Key information about House Prices Growth

• US house prices grew 3.3% YoY in Sep 2025, following an increase of 4.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2025, with an average growth rate of -12.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

CEIC calculates House Prices Growth from quarterly House Price Index. Federal Housing Finance Agency provides House Price Index with base January 1991=100.

Average House Prices in Canada increased to 688800 CAD in October from 687600 CAD in September of 2025. This dataset includes a chart with historical data for Canada Average House Prices.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

Housing Index in Hong Kong increased to 143.46 points in November 23 from 142.49 points in the previous week. This dataset provides - Hong Kong House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

VITAL SIGNS INDICATOR List Rents (EC9)

FULL MEASURE NAME List Rents

LAST UPDATED October 2016

DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

DATA SOURCE real Answers (1994 – 2015) no link

Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.

Key information about House Prices Growth

• Australia house prices grew 3.5% YoY in Jun 2025, following an increase of 4.2% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Sep 2004 to Jun 2025, with an average growth rate of 0.0%.

CEIC calculates quarterly House Price Index Growth from quarterly Residential Dwellings: Mean Price of Eight Capital Cities. The Australian Bureau of Statistics provides Residential Dwellings: Mean Price of Eight Capital Cities in local currency. House Price Index Growth prior to Q3 2012 is calculated from Residential Property Price Index: Weighted Average of Eight Capital Cities.

Graph and download economic data for All-Transactions House Price Index for California (CASTHPI) from Q1 1975 to Q3 2025 about appraisers, CA, HPI, housing, price index, indexes, price, and USA.

South Africa ZA: Price to Income Ratio: sa data was reported at 90.320 2015=100 in 2024. This records a decrease from the previous number of 92.422 2015=100 for 2023. South Africa ZA: Price to Income Ratio: sa data is updated yearly, averaging 98.221 2015=100 from Dec 1995 (Median) to 2024, with 30 observations. The data reached an all-time high of 132.663 2015=100 in 2007 and a record low of 71.323 2015=100 in 1997. South Africa ZA: Price to Income Ratio: sa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s South Africa – Table ZA.OECD.AHPI: House Price Index: Seasonally Adjusted: Non OECD Member: Annual. Nominal house prices divided by nominal disposable income per head. Net household disposable income is used. The population data come from the OECD national accounts database.

These National Statistics provide monthly estimates of the number of residential and non-residential property transactions in the UK and its constituent countries. National Statistics are accredited official statistics.

England and Northern Ireland statistics are based on information submitted to the HM Revenue and Customs (HMRC) Stamp Duty Land Tax (SDLT) database by taxpayers on SDLT returns.

Land and Buildings Transaction Tax (LBTT) replaced SDLT in Scotland from 1 April 2015 and this data is provided to HMRC by https://www.revenue.scot/">Revenue Scotland to continue the time series.

Land Transaction Tax (LTT) replaced SDLT in Wales from 1 April 2018. To continue the time series, the https://gov.wales/welsh-revenue-authority">Welsh Revenue Authority (WRA) have provided HMRC with a monthly data feed of LTT transactions since July 2021.

LTT figures for the latest month are estimated using a grossing factor based on data for the most recent and complete financial year. Until June 2021, LTT transactions for the latest month were estimated by HMRC based upon year on year growth in line with other UK nations.

LTT transactions up to the penultimate month are aligned with LTT statistics.

Go to Stamp Duty Land Tax guidance for the latest rates and information.

Go to Stamp Duty Land Tax rates from 1 December 2003 to 22 September 2022 and Stamp Duty: rates on land transfers before December 2003 for historic rates.

The latest release was published 09:30 28 November 2025 and was updated with provisional data from completed transactions during October 2025.

The next release will be published 09:30 09 January 2026 and will be updated with provisional data from completed transactions during November 2025.

https://webarchive.nationalarchives.gov.uk/ukgwa/20240320184933/https://www.gov.uk/government/statistics/monthly-property-transactions-completed-in-the-uk-with-value-40000-or-above">Archive versions of the Monthly property transactions completed in the UK with value of £40,000 or above are available via the UK Government Web Archive, from the National Archives.

