The average floor area of dwellings in England in 2024 varied by tenure type. In this year, the usable floor area was largest for owner occupiers (110 square meters), followed by private renters (75 square meters). The smallest dwellings were those belonging to social renters (66 square meters). In 2025, around 4 million houses in England were of the social housing kind.

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in the United States (MEDLISPRIPERSQUFEEUS) from Jul 2016 to Oct 2025 about square feet, listing, median, price, and USA.

The average square meter price of new residential real estate in Spain was the highest in Catalonia and the Community of Madrid in 2025. In the second quarter of the year, both regions boasted home prices of over 4,800 euros per square meter, with Catalonia at 4,893 euros and the Community of Madrid at 5,037 euros. That was substantially higher than the average for the country, which amounted to 3,151 euros per square meter. Overall, house prices in Spain have been on the rise since 2016.

House prices in Spain have grown year-on-year between 2016 and 2025, increasing by over ***** euros per square meter. In October 2025, the average house price per square meter reached ***** euros - far above the level of 2007 before the global financial crisis hit and the market plummeted.

The average size of newly built houses in Ireland shrunk by about ** square meters between 2011 and 2024. Conversely, scheme dwellings decreased in size slightly, while apartments remained stable. In 2024, the average size of newly completed single-family houses was *** square meters. Scheme dwellings had *** square meters, and apartments ** square meters.

Ireland Average Floor Area per Unit: Planning Permission Granted: Houses data was reported at 160.400 sq m in Jun 2018. This records an increase from the previous number of 153.400 sq m for Mar 2018. Ireland Average Floor Area per Unit: Planning Permission Granted: Houses data is updated quarterly, averaging 132.100 sq m from Mar 1977 (Median) to Jun 2018, with 160 observations. The data reached an all-time high of 220.000 sq m in Jun 2012 and a record low of 104.000 sq m in Jun 1977. Ireland Average Floor Area per Unit: Planning Permission Granted: Houses data remains active status in CEIC and is reported by Central Statistics Office of Ireland. The data is categorized under Global Database’s Ireland – Table IE.EA001: Planning Permissions Granted Statistics.

Peru Average House Price: Apartment Sales: Miraflores data was reported at 2,135.577 USD/sq m in Dec 2018. This records a decrease from the previous number of 2,185.704 USD/sq m for Sep 2018. Peru Average House Price: Apartment Sales: Miraflores data is updated quarterly, averaging 2,015.856 USD/sq m from Dec 2007 (Median) to Dec 2018, with 45 observations. The data reached an all-time high of 2,327.614 USD/sq m in Jun 2014 and a record low of 720.294 USD/sq m in Dec 2007. Peru Average House Price: Apartment Sales: Miraflores data remains active status in CEIC and is reported by Central Reserve Bank of Peru. The data is categorized under Global Database’s Peru – Table PE.EB002: House Price: by District.

Poland House Prices: per Square Meter: Primary Market: Transaction: 7 Cities data was reported at 14,264.650 PLN in Dec 2024. This records an increase from the previous number of 14,060.779 PLN for Sep 2024. Poland House Prices: per Square Meter: Primary Market: Transaction: 7 Cities data is updated quarterly, averaging 6,809.243 PLN from Sep 2006 (Median) to Dec 2024, with 74 observations. The data reached an all-time high of 14,264.650 PLN in Dec 2024 and a record low of 3,590.503 PLN in Sep 2006. Poland House Prices: per Square Meter: Primary Market: Transaction: 7 Cities data remains active status in CEIC and is reported by Narodowy Bank Polski. The data is categorized under Global Database’s Poland – Table PL.EB008: House Price.

This is the dataset of average housing prices in Ho Chi Minh City, Vietnam.

Data for districts 1 to 9.

Data collected from 2017 to 2022

Data in the form of Time Series Data for predicting house prices, holidays and weekends is not included in this dataset.

The Date column represents the transaction date Columns from District 1 to District 9 show the average transaction value per square meter.

