Evaluate Canada’s best mortgage rates in one place. RATESDOTCA’s Rate Matrix lets you compare pricing for all key mortgage types and terms. Rates are based on an average mortgage of $300,000

Rates have been trending downward in Canada for the last five years. The ebbs and flows are caused by changes in Canada’s bond yields (driven by Canadians economic developments and international rate movements, particularly U.S. rate fluctuations) and the overnight rate (which is set by the Bank of Canada). As of August 2022, there has been a 225 bps increase in the prime rate, since beginning of year 2022, from 2.45% to 4.70% as of Aug 24th 2022. The following are the historical conventional mortgage rates offered by the 6 major chartered banks in Canada in the past 20 years.

This paper examines some of the more recent mortgage products now available to borrowers. The authors describe how these products differ across important characteristics, such as the down payment requirement, repayment structure, and amortization schedule. The paper also presents a model with the potential to analyze the implications for various mortgage contracts for individual households, as well as to address many current housing market issues. The authors use the model to examine the implications of alternative mortgages for homeownership and to show that interest rate-adjustable mortgages and combo loans can help explain the rise-and fall-in homeownership since 1994.

Federal, state, and local predatory lending laws are designed to restrict and in some cases prohibit certain types of high-cost mortgage credit in the subprime market. Empirical evidence using the spatial variation in these laws shows that the aggregate flow of high-cost mortgage credit can increase, decrease, or be unchanged after these laws are enacted. Although it may seem counterintuitive to find that a law that prohibits lending could be associated with more lending, it is hypothesized that a law may reduce the cost of sorting honest loans from dishonest loans and lessens borrowers' fears of predation, thus stimulating the high-cost mortgage market.

Explore the historical Whois records related to lowest-mortgage-rates.net (Domain). Get insights into ownership history and changes over time.

The monthly interest rates on deposits from a sample of Norwegian banks and mortgage companies increased sharply between July 2018 and January 2025. Interest rates rose particularly fast throughout 2022 and 2023 and stood at 3.31 percent as of January 2025.

Price Mortgage, a reputable financial services provider, offers a wealth of information on the mortgage industry. Through their digital platform, users can gain insight into the latest mortgage market trends, rates, and regulations. The company's website serves as a valuable resource for mortgage professionals, lenders, and borrowers alike, providing a comprehensive overview of the mortgage landscape.

With a focus on mortgage origination and servicing, Price Mortgage has established itself as a trusted authority in the industry. Their online presence combines expert analysis, market news, and tools to help users navigate the complex world of mortgages. Whether seeking to stay informed about market fluctuations or to explore options for refinancing or purchasing a new home, Price Mortgage's digital platform is an essential destination for anyone involved in the mortgage sector.

The global Mortgage CRM Software market is experiencing robust growth, projected to reach $221 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 9% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the increasing adoption of digital technologies within the mortgage industry is driving demand for efficient customer relationship management (CRM) solutions. Mortgage lenders are leveraging CRM software to streamline processes, improve customer engagement, and enhance operational efficiency, leading to increased productivity and reduced operational costs. Secondly, the rising need for compliance and regulatory adherence is pushing mortgage companies to adopt sophisticated CRM systems that can manage sensitive customer data securely and efficiently. Finally, the emergence of cloud-based and web-based CRM solutions is making these systems more accessible and affordable for businesses of all sizes, including Small and Medium-sized Enterprises (SMEs) and large enterprises. Competition is relatively high, with established players like Salesforce and emerging specialized vendors catering to specific needs within the mortgage sector. Growth is expected to be particularly strong in North America and Europe, driven by high digital adoption rates and a relatively mature mortgage market in these regions. However, challenges remain, including the need for ongoing software maintenance and updates, as well as concerns about data security and integration with existing legacy systems. The market segmentation reveals a dynamic landscape. Cloud-based solutions are gaining significant traction due to their scalability, flexibility, and cost-effectiveness. The large enterprise segment is a significant revenue driver, however, the SME segment is demonstrating considerable growth potential, particularly with the increasing accessibility of affordable and user-friendly CRM solutions. The competitive landscape includes established players such as Salesforce and Zendesk Sell, along with niche players like Velocify LoanEngage and Floify, each offering specialized features and functionalities tailored to the specific needs of the mortgage industry. Geographic expansion is expected to be driven by increasing digitization and financial market maturity in developing economies. Continued innovation in artificial intelligence (AI) and machine learning (ML) integration is likely to further enhance the functionality and efficiency of mortgage CRM software, leading to further market growth in the years to come.

