Median price paid for residential property in England and Wales, by property type and administrative geographies. Quarterly rolling annual data. Formerly HPSSA dataset 9

Number of residential property sales and median price paid in England and Wales, by property type and parliamentary constituencies. Quarterly rolling annual data. Formerly part of HPSSA datasets 21, 24 and 26.

Median price paid for residential property in England and Wales, for all property types by lower layer super output area. Annual data..

Summary statistics for housing transactions by local authority in England and Wales, on an annual basis, updated quarterly using HM Land Registry Price Paid Data. Select values from the Year and Month dimensions for data for a 12-month period ending that month and year (e.g. selecting June and 2018 will return the twelve months to June 2018).

Number of properties sold from Land Registry data.

Excluded from the above figures are sales at less than market price (e.g. Right To Buy), sales below £1,000 and sales above £20m.

Relevant link: http://www.ons.gov.uk/ons/rel/regional-analysis/house-price-statistics-for-small-areas/index.html

Average (median) house prices and sale counts by dwelling type for local authorities, parliamentary constituencies and middle layer super output areas, 1995 to 2014.

Median prices for dwellings/townhouses, and apartments by their year of settlement for the City of Melbourne by CLUE Small area.

The figures of existing own homes are related to the stock of existing own homes. Besides the price indices, figures are also published about the numbers sold, the average purchase price, and the total sum of the purchase prices of the sold dwellings. The House Price Index of existing own homes is based on a complete registration of sales of dwellings by the Dutch Land Registry Office (Kadaster) and the (WOZ) value of all dwellings in the Netherlands. Indices may fluctuate, for example if a small number of dwellings are sold in a certain region. In such cases we recommended using the long-term figures. The average purchase price of existing own homes may differ from the price index of existing own homes. The change in the average purchase price, however, is not an indicator for price developments of existing own homes. Data available from: 1st quarter 1995 to 2017 Status of the figures: The figures are definite. Changes as of 20th April 2018: None, this table has been discontinued. This table is followed by the table House Price Index by region; existing own homes 2015 = 100. See paragraph 3 When will new figures be published? Does not apply.

The figures of existing own homes are related to the stock of existing own homes. Besides the price indices, figures are also published about the numbers sold, the average purchase price, and the total sum of the purchase prices of the sold dwellings. The House Price Index of existing own homes is based on a complete registration of sales of dwellings by the Dutch Land Registry Office (Kadaster) and the (WOZ) value of all dwellings in the Netherlands. Indices may fluctuate, for example if a small number of dwellings are sold in a certain region. In such cases we recommended using the long-term figures. The average purchase price of existing own homes may differ from the price index of existing own homes. The change in the average purchase price, however, is not an indicator for price developments of existing own homes.

Data available from: 1st quarter 1995

Status of the figures: The figures in this table are immediately definitive. The calculation of these figures is based on the number of notary transactions that are registered every month by the Dutch Land Registry Office (Kadaster). A revision of the figures is exceptional and occurs specifically if an error significantly exceeds the acceptable statistical margins. The numbers of existing owner-occupied sold homes can be recalculated by Kadaster at a later date. These figures are usually the same as the publication on Statline, but in some periods they differ. Kadaster calculates the average purchasing prices based on the most recent data. These may have changed since the first publication. Statistics Netherlands uses figures from the first publication in accordance with the revision policy described above.

Changes as of 28 January 2025: The figures for the 4th quarter and the year 2024 have been changed and become final.

Changes as of 22 January 2025: Figures for reporting period 4th quarter and the year 2024 are added. The figures of the Price Index for Existing own homes are in the current publication unfortunately provisional. The calculations are based on incomplete data. This may lead to an adjustment of the figures. The expectation is that this adjustment will be limited.

When will new figures be published? New figures are published about 22 days after the period under review.

Mexico's housing market demonstrates significant regional price variations, with Mexico City emerging as the most expensive area for residential property in 2024. The capital city's average house price of 3.91 million Mexican pesos far exceeds the national average of 1.73 million pesos, highlighting the stark contrast in property values across the country. This disparity reflects broader economic and demographic trends shaping Mexico's real estate landscape. Sustained growth in housing prices The Mexican housing market has experienced substantial growth over the past decade, with home prices more than doubling since 2010. By the third quarter of 2023, the nominal house price index reached 255.54 points, representing a 146 percent increase from the baseline year. Even when adjusted for inflation, the real house price index showed a notable 40 percent growth, underscoring the market's resilience and attractiveness to investors. The mortgage market is dominated by three main player types: Infonavit, Fovissste, and commercial banks including Sofomes. In 2023, Infonavit, a scheme by Mexico's National Housing Fund Institute which provides lending to workers in the formal sector, was responsible for the majority of mortgages granted to individuals. Challenges in mortgage lending Despite the overall growth in housing prices, Mexico's mortgage market has faced challenges in recent years. The number of new mortgage loans granted has declined over the past decade, falling by approximately 200,000 loans between 2008 and 2023. This decrease in lending activity may be attributed to various factors, including economic uncertainties and changing consumer preferences. The state of Mexico, which is home to 13 percent of the country's population, likely plays a significant role in shaping these trends, given its large demographic influence on the national housing market.

