of 2025 over the previous week. This dataset provides - United States MBA Mortgage Applications - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Local Authority Mortgage to rent (LAMTR) scheme is a government initiative to help homeowners who have mortgages through the local government sector and are at risk of losing their homes due to mortgage arrears. The scheme is one of the possible resolutions for people who have been involved in the Mortgage Arrears Resolution Process (MARP) with a local authority and whose mortgage has been determined as unsustainable. The most important step any household in mortgage arrears can take is to engage early with the Arrears Support Unit of the local authority. The Local Authority Mortgage to Rent scheme enables householders to stay in their home and their established community. Surrendering the ownership equity in a home is a very difficult decision; however, the Local Authority Mortgage to Rent scheme provides householders with stability and continuity, after an often long period of financial turmoil. Ownership of the home transfers to the local authority and the householder pays a differential rent. Donegal, Dublin city, Laois, Roscommon and Westmeath data includes local authority/borrower costs incurred for LAMTR transactions in 2014 but claimed by local authorities in 2015 Total figure for 2015 includes €14,331 in respect of local authority/borrower costs incurred for LAMTR transactions in 2014 but claimed by local authorities in 2015.

HMDA requires many Financial Institutions (FI)s to maintain, report, and publicly disclose information about applications for and originations of mortgage loans. HMDA s purposes are to provide the public and public officials with sufficient information to enable them to determine whether institutions are serving the housing needs of the communities and neighborhoods in which they are located, to assist public officials in distributing public sector investments in a manner designed to improve the private investment environment, and to assist in identifying possible discriminatory lending patterns and enforcing antidiscrimination statutes. This publicly-available data asset contains HMDA data collected in or after 2017 and has been modified to protect the privacy of individuals whose information is present in the dataset.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

30 Year Mortgage Rate in the United States decreased to 6.77 percent in June 26 from 6.81 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

DESCRIPTION

A banking institution requires actionable insights into mortgage-backed securities, geographic business investment, and real estate analysis. The mortgage bank would like to identify potential monthly mortgage expenses for each region based on monthly family income and rental of the real estate. A statistical model needs to be created to predict the potential demand in dollars amount of loan for each of the region in the USA. Also, there is a need to create a dashboard which would refresh periodically post data retrieval from the agencies. The dashboard must demonstrate relationships and trends for the key metrics as follows: number of loans, average rental income, monthly mortgage and owner’s cost, family income vs mortgage cost comparison across different regions. The metrics described here do not limit the dashboard to these few. Dataset Description

Variables

Description Second mortgage Households with a second mortgage statistics Home equity Households with a home equity loan statistics Debt Households with any type of debt statistics Mortgage Costs Statistics regarding mortgage payments, home equity loans, utilities, and property taxes Home Owner Costs Sum of utilities, and property taxes statistics Gross Rent Contract rent plus the estimated average monthly cost of utility features High school Graduation High school graduation statistics Population Demographics Population demographics statistics Age Demographics Age demographic statistics Household Income Total income of people residing in the household Family Income Total income of people related to the householder Project Task: Week 1

Data Import and Preparation:

Import data.

Figure out the primary key and look for the requirement of indexing.

Gauge the fill rate of the variables and devise plans for missing value treatment. Please explain explicitly the reason for the treatment chosen for each variable.

Exploratory Data Analysis (EDA):

Perform debt analysis. You may take the following steps:

Explore the top 2,500 locations where the percentage of households with a second mortgage is the highest and percent ownership is above 10 percent. Visualize using geo-map. You may keep the upper limit for the percent of households with a second mortgage to 50 percent

Use the following bad debt equation:

Bad Debt = P (Second Mortgage ∩ Home Equity Loan) Bad Debt = second_mortgage + home_equity - home_equity_second_mortgage Create pie charts to show overall debt and bad debt

Create Box and whisker plot and analyze the distribution for 2nd mortgage, home equity, good debt, and bad debt for different cities

Create a collated income distribution chart for family income, house hold income, and remaining income

Perform EDA and come out with insights into population density and age. You may have to derive new fields (make sure to weight averages for accurate measurements):

Use pop and ALand variables to create a new field called population density

Use male_age_median, female_age_median, male_pop, and female_pop to create a new field called median age

Visualize the findings using appropriate chart type

Create bins for population into a new variable by selecting appropriate class interval so that the number of categories don’t exceed 5 for the ease of analysis.

