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.

View monthly updates and historical trends for US House Price Index. from United States. Source: Federal Housing Finance Agency. Track economic data with …

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.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

ttd22/house-price dataset hosted on Hugging Face and contributed by the HF Datasets community

House Price Index YoY in the United States decreased to 1.80 percent in December from 2.10 percent in November of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

New housing price index (NHPI). Monthly data are available from January 1981. The table presents data for the most recent reference period and the last four periods. The base period for the index is (201612=100).

This dataset comprises information essential for predicting housing prices in Moscow and the Moscow Oblast region. Collected in November 2023, the data is current and pertinent for analysis. It includes various attributes crucial for predicting housing costs, such as location, size, amenities, and other relevant factors influencing property prices.

Description columns

Price: The price of the apartment in the specified currency. This is the primary target variable for prediction.

Apartment type: The type of apartment, such as studio, one-bedroom, two-bedroom, etc.

Metro station: The name of the nearest metro station to the apartment's location.

Minutes to metro: The time in minutes required to walk from the apartment to the nearest metro station.

Region: The region where the apartment is located (Moscow or Moscow Oblast).

Number of rooms: The total number of rooms in the apartment, including bedrooms, living rooms, etc.

Area: The total area of the apartment in square meters.

Living area: The living area of the apartment in square meters, i.e., the area usable for living.

Kitchen area: The area of the kitchen in square meters.

Floor: The floor on which the apartment is located.

Number of floors: The total number of floors in the building where the apartment is located.

Renovation: The level of renovation of the apartment, such as "no renovation", "cosmetic renovation", "euro renovation", etc.

Task for dataset users:

1. Using the housing price data for Moscow and the Moscow Oblast region, build a machine learning model to predict apartment prices. Your model should utilize various apartment attributes such as apartment type, proximity to metro stations, size, floor level, and renovation level to predict property prices. After constructing the model, analyze its performance and determine which factors have the most significant impact on housing prices in the Moscow and Moscow Oblast region.

A few questions to analyze this data set:

1. What are the most common types of apartments in the region?
2. Is there a relationship between housing prices and proximity to metro stations?
3. How does the level of renovation affect the price of an apartment?
4. Is there a price difference in housing between Moscow and the Moscow Oblast region?
5. Are there any preferences regarding floor levels?
6. Which factors have the greatest impact on housing prices?

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2024, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2024 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2024 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (3 December 2025).; U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

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

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

In 2025, Turkey had the highest inflation-adjusted house price index out of the ** European countries under observation, making it the country where house prices have increased the most since 2010. In Turkey, the house price index exceeded *** index points in the second quarter of 2025, showing an increase in real terms of *** percent since 2010, the baseline year for the index. Iceland and Hungary completed the top three, with an index value of *** and *** index points. In the past year, however, many European countries saw house prices decline in real terms. Where can I find other metrics on different housing markets in Europe? To assess the valuation in different housing markets, one can compare the house-price-to-income ratios of different countries worldwide. These ratios are calculated by dividing nominal house prices by nominal disposable income per head. There are also ratios that look at how residential property prices relate to domestic rents, such as the house-price-to-rent ratio for the United Kingdom. Unfortunately, these numbers are not available in a European overview. An overview of the price per square meter of an apartment in the EU-28 countries is available, however. One region, different markets An important trait of the European housing market is that there is not one market, but multiple. Property policy in Europe lies with the domestic governments, not with the European Union. This leads to significant differences between European countries, which shows in, for example, the homeownership rate (the share of owner-occupied dwellings of all homes). These differences also lead to another problem: the availability of data. Non-Europeans might be surprised to see that house price statistics vary in depth, as every country has their own methodology and no European body exists that tracks this data for the whole continent.

Using 2015 prices as a baseline, the cost of housing in selected developed and emerging economies was highest in Germany as of the last quarter of 2023. Housing costs in the country increased since the second quarter of 2020, reaching an index value of *** in the final quarter of 2023.

Dataset Introduction

This dataset contains property listings from various cities across Bangladesh, specifically including Dhaka, Chattogram, Cumilla, Narayanganj City, and Gazipur, with prices listed in Bangladeshi Taka (৳). The dataset provides valuable insights into various features of the properties, including the number of bedrooms, bathrooms, floor number, floor area in square feet, and their respective prices. The data has been collected from a real estate website, offering a comprehensive view of the housing market across these key cities in Bangladesh.

Feature Description

Title: The title or description of the property listing.

Bedrooms: The number of bedrooms in the property.