With this data, data scientists can: - Compare house prices between districts by year - Forecast house prices in the future, can use singular spectrum analysis to analyze - Evaluate model quality with RMSE or equivalent measures

Update: HousePricingHCM_v2.csv is including holidays and weekends, We use replacing missing data by average transactions of the days before and after the holiday to fill in the missing data

Graph and download economic data for Housing Inventory: Median Home Size in Square Feet in Los Angeles County, CA (MEDSQUFEE6037) from Jul 2016 to Oct 2025 about Los Angeles County, CA; Los Angeles; square feet; CA; median; and USA.

The average bid price of new housing in Europe was the highest in Luxembourg, at 8,760 euros per square meter. Since there is no central body that collects and tracks transaction activity or house prices across the whole continent or the European Union, only bid prices were considered. House prices have been soaring, with Sweden topping the ranking Considering the RHPI of houses in Europe (the price index in real terms, which measures price changes of single-family properties adjusted for the impact of inflation), however, the picture changes. Sweden, Luxembourg and Norway top this ranking, meaning residential property prices have surged the most in these countries. Real values were calculated using the so-called Personal Consumption Expenditure Deflator (PCE), This PCE uses both consumer prices as well as consumer expenditures, like medical and health care expenses paid by employers. It is meant to show how expensive housing is compared to the way of living in a country. Home ownership highest in Eastern Europe The home ownership rate in Europe varied from country to country. In 2020, roughly half of all homes in Germany were owner-occupied whereas home ownership was at nearly ** percent in Romania or around ** percent in Slovakia and Lithuania. These numbers were considerably higher than in France or Italy, where homeowners made up ** percent and ** percent of their respective populations.For more information on the topic of property in Europe, visit the following pages as a starting point for your research: real estate investments in Europe and residential real estate in Europe.

Prices & Characteristics of Spanish Homes

Uncovering Market Trends in Spain

By [source]

About this dataset

This dataset provides a wealth of information about the current Spanish housing market for potential buyers. This comprehensive data set includes research-level information about region, number of rooms, size, price, photos and more for different available properties across the country. This data can help researchers understand the wide pricing range and characteristics associated with these homes in great detail. For example, it allows us to uncover average price per square meter as well as differences in prices between larger and smaller locations. Further exploration also reveals correlations between price and surface area as well as number of rooms and pricing models - all immensely helpful to those wishing to purchase or rent properties in Spain! By further investigating this rich set of information provided by this dataset, prospective property buyers can be more informed when making decisions regarding their next home or investment opportunities within the Spanish housing market

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How to use the dataset

Welcome to the Prices and Characteristics of Spanish Houses for Sale dataset! This data set contains comprehensive information about Spanish houses for sale, including location, price, size, and number of rooms. Here’s a guide to help you get started.

• Explore the columns included in this dataset: the summary column provides an overview of the property while description provides more in-depth details. The location column offers geographical details about each house; photo displays a picture of each property; recomendado indicates whether or not it has been recommended; price gives you an idea of how much each house costs; size determines how large or small it is; rooms tells you how many bedrooms it has to offer; price/m2 states the Square Meter Price for each home; bathrooms lets you know how many bathrooms it has on the premises; Num Photos shows you the exact number of images available for that home and type directs which type it is (apartment); region helps pinpoint exactly where these homes are located.

• Analyze relationships between variables: use this dataset to uncover interesting correlations between pricing and other characteristics such as size and number of rooms, or between prices in different regions within Spain. You can also gain insight into average pricing by square meter across various locations - this data might be useful if you're looking at making a real estate investment decision based on market trends around Spain's housing sector!

• Research current market trends: review historical data points from within this dataset with regards to pricing changes over time, as well as differences in supply/demand dynamics across distinct locations within Spain's housing market - all these insights can be used when deciding whether or not now would be an ideal time to purchase property in certain areas!
  Overall, we hope that with this information at hand your research into Spain's current housing market will provide useful results and lend insight that may assist your purchase decision process when considering buying S[anish homes!

Research Ideas

• Comparing the average Spanish house price in different regions to determine if prices are more expensive in certain regions.
• Examining the correlation between size and number of rooms to understand which properties would be a better investment given their size.
• Analyzing the relationship between number of photos uploaded for a property and its price, to determine if there is any correlation between them or not

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: pisos.csv | Column name | Description | |:----------------|:------------------------------------------------------------| | summary | A brief description of the property. (Text) | | location | The geographical area or postcode of the property. (Text) | | photo...