Analyze the married, separated, and divorced population for these population brackets

Visualize using appropriate chart type

Please detail your observations for rent as a percentage of income at an overall level, and for different states.

Perform correlation analysis for all the relevant variables by creating a heatmap. Describe your findings.

Project Task: Week 2

Data Pre-processing:

The economic multivariate data has a significant number of measured variables. The goal is to find where the measured variables depend on a number of smaller unobserved common factors or latent variables.

Each variable is assumed to be dependent upon a linear combination of the common factors, and the coefficients are known as loadings. Each measured variable also includes a component due to independent random variability, known as “specific variance” because it is specific to one variable. Obtain the common factors and then plot the loadings. Use factor analysis to find latent variables in our dataset and gain insight into the linear relationships in the data.

  Following are the list of latent variables:

Highschool graduation rates

Median population age

Second mortgage statistics

Percent own

Bad debt expense

Data Modeling :

Build a linear Regression model to predict the total monthly expenditure for home mortgages loan.

  Please refer deplotment_RE.xlsx. Column hc_mortgage_mean is predicted variable. This is the mean monthly mortgage and owner costs of specified geographical location.

  Note: Exclude loans from prediction model which have NaN (Not a Numb...

15 Year Mortgage Rate in the United States decreased to 5.89 percent in June 26 from 5.96 percent in the previous week. This dataset includes a chart with historical data for the United States 15 Year Mortgage Rate.

The indicator reports the number of credits in progress during the year (without taking into account the year of signature of the contract) to the population aged 18 and over. All loans are registered with the National Bank (including credit openings of less than 1250 euros and repayable within 3 months, which mainly concern overdraft possibilities on bank account). Having credit is therefore not necessarily an indicator of "over-indebtedness risk". At the end of 2013, only 7.3% of Walloons with outstanding credits are in default of payment for credit. Note: the data at contract level are disseminated by postal code on the website of the personal credit centre. They have been aggregated at the municipal level by IWEPS. It is possible that this aggregation leads to some double counting. When a credit is taken out by several people who do not live in the same postal code, the data are included in the file for each of the postal codes concerned. If two contractors live in the same municipality but not in the same postal code, there will be duplication in the information related to the credit (amount, number, ...). These cases are probably rare because loans to several borrowers most often concern people domiciled at the same address. See also: - the website of the National Bank of Belgium (NBB), "\2".

This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...).

New Mortgage Lending Statistics. Published by Central Bank of Ireland. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).These data relate to new mortgage lending on residential property in Ireland on an annual basis. Data relates to those institutions [(banks and non-bank mortgage lenders)] who issue at least €50 million of new mortgage lending in a six-month period and are subsequently required to submit loan-level information to the Central Bank for the purposes of the macroprudential mortgage measures. The value and volume of new lending is provided, by borrower type, along with the distribution of lending by Loan-to-value and Loan-to-income ratio. Average characteristics are also provided. These data do not constitute official statistics. These data are published to support transparency and understanding of market developments....

