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 Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Mortgage Rate in Australia decreased to 5.84 percent in May from 5.98 percent in April of 2025. This dataset includes a chart with historical data for Australia Mortgage Rate.

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Online beschikbaar: [01-12-2014]
This article was published on the Guardian website at 20.25 BST on Thursday 11 June 2009. A version appeared on p1 of the Main section section of the Guardian on Friday 12 June 2009. It was last modified at 12.21 BST on Monday 19 May 2014.
© 2014 Guardian News and Media Limited or its affiliated companies. All rights reserved.

The benchmark interest rate in Philippines was last recorded at 5.25 percent. This dataset provides the latest reported value for - Philippines Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold. Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007.
From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank.
From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and 2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here:
http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf
Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter.
Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office.
The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

Chile Mortgage Rate: Bills: Term: 1 to 8 Years data was reported at 3.000 % pa in Jun 2015. This stayed constant from the previous number of 3.000 % pa for Apr 2015. Chile Mortgage Rate: Bills: Term: 1 to 8 Years data is updated monthly, averaging 4.319 % pa from Dec 2000 (Median) to Jun 2015, with 144 observations. The data reached an all-time high of 7.162 % pa in Dec 2000 and a record low of 0.000 % pa in Feb 2015. Chile Mortgage Rate: Bills: Term: 1 to 8 Years data remains active status in CEIC and is reported by Financial Market Commission. The data is categorized under Global Database’s Chile – Table CL.M008: Mortgage Rate. The data for Dec-2014 and Jan-2015 are as per information released by Superintendency of Banks and Financial Institutions. Being verified with source.

Analysis of ‘Annual Market Information Indices’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-data-usmart-io-org-ae1d5c14-c392-4c3f-9705-537427eeb413-dataset-viewdiscovery-datasetguid-c410c7a0-14c3-442b-b75f-4c230ec59406 on 13 January 2022.

--- Dataset description provided by original source is as follows ---

House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold. Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007.
From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank.
From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and 2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here:
http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf
Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter.
Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office.
The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

--- Original source retains full ownership of the source dataset ---

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

New Zonage “A/B/C” applicable from 01/10/2014 (Ministerial Decree of 01 August 2014). The “A/B/C” zoning, created in 2003 at the time when Robien’s rental investment scheme was introduced, characterises the tension of the local real estate market, i.e. the adequacy of the demand for and the supply of available housing on a territory. It consists of five modalities ranging from the most tense (Abis) to the most relaxed (C).Franche-Comté is only affected by zones B2 and C. Several financial schemes use this zoning to determine the eligibility of territories for aid or to adjust their parameters (level of aid, ceiling of rents, etc.). These include the Intermediate Rental Investment Facility for Individuals (see Duflot Zoning), the Old Borloo, the Intermediate Rental Loan (PLI), the Zero Rate Loan (PTZ), the Social Accession Rental Loan (PSLA) and the Social Access Loan (PAS) to property, and the reduced rate VAT in the ANRU area.Some ANAH aid to social lenders is also linked to a ceiling on rent and the amount of resources of the tenant, which varies according to the zoning A/B/C. Following a consultation conducted by the Regional Prefect with the local authorities in the 4th quarter of 2013, the new zoning A/B/C was adopted by the Minister in charge of Housing on 1 August 2014. For Franche-Comté, 19 new municipalities were reclassified from C to B2, while no decommissioning was recorded. Its entry into force varies between 1 October 2014 and 1 February 2015 depending on the arrangements attached to it: as of 1 October 2014 for: — the zero-rate loan; — the guarantee scheme of the FGAS; — the reduced rate VAT scheme for intermediate rental accommodation (279-0a A of the CGI); — the aid scheme for intermediate rental investment for private individuals (199 novitiies of the General Tax Code (CGI); — promises of sales of public land, pursuant to Article R. 3211-15 of the General Code of Ownership of Public Persons; on 1 January 2015 for: — the benefit of aid from the National Housing Agency, the ‘old Borloo’ tax scheme; — the intermediate rental loan; — reduced VAT in ANRU area; — devices related to HLM promotion; — the assessment of resources for new intermediate dwellings held by HLML bodies in the context of their service of general economic interest; as of 1 February 2015 for: — approvals of social loans for leasing-accession. Data sources: order of the Minister of Housing dated 01 August 2014

Analysis of ‘SBA Loans Case Data Set’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/larsen0966/sba-loans-case-data-set on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Should This Loan be Approved or Denied?

