30 Year Mortgage Rate in the United States decreased to 6.23 percent in November 26 from 6.26 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

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 ...

Fixed 30-year mortgage rates in the United States averaged 6.40 percent in the week ending November 21 of 2025. This dataset provides the latest reported value for - United States MBA 30-Yr Mortgage Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Context

The Federal Reserve sets interest rates to promote conditions that achieve the mandate set by the Congress — high employment, low and stable inflation, sustainable economic growth, and moderate long-term interest rates. Interest rates set by the Fed directly influence the cost of borrowing money. Lower interest rates encourage more people to obtain a mortgage for a new home or to borrow money for an automobile or for home improvement. Lower rates encourage businesses to borrow funds to invest in expansion such as purchasing new equipment, updating plants, or hiring more workers. Higher interest rates restrain such borrowing by consumers and businesses.

Content

This dataset includes data on the economic conditions in the United States on a monthly basis since 1954. The federal funds rate is the interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight. The rate that the borrowing institution pays to the lending institution is determined between the two banks; the weighted average rate for all of these types of negotiations is called the effective federal funds rate. The effective federal funds rate is determined by the market but is influenced by the Federal Reserve through open market operations to reach the federal funds rate target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds target rate; the target rate transitioned to a target range with an upper and lower limit in December 2008. The real gross domestic product is calculated as the seasonally adjusted quarterly rate of change in the gross domestic product based on chained 2009 dollars. The unemployment rate represents the number of unemployed as a seasonally adjusted percentage of the labor force. The inflation rate reflects the monthly change in the Consumer Price Index of products excluding food and energy.

Acknowledgements

The interest rate data was published by the Federal Reserve Bank of St. Louis' economic data portal. The gross domestic product data was provided by the US Bureau of Economic Analysis; the unemployment and consumer price index data was provided by the US Bureau of Labor Statistics.

Inspiration

How does economic growth, unemployment, and inflation impact the Federal Reserve's interest rates decisions? How has the interest rate policy changed over time? Can you predict the Federal Reserve's next decision? Will the target range set in March 2017 be increased, decreased, or remain the same?

This dataset provides values for 30 YEAR MORTGAGE RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Historical Prime Rates and Related Significant Events 1956-Present

Understanding Lending and Economic Trends

By Brandon Gadoci [source]

About this dataset

This dataset looks back at the history of lending rates from 1956 to present and investigates the effects of significant historical events on prime lending rate. The data, which was sourced from trusted sources, provides an insight into how major political and economic developments have influenced the cost of borrowing in different countries. By examining which events had an impact on interest rates and by how much, this dataset could prove invaluable for researchers looking to understand historical financial trends or for investors trying to understand past market behaviour. Take a step back in time with this comprehensive collection of lending data – it could be the key to unlocking greater insights into our financial history!

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How to use the dataset

This dataset contains historical prime rates from 1956 to present, as well as significant events that may have affected the prime lending rate. With this data, you can analyze changes in the average majority prime rate charged by banks and any events that may have contributed to this change.

To get started with this dataset, you'll want to make sure you understand the columns it contains: Year: This is the year of the data point. (Integer)
Average Majority Prime Rate Charged By Banks: This is average prime rate charged by banks in the majority of he year for a given time period. (Float)
Significant Events: Significant events that may have impacted or shifted the Prime Lending Rate during a certain period or throughout history. (String)

You can then use this information to begin exploring and comparing periods where there were drastic shifts inside of one year within this data set as it provides an overall view intoprime lending during these different times periods along with what plausible external or internal factors could’ve caused them. To do so, you can use descriptive statistics such a means and medians, along with graphing tools such as line charts and scatter plots to observe any correlations between fluctuations inPrime Lending Rates and Significant Events taking place concurrently at different points in time throughout history over six decades §§ when both economic states seem prosperous or abysmal for comparison purposes so we can identify driving forces behind certain trends inside our data set

Research Ideas

• Create a timeline visualization of major prime rate events in the US to show the influence of various political and economic factors on interest rates.
• Superimpose this data over monthly trends of mortgage and auto loan interest rates to illustrate the impact that movements in the prime lending rate have on consumer borrowing.
• Determine which banks currently offer loans with the lowest prime rates, by tracking historic trends against current market conditions for lenders

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: historical_prime rate.csv | Column name | Description | |:-------------------------------------------------|:---------------------------------------------------------------------------| | Year | Year of the average majority prime rate charged by banks. (Integer) | | Average majority prime rate charged by banks | The average majority prime rate charged by banks in a given year. (Float) | | Significant Events | Significant events that may have had an effect on the prime rate. (String) |

Acknowledgements

If you use this dataset in your research, please cr...

