House prices grew year-on-year in most states in the U.S. in the first quarter of 2025. Hawaii was the only exception, with a decline of **** percent. The annual appreciation for single-family housing in the U.S. was **** percent, while in Rhode Island—the state where homes appreciated the most—the increase was ******percent. How have home prices developed in recent years? House price growth in the U.S. has been going strong for years. In 2025, the median sales price of a single-family home exceeded ******* U.S. dollars, up from ******* U.S. dollars five years ago. One of the factors driving house prices was the cost of credit. The record-low federal funds effective rate allowed mortgage lenders to set mortgage interest rates as low as *** percent. With interest rates on the rise, home buying has also slowed, causing fluctuations in house prices. Why are house prices growing? Many markets in the U.S. are overheated because supply has not been able to keep up with demand. How many homes enter the housing market depends on the construction output, whereas the availability of existing homes for purchase depends on many other factors, such as the willingness of owners to sell. Furthermore, growing investor appetite in the housing sector means that prospective homebuyers have some extra competition to worry about. In certain metros, for example, the share of homes bought by investors exceeded ** percent in 2025.

Turkey experienced the highest annual change in house prices in 2025, followed by North Macedonia and Portugal. In the second quarter of the year, the nominal house price in Turkey grew by **** percent, while in North Macedonia and Portugal, the increase was **** and **** percent, respectively. Meanwhile, some countries saw prices fall throughout the year. That has to do with an overall cooling of the global housing market that started in 2022. When accounting for inflation, house price growth was slower, and even more countries saw the market shrink.

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

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.

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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

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.

Housing Index in China remained unchanged at -2.20 percent in October. This dataset provides the latest reported value for - China Newly Built House Prices YoY Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The average sales price of new homes in the United States experienced a slight decrease in 2024, dropping to 512,2000 U.S. dollars from the peak of 521,500 U.S. dollars in 2022. This decline came after years of substantial price increases, with the average price surpassing 400,000 U.S. dollars for the first time in 2021. The recent cooling in the housing market reflects broader economic trends and changing consumer sentiment towards homeownership. Factors influencing home prices and affordability The rapid rise in home prices over the past few years has been driven by several factors, including historically low mortgage rates and increased demand during the COVID-19 pandemic. However, the market has since slowed down, with the number of home sales declining by over two million between 2021 and 2023. This decline can be attributed to rising mortgage rates and decreased affordability. The Housing Affordability Index hit a record low of 98.1 in 2023, indicating that the median-income family could no longer afford a median-priced home. Future outlook for the housing market Despite the recent cooling, experts forecast a potential recovery in the coming years. The Freddie Mac House Price Index showed a growth of 6.5 percent in 2023, which is still above the long-term average of 4.4 percent since 1990. However, homebuyer sentiment remains low across all age groups, with people aged 45 to 64 expressing the most pessimistic outlook. The median sales price of existing homes is expected to increase slightly until 2025, suggesting that affordability challenges may persist in the near future.

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

View monthly updates and historical trends for US Existing Home Median Sales Price. from United States. Source: National Association of Realtors. Track ec…

In this Economic Commentary , we compare characteristics of the 2000–2006 house-price boom that preceded the Great Recession to the house-price boom that began in 2020 during the COVID-19 pandemic. These two episodes of high house-price growth have important differences, including the behavior of rental rates, the dynamics of housing supply and demand, and the state of the mortgage market. The absence of changes in fundamentals during the 2000s is consistent with the literature emphasizing house-price beliefs during this prior episode. In contrast to during the 2000s boom, changes in fundamentals (including rent and demand growth) played a more dominant role in the 2020s house-price boom.

Danish residential house prices (1992-2024)

About the dataset (cleaned data)

The dataset (parquet file) contains approximately 1,5 million residential household sales from Denmark during the periode from 1992 to 2024. All cleaned data is merged into one parquet file here on Kaggle. Note some cleaning might still be nessesary, see notebook under code.

Also, added a random sample (100k) of the dataset as a csv file.

Done in Python version: 2.6.3.

Raw data

Raw data and more info is avaible on Github repositary: https://github.com/MartinSamFred/Danish-residential-housingPrices-1992-2024.git

The dataset has been scraped and cleaned (to some extent). Cleaned files are located in: \Housing_data_cleaned \ named DKHousingprices_1 and 2. Saved in parquet format (and saved as two files due to size).

Cleaning from raw files to above cleaned files is outlined in BoligsalgConcatCleanigGit.ipynb. (done in Python version: 2.6.3)

Webscraping script: Webscrape_script.ipynb (done in Python version: 2.6.3)

Provided you want to clean raw files from scratch yourself:

Uncleaned scraped files (81 in total) are located in \Housing_data_raw \ Housing_data_batch1 and 2. Saved in .csv format and compressed as 7-zip files.

