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.

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.

Financial Access and Usage

Global Comparative Ratios Across 189 Jurisdictions

By International Monetary Fund [source]

About this dataset

This dataset provides an unprecedented opportunity to explore global financial access and usage trends from 2004-2016 from 189 of the world's reporting jurisdictions—which cover over 99 percent of the total adult population. With 152 time series and 47 indicator ratios, this Financial Access Survey gives insight into ways that access to and usage of financial services differ by households vs small/medium enterprises, life vs non-life insurance, deposits & microfinance institutions as well as credit unions & financial cooperatives. Utilizing this data, we can gain a better understanding of how policies or shifts in the global economy may influence or relate to access or utilization of services in certain regions while having comparable cross-economy comparisons. The IMF Monetary and Financial Statistics Manual Compilation Guide is utilized for all methodologies used in accumulating these datasets, while all data is available “as-is” with no guarantee provided either express or implied. Are you looking for ways to implement insightful macroeconomic analyses? Download FAS 2004–2016 now!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The Financial Access Survey provides global supply-side data on access to and usage of financial services by households and firms for 189 reporting jurisdictions, covering 99 percent of the world’s adult population. With a robust selection of time series in this dataset, users can make meaningful insights into trends over time or across countries concerning various indicators related to access and usage of financial services. To help users navigate this large dataset, the following guide explains how to use the data most effectively.

Understanding The Dataset Columns

The columns in the dataset provide information about each indicator such as country name, indicator name, code for that indicator, its attribute (i.e., rate/ratio), when data is available for that particular indicator. Once you have identified an interesting measure/indicator whether it be credit union density or life insurance penetration rate measure in a given country during a certain year period then you can look up those numbers from the rows provided in this dataset .

Understanding The Different Years Available & Comparing Numbers Over Time

It is useful for users to compare different indicators over time by looking at specific years within this dataset which will allow us to see if rates are increasing or decreasing worldwide patterns across these trends among different countries based on these various measures listed provided in this survey such as mortgage lending rate or ratio GDP per capita etc that have been collected . We can therefore make use of our knowledge off economic changes that have occurred over time within certain parts of world , no matter if they are longer term economic effects due increases certain capabilities within a geographical area or shorter term changes due taxation laws by governments etc driving some people either towards using or away from using certain kinds financial products .

#### Comparing Between Countries

This datasets allows us direct comparisons between different countries with regards how many people are currently making use particular types off finances services , we certainly be able analyse current international relationships between services providers as well customers where ever concerned about particular attributes mentioned above whether being deposit interest rates small business credits terms tenders so forth . Knowing more about related dynamics helps build better user experiences with providers who understand needs risks impacts generating larger customer bases globally which often beneficial both parties involved exchange relationship so not forget always keep cross border motif whenever eye process from afar !

Research Ideas

• Comparing the access to and usage of financial services in different countries to better inform research policy decisions.
• Analyzing trends in financial access and usage by jurisdiction over time, to identify areas needing improvement in order to promote financial inclusion and stability.
• Cross-referencing FAS data with macroeconomic indicators such as GDP information to measure the potential impact of changes in level of access on economic growth or other metrics specific to a country or region of interest

Acknowledgements

If you use this dataset in yo...

DESCRIPTION

Create a model that predicts whether or not a loan will be default using the historical data.

Problem Statement:

For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.

Domain: Finance

Analysis to be done: Perform data preprocessing and build a deep learning prediction model.

Content:

Dataset columns and definition:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.

purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").

int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.

installment: The monthly installments owed by the borrower if the loan is funded.

log.annual.inc: The natural log of the self-reported annual income of the borrower.

dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).

fico: The FICO credit score of the borrower.

days.with.cr.line: The number of days the borrower has had a credit line.

revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).

revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).

inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.

delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.

pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Steps to perform:

Perform exploratory data analysis and feature engineering and then apply feature engineering. Follow up with a deep learning model to predict whether or not the loan will be default using the historical data.

Tasks:

1. Feature Transformation

Transform categorical values into numerical values (discrete)

1. Exploratory data analysis of different factors of the dataset.

2. Additional Feature Engineering

You will check the correlation between features and will drop those features which have a strong correlation

This will help reduce the number of features and will leave you with the most relevant features

1. Modeling

After applying EDA and feature engineering, you are now ready to build the predictive models

In this part, you will create a deep learning model using Keras with Tensorflow backend

Context

Case study ABC is a multinational corporation and finance company which provides is into personal loan business. This company provides personal loans to individuals based on their credit policy. In 2015, the company has seen an increases in the loss rate. Which means that the number of people defaulting on the loan is increasing. The company believes that targeting the customers who are more risky at early stage of defaulting would help in reducing the loss rate.