The benchmark interest rate in Sweden was last recorded at 1.75 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

UK Mortgage Rates

Mortgage products in the united kingdom

By Jeff [source]

About this dataset

This dataset contains information on thousands of mortgage products available in the UK, including the interest rate, APR, revert rate, fees, and initial rate period. This data can be used to compare different mortgage products and find the best deal for your needs

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How to use the dataset

This dataset contains information on thousands of mortgage products available in the UK, including the interest rate, APR, revert rate, fees, and initial rate period.

To use this dataset, simply download it and then import it into your favorite spreadsheet program. You can then use the data to compare mortgage rates across different products and banks.

This dataset can be used to help you: - Compare mortgage rates from different banks - Find the best mortgage product for your needs - Understand how fees and other charges affect the overall cost of a mortgage

Research Ideas

• Analysing the different mortgage products available on the market
• Benchmarking against other products in order to get a competitive rate
• Finding products that have low fees and revert rates

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: UK_Mortgage_Rate.csv | Column name | Description | |:----------------------------|:----------------------------------------------------------------| | SKU | The product's SKU. (String) | | BANK_NAME | The name of the bank that offers the mortgage product. (String) | | MTG_PRODUCT_SUBTITLE | The subtitle of the mortgage product. (String) | | MTG_PRODUCT_TYPE_RAW | The raw product type of the mortgage product. (String) | | MTG_PRODUCT_YEARS | The number of years of the mortgage product. (Integer) | | MTG_INITIAL_RATE_PCT | The initial rate percentage of the mortgage product. (Float) | | MTG_APR_PCT | The APR percentage of the mortgage product. (Float) | | MTG_REVERT_RATE | The revert rate of the mortgage product. (Float) | | MTG_FEES_TOTAL | The total fees of the mortgage product. (Float) | | MTG_INITIAL_RATE_MONTHS | The initial rate months of the mortgage product. (Integer) | | SCAN_DATE | The date that the mortgage product was scanned. (Date) |

Acknowledgements

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

Mortgage rates surged at an unprecedented pace in 2022, with the average 10-year fixed rate doubling between March and December of that year. In response to mounting inflation, the Bank of England implemented a series of rate hikes, pushing borrowing costs steadily higher. By October 2025, the average 10-year fixed mortgage rate stood at **** percent. As financing becomes more expensive, housing demand has cooled, weighing on market sentiment and slowing house price growth. How have the mortgage hikes affected the market? After surging in 2021, the number of residential properties sold fell significantly in 2023, dipping to just above *** million transactions. This contraction in activity also dampened mortgage lending. Between the first quarter of 2023 and the first quarter of 2024, the value of new mortgage loans declined year-on-year for five consecutive quarters. Even as rates eased modestly in 2024 and housing activity picked up slightly, volumes remained well below the highs recorded in 2021. How are higher mortgages impacting homebuyers? For homeowners, the impact is being felt most acutely as fixed-rate deals expire. Mortgage terms in the UK typically range from two to ten years, and many borrowers who locked in historically low rates are now facing significantly higher repayments when refinancing. By the end of 2026, an estimated five million homeowners will see their mortgage deals expire. Roughly two million of these loans are projected to experience a monthly payment increase of up to *** British pounds by 2026, putting additional pressure on household budgets and constraining affordability across the market.