There is a newer and more authoritative version of this layer here! It is owned by the University of Richmond's Digital Scholarship Lab and contains data on many more cities.The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Banks received federal backing to lend money for mortgages based on these grades. Many banks simply refused to lend to areas with the lowest grade, making it impossible for people in many areas to become homeowners. While this type of neighborhood classification is no longer legal thanks to the Fair Housing Act of 1968 (which was passed in large part due to the activism and work of the NAACP and other groups), the effects of disinvestment due to redlining are still observable today. For example, the health and wealth of neighborhoods in Chicago today can be traced back to redlining (Chicago Tribune). In addition to formerly redlined neighborhoods having fewer resources such as quality schools, access to fresh foods, and health care facilities, new research from the Science Museum of Virginia finds a link between urban heat islands and redlining (Hoffman, et al., 2020). This layer comes out of that work, specifically from University of Richmond's Digital Scholarship Lab. More information on sources and digitization process can be found on the Data and Download and About pages. This layer includes 7,148 neighborhoods spanning 143 cities across the continental United States. NOTE: As mentioned above, over 200 cities were redlined and therefore this is not a complete dataset of every city that experienced redlining by the HOLC in the 1930s. More cities are available in this feature layer from University of Richmond.Cities included in this layerAlabama: Birmingham, Mobile, MontgomeryCalifornia: Fresno, Los Angeles, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: DenverConnecticut: East Hartford, New Britain, New Haven, StamfordFlorida: Jacksonville, Miami, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Chattanooga, Columbus, MaconIllinois: Aurora, Chicago, Decatur, Joliet, GaryIndiana: Evansville, Fort Wayne, Indianapolis, Gary, Muncie, South Bend, Terre HauteKansas: Greater Kansas City, WichitaKentucky: Lexington, LouisvilleLouisiana: New OrleansMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Haverhill, Holyoke Chicopee, Lexington, Malden, Medford, Melrose, Milton, Needham, Newton, Quincy, Revere, Saugus, Somerville, Waltham, Watertown, Winchester, WinthropMaryland: BaltimoreMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Kalamazoo, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Duluth, MinneapolisMissouri: Greater Kansas City, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Greensboro, Winston SalemNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen Co., Camden, Essex County, Hudson County, TrentonNew York: Bronx, Brooklyn, Buffalo, Elmira, Binghamton/Johnson City, Lower Westchester Co., Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Staten Island, Syracuse, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorrain, Portsmouth, Springfield, Toledo, Warren, YoungstownOregon: PortlandPennsylvania: Altoona, Erie, Johnstown, New Castle, Philadelphia, PittsburghSouth Carolina: AugustaTennessee: Chattanooga, KnoxvilleTexas: DallasVirginia: Lynchburg, Norfolk, Richmond, RoanokeWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Milwaukee, Oshkosh, RacineWest Virginia: Charleston, WheelingAn example of a map produced by the HOLC of Philadelphia:

Home Loans in the United Kingdom increased to 2054 GBP Million in May from -776 GBP Million in April of 2025. This dataset provides - United Kingdom Mortgage Lending- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Description Summary Belvoir Group (BLV) is one of the largest property franchisors in the UK. BLV is a resilient business with 26 years of consecutive earnings growth, high margins and low capital needs. The company has been growing FCF per share at 15-25% per year, while also paying a 4.5% dividend yield. At a 10% 2023 FCF yield with a net cash balance sheet, BLV provides an attractive opportunity for small funds and personal accounts. Business background BLV started out in 1995 as a pure-play lettings franchisor. Over time they complemented the business with estate sales and financial services. Today, BLV operates 338 franchised offices under six different regional brands: Belvoir (159 offices), Northwood (91), Newton Fallowell (39), Nicholas Humphreys (20), Lovelle (16) and Mr and Mrs Clarke (13). They also operate a network of 284 mortgage advisors. best stock screener best investing platforms best stock screeners At its core this is a franchise business. BLV derives 80% of its gross profit from royalties paid by their property franchisees. BLV provides them with central support (e.g. a known brand, operational best practices, back-office, training and certifications, valuation and rental data/services, regional advertising and assistance in doing acquisitions) in return for a 10-12% royalty fee of the monthly revenue. As most know, franchise businesses possess attractive features with recurring revenues, high incremental margins and little capex. BLV’s franchisees are largely local entrepreneurs with 100% skin in the game. The majority operate just one to three offices. There are no large established franchisees as is the case with the major hotel and fast-food chains. The £150-200k start-up costs of running a BLV office, means that the franchisees generally have put all their money into the business. Alignment of incentives on the operating level doesn’t get much better than this. This is why the franchisees consistently outgrow the industry by a few percentage points. When the first lockdown ended, it took some of the corporate estate agency chains two months to reopen, while BLV’s franchisees opened their doors on the first day possible. The company reports into two divisions, property franchising and financial services. The former can be split between lettings and estate sales. Lettings (60% of gross profit). This is by far the best part of the business. Lettings is the managing of residential property on behalf of a landlord. This includes finding a tenant, doing the related administration/compliance, property visits and managing the tenant relationship. The franchisees charge landlords 1-1.5% of the monthly rent. Through its franchisees, BLV manages 75.5k properties, up from 37k in 2015. This segment has been growing gross profit at a low teens CAGR (incl. M&A). Lettings is a resilient business as people have to pay rent no matter the state of the economy. Organic growth has been positive every year since inception. Estate sales (20% of gross profit). The business of selling houses, which clearly is not as attractive as lettings. BLV’s franchisees charge a 1% commission on the value of the house. BLV sold 11k houses in 2022, up from 7k in 2017. Like lettings, gross profit has been growing at a low teens CAGR (incl. M&A). This segment is less cyclical than the overall housing market as they have historically grown above market, continue to attract new franchisees and generate 93% of gross profit outside of the Greater London area (less prone to boom and busts cycles). In 2022, BLV saw a 11% decrease in housing transactions compared to a 15% drop for the overall UK housing market. Nvidia EV/EBITDA Kroger EV/EBITDA Kraft EV/EBITDA Chevron EV/EBITDA Verizon EV/EBITDA Financial services (20% of gross profit). BLV manages a network of 284 mortgage advisors. The majority of them (85%) are self-employed. While technically not a franchise business, it works similarly. BLV provides central support and leads in return for a 25% cut of the fees. BLV works with one of UK’s leading mortgage intermediaries, the Mortgage Advice Bureau (MAB). MAB offers BLV access to >90 lenders, looks after compliance and processes the mortgages. The typical mortgage fee is 0.3% of the amount borrowed. In terms of cyclicality, this segment sits between lettings and estate sales. More than 90% of mortgages in the UK are two to five years in length, after which it typically gets refinanced. As such, there is a stable stream of mortgage renewals each year. Around half of the segment is refinancing-related and the other half is tied to housing transactions. Industry overview The UK counts 4.6m private-rented properties and sells 1.2m houses in a normal year. The majority of these are managed and sold by one of the more than 20k estate agencies/lettings offices. At least 15k of these are independents. The rest consists of agency networks that range from a handful of offices to in the hundreds. The largest...