The English Housing Survey (EHS) is a continuous national survey commissioned by the Department for Communities and Local Government (DCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England. The EHS brings together two previous surveys into a single fieldwork operation: the English House Condition Survey (EHCS) (available from the UK Data Archive under GN 33158) and the Survey of English Housing (SEH) (available from the Archive under GN 33277). The EHS covers all housing tenures and provides valuable information and evidence to inform the development and monitoring of the department's housing policies. Results from the survey are also used by a wide range of other users including other government departments, local authorities, housing associations, landlords, academics, construction industry professionals, consultants, and the general public. The EHS has a complex multi-stage methodology consisting of two main elements; an initial interview survey of around 14,000 households and a follow-up physical inspection. Some further elements are also periodically included in or derived from the EHS: for 2008 and 2009, a desk-based market valuation was conducted of a sub-sample of 8,000 dwellings (including vacant ones), but this was not carried out from 2010 onwards. A periodic follow-up survey of private landlords and agents (the Private Landlords Survey (PLS)) is conducted using information from the EHS interview survey. Fuel Poverty datasets are also available from 2003, created by the Department for Energy and Climate Change (DECC).

The EHS interview survey sample formed part of the Integrated Household Survey (IHS) (available from the Archive under GN 33420) from April 2008 to April 2011. During this period the core questions from the IHS formed part of the EHS questionnaire.

The EHS is used to derive two types of datasets: Household and Housing Stock. These are available separately for the End User Licence (EUL) and Special Licence (SL) versions, but are combined into one study for the Secure Access EHS:

• Household datasets comprise the full interview data (plus associated derived variables) for all cases where an interview has been completed. Datasets are provided for single financial years together with annual weights. The survey consists of a detailed interview using a CAPI based program. An interview is first conducted with the householder. Household datasets should be used for any analysis where only information from the household interview is required.
• Housing Stock data are available for all cases where a physical survey has been completed. For occupied properties, the datasets include data from the household interview as well as data from the physical survey. For vacant properties, only data for the physical survey are provided. The data are made available for a two year rolling sample i.e. approximately 12,000 cases together with the appropriate 2-year weights. This means that if you use more than one housing stock dataset, you must use either odd or even years. For example, you need to use the Housing Stock Dataset for '2012' and '2014' or '2013' and '2015', but not the dataset for '2014' and '2013' as you would double-count the cases surveyed between April 2013 and March 2014. The Housing Stock datasets should be used for any analysis requiring information relating to the physical characteristics and energy efficiency of the housing stock. Derived datasets provide key analytical variables compiled post-fieldwork including energy efficiency ratings, decent home indicators and equivalised income.

Secure Access EHS data:
Secure Access datasets for the EHS up to 2013/14 are available under UK Data Archive SN 6923 and include two detailed geographical variables that are not available in the standard EUL versions: Postcodes and Lower Layer Super Output Areas. These variables have been merged into the General derived data file within each Household and Housing Stock dataset. The two variables are also available in a separate Detailed Geography data file, along with the key variable 'aacode', allowing the user to merge with other files of their choice. All other files are the same as in the EUL versions. Secure Access datasets for the EHS from 2014/15 are available under Archive SN 8121. From the submission of the 2014/15 datasets onwards, the EHS Secure Access approach was changed by the Department of Communities and Local Government (DCLG). The Postcode and Lower Layer Super Output Area variables remain available and DCLG also provide versions of the full EHS datasets as used internally, i.e. not disclosure controlled, for Secure Access. For the 2015/16 datasets, the Secure Access version includes the Special Licence version data also available under SNs 8254 and 8255, as well as detailed geography files containing postcodes and Lower Layer Super Output Area variables.

Prospective users of the Secure Access version of the EHS will need to fulfil additional requirements, commencing with the completion of extra application forms to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access users must also complete face-to-face training and agree to Secure Access' User Agreement and Licence Compliance Policy (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL and/or Special Licence versions of the data prior to ordering a Secure Access version.