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. “&” was substituted by “&”, “<” 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-Garzón, 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

The benchmark interest rate in Mexico was last recorded at 7.25 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in the United States was last recorded at 4 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

• Agriculture, Hunting, Forestry and Fishing refers to loans granted for the purpose of financing customers in the cultivation of crops, livestock farming, timber extraction, forest management, poultry, farming, fishing and agricultural services. • Mining and Quarrying refers to loans granted to finance coal mining, crude petroleum and natural gas production, metal ore mining and quarrying. • Manufacturing refers to loans granted to finance customers in the manufacturing of a multitude of goods, including processing of food, rubber, palm oil, etc., manufacture of wearing apparel, leather goods, wood and wood products, paper and paper products, printing, publishing, manufacture of chemical and chemical products, petroleum, coal, rubber and plastic products, manufacture of iron and steel products, manufacture of fabricated metal products, machinery and equipment, etc. • Electricity, Gas and Water refers to loans granted to finance customers in generation, transmission and distribution of electrical energy for sale to households, industrial and commercial users, production of gas in gas works, distribution of manufactured gas and natural gas. • Wholesale and Retail Trade, Restaurants and Hotels refers to loans granted to finance customers in wholesale trade, retail trade and those operating restaurants and hotels. • Broad Property Sector Of which:  Construction refers to loans granted to finance customers in general contracting including civil engineering work, special contracting work, construction of industrial buildings and factories, construction of infrastructure, commercial complexes, residential dwellings and other construction activity.  Residential Property refers to loans granted for the purchase or refinancing the purchase of residential property which were classified as low cost (RM25, 000 and below), lower medium cost (RM25, 001 -RM60, 000), medium cost (RM60,001-RM100,000), higher medium cost (RM100,001 -RM150,000) and higher cost houses (more that RM150,000).  Non-Residential Property refers to loans granted for the purchase and refinancing of the purchase of non-residential property. Non-residential means landed property, which are not used for human dwelling purposes. It includes industrial buildings, factories, land, commercial complexes, warehouses and other structures not meant for human dwelling.  Real Estate refers to loans granted to companies involved in letting and operating real estate services on own account. Include renting of land to others, development and sale of land on own account, sub-dividing real property etc. Include real estate agents, brokers and managers engaged in renting, buying, selling and managing real estate for others for a fee and commission.  Transport, Storage and Communication refers to loans granted to finance customers in the provision of transport, storage and communication services to others. • Finance, Insurance and Business Services Of which:  Finance refers to loans granted to banking institutions and non-bank financial institutions.  Insurance refers to life insurance, reinsurance and general insurance services, insurance broking and loss assessing for insurance claims purposes.  Business Services refer to loans extended for provision of legal services, accounting services, auditing services, data collection etc. • Consumption Credit Of which:  Personal Uses refer to loans granted to individuals only for private use, exclude loans to purchase securities, consumer durables, transport vehicles, residential property, non-residential property and loans or credit obtained through the use of credit cards.  Purchase of Consumer Durables refers to loans granted for the acquisition of consumer durable goods such as televisions, refrigerators, washing machines etc.  Purchase of Passenger Cars refers to loans for the purchase of motor vehicles which are used primarily to carry a limited number of people, and includes multipurpose vehicles fitted to carry passengers.  Credit Cards refer to credit extended to customers using credit/charge cards issued by a reporting institution and includes cash withdrawals through such cards.  Purchase of Securities refers to loans granted to finance both primary and secondary market purchases of securities. Include loans granted to substitute for another loan granted previously by another party for the purchase of securities.  Purchase of Transport Vehicles refers to loans granted for the purposes of financing the purchases of motor vehicles other than passenger cars and other transport vehicles.  Community, Social and Personal Services refer to loans granted to finance customers for services such as public administration and defense, sanitary and similar services, social and related community services, recreation and cultural services and personal and household services.

Dataset underlying the Seniors First reverse mortgage comparison widget. Displays indicative rate types, features, and eligibility details for multiple Australian reverse-mortgage providers. Data is aggregated and refreshed periodically for consumer education.

Brief

The dataset contains key factors that could influence Residential home prices in the last 20 years in the United States. This factor falls into two categories i.e. Supply & Demand

The S&P Case-Shiller Housing Price Index(HPI) is taken as the y variable, or dependent variable, as an indicator of change in prices.

