The price of existing dwellings in the UK increased significantly between 1990 and 2024. Existing housing reached a value of 335,000 British pounds in 2024. That was slightly higher than the previous year, when house prices were 331,000 pounds, but a substantial increase since 2019 when prices started to rise rapidly. Overall, flats or maisonettes in converted houses were the most affordable housing type in the UK in 2024.

The price of existing dwelling units in Great Britain significantly grew between 1990 and 2023. The simple average price of existing homes amounted to ******* British pounds in 2023. That was a decrease of ****** British pounds from a year ago, when prices were are their peak.

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.

California Housing Prices: 1990 Census Data

This dataset contains information derived from the 1990 census in California, focusing on housing characteristics and median house values at the block group level. Each row in the dataset represents a block group, which is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a cluster of typically 600 to 3,000 people).

About the Features:

The dataset includes the following features:

1. longitude: Numerical. Represents the longitudinal coordinate of the block group. A higher value indicates a location farther west in California.

2. latitude: Numerical. Represents the latitudinal coordinate of the block group. A higher value indicates a location farther north in California.

3. housingMedianAge: Numerical. Represents the median age of the houses within a block group, measured in years. A lower value indicates newer buildings.

4. totalRooms: Numerical. Represents the total number of rooms within all the housing units in the block group.

5. totalBedrooms: Numerical. Represents the total number of bedrooms within all the housing units in the block group.

6. population: Numerical. Represents the total number of people residing within the block group.

7. households: Numerical. Represents the total number of households within the block group. A household is defined as a group of people residing within a home unit.

8. medianIncome: Numerical. Represents the median income for households within the block group, measured in tens of thousands of US Dollars. For example, a value of 5.0 corresponds to an income of $50,000.

9. medianHouseValue: Target Variable. Numerical. Represents the median house value for households within the block group, measured in US Dollars. This is the variable you would typically aim to predict in a regression task.

10. oceanProximity: Categorical. Indicates the location of the house with respect to the ocean or sea. Possible values might include:

    • NEAR BAY
    • <1H OCEAN (less than 1 hour to the ocean)
    • INLAND
    • NEAR OCEAN
    • ISLAND

Potential Uses:

This dataset is well-suited for various machine learning tasks, including:

• Regression: Predicting the medianHouseValue based on the other features.
• Exploratory Data Analysis (EDA): Understanding the relationships between different housing characteristics and their geographical distribution.
• Feature Engineering: Creating new features from the existing ones to potentially improve prediction accuracy.

Considerations:

• The data is from the 1990 census, so housing market conditions and prices have likely changed significantly since then.
• The oceanProximity feature is categorical and will need to be handled appropriately (e.g., through one-hot encoding or other categorical encoding techniques) for many machine learning models.
• Missing values might exist in some of the numerical features and would need to be addressed during data preprocessing.

This dataset provides a valuable snapshot of housing characteristics in California from 1990 and serves as a good starting point for practicing regression and data analysis techniques.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q4 2025 about sales, housing, and USA.

This dataset was obtained from the StatLib repository: https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html

The target variable is the median house value for California districts, expressed in hundreds of thousands of dollars ($100,000).

This dataset was derived from the 1990 U.S. census, using one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).

A household is a group of people residing within a home. Since the average number of rooms and bedrooms in this dataset are provided per household, these columns may take surprisingly large values for block groups with few households and many empty houses, such as vacation resorts.

The dataset can also be downloaded/loaded using the sklearn.datasets.fetch_california_housing function.

Acknowledgments

Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, Statistics and Probability Letters, 33 (1997) 291-297

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q4 2025 about sales, housing, median, and USA.

House Price Index: Annual: High Rise Unit: Johor data was reported at 157.700 1990=100 in 2001. This records a decrease from the previous number of 163.100 1990=100 for 2000. House Price Index: Annual: High Rise Unit: Johor data is updated yearly, averaging 149.850 1990=100 from Dec 1988 (Median) to 2001, with 14 observations. The data reached an all-time high of 196.000 1990=100 in 1997 and a record low of 93.900 1990=100 in 1988. House Price Index: Annual: High Rise Unit: Johor data remains active status in CEIC and is reported by Valuation and Property Services Department, Ministry of Finance. The data is categorized under Global Database’s Malaysia – Table MY.EB003: House Price Index: 1990=100.

Graph and download economic data for Residential Property Prices for Hungary (QHUN628BIS) from Q1 1990 to Q3 2025 about Hungary, residential, HPI, housing, price index, indexes, and price.

Similar Datasets:

• Boston House Prices: LINK
• Gender Pay Gap Dataset: LINK
• Company Bankruptcy Prediction: LINK
• Spanish Wine Quality Dataset: LINK

Context

This is the dataset is a modified version of the California Housing Data used in the paper Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being too toyish and too cumbersome.

The data contains information from the 1990 California census. So although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning.

Modifications with respect to the original data

This dataset includes 5 extra features defined by me: "Distance to coast", "Distance to Los Angeles", "Distance to San Diego", "Distance to San Jose", and "Distance to San Francisco". These extra features try to account for the distance to the nearest coast and the distance to the centre of the largest cities in California.