Additional files added/appended to the Cleaned files are located in \Addtional_data and named DK_inflation_rates, DK_interest_rates, DK_morgage_rates and DK_regions_zip_codes. Saved in .xlsx format.

Content

Each row in the dataset contains a residential household sale during the period 1992 - 2024.

“Cleaned files” columns:

0 'date': is the transaction date

1 'quarter': is the quarter based on a standard calendar year

2 'house_id': unique house id (could be dropped)

3 'house_type': can be 'Villa', 'Farm', 'Summerhouse', 'Apartment', 'Townhouse'

4 'sales_type': can be 'regular_sale', 'family_sale', 'other_sale', 'auction', '-' (“-“ could be dropped)

5 'year_build': range 1000 to 2024 (could be narrowed more)

6 'purchase_price': is purchase price in DKK

7 '%_change_between_offer_and_purchase': could differ negatively, be zero or positive

8 'no_rooms': number of rooms

9 'sqm': number of square meters

10 'sqm_price': 'purchase_price' divided by 'sqm_price'

11 'address': is the address

12 'zip_code': is the zip code

13 'city': is the city

14 'area': 'East & mid jutland', 'North jutland', 'Other islands', 'Capital, Copenhagen', 'South jutland', 'North Zealand', 'Fyn & islands', 'Bornholm'

15 'region': 'Jutland', 'Zealand', 'Fyn & islands', 'Bornholm'

16 'nom_interest_rate%': Danish nominal interest rate show pr. quarter however actual rate is not converted from annualized to quarterly

17 'dk_ann_infl_rate%': Danish annual inflation rate show pr. quarter however actual rate is not converted from annualized to quarterly

18 'yield_on_mortgage_credit_bonds%': 30 year mortgage bond rate (without spread)

Uses

Various (statistical) analysis, visualisation and I assume machine learning as well.

Practice exercises etc.

Uncleaned scraped files are great to practice cleaning, especially string cleaning. I’m not an expect as seen in the coding ;-).

Disclaimer

The data and information in the data set provided here are intended to be used primarily for educational purposes only. I do not own any data, and all rights are reserved to the respective owners as outlined in “Acknowledgements/sources”. The accuracy of the dataset is not guaranteed accordingly any analysis and/or conclusions is solely at the user's own responsibly and accountability.

Acknowledgements/sources

All data is publicly available on:

Boliga: https://www.boliga.dk/

Finans Danmark: https://finansdanmark.dk/

Danmarks Statistik: https://www.dst.dk/da

Statistikbanken: https://statistikbanken.dk/statbank5a/default.asp?w=2560

Macrotrends: https://www.macrotrends.net/

PostNord: https://www.postnord.dk/

World Data: https://www.worlddata.info/

Dataset picture / cover photo: Nick Karvounis (https://unsplash.com/)

Have fun… :-)

The U.S. housing market has slowed, after ** consecutive years of rising home prices. In 2021, house prices surged by an unprecedented ** percent, marking the highest increase on record. However, the market has since cooled, with the Freddie Mac House Price Index showing more modest growth between 2022 and 2024. In 2024, home prices increased by *** percent. That was lower than the long-term average of *** percent since 1990. Impact of mortgage rates on homebuying The recent cooling in the housing market can be partly attributed to rising mortgage rates. After reaching a record low of **** percent in 2021, the average annual rate on a 30-year fixed-rate mortgage more than doubled in 2023. This significant increase has made homeownership less affordable for many potential buyers, contributing to a substantial decline in home sales. Despite these challenges, forecasts suggest a potential recovery in the coming years. How much does it cost to buy a house in the U.S.? In 2023, the median sales price of an existing single-family home reached a record high of over ******* U.S. dollars. Newly built homes were even pricier, despite a slight decline in the median sales price in 2023. Naturally, home prices continue to vary significantly across the country, with West Virginia being the most affordable state for homebuyers.

Single Family Home Prices in the United States increased to 415200 USD in October from 412300 USD in September of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

House Price Index YoY in the United States decreased to 1.70 percent in September from 2.40 percent in August of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

This dataset provides a comprehensive overview of new housing price indexes in Canada. The data is sourced from a reliable statistical survey, offering a detailed breakdown of housing prices across different components such as total house and land, house only, and land only. The dataset is structured to include key metrics such as geographical location, price index classification, and specific price values, providing a robust foundation for analyzing housing price dynamics within the country.