The average purchase price per square meter for family houses in Czechia generally increased in the observed period. In Prague, the capital city region, the average purchase price per square meter stood at ******* Czech koruna in 2023. The lowest price in 2023 was in Zlín region when it amounted to ****** CZK.

Hungary House Price: Average per Square Meter: Secondary Market data was reported at 188.000 HUF/sq m th in 2016. This records an increase from the previous number of 175.000 HUF/sq m th for 2015. Hungary House Price: Average per Square Meter: Secondary Market data is updated yearly, averaging 157.000 HUF/sq m th from Dec 2007 (Median) to 2016, with 10 observations. The data reached an all-time high of 188.000 HUF/sq m th in 2016 and a record low of 145.000 HUF/sq m th in 2013. Hungary House Price: Average per Square Meter: Secondary Market data remains active status in CEIC and is reported by Hungarian Central Statistical Office. The data is categorized under Global Database’s Hungary – Table HU.P004: Average House Price.

China Property Price: YTD Avg: Overall data was reported at 9,510.153 RMB/sq m in Mar 2025. This records a decrease from the previous number of 9,547.228 RMB/sq m for Feb 2025. China Property Price: YTD Avg: Overall data is updated monthly, averaging 5,157.474 RMB/sq m from Dec 1995 (Median) to Mar 2025, with 352 observations. The data reached an all-time high of 11,029.538 RMB/sq m in Feb 2021 and a record low of 599.276 RMB/sq m in Feb 1996. China Property Price: YTD Avg: Overall data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Price – Table CN.PD: NBS: Property Price: Monthly.

Dataset

This dataset was created by Aleksandr Iavorskii

Contents

Content

The dataset consists of Price of Houses in King County , Washington from sales between May 2014 and May 2015. Along with house price it consists of information on 18 house features, date of sale and ID of sale.

Attribute information

1. id - Unique id for each home sold
2. date - Date of the home saled
3. price - Price of each home sold
4. bedrooms - Number of bedrooms
5. bathrooms - Number of bathrooms
6. sqft _ living - Square footage of the apartments interior living space
7. sqft _ lot - Square footage of the land space
8. floors - Number of floors
9. waterfront - A dummy variable for whether the apartment was overlooking the waterfront or not
10. view - An index from 0 to 4 of how good the view of the property was
11. condition - an index from 1 to 5 on the condition of the apartment
12. grade - An index from 1 to 13 , where 1-3falls short of building construction and design, 7 has an average level of construction and design , and 11-13 have a high quality level of construction and design
13. sqft _ above - the square footage of the interior housing space that is above ground level
14. sqft _ basement - the square footage of the inerior housing space that is below ground level
15. yr _ built - The year of the house was initially built
16. yr _ renovated - The year of the house's last renovation
17. zipcode - What zipcode area the house is in
18. lat - Lattitude
19. long - Longitude
20. sqft _ living15 - The square footage of inerior housing living space for the nearest nearest 15 neighbours
21. sqft _ lot15 - the square footage of the land lots of the nearest 15 neighbours

House Price Prediction Dataset.

The dataset contains 2000 rows of house-related data, representing various features that could influence house prices. Below, we discuss key aspects of the dataset, which include its structure, the choice of features, and potential use cases for analysis.

1. Dataset Features

The dataset is designed to capture essential attributes for predicting house prices, including:

Area: Square footage of the house, which is generally one of the most important predictors of price. Bedrooms & Bathrooms: The number of rooms in a house significantly affects its value. Homes with more rooms tend to be priced higher. Floors: The number of floors in a house could indicate a larger, more luxurious home, potentially raising its price. Year Built: The age of the house can affect its condition and value. Newly built houses are generally more expensive than older ones. Location: Houses in desirable locations such as downtown or urban areas tend to be priced higher than those in suburban or rural areas. Condition: The current condition of the house is critical, as well-maintained houses (in 'Excellent' or 'Good' condition) will attract higher prices compared to houses in 'Fair' or 'Poor' condition. Garage: Availability of a garage can increase the price due to added convenience and space. Price: The target variable, representing the sale price of the house, used to train machine learning models to predict house prices based on the other features.