• See definition in About this file below

Interesting Questions

Level 1: Specific data Questions:

1. What is the account roll forward rate for the months of Mar , Apr, May and June for TB0?

2. What is the Princple outstanding balance roll forward rate for the same months as above for TB0?

3. What is the average attempt intensity, RPC rate and PTP rate on accounts for the months of mar, Apr and May

Level 2: Open Ended questions

1. What QCs will you do on this data to ensure the data is accurate and useable?

 Show the results of the QCs as well

1. Is there any correlation between the princple balance remaining and the term completed  Term Completed: The months on books / tenure

2. Identify the agents who are showing the best and worst performance.  Please provide the metrics that you used to rate these agents  Please prepare one paragraph explaining the approach and results

Level 3: Analytics question

1. Profile/Classify the accounts the accounts based on riskiness to roll forward  You can either use simple data manipulation to understand the which group of accounts have the highest risk or  Build any regression model / decision tree to identify the riskiness

Level 4: Strategy Question:

1. Based on the analysis that you have done till now, could you please come up with a strategy that you would like to implement to improve the collection efficiency (i.e., reduce roll forward rate). Please note that the aim of the company is to reduce the principle outstanding balance that has rolled forward.

Data Available

Loan Details: Contains information regarding the loan

Loan Status Mar to May: The status of the loan at the beginning of the month from Mar to May. Only TB0 Accounts have been given for the purpose of this case study

Loan Status Apr to June: The status of the loan at the beginning of the month from Apr to June. Only the status of the loans that have been extracted for TB0 have been obtained

Historical 6 months details: Various metrics have been calculated for the past 6 months for each of the TB0 accounts that are appearing in March to April

Variable Names:

Paidcure: The number of times in the past 6 months an account has paid full amount due so that he is no longer delinquenty

Paiduncure: The number of times in the past 6 months an account has paid a partial amount of the total due amount so that account is still delinquent

Unpaid: The number of times in the past 6 months an account has not paid any amount of the total due amount so that account is still delinquent

Rollb: The number of times in past 6 months that an account has rolled backwards. i.e If account is in TB2 in current month and moved to TB1 or TB0 or Regular in the following month

Rollf: The number of times in past 6 months that an account has rolled forwards. i.e If account is in TB2 in current month and moved to TB3 in the following month

Num6mondel: The number of times an account is delinquent in past 6 months

Num3mondel: The number of times an account is delinquent in past 3 months

num6mosdel_2plus: The number of times an account is in bucket TB2, TB3, TB4, TB5 or TB6 in past 6 months

num3mosdel_2plus The number of times an account is in bucket TB2, TB3, TB4, TB5 or TB6 in past 3 months

max6del: The maximum bucket in the account was present in the past 6 months. For example if the maximum bucket is TB4 in past 6 months, then the value is 4

max3del: The maximum bucket in the account was present in the past 3 months. For example if the maximum bucket is TB5 in past 6 months, then the value is 5

Call Details:

Contains a summary of calls made to the TB0 accounts for Mar-May data

LoanID : The agent who called the customer who is delinquent

Total_contacts: The total number of times an agent has tried to call the account

Right Party Contact: The number of times an agent was able to reach the borrower

Promise to pay: The number of times the customer promised to pay when the call was made

Loan Id mapping: The call details and rest of the data available have two different Ids captured. This mapping file maps the loan id between the tables so that there can be a unique identifier for each consumer

Notes

Assumptions - Loan_id: unique ID associated to every loan - Month - Month of the same year - Bucket - Categorizing accounts at various level of default or non default

Acknowledgement

Febina Abdulrahiman

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.

Rental Affordability Analysis Based on Median Income

Trends in Tier-Based Affordability Across the U.S

By Zillow Data [source]

About this dataset

This dataset contains rental affordability data for different regions in the US, giving valuable insights into regional rental markets. Renters can use this information to identify where their budget will go the farthest. The cities are organized by rent tier in order to analyze affordability trends within and between different housing stock types. Within each region, the data includes median household income, Zillow Rent Index (ZRI), and percent of income spent on rent.

