The Federal National Mortgage Association, commonly known as Fannie Mae, was created by the U.S. congress in 1938, in order to maintain liquidity and stability in the domestic mortgage market. The company is a government-sponsored enterprise (GSE), meaning that while it was a publicly traded company for most of its history, it was still supported by the federal government. While there is no legally binding guarantee of shares in GSEs or their securities, it is generally acknowledged that the U.S. government is highly unlikely to let these enterprises fail. Due to these implicit guarantees, GSEs are able to access financing at a reduced cost of interest. Fannie Mae's main activity is the purchasing of mortgage loans from their originators (banks, mortgage brokers etc.) and packaging them into mortgage-backed securities (MBS) in order to ease the access of U.S. homebuyers to housing credit. The early 2000s U.S. mortgage finance boom During the early 2000s, Fannie Mae was swept up in the U.S. housing boom which eventually led to the financial crisis of 2007-2008. The association's stated goal of increasing access of lower income families to housing finance coalesced with the interests of private mortgage lenders and Wall Street investment banks, who had become heavily reliant on the housing market to drive profits. Private lenders had begun to offer riskier mortgage loans in the early 2000s due to low interest rates in the wake of the "Dot Com" crash and their need to maintain profits through increasing the volume of loans on their books. The securitized products created by these private lenders did not maintain the standards which had traditionally been upheld by GSEs. Due to their market share being eaten into by private firms, however, the GSEs involved in the mortgage markets began to also lower their standards, resulting in a 'race to the bottom'. The fall of Fannie Mae The lowering of lending standards was a key factor in creating the housing bubble, as mortgages were now being offered to borrowers with little or no ability to repay the loans. Combined with fraudulent practices from credit ratings agencies, who rated the junk securities created from these mortgage loans as being of the highest standard, this led directly to the financial panic that erupted on Wall Street beginning in 2007. As the U.S. economy slowed down in 2006, mortgage delinquency rates began to spike. Fannie Mae's losses in the mortgage security market in 2006 and 2007, along with the losses of the related GSE 'Freddie Mac', had caused its share value to plummet, stoking fears that it may collapse. On September 7th 2008, Fannie Mae was taken into government conservatorship along with Freddie Mac, with their stocks being delisted from stock exchanges in 2010. This act was seen as an unprecedented direct intervention into the economy by the U.S. government, and a symbol of how far the U.S. housing market had fallen.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Mortgage Rates: Hot Economic Conjecture Puts the Squeeze on Homebuyers

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

View weekly updates and historical trends for 30 Year Mortgage Rate. from United States. Source: Freddie Mac. Track economic data with YCharts analytics.

In the United States, interest rates for all mortgage types started to increase in 2021. This was due to the Federal Reserve introducing a series of hikes in the federal funds rate to contain the rising inflation. In the second quarter of 2025, the 30-year fixed rate dropped slightly, to **** percent. The rate remained below the peak of **** percent in the fourth quarter of 2023. Why have U.S. home sales decreased? Cheaper mortgages normally encourage consumers to buy homes, while higher borrowing costs have the opposite effect. As interest rates increased in 2022, the number of existing homes sold plummeted. Soaring house prices over the past 10 years have further affected housing affordability. Between 2014 and 2024, the median price of an existing single-family home risen by about ** percent. On the other hand, the median weekly earnings have risen much slower. Comparing mortgage terms and rates Between 2008 and 2024, the average rate on a 15-year fixed-rate mortgage in the United States stood between **** and **** percent. Over the same period, a 30-year mortgage term averaged a fixed-rate of between **** and **** percent. Rates on 15-year loan terms are lower to encourage a quicker repayment, which helps to improve a homeowner’s equity.

Discover the booming US mortgage lending market! This in-depth analysis reveals key trends, growth drivers, and forecasts for 2025-2033, including regional breakdowns and leading players like Bank of America and Chase. Explore market segmentation, CAGR projections, and competitive insights. Recent developments include: August 2023: Spring EQ, a provider of home equity financing solutions, has entered into a definitive agreement to be acquired by an affiliate of Cerberus Capital Management, L.P., a global leader in alternative investing. The main aim of the partnership is to support Spring EQ's mission to deliver offerings and expand its leadership in the home equity financing market., June 2023: VIU by HUB, a digital insurance brokerage platform subsidiary of Hub International Limited, has entered into a new partnership with Unison, a home equity-sharing company. The collaboration will allow homeowners to compare insurance coverage quotes from various carriers and receive expert advice throughout the process.. Key drivers for this market are: Home Renovation Trends are Driving the Market. Potential restraints include: Home Renovation Trends are Driving the Market. Notable trends are: Home Equity Lending Market is Being Stimulated By Rising Home Prices.