Mortgage Approvals in Spain decreased to 39176 Units in April from 42831 Units in March of 2025. This dataset provides - Spain Mortgage Approvals- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Weekly updated dataset of Santander mortgage offerings, including interest rates, APRC, fees, and LTV for each product.

Consumer Complaints Dataset

Description:

This dataset provides detailed information about consumer complaints spanning various financial products and services.

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It includes data on the date of complaint, product and sub-product details, specific issues raised, company responses, and more. The dataset covers a wide range of time periods, allowing users to analyze trends in consumer complaints over the years.

Key Columns

Date received: Description: The date when the consumer complaint was received. Format: MM/DD/YYYY

Product: Description: Type of financial product or service associated with the complaint. Example: Credit reporting, Debt collection, Mortgage, etc.

Sub-product: Description: A more specific category under the main product. Example: FHA mortgage, Credit card, Personal loan, etc.

Issue: Description: Specific problem or reason for the consumer's complaint. Example: Incorrect information on your report, Improper use of your report, Application denials, etc.

Sub-issue: Description: Further details specifying the issue. Example: Information belongs to someone else, Reporting company used your report improperly, etc.

Consumer complaint narrative: Description: Free-form text where consumers provide detailed complaints.

Company public response: Description: Public response provided by the company addressing the consumer's complaint.

Company: Description: Name of the company against which the complaint is filed.

State: Description: State where the consumer resides.

ZIP code: Description: ZIP code of the consumer's location.

Tags: Description: Additional labels or tags associated with the complaint.

Consumer consent provided?: Description: Indicates whether the consumer provided consent regarding the complaint.

Submitted via: Description: The channel through which the complaint was submitted (e.g., Web, Referral).

Date sent to company: Description: Date when the complaint was forwarded to the company.

Company response to consumer: Description: The company's response to the consumer's complaint.

Timely response: Description: Indicates whether the company responded to the complaint in a timely manner.

Consumer disputed: Description: Indicates whether the consumer disputed the company's response.

Complaint ID: Description: Unique identifier for each consumer complaint.

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Usage Suggestions:

Explore patterns and trends in consumer complaints, analyze the responsiveness of companies, and gain insights into the distribution of complaints across different products and regions.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.