Supply_dataset(Monthly_data)

• Building Permits(Permit Number)-Number of building permits allotted
• Construction Spending (Million $)-The amount spent (in millions of USD) is a measure of the activity in the construction industry.
• Housing Starts(New Housing Project)-This is a measure of the number of units of new housing projects started in a given period.
• Homes Sold(units)-House for sale is a basic measure of supply.

Demand_dataset(Quaterly_data)

• Mortgage Rates(%)
• USA GDP(Billions$ )-Quarterly Real GDP (adjusted for inflation)
• Unemployment(%)
• Delinquency Rate(%) on Mortgages(Foreclosure on the mortgage)-an indicator of the number of foreclosures in real estate.

Inspiration

Building a Data Science model to find the factors which influenced the home prices the most in the last 20 years.

Summary

https://docs.google.com/presentation/d/1SFQg-cwu2JRr-85uvU1jYY4KDtTjqKuG/edit#slide=id.p3

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.

The benchmark interest rate in Russia was last recorded at 16.50 percent. This dataset provides the latest reported value for - Russia Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Interest-Expense Time Series for Comerica Incorporated. Comerica Incorporated, together with its subsidiaries, provides financial services in the United States, Canada, and Mexico. The company operates through Commercial Bank, Retail Bank, and Wealth Management segments. The Commercial Bank segment offers various products and services, including commercial loans and lines of credit, deposits, cash management, payment solutions, card services, capital market products, international trade finance, letters of credit, foreign exchange management services, and loan syndication services for small and middle market businesses, multinational corporations, and governmental entities. The Retail Bank segment provides personal financial services, such as consumer lending, consumer deposit gathering, and mortgage loan origination; and various consumer products that include deposit accounts, installment loans, credit cards, home equity lines of credit, and residential mortgage loans. The Wealth Management segment offers products and services comprising financial planning, trust and fiduciary services, investment management and advisory, brokerage, private banking, and business transition planning services for affluents, high-net worth and ultra-high-net-worth individuals and families, business owners and executives, and institutional clients. The company was formerly known as DETROITBANK Corporation and changed its name to Comerica Incorporated in July 1982. Comerica Incorporated was founded in 1849 and is headquartered in Dallas, Texas.

Interest-Income Time Series for KeyCorp. KeyCorp operates as the holding company for KeyBank National Association that provides various retail and commercial banking products and services in the United States. It operates in two segments, Consumer Bank and Commercial Bank. The company offers various deposits and investment products; commercial leasing, investment management, consumer finance; personal finance and financial wellness, lending, student loan refinancing, mortgage and home equity, credit card, treasury, and business advisory; and wealth management and investment services for institutional, non-profit, and high-net-worth clients. It also provides lending, cash management, equipment financing, and commercial mortgage loans; and capital market products and services, such as syndicated finance, debt and equity underwriting, fixed income and equity sales and trading, derivatives, foreign exchange, mergers and acquisition, other advisory, and public finance to large corporate and institutional clients. In addition, the company offers personal and institutional trust custody services, personal financial and planning services, access to mutual funds, treasury services, and international banking services. Further, it provides community development financing, securities underwriting, brokerage, and investment banking services, as well as merchant services. The company was founded in 1849 and is headquartered in Cleveland, Ohio.