Related UK Data Archive studies:
EUL versions of the EHS studies are available under GN 33422, and further details and links for these can be found via the EHS list of datasets. From 2014/15 data onwards, the EUL versions of the EHS only include derived variables. In addition the number of variables on the new EUL datasets has been reduced and disclosure control increased on certain remaining variables. New Special Licence versions of the EHS have been deposited, which are of a similar nature to previous EHS EUL datasets and include derived and raw datasets. Special Licence versions of the data from 2014/15 onwards are available under Archive GN 33515.

Further information about the EHS and the latest news, reports and tables can be found on the GOV.UK English Housing Survey web pages.

For the sixth edition (March 2017), the physical data file for each year has been updated; a new cavity wall insulation variable, wins95x, has been added. In compliance with Building Regulations, an increasing proportion of dwellings built in 1991 or after with cavity walls had cavity wall insulation fitted at the time of construction (known as 'as built' insulation), although compliance could also be achieved through other techniques. The non-intrusive survey undertaken in the EHS would not always be able to identify as built insulation (though the surveyor might have found out from the occupant), so dwellings built in 1991 or after with cavity walls with no evidence of insulation in the survey have been assumed to be insulated. The category 'cavity walls with evidence of insulation' includes both dwellings with evidence of cavity wall insulation (e.g. drill holes or information from occupants) and those built in 2003 or after. A separate category identifies cavity walled dwellings built in 1991 or after where no evidence of cavity wall insulation was seen by the surveyors and where no assumptions have been made based on the construction date. This category therefore includes dwellings built in 1991 or after up to and including in 2002, with no evidence of CWI from the physical survey. For the 2014/15 Headline Report a new variable for cavity wall insulation was introduced (wins95x, which has been added to EHS physical files from 2007/8 onwards). From the submission of the 2015 data, wins95x will replace wins90x.

Description Summary Belvoir Group (BLV) is one of the largest property franchisors in the UK. BLV is a resilient business with 26 years of consecutive earnings growth, high margins and low capital needs. The company has been growing FCF per share at 15-25% per year, while also paying a 4.5% dividend yield. At a 10% 2023 FCF yield with a net cash balance sheet, BLV provides an attractive opportunity for small funds and personal accounts. Business background BLV started out in 1995 as a pure-play lettings franchisor. Over time they complemented the business with estate sales and financial services. Today, BLV operates 338 franchised offices under six different regional brands: Belvoir (159 offices), Northwood (91), Newton Fallowell (39), Nicholas Humphreys (20), Lovelle (16) and Mr and Mrs Clarke (13). They also operate a network of 284 mortgage advisors. best stock screener best investing platforms best stock screeners At its core this is a franchise business. BLV derives 80% of its gross profit from royalties paid by their property franchisees. BLV provides them with central support (e.g. a known brand, operational best practices, back-office, training and certifications, valuation and rental data/services, regional advertising and assistance in doing acquisitions) in return for a 10-12% royalty fee of the monthly revenue. As most know, franchise businesses possess attractive features with recurring revenues, high incremental margins and little capex. BLV’s franchisees are largely local entrepreneurs with 100% skin in the game. The majority operate just one to three offices. There are no large established franchisees as is the case with the major hotel and fast-food chains. The £150-200k start-up costs of running a BLV office, means that the franchisees generally have put all their money into the business. Alignment of incentives on the operating level doesn’t get much better than this. This is why the franchisees consistently outgrow the industry by a few percentage points. When the first lockdown ended, it took some of the corporate estate agency chains two months to reopen, while BLV’s franchisees opened their doors on the first day possible. The company reports into two divisions, property franchising and financial services. The former can be split between lettings and estate sales. Lettings (60% of gross profit). This is by far the best part of the business. Lettings is the managing of residential property on behalf of a landlord. This includes finding a tenant, doing the related administration/compliance, property visits and managing the tenant relationship. The franchisees charge landlords 1-1.5% of the monthly rent. Through its franchisees, BLV manages 75.5k properties, up from 37k in 2015. This segment has been growing gross profit at a low teens CAGR (incl. M&A). Lettings is a resilient business as people have to pay rent no matter the state of the economy. Organic growth has been positive every year since inception. Estate sales (20% of gross profit). The business of selling houses, which clearly is not as attractive as lettings. BLV’s franchisees charge a 1% commission on the value of the house. BLV sold 11k houses in 2022, up from 7k in 2017. Like lettings, gross profit has been growing at a low teens CAGR (incl. M&A). This segment is less cyclical than the overall housing market as they have historically grown above market, continue to attract new franchisees and generate 93% of gross profit outside of the Greater London area (less prone to boom and busts cycles). In 2022, BLV saw a 11% decrease in housing transactions compared to a 15% drop for the overall UK housing market. Nvidia EV/EBITDA Kroger EV/EBITDA Kraft EV/EBITDA Chevron EV/EBITDA Verizon EV/EBITDA Financial services (20% of gross profit). BLV manages a network of 284 mortgage advisors. The majority of them (85%) are self-employed. While technically not a franchise business, it works similarly. BLV provides central support and leads in return for a 25% cut of the fees. BLV works with one of UK’s leading mortgage intermediaries, the Mortgage Advice Bureau (MAB). MAB offers BLV access to >90 lenders, looks after compliance and processes the mortgages. The typical mortgage fee is 0.3% of the amount borrowed. In terms of cyclicality, this segment sits between lettings and estate sales. More than 90% of mortgages in the UK are two to five years in length, after which it typically gets refinanced. As such, there is a stable stream of mortgage renewals each year. Around half of the segment is refinancing-related and the other half is tied to housing transactions. Industry overview The UK counts 4.6m private-rented properties and sells 1.2m houses in a normal year. The majority of these are managed and sold by one of the more than 20k estate agencies/lettings offices. At least 15k of these are independents. The rest consists of agency networks that range from a handful of offices to in the hundreds. The largest...