If you like the data set and download it, an upvote would be appreciated.

The Small Business Administration (SBA) was founded in 1953 to assist small businesses in obtaining loans. Small businesses have been the primary source of employment in the United States. Helping small businesses help with job creation, which reduces unemployment. Small business growth also promotes economic growth. One of the ways the SBA helps small businesses is by guaranteeing bank loans. This guarantee reduces the risk to banks and encourages them to lend to small businesses. If the loan defaults, the SBA covers the amount guaranteed, and the bank suffers a loss for the remaining balance.

There have been several small business success stories like FedEx and Apple. However, the rate of default is very high. Many economists believe the banking market works better without the assistance of the SBA. Supporter claim that the social benefits and job creation outweigh any financial costs to the government in defaulted loans.

The Data Set

The original data set is from the U.S.SBA loan database, which includes historical data from 1987 through 2014 (899,164 observations) with 27 variables. The data set includes information on whether the loan was paid off in full or if the SMA had to charge off any amount and how much that amount was. The data set used is a subset of the original set. It contains loans about the Real Estate and Rental and Leasing industry in California. This file has 2,102 observations and 35 variables. The column Default is an integer of 1 or zero, and I had to change this column to a factor.

For more information on this data set go to https://amstat.tandfonline.com/doi/full/10.1080/10691898.2018.1434342

--- Original source retains full ownership of the source dataset ---

House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold.
Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007.
From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank.
From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and
2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here:
http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf
Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter.
Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office.
The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

Bank marketing campaigns dataset analysis # Opening a Term Deposit

It is a dataset that describing Portugal bank marketing campaigns results. Conducted campaigns were based mostly on direct phone calls, offering bank client to place a term deposit. If after all marking afforts client had agreed to place deposit - target variable marked 'yes', otherwise 'no'

Sourse of the data https://archive.ics.uci.edu/ml/datasets/bank+marketing

Citation Request:

This dataset is public available for research. The details are described in S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

1. Title: Bank Marketing (with social/economic context)

2. Sources Created by: Sérgio Moro (ISCTE-IUL), Paulo Cortez (Univ. Minho) and Paulo Rita (ISCTE-IUL) @ 2014

3. Past Usage:

   The full dataset (bank-additional-full.csv) was described and analyzed in:

   S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems (2014), doi:10.1016/j.dss.2014.03.001.

4. Relevant Information:

   This dataset is based on "Bank Marketing" UCI dataset (please check the description at: http://archive.ics.uci.edu/ml/datasets/Bank+Marketing). The data is enriched by the addition of five new social and economic features/attributes (national wide indicators from a ~10M population country), published by the Banco de Portugal and publicly available at: https://www.bportugal.pt/estatisticasweb. This dataset is almost identical to the one used in Moro et al., 2014. Using the rminer package and R tool (http://cran.r-project.org/web/packages/rminer/), we found that the addition of the five new social and economic attributes (made available here) lead to substantial improvement in the prediction of a success, even when the duration of the call is not included. Note: the file can be read in R using: d=read.table("bank-additional-full.csv",header=TRUE,sep=";")

The binary classification goal is to predict if the client will subscribe a bank term deposit (variable y).

1. Number of Instances: 41188 for bank-additional-full.csv

2. Number of Attributes: 20 + output attribute.

3. Attribute information:

   For more information, read [Moro et al., 2014].