Unternehmensfinanzierung. Nutzung von Krediten. Schwierigkeiten bei Kreditaufnahme. Vorgehen von Kreditinstituten in Bezug auf Finanzierungsmöglichkeiten. Finanzierungsberatung. Themen: Finanzielle Situation des Unternehmens; 3-Jahres Plan; wichtigste Maßnahmen zur Festigung des Unternehmens: qualifizierte Mitarbeiter, der Branche angepasstere soziale und steuerliche Bestimmungen, größere Produktionskapazität, einfacher Zugang zu Finanzierungsmitteln, strengere Regulierung der Konkurrenz aus Nicht-EU-Ländern, Beratung und Unterstützung für die Unternehmensentwicklung; Inanspruchnahme finanzieller Leistungen (Dispositionskredit, Leasing/Mieten, Diskont/Factoring, Kapitalerhöhung für Wagniskapitalfonds und für Privatpersonen, (Kurz-)Darlehen, öffentliche Fördermittel); Höhe des letzten Kreditantrags; Verwendungsabsicht für den Kredit; Schwierigkeiten einen Kredit unter 250.000 Euro zu bekommen im Vergleich zu anderen Finanzierungsformen; Gründe für Kreditaufnahme (niedrigere Zinssätze, einfacherer Bewilligungsvorgang, geringere Anforderung auf Kreditsicherheit, kürzere Bearbeitungszeit für Kreditbewilligung); Einschätzung der Unternehmensfinanzierung als ausreichend für Projektrealisierung; primäre Anlaufstellen für den Erhalt von Finanzmitteln; Erschließungsmöglichkeiten für Kapital um finanzielle Bedürfnisse des Unternehmens zu erfüllen; Wege für die Kapitalerschließung des Unternehmens; Einschätzung der Schwierigkeit heutzutage einen Kredit bei Banken zu bekommen im Vergleich zu früher; Gründe, warum es heutzutage schwieriger ist einen Kredit bei einer Bank zu bekommen; Einstellung zu: Kreditabhängigkeit bei Durchführung von Projekten, nicht auf die Belange des Unternehmens zugeschnittene Angebote der Banken; geringe Risikobereitschaft von Banken bei Kreditvergabe; Verständnis für die spezifischen Belange der eigenen Branche durch den zuständigen Bankangestellten; ausreichende Unterstützung bei der Finanzierung durch die Bank; Beurteilung des firmeninternen Finanzmanagements; primäre Anlaufstelle für Finanzierungsberatung. Demographie: Angaben zum Unternehmen: Anzahl der Mitarbeiter, Entwicklung der Anzahl der Beschäftigten seit 2004, Unternehmensgröße, Hauptgeschäftsfeld des Unternehmens, Gründungsjahr, Aktienanteil des Unternehmens; Jahresumsatz des Unternehmens im letzten Geschäftsjahr. Zusätzlich verkodet wurde: Land; Befragten-ID; Interviewsprache; Gewichtungsfaktor. Access to finance of small and medium enterprises. Topics: development of the following indicators in the last six months: turnover, profit, profit margin, level of debt, cash flow, investment, level of exports, research and development, market share; existence of a development plan for the next three years; most important element to ensure the company’s development: better qualified people on the market, social and fiscal regulations more suited to the sector of activity, greater production capacity, easy access to means of financing, stricter regulation regarding competition from outside the EU, advice and support service for the development of the company; use of selected types of financing in the past: overdraft, leasing or renting, discount or factoring, increase in capital dedicated to venture capital funds or to private individuals, loans shorter or longer than a 3-year term, public subsidies; approximate amount of last loan; recent request for a loan less than 25000 €; needs to be met by this loan; assessment of the difficulties to obtain a loan less than 25000 € compared to other forms of company’s financing; most important elements to resort a loan less than 25000 €: lower interest rates, simpler procedures for granting loans, less demanding on guarantee requirements, shorter delays for granting loans; assessment of the current financing of the company as sufficient; institutions contacted to obtain financing: banks, public institutions, private financing companies, leasing or renting companies, venture capital companies, private investors; expectations regarding the increase of the company’s capital within the next years; measures to increase the company’s capital: opening-up capital to private individual investors or to venture capital companies, management buy-out, going on the stock exchange, opening-up capital to the company’s employees; assessment of the access to bank loans as easy; assessment of the development of the impediments to access bank loans compared to a few years ago; reasons that impede obtaining a bank loan compared to a few years ago: interest rates are too high, banks request too much information, loan granting procedures are too long, administrative side of the loan application is very demanding; approval of the following statements: loan is needed to conclude projects, unsuitable offers from banks, risk-averseness of banks, banker understands specifics of the company’s sector, banker sufficiently supports the company in terms of its financing; assessment how the company’s needs regarding financial management are met internally; preferred sources of information on financing. Demography: information about the company: number of employees, development of the number of employees since 2004; company size; main activity of the company; year of company establishment; shareholding of the company; turnover of the company in the own country in the last fiscal year. Additionally coded was: country; respondent ID; language of the interview; weighting factor.