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
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3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
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8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

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Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

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And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

Bank Lending Rate in Portugal decreased to 3.83 percent in May from 4.23 percent in April of 2025. This dataset provides - Portugal Bank Lending Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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The indicator shows the number of social mortgages granted during the year. The Walloon Social Credit Society (SWCS) and the Housing Fund for Large Families of Wallonia (FLW) are particularly competent to grant mortgages at favourable rates to households of modest conditions. The composition of the household determines the competent body. If the household has at least three dependent children*, it is the FLW that processes the request, otherwise it is the SWCS. In the case of social loans, the rates charged are lower than those found in the conventional banking market. They also apply more flexible conditions in terms of borrowed quotity and income. They are set by scales that depend for the FLW on the composition and income of the household, and for the SWCS on the level of income and the amount borrowed. This policy of social loans reflects the willingness of the public authorities to help households of modest conditions access to real estate property. See also: — the website of the ‘\2’, in particular to find out how dependent children are counted: — the website of the “\2”.

The number of visits to Zillow website and mobile application increased by almost 30 percent from 2019 to 2022, peaking at 10.5 billion visits. In 2023, the visits count decreased by five percent due to macro housing market factors including low housing inventory, fewer new for-sale listings, increases and volatility in mortgage interest rates as well as home price fluctuations.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Beginning in data year 2020, categories were added to Mortgage Status to account for the variety of mortgage arrangements that may exist. See “American Community Survey Subject Definitions” for more information on Mortgage Status..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In May 2025, global inflation rates and central bank interest rates showed significant variation across major economies. Most economies initiated interest rate cuts from mid-2024 due to declining inflationary pressures. The U.S., UK, and EU central banks followed a consistent pattern of regular rate reductions throughout late 2024. In early 2025, Russia maintained the highest interest rate at 20 percent, while Japan retained the lowest at 0.5 percent. Varied inflation rates across major economies The inflation landscape varies considerably among major economies. China had the lowest inflation rate at -0.1 percent in May 2025. In contrast, Russia maintained a high inflation rate of 9.9 percent. These figures align with broader trends observed in early 2025, where China had the lowest inflation rate among major developed and emerging economies, while Russia's rate remained the highest. Central bank responses and economic indicators Central banks globally implemented aggressive rate hikes throughout 2022-23 to combat inflation. The European Central Bank exemplified this trend, raising rates from 0 percent in January 2022 to 4.5 percent by September 2023. A coordinated shift among major central banks began in mid-2024, with the ECB, Bank of England, and Federal Reserve initiating rate cuts, with forecasts suggesting further cuts through 2025 and 2026.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Beginning in data year 2020, categories were added to Mortgage Status to account for the variety of mortgage arrangements that may exist. See "American Community Survey Subject Definitions" for more information on Mortgage Status..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

This data collection provides information on the characteristics of a national sample of housing units. Data include the year the structure was built, type and number of living quarters, presence of a garage, occupancy status, access, number of rooms and bedrooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Similar data are provided for housing units previously occupied by respondents who had recently moved. Supplemental sections provide data on energy-related characteristics, such as the presence of storm doors, storm windows, and other types of insulation, and use of supplemental heating equipment. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, cracks or holes in walls, ceilings, and floors, breakdowns of plumbing facilities and equipment, use of exterminator service, and respondent's overall opinion of structure. For quality of neighborhood, variables include existence of boarded-up buildings, noise, lack of street lighting, heavy traffic, objectionable odors, crime, and respondent's overall opinion of neighborhood. Extensive information is provided on mobile homes including type of foundation, width of home, quality of the structure, problems, if any with installation of mobile home on the present site, and amount of real estate and property taxes, and site rent. Information on condominiums and cooperatives covers number of units in the development, amount of mortgage payment, real estate tax, condominium fee, and utility costs. In addition to housing characteristics, demographic data are provided on the household members, such as sex, age, race, marital status, relationship to the household head, and income. Additional data are provided on the head of the household including years of school completed, Hispanic origin, and length of residence. For each employed respondent travel-to-work information such as principal mode of transportation, carpool occupancy, type of public transportation used, and time and distance from home to work was also collected.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Beginning in data year 2020, categories were added to Mortgage Status to account for the variety of mortgage arrangements that may exist. See “American Community Survey Subject Definitions” for more information on Mortgage Status..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Graph and download economic data for Charge-Off Rate on Commercial Real Estate Loans (Excluding Farmland), Booked in Domestic Offices, All Commercial Banks (CORCREXFACBS) from Q1 1991 to Q1 2025 about farmland, charge-offs, domestic offices, real estate, commercial, domestic, loans, banks, depository institutions, rate, and USA.