   Input variables:

   bank client data:

   *1 - age (numeric)

   *2 - job : type of job (categorical: "admin.","blue-collar","entrepreneur","housemaid","management","retired","self-employed","services","student","technician","unemployed","unknown")

   *3 - marital : marital status (categorical: "divorced","married","single","unknown"; note: "divorced" means divorced or widowed)

   *4 - education (categorical: "basic.4y","basic.6y","basic.9y","high.school","illiterate","professional.course","university.degree","unknown")

   • 5 - default: has credit in default? (categorical: "no","yes","unknown")

   • 6 - housing: has housing loan? (categorical: "no","yes","unknown")

   • 7 - loan: has personal loan? (categorical: "no","yes","unknown")

   related with the last contact of the current campaign:

   • 8 - contact: contact communication type (categorical: "cellular","telephone")

   *9 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec")

   *10 - day_of_week: last contact day of the week (categorical: "mon","tue","wed","thu","fri")

   *11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y="no"). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

   other attributes:

   *12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)

   *13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)

   *14 - previous: number of contacts performed before this campaign and for this client (numeric)

   1515 - poutcome: outcome of the previous marketing campaign (categorical: "failure","nonexistent","success")

   social and economic context attributes

   *16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)

   *17 - cons.price.idx: consumer price index - monthly indicator (numeric)

   *18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)

   *19 - euribor3m: euribor 3 month rate - daily indicator (numeric)

   • 20 - nr.employed: number of employees - quarterly indicator (numeric)

   Output variable (desired target): * 21 - y - h...

The benchmark interest rate in Hong Kong was last recorded at 4.75 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.

The benchmark interest rate in Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States WAS: Total Points: FRM 30-Year data was reported at 0.450 % in 20 Jul 2018. This records a decrease from the previous number of 0.460 % for 13 Jul 2018. United States WAS: Total Points: FRM 30-Year data is updated weekly, averaging 1.230 % from Jan 1990 (Median) to 20 Jul 2018, with 1490 observations. The data reached an all-time high of 3.340 % in 08 Mar 1991 and a record low of 0.130 % in 30 May 2014. United States WAS: Total Points: FRM 30-Year data remains active status in CEIC and is reported by Mortgage Bankers Association. The data is categorized under Global Database’s USA – Table US.M013: Weekly Applications Survey: Mortgage Interest Rate.

Chile Mortgage Rate: Bills: Term: 8 to 12 Years data was reported at 2.100 % pa in Aug 2016. This records a decrease from the previous number of 2.700 % pa for Feb 2016. Chile Mortgage Rate: Bills: Term: 8 to 12 Years data is updated monthly, averaging 4.300 % pa from Dec 2000 (Median) to Aug 2016, with 165 observations. The data reached an all-time high of 7.300 % pa in Dec 2000 and a record low of 0.000 % pa in Apr 2015. Chile Mortgage Rate: Bills: Term: 8 to 12 Years data remains active status in CEIC and is reported by Financial Market Commission. The data is categorized under Global Database’s Chile – Table CL.M008: Mortgage Rate. The data for Dec-2014 and Jan-2015 are as per information released by Superintendency of Banks and Financial Institutions. Being verified with source.

The benchmark interest rate in Australia was last recorded at 3.85 percent. This dataset provides - Australia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Malaysia was last recorded at 2.75 percent. This dataset provides - Malaysia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Data on the number of OSAP loan recipients who received repayment assistance: * 2010-11 OSAP loan recipients who received repayment assistance before July 2013. * 2011-12 OSAP loan recipients who received repayment assistance before July 2014. * 2012-13 OSAP loan recipients who received repayment assistance before July 2015. * 2013-14 OSAP loan recipients who received repayment assistance before July 2016. * 2014-15 OSAP loan recipients who received repayment assistance before July 2017. * 2015-16 OSAP loan recipients who received repayment assistance before July 2018. * 2016-17 OSAP loan recipients who received repayment assistance before July 2019. * 2017-18 OSAP loan recipients who received repayment assistance before July 2020. Data is presented at the following levels: * all of Ontario * postsecondary sector * individual postsecondary institution * individual program of individual postsecondary institution Data fields are: * postsecondary sector (university, college of applied arts and technology, private career college, other private or publicly funded postsecondary institutions) * institution name * program name (starting with the 2014 rates) * number of OSAP loan recipients who last received an OSAP loan in 2010-11, 2011-12, 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 2017-18 * number of Repayment Assistance Plan participants as of July 2013, July 2014, July 2015, July 2016, July 2017, July 2018, July 2019, and July 2020 * repayment assistance participation rate for 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 Get more information about OSAP loan default rates. *[OSAP]: Ontario Student Assistance Program