In this paper, we develop a DSGE model including heterogeneous households, introduce the financial friction of credit constraint mechanism, and study the impact of house price shocks on the consumption of heterogeneous household. Based on this, the CHFS data in 2011, 2013, 2015, 2017, and 2019 were used to test the marginal propensity to consume for housing wealth appreciation under different credit constraints. Results show that: Firstly, the financial accelerator mechanism plays an important role in the transmission of housing price shocks to household consumption. The looser the degree of credit constraints, the more obvious the rise in housing prices will be to the consumption expenditure of borrowing household. Secondly, the impact of housing wealth appreciation on household consumption under different credit constraints is heterogeneous. Among them, housing wealth appreciation has a significant positive impact on household consumption expenditure with multiple houses, credit cards, non-loan restrictions, while the marginal effect on the consumption expenditure of households with only one house, loan limited, and no credit cards decreases. Thirdly, for every 1% increase in the housing wealth appreciation, household consumption will increase significantly by 0.10–0.14%.

The average resale house price in Canada was forecast to reach nearly ******* Canadian dollars in 2026, according to a January forecast. In 2024, house prices increased after falling for the first time since 2019. One of the reasons for the price correction was the notable drop in transaction activity. Housing transactions picked up in 2024 and are expected to continue to grow until 2026. British Columbia, which is the most expensive province for housing, is projected to see the average house price reach *** million Canadian dollars in 2026. Affordability in Vancouver Vancouver is the most populous city in British Columbia and is also infamously expensive for housing. In 2023, the city topped the ranking for least affordable housing market in Canada, with the average homeownership cost outweighing the average household income. There are a multitude of reasons for this, but most residents believe that foreigners investing in the market cause the high housing prices. Victoria housing market The capital of British Columbia is Victoria, where housing prices are also very high. The price of a single family home in Victoria's most expensive suburb, Oak Bay was *** million Canadian dollars in 2024.

Housing Index in Singapore increased to 210.70 points in the first quarter of 2025 from 209.40 points in the fourth quarter of 2024. This dataset provides the latest reported value for - Singapore Property Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.