Unlock the potential of the booming mortgage refinancing market! Explore detailed analysis of market size, growth trends, key players (Wells Fargo, Bank of America, Rocket Companies), and regional variations from 2019-2033. Discover valuable insights into fixed-rate, adjustable-rate, and cash-out refinancing options.

An index that can be used to gauge broad financial conditions and assess how these conditions are related to future economic growth. The index is broadly consistent with how the FRB/US model generally relates key financial variables to economic activity. The index aggregates changes in seven financial variables: the federal funds rate, the 10-year Treasury yield, the 30-year fixed mortgage rate, the triple-B corporate bond yield, the Dow Jones total stock market index, the Zillow house price index, and the nominal broad dollar index using weights implied by the FRB/US model and other models in use at the Federal Reserve Board. These models relate households' spending and businesses' investment decisions to changes in short- and long-term interest rates, house and equity prices, and the exchange value of the dollar, among other factors. These financial variables are weighted using impulse response coefficients (dynamic multipliers) that quantify the cumulative effects of unanticipated permanent changes in each financial variable on real gross domestic product (GDP) growth over the subsequent year. The resulting index is named Financial Conditions Impulse on Growth (FCI-G). One appealing feature of the FCI-G is that its movements can be used to measure whether financial conditions have tightened or loosened, to summarize how changes in financial conditions are associated with real GDP growth over the following year, or both.

Jumbo 30-Year Fixed Mortgage Rates

Zillow Home Value Forecast and Cash Buyer Data

By Zillow Data [source]

About this dataset

This dataset tracks the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours. It provides insight into changes in the housing market and helps consumers make wiser decisions with their investments. In addition to tracking monthly mortgage rates, our dataset also covers consumer's home types and housing stock, cash buyer data, Zillow Home Value Forecast (ZHVF), negative equity metrics, affordability forecasts for both mortgages and rents as well as historic data including historical ZHVI and household income. With this unique blend of financial and real estate information, users are empowered to make more informed decisions about their investments. The data is updated weekly with the most recent statistics available so that users always have access to up-to-date information

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How to use the dataset

How to Use This Dataset:

• To start exploring this dataset, identify what type of home you are interested in by selecting one of the four categories: “all homes” (Zillow defines all homes as single family, condominiums and coops with a county record); multifamily 5+; duplex/triplex; or condos/coops.
• Understand additional data products that are included such as Zillow Home Value Forecast (ZHVF), Cash Buyers % share, affordability metrics like mortgage affordability or rental affordability and historical ZHVI values along with its median value for particular households or geographies which needs deeper insights into other endogenous variables such detailed information like how many bedrooms a house has etc.

• Choose your geographic region on which you would want to collect more information– regions could include city breakdowns from nationwide level down till specific metropolitan etc . Also use special crosswalks available if needed between federally defined metrics for counties / metro areas combined with Zillow's own ones for greater accuracy when analysing external facors effect on data . To download all datasets at once - click here. .

• Gather more relevant external factors for analysis such as home values forecasts using our published methodology post given url , further to mention TransUnion credit bureau related debt amounts also consider median household incomes vis Bureaus of Labor Cost Indexes ; All these give us greater dimensional insights into market dynamics affecting any particular region finally culminating into deeper research findings when taken together . The reasons behind any fluctions observed can be properly derived as a result .

          Finally make sure that proper attribution is alwys done following mentioned Terms Of Use while downloading since 'All Data Accessed And Downloaded From This Page Is Free For Public Use By Consumers , Media
  

Research Ideas

• Using the Mortgage Rate Data to devise strategies to help persons purchasing jumbo mortgages determine the best time and rates to acquire a loan.
• Analyzing trends in the market by investigating changes in affordability over time by studying rent and mortgage affordability, price-to-income ratios, and historical ZHVIs with cash buyers.
• Comparing different areas of housing markets over diverse geographies using data on all homes, condos/co-ops, multifamily dwellings 5+ units, duplexes/triplexes across various counties or metro areas

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: MortgageRateJumboFixed.csv | Column name | Description | |:---------------------------|:---------------------------------------------------------------------------------------------------------------| | Date | The date of the mortgage rate. (Date) | | TimePeriod | The time period of the ...

