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

Graph and download economic data for 15-Year Fixed Rate Mortgage Average in the United States (MORTGAGE15US) from 1991-08-30 to 2025-11-26 about 15-year, mortgage, fixed, interest rate, interest, rate, and USA.

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 table contains 102 series, with data starting from 2013, and some select series starting from 2016. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Components (51 items: Total, funds advanced, residential mortgages, insured; Variable rate, insured; Fixed rate, insured, less than 1 year; Fixed rate, insured, from 1 to less than 3 years; ...), and Unit of measure (2 items: Dollars; Interest rate). For additional clarification on the component dimension, please visit the OSFI website for the Report on New and Existing Lending.

Comprehensive proprietary research analyzing 312,367 assumable mortgage homes from 2023-2025 across all 50 states, including interest rates, savings analysis, state distribution, price ranges, and down payment requirements.

Graph and download economic data for Bank Prime Loan Rate Changes: Historical Dates of Changes and Rates (PRIME) from 1955-08-04 to 2025-10-30 about prime, loans, interest rate, banks, depository institutions, interest, rate, and USA.

In September 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 September 2025, Russia maintained the highest interest rate at 17 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 -0.3 percent in September 2025. In contrast, Russia maintained a high inflation rate of 8 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.

Our People 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
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
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.

People Data 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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Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

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

Here's the schema of People Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_description
company_sic...

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 ** percent from 2019 to 2022, peaking at **** 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.

The average monthly unique users of Zillow website and mobile applications moderately increased from 2020 to 2022, peaking at *** million users. In 2023, the user count decreased by ***** percent due to macro housing market factors, such as low housing inventory, fewer new listings, mortgage rate volatility, and home price fluctuations.

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 Q2 2025 about farmland, charge-offs, domestic offices, real estate, commercial, domestic, loans, banks, depository institutions, rate, and USA.

Key Table Information.Table Title.Mortgage Status and Selected Monthly Owner Costs.Table ID.ACSDT1Y2024.B25087.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.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.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..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.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.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..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.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 tow...

The benchmark interest rate in India was last recorded at 5.50 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Key Table Information.Table Title.Financial Characteristics for Housing Units With a Mortgage.Table ID.ACSST1Y2024.S2506.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Subject Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.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.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..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.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.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..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.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...

Snapshot img

Loan Servicing Software Market Size 2025-2029

The loan servicing software market size is forecast to increase by USD 3.43 billion, at a CAGR of 13.4% between 2024 and 2029.

The market is driven by the increasing demand for efficiency in lending operations. Lenders seek to streamline their processes and reduce operational costs, making automated loan servicing solutions increasingly valuable. Strategic partnerships and acquisitions among market participants further fuel market expansion, as they collaborate to offer comprehensive solutions and expand their reach. Creditworthiness is assessed using credit scoring algorithms, alternative data sources, and AI, ensuring lenders mitigate default risk. However, the market faces challenges from open-source loan servicing software, which can offer cost-effective alternatives to proprietary solutions.
As competition intensifies, companies must differentiate themselves through superior functionality, customer service, and integration capabilities to maintain market share. To capitalize on opportunities and navigate challenges effectively, market players should focus on continuous innovation, strategic partnerships, and robust customer support.

What will be the Size of the Loan Servicing Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the need for system scalability, regulatory reporting, and enhanced user experiences. Loan servicers seek solutions that seamlessly integrate escrow management, automated payment processing, machine learning, and predictive analytics. Hybrid loan servicing models, which combine on-premise and cloud-based systems, are gaining popularity. Loan portfolio management, loan servicing workflow, and loan origination systems are key areas of focus. Mobile loan servicing and loan servicing consulting are also important, as servicers strive for increased efficiency and improved customer communication management. Risk management, data migration, API integration, and document management are essential components of modern loan servicing solutions.

Default management, foreclosure management, and audit trail are also critical, ensuring regulatory compliance and data integrity. Loan servicing reporting, fraud detection, and loan servicing analytics are crucial for effective decision-making. User experience and loan servicing training are also prioritized, as servicers aim to provide exceptional customer satisfaction. Artificial intelligence and machine learning are transforming loan servicing, enabling predictive analytics and automated loan modification processing. Regulatory reporting and system scalability remain top priorities, as servicers navigate the evolving loan servicing landscape.

How is this Loan Servicing Software Industry segmented?

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

Application

  Banks
  Credit unions
  Mortgage lenders
  Brokers
  Others


Deployment

  Cloud-based
  On-premises


Component

  Software
  Services


Sector

  Large enterprises
  Small and medium enterprises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The banks segment is estimated to witness significant growth during the forecast period.

Loan servicing software is a crucial component of loan origination and servicing technologies (LOS) utilized by banks and financial institutions (BFSI). This software streamlines daily operations by enabling BFSI to accept loan applications online through their websites. The convenience of digital applications aligns with customers' preferences for using the Internet and smartphones. LOS solutions offer features such as EMI calculators, loan eligibility ready reckoners, and document checklists, facilitating a seamless application process 24/7. Pre-configured workflows for credit scoring, document checklist, and approvals significantly reduce turnaround time, enhancing operational efficiency by up to 50%. Escrow management, automated payment processing, and loan portfolio management are integral functions of loan servicing software.

Machine learning and predictive analytics optimize risk management, while user experience and document management ensure customer satisfaction. Cloud-based loan servicing and mobile loan servicing cater to the evolving needs of customers. Loan servicing consulting and automation services help institutions optimize their loan servicing processes.