The distances were calculated using the Haversine formula with the Longitude and Latitude:

https://wikimedia.org/api/rest_v1/media/math/render/svg/a65dbbde43ff45bacd2505fcf32b44fc7dcd8cc0" alt="">

where:

• phi_1 and phi_2 are the Latitudes of point 1 and point 2, respectively
• lambda_1 and lambda_2 are the Longitudes of point 1 and point 2, respectively
• r is the radius of the Earth (6371km)

Content

The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. The columns are as follows, their names are pretty self-explanatory:

1) Median House Value: Median house value for households within a block (measured in US Dollars) [$] 2) Median Income: Median income for households within a block of houses (measured in tens of thousands of US Dollars) [10k$] 3) Median Age: Median age of a house within a block; a lower number is a newer building [years] 4) Total Rooms: Total number of rooms within a block 5) Total Bedrooms: Total number of bedrooms within a block 6) Population: Total number of people residing within a block 7) Households: Total number of households, a group of people residing within a home unit, for a block 8) Latitude: A measure of how far north a house is; a higher value is farther north [°] 9) Longitude: A measure of how far west a house is; a higher value is farther west [°] 10) Distance to coast: Distance to the nearest coast point [m] 11) Distance to Los Angeles: Distance to the centre of Los Angeles [m] 12) Distance to San Diego: Distance to the centre of San Diego [m] 13) Distance to San Jose: Distance to the centre of San Jose [m] 14) Distance to San Francisco: Distance to the centre of San Francisco [m]

Source

This data was entirely modified and cleaned by me. The original data (without the distance features) was initially featured in the following paper: Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.

The original dataset can be found under the following link: https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html

House Price Index: Annual: High Rise Unit: Pulau Pinang data was reported at 150.300 1990=100 in 2001. This records a decrease from the previous number of 167.100 1990=100 for 2000. House Price Index: Annual: High Rise Unit: Pulau Pinang data is updated yearly, averaging 147.650 1990=100 from Dec 1988 (Median) to 2001, with 14 observations. The data reached an all-time high of 170.800 1990=100 in 1997 and a record low of 77.900 1990=100 in 1988. House Price Index: Annual: High Rise Unit: Pulau Pinang data remains active status in CEIC and is reported by Valuation and Property Services Department, Ministry of Finance. The data is categorized under Global Database’s Malaysia – Table MY.EB003: House Price Index: 1990=100.

Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q4 2025 about appraisers, HPI, housing, price index, indexes, price, and USA.

The average sales price of new homes in the United States experienced a slight increase in 2024, rising to 514,500 U.S. dollars. This was below 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.

This statistic shows the average selling price of houses in Flanders, Wallonia and the Brussels Capital Region (Belgium) from 1996 to 2017 (in euros). Belgium as a country has the unique situation that it has four governments: one national and three regional. These governments sometimes work together, but also do things differently from another. When looking at Belgium, one therefore also has to look at the regional level. According to the source, the numbers provided concern 'ordinary houses' (in Flemish Dutch: gewone woonhuizen). This in contrast to other numbers, which cover 'villa's, bungalows and mansions', 'apartments, flats and studios' (in Flemish Dutch: villa's, bungalows, landhuizen) and 'building lots' (in Flemish Dutch: bouwgronden). In 2017, a house would cost 234,000 euros on average in the Flemish region in Belgium.

During the coronavirus (COVID-19) crisis in 2020, house prices in Belgium continued increasing. Unsurprisingly, the Brussels-Capital Region was the most expensive region for housing.

Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q3 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.

The U.S. housing market continues to evolve, with the median price for existing homes forecast to fall to ******* U.S. dollars by 2027. This projection comes after a period of significant growth and recent fluctuations, reflecting the complex interplay of economic factors affecting the real estate sector. The rising costs have not only impacted home prices but also down payments, with the median down payment more than doubling since 2012. Regional variations in housing costs Home prices and down payments vary dramatically across the United States. While the national median down payment stood at approximately ****** U.S. dollars in early 2024, homebuyers in states like California, Massachusetts, and Hawaii faced down payments exceeding ****** U.S. dollars. This disparity highlights the challenges of homeownership in high-cost markets and underscores the importance of location in determining housing affordability. Market dynamics and future outlook The housing market has shown signs of cooling after years of rapid growth, with a modest price increase of *** percent in 2024. This slowdown can be attributed in part to rising mortgage rates, which have tempered demand. Despite these challenges, most states continued to see year-over-year price growth in 2025, with Rhode Island and West Virginia leading the packby home appreciation. As the market adjusts to new economic realities, potential homebuyers and investors alike will be watching closely for signs of stabilization or renewed growth in the coming years.

House Price Index: High Rise Unit: Selangor data was reported at 137.700 1990=100 in Dec 2001. This records an increase from the previous number of 136.400 1990=100 for Jun 2001. House Price Index: High Rise Unit: Selangor data is updated semiannually, averaging 137.050 1990=100 from Jun 1997 (Median) to Dec 2001, with 10 observations. The data reached an all-time high of 144.900 1990=100 in Jun 1997 and a record low of 115.000 1990=100 in Jun 1999. House Price Index: High Rise Unit: Selangor data remains active status in CEIC and is reported by Valuation and Property Services Department, Ministry of Finance. The data is categorized under Global Database’s Malaysia – Table MY.EB003: House Price Index: 1990=100.

Context

California House Pricing(1990)

Content

Prediction of House values in California in the year 1990.

Acknowledgements

Aurelien Geron

Inspiration

Will we see the fall in housing prices in the near future?

Graph and download economic data for All-Transactions House Price Index for Los Angeles County, CA (ATNHPIUS06037A) from 1975 to 2024 about Los Angeles County, CA; Los Angeles; HPI; CA; housing; price index; indexes; price; and USA.

Summary of UK House Price Index (HPI) price statistics covering England, Scotland, Wales and Northern Ireland. Full UK HPI data are available on GOV.UK.