Snapshot img

Home Equity Lending Market Size 2025-2029

The home equity lending market size is forecast to increase by USD 48.16 billion, at a CAGR of 4.7% between 2024 and 2029.

The market is experiencing significant growth, fueled primarily by the massive increase in home prices and the resulting rise in residential properties with substantial equity. This trend presents a lucrative opportunity for lenders, as homeowners with substantial equity can borrow against their homes to fund various expenses, from home improvements to debt consolidation. However, this market also faces challenges. Lengthy procedures and complex regulatory requirements can hinder the growth of home equity lending, making it essential for lenders to streamline their processes and ensure compliance with evolving regulations.
Additionally, economic uncertainty and potential interest rate fluctuations may impact borrower demand, requiring lenders to adapt their strategies to remain competitive. To capitalize on market opportunities and navigate challenges effectively, lenders must focus on enhancing the borrower experience, leveraging technology to streamline processes, and maintaining a strong regulatory compliance framework.

What will be the Size of the Home Equity Lending Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, shaped by various economic and market dynamics. Fair lending practices remain a crucial aspect, with entities ensuring borrowers' creditworthiness through rigorous risk assessments. Economic conditions, employment history, and credit score are integral components of this evaluation. Mortgage insurance (PMIs) and mortgage-backed securities (MBS) are employed to mitigate risk in the event of default. Verification of income, property value, and consumer protection are also essential elements in the home equity lending process. Housing prices, Homeowners Insurance, and property value are assessed to determine the loan-to-value ratio (LTV) and interest rate risk. Prepayment penalties, closing costs, and loan term are factors that influence borrowers' financial planning and decision-making.

The regulatory environment plays a significant role in shaping market activities. Consumer confidence, financial literacy, and foreclosure prevention initiatives are key areas of focus. real estate market volatility and mortgage rates impact the demand for home equity loans, with cash-out refinancing and debt consolidation being popular applications. Amortization schedules, mortgage broker involvement, and escrow accounts are essential components of the loan origination process. Market volatility and housing market trends continue to unfold, requiring ongoing risk assessment and adaptation.

How is this Home Equity Lending Industry segmented?

The home equity lending industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Source

  Mortgage and credit union
  Commercial banks
  Others


Distribution Channel

  Offline
  Online


Purpose

  Home Improvement
  Debt Consolidation
  Investment


Loan Type

  Fixed-Rate
  Variable-Rate


Geography

  North America

    US
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    UAE


  APAC

    Australia
    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Source Insights

The mortgage and credit union segment is estimated to witness significant growth during the forecast period.

In the realm of home equity lending, mortgage and credit unions emerge as trusted partners for consumers. These financial institutions offer various services beyond home loans, including deposit management, checking and savings accounts, and credit and debit cards. By choosing a mortgage or credit union for home equity lending, consumers gain access to human advisors who can guide them through the intricacies of finance. Mortgage and credit unions provide competitive rates on home equity loans, making them an attractive option. Consumer protection is a priority, with fair lending practices and rigorous risk assessment ensuring creditworthiness. Economic conditions, employment history, and credit score are all taken into account during the loan origination process.

Home equity loans can be used for various purposes, such as home improvement projects, debt consolidation, or cash-out refinancing. Consumer confidence plays a role in loan origination, with interest rates influenced by market volatility and economic conditions. Fixed-rate and adjustable-rate loans are available, each with its advantag

Lehman Brothers, the fourth largest investment bank on Wall Street, declared bankruptcy on the 15th of September 2008, becoming the largest bankruptcy in U.S. history. The investment house, which was founded in the mid-19th century, had become heavily involved in the U.S. housing bubble in the early 2000s, with its large holdings of toxic mortgage-backed securities (MBS) ultimately causing the bank's downfall. The bank had expanded rapidly following the repeal of the Glass-Steagall Act in 1999, which meant that investment banks could also engage in commercial banking activities. Lehman vertically integrated their mortgage business, buying smaller commercial enterprises that originated housing loans, which allowed the bank to expand its MBS holdings. The downfall of Lehman and the crash of '08 As the U.S. housing market began to slow down in 2006, the default rate on housing loans began to spike, triggering losses for Lehman from their MBS portfolio. Lehman's main competitor in mortgage financing, Bear Stearns, was bought by J.P. Morgan Chase in order to prevent bankruptcy in March 2008, leading investors and lenders to become increasingly concerned about the bank's financial health. As the bank relied on short-term funding on money markets in order to meet its obligations, the news of its huge losses in the third-quarter of 2008 further prevented it from funding itself on financial markets. By September, it was clear that without external assistance, the bank would fail. As its losses from credit default swaps mounted due to the deepening crash in the housing market, Lehman was forced to declare bankruptcy on September 15, as no buyer could be found to save the bank. The collapse of Lehman triggered panic in global financial markets, forcing the U.S. government to step in and bail-out the insurance giant AIG the next day on September 16. The effects of this financial crisis hit the non-financial economy hard, causing a global recession in 2009.