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Real Estate Market Size 2025-2029

The real estate market size is valued to increase USD 1258.6 billion, at a CAGR of 5.6% from 2024 to 2029. Growing aggregate private investment will drive the real estate market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 64% growth during the forecast period.
By Type - Residential segment was valued at USD 1440.30 billion in 2023
By Business Segment - Rental segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 48.03 billion
Market Future Opportunities: USD 1258.60 billion
CAGR from 2024 to 2029 : 5.6%

Market Summary

In the dynamic realm of global real estate, private investment continues to surge, reaching an impressive USD 2.6 trillion in 2020. This significant influx of capital underscores the sector's enduring appeal to investors, driven by factors such as stable returns, inflation hedging, and the ongoing demand for shelter and commercial real estate space. Simultaneously, marketing initiatives have gained momentum, with digital platforms and virtual tours becoming increasingly popular.
However, regulatory uncertainty looms, posing challenges for market participants. Amidst this complex landscape, real estate remains a vital component of the global economy, continually evolving to meet the shifting needs of businesses and individuals alike.

What will be the Size of the Real Estate Market during the forecast period?

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How is the Real Estate Market Segmented ?

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

Type

  Residential
  Commercial
  Industrial


Business Segment

  Rental
  Sales


Manufacturing Type

  New construction
  Renovation and redevelopment
  Land development


Geography

  North America

    US
    Canada


  Europe

    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The residential segment is estimated to witness significant growth during the forecast period.

Amidst the dynamic real estate landscape, the residential sector encompasses the buying and selling of various dwelling types, including single-family homes, apartments, townhouses, and more. This segment experiences continuous growth, fueled by increasing millennial homeownership rates and urbanization trends. Notably, the APAC region, specifically China, dominates the market share, driven by escalating homeownership numbers. Concurrently, the Indian real estate sector thrives due to the demand for affordable housing, with initiatives like Pradhan Mantri Awas Yojana (PMAY) spurring the development of affordable housing projects. In this evolving market, various aspects such as environmental impact studies, capital appreciation potential, title insurance coverage, building lifecycle costs, mortgage interest rates, and structural engineering analysis play crucial roles.

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The Residential segment was valued at USD 1440.30 billion in 2019 and showed a gradual increase during the forecast period.

Property tax appeals, property insurance premiums, property tax assessments, property marketing strategies, building material pricing, property management software, land surveying techniques, zoning regulations compliance, architectural design features, building code compliance, multifamily property management, rental yield calculations, construction cost estimation, energy efficiency ratings, green building certifications, tenant screening processes, investment property returns, property development plans, geotechnical site investigations, sustainable building practices, due diligence procedures, HVAC system efficiency, property renovation costs, market value appraisals, building permit acquisition, and property valuation models significantly impact the sector's progression. As of 2021, the market is projected to reach a value of USD 33.3 trillion, underscoring its substantial influence on the global economy.

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Regional Analysis

APAC is estimated to contribute 64% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How Real Estate Market Demand is Rising in APAC Request Free Sample

The APAC region held the largest share of the market in 2024, driven by factors such as rapid urbanization and increasing spending capacity. This trend is expected to continue during the forecast period. The overall health of the economy signi

The Real Estate Services industry has faced mixed conditions over recent years. Despite the recent improvement in housing supply and the piling up of inventory, prices remain elevated relative to pre-pandemic levels, offsetting revenue declines for real estate agents. A demand-supply imbalance led to historically high housing prices in 2021-22, though tighter loan-to-value ratio (LVR) regulations and heightened interest rates curbed real estate activity and weakened prices over the two years through 2023-24. The bright-line test extension in 2021 cooled speculative investment, diminishing property investors' interest. Residential property transactions plunged in 2022-23 as cost-of-living pressures and soaring borrowing expenses weighed on mortgage affordability. As inflation moderates and the official cash rate has come down since August 2024, sales volumes and demand will pick up. That's why revenue is forecast to climb 2.8% in 2024-25. However, a plunge in property transactions is why revenue is expected to have dipped at an annualised 0.4% over the five years through 2024-25 to $6.2 billion. The commercial market has faced shifting tenant preferences, particularly around remote work arrangements, contributing to elevated office vacancy rates. Nonetheless, booming demand for industrial space and interest in green buildings has yielded new opportunities. Concurrently, the widespread adoption of artificial intelligence has boosted operational efficiency for many real estate agencies, underpinning growth in their profit margins and alleviating some wage pressures. The Coalition government’s reinstatement of 80% interest deductibility for residential investment properties in April 2024, with a plan to reach 100% by April 2025, alongside the rollback of the bright-line test from 10 to 2 years, will spur investor activity and escalate property prices. These policy changes will entice property investors, expanding this market's revenue share over the coming years and benefiting real estate agencies. Consecutive cuts to the official cash rate to counter subdued economic activity will strengthen mortgage affordability and promote a resurgence in the residential property market. However, an expanding housing supply – aided by funding for social housing units and relaxed planning restrictions – will temper price escalation and slow agencies' commission growth over the coming years. Rising competition among real estate agencies and the continued adoption of digital tools, from big data analytics to advanced customer management solutions, will intensify market dynamics, creating opportunities and challenges for prospective and existing agents. Overall, revenue is forecast to climb at an annualised 2.2% over the five years through 2029-30 to $6.9 billion.