The Zillow Home Value Forecast (ZHVF) is used to calculate future combined mortgage pay/rent payments in each region using current median home prices, actual outstanding debt amounts and 30-year fixed mortgage interest rates reported through partnership with TransUnion credit bureau. Zillow also provides a breakdown of cash vs financing purchases for buyers looking for an investment or cash option solution.

This dataset provides an effective tool for consumers who want to better understand how their budget fits into diverse rental markets across the US; from condominiums and co-ops, multifamily residences with five or more units, duplexes and triplexes - every renter can determine how their housing budget should be adjusted as they consider multiple living possibilities throughout the country based on real-time price data!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Introduction

Getting Started

• First, you'll need to download the TieredAffordability_Rental.csv dataset from this Kaggle page onto your computer or device.

• After downloading the data set onto your device, open it with any CSV viewing software of your choice (ex: Excel). It will include columns for RegionName**RegionName** , homes type/housing stock (All Homes or Condo/Co-op) SizeRank , Rent tier tier , Date date , median household income income , Zillow Rent Index zri and PercentIncomeSpentOnRent percentage (what portion of monthly median house-hold goes toward monthly mortgage payment) .

• To begin analyzing rental prices across different regions using this dataset, look first at column four: SizeRank; which ranks each region based on size - smallest regions listed first and largest at last - so that you can compare a similar range of Regions when looking at affordability by home sizes larger than one unit multiplex dwellings.*Duples/Triplex*. Once there is an understanding of how all homes compare overall now it is time to consider home types Multifamily 5+ units according to rent tiers tier .

• Next, choose one or more region(s) for comparison based on their rank in SizeRank column –so that all information gathered about them reflects what portionof households fall into certain categories ; eg; All Homes / Small Home /Large Home / MultiPlex Dwelling and what tier does each size rank falls into eg.: Affordable/Slightly Expensive/ Moderately Expensive etc.. This will enable further abstraction from other elements like date vs inflation rate per month or periodical intervals set herein by Rate segmentation i e dates givenin ‘Date’Columns – making the task easier and more direct while analyzing renatalAffordibility Analysis Based On Median Income zri 00 zwi & PCISOR 00 PCIRO

Research Ideas

• Use the PercentIncomeSpentOnRent column to compare rental affordability between regions within a particular tier and determine optimal rent tiers for relocating families.
• Analyze how market conditions are affecting rental affordability over time by using the income, zri, and PercentageIncomeSpentOnRent columns.
• Identify trends in housing prices for different tiers over the years by comparing SizeRank data with Zillow Home Value Forecast (ZHVF) numbers across different regions in order to identify locations that may be headed up or down in terms of home values (and therefore rent levels)

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: TieredAffordability_Rental.csv | Column name | Description | |:-----------------------------|:-------------------------------------------------------------| | RegionName | The name of the region. (String) ...

MyHom is a finance company that lends housing loans at the best and most affordable interest rates to customers. In recent times, the company incurred heavy losses due to loan defaults. Most applicants failed to repay the loan as per the promissory note.

In order to avoid such losses, the company has decided to build a system for identifying the loan defaulters automatically based on data. This will help the company to identify the potential applicants and ensure the smooth running of the entire process.

Now, the company challenges the Data Science community to build a smart AI system to predict the probability of an applicant defaulting the loan or not in the future.

You are provided with the past applicant’s data containing the demographic information, loan attributes, and target variable indicating if an applicant will default the loan or not.

Variable - Description loan_id - Unique identifier of a loan age - Age of the Applicant Education - Applicant Education proof_submitted - Type of proof submitted loan_amount - Loan Amount Disbursed asset_cost - The total asset value of the applicant no_of_loans - No. of the loans taken by the applicant no_of_curr_loans - No. of active loans held by the applicant last_delinq_none - The loan defaulted in at least one of the past loans loan_default (Target Variable) - 0/1 indicating if an applicant will default the loan or not

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.