Bathrooms: The number of bathrooms in the property.

Floor_no: The floor number on which the property is located.

Occupancy_status: Indicates whether the property is vacant or occupied.

Floor_area: The total floor area of the property in square feet.

City: The city where the property is located. This dataset includes listings from Dhaka, Chattogram, Cumilla, Narayanganj City, and Gazipur.

Price_in_taka: The listing price of the property in Bangladeshi Taka (৳).

Location: The specific location or address within the city.

Usages and Applications

This dataset can be utilized in several ways:

Market Analysis: Understanding the pricing trends across different cities in Bangladesh. It can help identify which cities or neighborhoods are more expensive and which are more affordable.

Investment Decisions: Investors can use this data to evaluate potential real estate investments by comparing properties in terms of price, size, and location across different cities.

Real Estate Valuation: Property developers and agents can assess the market value of similar properties, enabling them to set competitive prices for new developments or resale properties in various regions.

Machine Learning Opportunities

This dataset presents several opportunities for applying machine learning techniques:

Price Prediction: Using features such as floor area, number of bedrooms, and location, machine learning models can be trained to predict the price of a property. This can be especially useful for buyers and sellers looking for price guidance across different cities.

Clustering: By clustering properties based on features like location, size, and price, one could identify distinct property segments or neighborhoods in Dhaka, Chattogram, Cumilla, Narayanganj City, and Gazipur with similar characteristics.

Demand Forecasting: Analyzing trends in the dataset over time can help predict future demand for housing in these cities, which could be valuable for both real estate developers and policymakers.

Anomaly Detection: Identifying properties that are significantly over- or under-priced compared to similar properties, which could indicate potential issues or opportunities in the market.

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

This dataset contains 2,000 entries of house price data from all states in Malaysia, providing a comprehensive overview of the country’s real estate market for 2025. Sourced from Brickz, a trusted platform for property transaction insights, it includes detailed information such as property location, tenure, type, median prices, and transaction counts. This dataset is ideal for real estate market analysis, predictive modeling, and exploring trends across Malaysia’s diverse property market.

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Data Columns (Total 8 Columns):

1. Township: The specific township where the property is located (e.g., Cheras, Subang Jaya).
2. Area: The locality or broader area encompassing the township (e.g., Klang Valley, Penang Island).
3. State: The Malaysian state where the property is situated (e.g., Selangor, Johor, Penang).
4. Tenure: The property ownership type (e.g., Freehold, Leasehold).
5. Type: The category of property (e.g., Terrace, Condominium, Semi-Detached).
6. Median_Price: The median price (in MYR) for properties in the specified township or area.
7. Median_PSF: The median price per square foot (in MYR) for properties.
8. Transactions: The number of recorded property transactions.

Future Plans:

• Expanded Coverage: This dataset will be regularly updated with additional property data to make it even more versatile.
• Enhanced Features: Future updates may include rental prices, amenities, or property-specific details to offer deeper insights into Malaysia’s housing market.

Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

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

This house costing data has 21609 rows of information about different Houses. It's the imputed version of my previous dataset titled Raw Housing Cost Data. Same as earlier, there are a total of 21 columns, out of which Sale Price can be supposedly taken as a dependent variable. The other variables are different features, locations and date, etc. regarding the houses. Several EDA or regression modelling can be performed in this data after a little polishing and treatment.

According to ValuStrat, the average residential prices in Dubai reached around 1,448 United Arab Emirates dirhams (AED) in December 2024, rising from around 894 AED in 2020. Real estate market in Dubai Despite the impact of the global COVID-19 pandemic on the real estate market, Dubai's real estate sector continues to show resilience and remains a lucrative investment option. In the first quarter of 2021, the real estate transactions in Dubai amounted to approximately 25 billion U.S. dollars in value. With its emphasis on the goal of transforming into a high-end tourist destination, Dubai has become an appealing choice for real estate investors. In 2019, investment villas made up most urban buildings in the emirate, with around 72,000 units. Residential market outlook The residential market in Dubai has experienced substantial growth in recent years. In 2022, it was projected to witness the addition of approximately 45 thousand new apartments and seven thousand new villas. These additions contribute to the existing supply of 743,000 residential units in the emirate for that year. According to the same source, in December 2022, the capital prices of residential apartments in Jumeirah Beach Residence, Dubai, stood at approximately 2.5 million United Arab Emirates dirhams. This represented a 5.7 percent growth in capital values compared to the prior year. With its strong market presence and attractive investment opportunities, Dubai's residential market remains a key player in the region.