Explore the dynamic global refinancing market, analyzing its growth drivers, emerging trends like digital transformation and cash-out options, and key industry players. Discover market size, CAGR, and regional insights from 2019-2033.

Unlock the potential of your home equity! Explore the booming home equity loan market, projected to reach $41.18 billion by 2033. Learn about market trends, top lenders (Bank of America, Discover Bank, LoanDepot), and regional insights in our comprehensive analysis. Discover how HELOCs and fixed-rate loans are shaping the future of home financing. Recent developments include: In April 2022, Redfin a real estate company based in Seattle (United States) acquired Bay Equity Home Loans with a sum of USD 137.8 Million. The merger accelerates Redfin’s strategy for expanding its business with customers to buy, sell, rent, and finance a home., In July 2022, Ontario Teachers’ Pension Plan Board acquired HomeQ which exists as a parent company of HomeEquity Bank, from Birch Hill Equity Partners Management Inc. HomeEquity Bank exist as a Canadian Bank offering a range of reverse mortgage solutions product and Ontario Teachers' Pension Plan Board is a global investor.. Key drivers for this market are: Increase In Sales of Household Units, Higher Duration of Repayment. Potential restraints include: Increase In Sales of Household Units, Higher Duration of Repayment. Notable trends are: Access to Large Amount of Loan.

France FR: Lending Rate: Households: House Purchase: Stocks data was reported at 2.283 % pa in 2017. This records a decrease from the previous number of 2.757 % pa for 2016. France FR: Lending Rate: Households: House Purchase: Stocks data is updated yearly, averaging 4.089 % pa from Dec 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 5.473 % pa in 2003 and a record low of 2.283 % pa in 2017. France FR: Lending Rate: Households: House Purchase: Stocks data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s France – Table FR.IMF.IFS: Lending, Saving and Deposit Rates: Annual.

In 2024, the average mortgage rates in European countries varied from *** percent in Bulgaria to over nine percent in Hungary. The mortgage rate for a home purchase is decided depending on the individual situation of the homebuyer, their credit history, and income, but they also follow macro determinants including the base lending rate, inflation, economic growth, and the health of the housing market. Starts, completions and prices The supply of new housing varies in different countries in Europe. In 2023, the number of new housing units completed per 1,000 citizens was between *** and seven, with this number varying greatly in different countries. Ireland and Poland were among the countries with most completed housing units. When it comes to housing starts, Ireland tops the ranking. The average transaction price of a new dwelling in 2023 ranged anywhere from roughly ***** euros per square meter to under ***** euros per square meter. Housing stock As the most populous country in Europe, Germany has the largest housing stock. Comparing the number of housing units per 1,000 citizens is an easy way to identify housing shortages. In Greece and the UK, for example, the number of dwellings per 1,000 citizens measured less than ***, compared to Bulgaria and Spain, where it was around ***.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

AGM^E Federal Agricultural Mortgage Corporation 5.750% Non-Cumulative Preferred Stock Series E

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Negative Equity in U.S. Housing Market

Measuring Home Values, Debt, and Credit Risk

By Zillow Data [source]

About this dataset

This dataset, Negative Equity in the US Housing Market, provides an in-depth look into the negative equity occurring across the United States during this single quarter. Included are metrics such as total amount of negative equity in millions of dollars, total number of homes in negative equity, percentage of homes with mortgages that are in negative equity and more. These data points provide helpful insights into both regional and national trends regarding the prevalence and rate of home mortgage delinquency stemming from a diminishment of value from peak levels.

Home types available for analysis include 'all homes', condos/co-ops, multifamily units containing five or more housing units as well as duplexes/triplexes. Additionally, Cash buyers rates for particular areas can also be determined by referencing this collection. Further metrics such as mortgage affordability rates and impacts on overall indebtedness are readily calculated using information related to Zillow's Home Value Index (ZHVI) forecast methodology and TransUnion data respectively.