Key Features

• Price Index Metrics: The dataset includes price indexes for total house and land, house only, and land only, providing a complete picture of housing price dynamics across different components.
• Geographical Focus: Data is specific to Canada, providing insights into national housing price trends and patterns.
• Unit of Measurement: Information is presented in index units (201612=100), allowing for straightforward analysis and comparison.
• Temporal Precision: The data is time-stamped for January 1981, ensuring relevance and accuracy for temporal analysis.

Potential Uses

• Real Estate Market Analysis: Assist in understanding the housing price dynamics in Canada, which is crucial for real estate market forecasting and planning.
• Investment Decisions: Provide insights into optimal investment strategies for real estate in various regions.
• Economic Policy: Support policymakers in monitoring and ensuring compliance with housing market trends and economic standards.
• Market-Specific Insights: Evaluate the impact of housing price trends on specific regions and potential growth or decline areas.
• Strategic Planning: Inform strategic planning for real estate developers and policymakers by providing a clear snapshot of current housing price levels and trends.

Purpose and brief description The house price index measures the inflation in the residential property market. The house price index reflects price developments for all residential properties purchased by households (apartments, terraced houses, detached houses), regardless of whether they are new or existing. Only market prices are taken into account, so self-build homes are excluded. The price of the land is included in the price of the properties. Population Real estate transactions involving residential properties Periodicity Quarterly. Release calendar Results available 3 months after the reference period Definitions House price index: The house price index measures changes in the prices of new or existing dwellings, regardless of their use or previous owner. Inflation - house price index: Inflation is defined as the ratio between the value of a given quarter and that of the same quarter of the previous year. Weighting - house price index: Weighting based on the national accounts (gross fixed capital formation in housing) and the total number of real estate transactions involving residential properties. Type of dwelling according to the classification set out in Regulation (EU) No 93/2013 on housing price indices. Technical information The house price index measures the price evolution of real estate prices on the market of private property. The index follows price changes of new or existing residential real estate purchased by households, irrespective of their purpose (letting or owner-occupying). Only market prices are taken into account. Houses built by their owners are therefore not included. The price of the building plot is included in the house price. The house price index is based on real estate transaction data from the General Administration of the Patrimonial Documentation of the FPS Finances. The prices used are those included in the deeds of sale. Given the time between the date on which the preliminary sales agreement is signed and the date on which the deed is executed (between three and four months), this index measures the price evolution with a delay compared to the actual date on which the sales price is set. This delay is inherent to the data source. The house price index is calculated by the European Union Member States, Norway and Iceland. Eurostat calculates the index for the Euro area (as well as for the European Union as a whole) using the harmonised indices of the Member States. Given the role of the housing market in the economic and financial crisis of 2008, the house price index was included in the indicators used in the procedure to prevent and correct macroeconomic imbalances in the European Union. The house price index is calculated under the European Regulation 2016/792 on harmonised indices of consumer prices and the house price index and 2023/1470 laying down the methodological and technical specifications as regards the house price index and the owner-occupied housing price index. Data are available from 2005 onward for Belgium as well as for the European Union and the majority of European countries. The house price index can be broken down by new houses and existing houses. The weights of these two items in the overall index are determined by the gross fixed capital formation in houses (for the new houses) and the total value of transactions of the previous year (for the existing houses). Until 2013, the house price index of new houses was roughly estimated based on the output price index in the construction sector. Since 2014, it is also based on real estate transaction data. House price index for existing houses is available per region since 2010. The data have therefore been completely reviewed when the results for the fourth quarter of 2023 were published in March 2024. Since the houses that are put up for sale differ from one quarter to another, the changes in characteristics are processed with hedonic regression models to eliminate price fluctuations due to changes in characteristics of the properties sold. These models aim to estimate the theoretical price based on the characteristics and location of the houses sold. The index is then calculated based on changes in the average prices observed and adjusted by a factor depending on the differences in quality observed between dwellings sold during the different periods.

Graph and download economic data for All-Transactions House Price Index for Detroit-Dearborn-Livonia, MI (MSAD) (ATNHPIUS19804Q) from Q2 1976 to Q3 2025 about Detroit, MI, appraisers, HPI, housing, price index, indexes, price, and USA.

Key information about House Prices Growth

• Mexico house prices grew 8.9% YoY in Sep 2025, following an increase of 8.7% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2006 to Sep 2025, with an average growth rate of 10.2%.
• House price data reached an all-time high of 11.7% in Mar 2023 and a record low of 2.2% in Jun 2010.

House Price Index MoM in the United States decreased to 0 percent in September from 0.40 percent in August of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index MoM.