Most of the text in this description originally appeared on the Mapping Inequality Website. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers, "HOLC staff members, using data and evaluations organized by local real estate professionals--lenders, developers, and real estate appraisers--in each city, assigned grades to residential neighborhoods that reflected their "mortgage security" that would then be visualized on color-coded maps. Neighborhoods receiving the highest grade of "A"--colored green on the maps--were deemed minimal risks for banks and other mortgage lenders when they were determining who should received loans and which areas in the city were safe investments. Those receiving the lowest grade of "D," colored red, were considered "hazardous." Conservative, responsible lenders, in HOLC judgment, would "refuse to make loans in these areas [or] only on a conservative basis." HOLC created area descriptions to help to organize the data they used to assign the grades. Among that information was the neighborhood's quality of housing, the recent history of sale and rent values, and, crucially, the racial and ethnic identity and class of residents that served as the basis of the neighborhood's grade. These maps and their accompanying documentation helped set the rules for nearly a century of real estate practice. " HOLC agents grading cities through this program largely "adopted a consistently white, elite standpoint or perspective. HOLC assumed and insisted that the residency of African Americans and immigrants, as well as working-class whites, compromised the values of homes and the security of mortgages. In this they followed the guidelines set forth by Frederick Babcock, the central figure in early twentieth-century real estate appraisal standards, in his Underwriting Manual: "The infiltration of inharmonious racial groups ... tend to lower the levels of land values and to lessen the desirability of residential areas." These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today. Mapping Inequality, we hope, will allow and encourage you to grapple with this history of government policies contributing to inequality." Data was copied from the Mapping Inequality Website for communities in Western Pennsylvania where data was available. These communities include Altoona, Erie, Johnstown, Pittsburgh, and New Castle. Data included original and georectified images, scans of the neighborhood descriptions, and digital map layers. Data here was downloaded on June 9, 2020.

The American Community Survey (ACS) is a nationwide survey conducted by the U.S. Census Bureau that is designed to provide communities a fresh look at how they are changing. It is a critical element in the Census Bureau's reengineered decennial census program, incorporating the detailed socioeconomic and housing questions that were previously asked on the decennial census long form into the ACS questionnaire. The ACS now collects and produces this detailed population and housing information every year instead of every ten years. Data are collected on an on-going basis throughout the year and are released each year for large geographic areas, those with 65,000 persons or more. However, sample sizes are not large enough for annual releases that cover smaller areas, those with less than 65,000 persons. Data that are suitable for areas with 20,000 to 65,000 persons are accumulated over three years and termed a three-year period estimate, the first of which was for the 2005-2007 period. Data that are suitable for areas with less than 20,000 persons are accumulated over five years and termed a five-year period estimate, the first of which was for the 2005-2009 period. The data in this series of RGIS Clearinghouse tables are for all New Mexico counties and are based on the 2005-2009 ACS Five-Year Period Estimates collected between January 2005 and December 2009. These data tables are a summary of all major housing topics published through the ACS, providing information about the condition of housing, and illuminating various financial characteristics of the housing stock. Major topics include housing occupancy, year structure built, rooms and bedrooms, housing tenure (owners and renters), year householder moved into unit, vehicles available, type of house heating fuel, units without complete plumbing and kitchen facilities or without telephone service, occupants per room, home value, mortgage status, monthly owner costs, owner costs as a percentage of household income, gross rent, and gross rent as a percentage of household income. Percentages are shown along with numeric estimates for most data items. Because the data are based on a sample the Census Bureau also provides information about the magnitude of sampling error. Consequently, the estimated margin of error (MOE) is shown next to each data item. Each housing topic is covered in a separate file in both Excel and CSV formats. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

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.

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.

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

This research proposes a credit score model for cooperatives using machine learning. Until now, there is no standard credit score assessment in savings and loan cooperatives in Indonesia. There are still many savings and loan cooperatives that provide loans due to closeness to the management or manager of the cooperative. The purpose of this research is to obtain a credit scoring method through machine learning that is effective, efficient and high accuracy.To predict the chance of default, this research uses seven machine learning algorithms namely Logistic Regression Classifier, Support Vector Machine Classifier, K-Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, XGBoost Classifier, and Light Gradient Boosting Machine Classifier. The data taken from the loan data of 851 members of Bank BPD Jateng "Yakekar" Cooperative, Semarang, Indonesia.The results show that Logistic Regression, Support Vector Machine Classifier, and K-Neighbors Classifier are the models that have relatively better performance in identifying 'Current' collectibility. However, all models have difficulty in classifying other collectibility ('Bad' and 'Doubtful') with low precision and recall.

The benchmark interest rate in Canada was last recorded at 2.25 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Norway was last recorded at 4 percent. This dataset provides the latest reported value for - Norway 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 Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Mortgage Rate in Sweden decreased to 2.80 percent in September from 2.84 percent in August of 2025. This dataset includes a chart with historical data for Sweden Average Interest Rate on New Agreements for Mortgages to Households.

The benchmark interest rate in Hong Kong was last recorded at 4.25 percent. This dataset provides the latest reported value for - Hong Kong Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.