This table shows the average purchase price that has been paid in the reporting period for existing own homes purchased by a private individual. The average purchase price of existing own homes may differ from the price index of existing own homes. The average purchase price is no indicator for price developments of owner-occupied residential property. The average purchase price reflects the average price of dwellings sold in a particular period. The fact that de dwellings sold differs from one period to another is not taken into account. The following instance explains which problems are entailed by the continually changing of the quality of the dwellings sold. Suppose in February of a particular year mainly big houses with extensive gardens beautifully situated alongside canals are sold, whereas in March many small terraced houses are sold. In that case the average purchase price in February will be higher than in March but this does not mean that house prices are increased. See note 3 for a link to the article 'Why the average purchase price is not an indicator'.

Data available from: 1995

Status of the figures: The figures in this table are immediately definitive. The calculation of these figures is based on the number of notary transactions that are registered every month by the Dutch Land Registry Office (Kadaster). A revision of the figures is exceptional and occurs specifically if an error significantly exceeds the acceptable statistical margins. The average purchasing prices of existing owner-occupied sold homes can be calculated by Kadaster at a later date. These figures are usually the same as the publication on Statline, but in some periods they differ. Kadaster calculates the average purchasing prices based on the most recent data. These may have changed since the first publication. Statistics Netherlands uses figures from the first publication in accordance with the revision policy described above.

Changes as of 17 February 2025: Added average purchase prices of the municipalities for the year 2024.

When will new figures be published? New figures are published approximately one to three months after the period under review.

As of the second quarter of 2024, the residential property price index score for small houses in Indonesia stood at around 112.16. The overall residential property price index in Indonesia has gradually increased over the past few years.

Indonesia Residential Property Price Index: BI: Pekanbaru: Small data was reported at 112.421 2002=100 in Jun 2019. This records an increase from the previous number of 112.413 2002=100 for Mar 2019. Indonesia Residential Property Price Index: BI: Pekanbaru: Small data is updated quarterly, averaging 108.265 2002=100 from Mar 2017 (Median) to Jun 2019, with 10 observations. The data reached an all-time high of 112.421 2002=100 in Jun 2019 and a record low of 100.000 2002=100 in Sep 2017. Indonesia Residential Property Price Index: BI: Pekanbaru: Small data remains active status in CEIC and is reported by Bank of Indonesia. The data is categorized under Global Database’s Indonesia – Table ID.EF005: Residential Property Price Index: by Cities.

Number of residential property sales and price paid (median and lower quartile) in England and Wales, by property type and national parks. Quarterly rolling annual data.

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a webmap of a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup. The map is visualized based on market typology score with strongest market in pink, and weakest market in dark blue.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to help inform development of the City's new Comprehensive Plan, Rochester 2034 , and retained czb, LLC - a firm with national expertise based in Alexandria, VA - to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. And, importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long appreciated that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment, and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/

Indonesia Residential Property Price Index: BI: Denpasar: Small data was reported at 192.479 2002=100 in Jun 2019. This records an increase from the previous number of 192.153 2002=100 for Mar 2019. Indonesia Residential Property Price Index: BI: Denpasar: Small data is updated quarterly, averaging 140.245 2002=100 from Mar 2002 (Median) to Jun 2019, with 70 observations. The data reached an all-time high of 194.176 2002=100 in Dec 2016 and a record low of 100.000 2002=100 in Mar 2002. Indonesia Residential Property Price Index: BI: Denpasar: Small data remains active status in CEIC and is reported by Bank of Indonesia. The data is categorized under Global Database’s Indonesia – Table ID.EF005: Residential Property Price Index: by Cities.

In the fourth quarter of 2024, the sales growth of the small house market in Indonesia contracted by nearly 24 percent. During the same period, the overall sales growth of the residential property market in Indonesia fell by approximately 15 percent.

Indonesia Residential Property Price Index: BI: Bandung: Small data was reported at 261.335 2002=100 in Jun 2019. This records an increase from the previous number of 260.892 2002=100 for Mar 2019. Indonesia Residential Property Price Index: BI: Bandung: Small data is updated quarterly, averaging 170.510 2002=100 from Mar 2002 (Median) to Jun 2019, with 70 observations. The data reached an all-time high of 261.335 2002=100 in Jun 2019 and a record low of 100.000 2002=100 in Mar 2002. Indonesia Residential Property Price Index: BI: Bandung: Small data remains active status in CEIC and is reported by Bank of Indonesia. The data is categorized under Global Database’s Indonesia – Table ID.EF005: Residential Property Price Index: by Cities.

As of the fourth quarter of 2023, the residential property price index for small houses in Indonesia increased by around 2.15 percent, indicating the highest growth compared to the other housing types. The overall residential property price index in Indonesia has gradually increased over the past few years.