Other variables featured within this dataset include characteristics like region type (i.e city, county ..etc), size rank based on population values , percentage change in ZHVI since peak levels as well as loan-to-value ratio greater than 200 across all regions constituted herein (NE). Moreover Zillow's own Secondary Mortgage Market Survey data is utilized to acquire average mortgage quote rates while correlative Census Bureau NCHS median household income figures represent typical assessable proportions between wages and debt obligations . So whether you're looking to assess effects along metro lines or detailed buffering through zip codes , this database should prove sufficient for insightful explorations! Nonetheless users must strictly adhere to all conditions encompassed within Terms Of Use commitments put forth by our lead provider before accessing any resources included herewith

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

Research Ideas

• Analyzing regional and state trends in negative equity: Analyze geographic differences in the percentage of mortgages “underwater”, total amount of negative equity, number of homes at least 90 days late, and other key indicators to provide insight into the factors influencing negative equity across regions, states and cities.
• Tracking the recovery rate over time: Track short-term changes in numbers related to negative equity (e.g., region or area ZHVI Change from Peak) to monitor recovery rates over time as well as how different policy interventions are affecting homeownership levels in affected areas.
• Exploring best practices for promoting housing affordability: Compare affordability metrics (e.g., mortgage payments, price-to-income ratios) across different geographic locations over time to identify best practices for empowering homeowners and promoting stability within the housing market while reducing local inequality impacts related to availability of affordable housing options and access to credit markets like mortgages/loans etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: NESummary_2017Q1_Public.csv | Column name | Description | |:------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | RegionType | The type of region (e.g., city, county, metro etc.) (String) | | City | Name of the city (String) | | County | Name of the county (String) | | State | Name of the state (String) | | Metro ...

Discover the booming Canadian home lending market! Explore key trends, growth projections (CAGR > 5%), leading lenders (HSBC, Tangerine, BMO), and market segmentation insights for 2025-2033. Analyze the impact of interest rates, regulations, and economic factors on this dynamic sector. Recent developments include: On March 15, 2022, First Ontario Credit Union announced its merger with Heritage savings & Credit union to offer the best in financial products and services., On February 09, 2022, Hello safe announced a new partnership with Hard bacon, a personal finance application used by more than 35,000 Canadians, this partnership is to leverage Hard bacon's portfolio of comparison tools.. Notable trends are: A Rise in Home Prices Boosting Home Equity Lending Market.

Greece GR: Lending Rate: Households: House Purchase: Stocks data was reported at 2.371 % pa in 2017. This records a decrease from the previous number of 2.636 % pa for 2016. Greece GR: Lending Rate: Households: House Purchase: Stocks data is updated yearly, averaging 3.824 % pa from Dec 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 5.327 % pa in 2003 and a record low of 2.371 % pa in 2017. Greece GR: Lending Rate: Households: House Purchase: Stocks data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Greece – Table GR.IMF.IFS: Lending, Saving and Deposit Rates: Annual.

Luxembourg LU: Lending Rate: Households: House Purchase: Stocks data was reported at 1.900 % pa in 2017. This records a decrease from the previous number of 1.979 % pa for 2016. Luxembourg LU: Lending Rate: Households: House Purchase: Stocks data is updated yearly, averaging 2.350 % pa from Dec 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 4.978 % pa in 2008 and a record low of 1.900 % pa in 2017. Luxembourg LU: Lending Rate: Households: House Purchase: Stocks data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Luxembourg – Table LU.IMF.IFS: Lending, Saving and Deposit Rates: Annual.

Discover the latest trends and insights into the booming US mortgage lending market. Our analysis reveals impressive growth projections, key players, and emerging opportunities within the fixed-rate loans, HELOCs, and online/offline lending segments. Learn more about market size, CAGR, and future forecasts. Recent developments include: August 2023: Spring EQ, a provider of home equity financing solutions, has entered into a definitive agreement to be acquired by an affiliate of Cerberus Capital Management, L.P., a global leader in alternative investing. The main aim of the partnership is to support Spring EQ's mission to deliver offerings and expand its leadership in the home equity financing market., June 2023: VIU by HUB, a digital insurance brokerage platform subsidiary of Hub International Limited, has entered into a new partnership with Unison, a home equity-sharing company. The collaboration will allow homeowners to compare insurance coverage quotes from various carriers and receive expert advice throughout the process.. Key drivers for this market are: Home Renovation Trends are Driving the Market. Potential restraints include: Home Renovation Trends are Driving the Market. Notable trends are: Home Equity Lending Market is Being Stimulated By Rising Home Prices.