2. Feature Distributions

Area Distribution: The area of the houses in the dataset ranges from 500 to 5000 square feet, which allows analysis across different types of homes, from smaller apartments to larger luxury houses. Bedrooms and Bathrooms: The number of bedrooms varies from 1 to 5, and bathrooms from 1 to 4. This variance enables analysis of homes with different sizes and layouts. Floors: Houses in the dataset have between 1 and 3 floors. This feature could be useful for identifying the influence of multi-level homes on house prices. Year Built: The dataset contains houses built from 1900 to 2023, giving a wide range of house ages to analyze the effects of new vs. older construction. Location: There is a mix of urban, suburban, downtown, and rural locations. Urban and downtown homes may command higher prices due to proximity to amenities. Condition: Houses are labeled as 'Excellent', 'Good', 'Fair', or 'Poor'. This feature helps model the price differences based on the current state of the house. Price Distribution: Prices range between $50,000 and $1,000,000, offering a broad spectrum of property values. This range makes the dataset appropriate for predicting a wide variety of housing prices, from affordable homes to luxury properties.

3. Correlation Between Features

A key area of interest is the relationship between various features and house price: Area and Price: Typically, a strong positive correlation is expected between the size of the house (Area) and its price. Larger homes are likely to be more expensive. Location and Price: Location is another major factor. Houses in urban or downtown areas may show a higher price on average compared to suburban and rural locations. Condition and Price: The condition of the house should show a positive correlation with price. Houses in better condition should be priced higher, as they require less maintenance and repair. Year Built and Price: Newer houses might command a higher price due to better construction standards, modern amenities, and less wear-and-tear, but some older homes in good condition may retain historical value. Garage and Price: A house with a garage may be more expensive than one without, as it provides extra storage or parking space.

4. Potential Use Cases

The dataset is well-suited for various machine learning and data analysis applications, including:

House Price Prediction: Using regression techniques, this dataset can be used to build a model to predict house prices based on the available features. Feature Importance Analysis: By using techniques such as feature importance ranking, data scientists can determine which features (e.g., location, area, or condition) have the greatest impact on house prices. Clustering: Clustering techniques like k-means could help identify patterns in the data, such as grouping houses into segments based on their characteristics (e.g., luxury homes, affordable homes). Market Segmentation: The dataset can be used to perform segmentation by location, price range, or house type to analyze trends in specific sub-markets, like luxury vs. affordable housing. Time-Based Analysis: By studying how house prices vary with the year built or the age of the house, analysts can derive insights into the trends of older vs. newer homes.

5. Limitations and ...

Key information about House Prices Growth

• Croatia house prices grew 15.9% YoY in Jun 2025, following an increase of 14.8% YoY in the previous half a year.
• YoY growth data is updated half a yearly, available from Jun 1996 to Jun 2025, with an average growth rate of -2.5%.
• House price data reached an all-time high of 25.8% in Dec 2007 and a record low of -12.6% in Jun 2010.

CEIC calculates Houses Price Growth from semiannual Average Price of New Dwellings per Square Meter. Croatian Bureau of Statistics provides Average Price of New Dwellings per Square Meter in EUR. Houses Price Growth prior to H1 2023 is calculated from Average Price of New Dwellings per Square Meter in local currency.

Hungary House Price: Average per Square Meter: Secondary Market: ow Detached data was reported at 118.000 HUF/sq m th in 2017. This records an increase from the previous number of 109.000 HUF/sq m th for 2016. Hungary House Price: Average per Square Meter: Secondary Market: ow Detached data is updated yearly, averaging 107.000 HUF/sq m th from Dec 2007 (Median) to 2017, with 11 observations. The data reached an all-time high of 129.000 HUF/sq m th in 2007 and a record low of 96.000 HUF/sq m th in 2013. Hungary House Price: Average per Square Meter: Secondary Market: ow Detached data remains active status in CEIC and is reported by Hungarian Central Statistical Office. The data is categorized under Global Database’s Hungary – Table HU.P004: Average House Price.