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.

This dataset provides a rich, time-series view of how key macroeconomic indicators have shaped the U.S. housing market over the last 20 years. It is built around the S&P Case-Shiller U.S. National Home Price Index (CSUSHPISA) — a widely trusted benchmark for tracking national home price trends — and enhanced with a curated selection of economic factors sourced from the Federal Reserve Economic Database (FRED).

What's Inside? The dataset spans January 2004 to June 2024 (monthly frequency), and includes the following: Feature Description

Home_Price_Index Case-Shiller Home Price Index (target)
Interest_Rate Federal Funds Rate
Mortgage_Rate 30-Year Fixed Mortgage Average
Unemployment_Rate National unemployment rate
Median_Income Median personal income (annual, forward-filled monthly) Inflation_CPI Consumer Price Index
Building_Permits Housing construction permit approvals
Housing_Starts New housing construction starts
US_Population Monthly estimated population
Consumer_Sentiment University of Michigan Consumer Sentiment Index

In addition to these core features, we’ve added: --Lag features (1-month, 3-month) to capture trend memory --Rolling averages to smooth volatility --Ratios like income-to-mortgage and permit-to-population --Percentage change columns to measure economic shifts over time These transformations make the dataset ideal for predictive modeling, exploratory data analysis, and economic storytelling.

Source --All raw data was retrieved via FRED (Federal Reserve Economic Data), ensuring official, up-to-date, and well-maintained inputs.

Use Cases --Time series forecasting (e.g., Ridge, ARIMA, XGBoost) --Macroeconomic trend analysis --Housing market dashboards --Educational projects on feature engineering --Model interpretability experiments

Frequency --All data is aggregated/resampled to monthly granularity for consistency.

License CC BY 4.0 — free to use with attribution

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Context:

My dataset is a valuable collection of real estate information sourced from REALTING.com, an international affiliate sales system known for facilitating safe and convenient property transactions worldwide. REALTING.com has a strong foundation, with its founders boasting approximately 20 years of experience in creating information technologies for the real estate market. This dataset offers insights into various properties across the globe, making it a valuable resource for real estate market analysis, property valuation, and trend prediction.

Content:

The dataset contains information on a diverse range of properties, each represented by a row of data. Here are the key columns and their contents:

• Title: A brief description or name of the property listing.
• Country: The country where the property is located.
• Location: The specific address or location of the property within the country.
• Building Construction Year: The year in which the building was constructed.
• Building Total Floors: The total number of floors or stories in the building.
• Apartment Floor: The floor on which the apartment is situated within the building.
• Apartment Rooms: The total number of rooms in the apartment.
• Apartment Bedrooms: The number of bedrooms in the apartment.
• Apartment Bathrooms: The number of bathrooms in the apartment.
• Apartment Total Area: The total area of the apartment in square meters.
• Apartment Living Area: The living area of the apartment in square meters.
• Price in USD: The price of the property listed in United States Dollars (USD).
• Image: References or links to images associated with the property listing.
• URL: Web links to the full property listing or more detailed information.

This dataset is rich in real estate-related information, making it suitable for various analytical tasks such as market research, property comparison, geographical analysis, and more. The dataset's global scope and diverse property attributes provide a comprehensive view of the international real estate market, offering ample opportunities for data-driven insights and decision-making.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

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.

Brief

The dataset contains key factors that could influence Residential home prices in the last 20 years in the United States. This factor falls into two categories i.e. Supply & Demand

The S&P Case-Shiller Housing Price Index(HPI) is taken as the y variable, or dependent variable, as an indicator of change in prices.

Supply_dataset(Monthly_data)

• Building Permits(Permit Number)-Number of building permits allotted
• Construction Spending (Million $)-The amount spent (in millions of USD) is a measure of the activity in the construction industry.
• Housing Starts(New Housing Project)-This is a measure of the number of units of new housing projects started in a given period.
• Homes Sold(units)-House for sale is a basic measure of supply.

Demand_dataset(Quaterly_data)

• Mortgage Rates(%)
• USA GDP(Billions$ )-Quarterly Real GDP (adjusted for inflation)
• Unemployment(%)
• Delinquency Rate(%) on Mortgages(Foreclosure on the mortgage)-an indicator of the number of foreclosures in real estate.

Inspiration

Building a Data Science model to find the factors which influenced the home prices the most in the last 20 years.

Summary

https://docs.google.com/presentation/d/1SFQg-cwu2JRr-85uvU1jYY4KDtTjqKuG/edit#slide=id.p3

After a period of rapid increase, house price growth in the UK has moderated. In 2025, house prices are forecast to increase by ****percent. Between 2025 and 2029, the average house price growth is projected at *** percent. According to the source, home building is expected to increase slightly in this period, fueling home buying. On the other hand, higher borrowing costs despite recent easing of mortgage rates and affordability challenges may continue to suppress transaction activity. Historical house price growth in the UK House prices rose steadily between 2015 and 2020, despite minor fluctuations. In the following two years, prices soared, leading to the house price index jumping by about 20 percent. As the market stood in April 2025, the average price for a home stood at approximately ******* British pounds. Rents are expected to continue to grow According to another forecast, the prime residential market is also expected to see rental prices grow in the next five years. Growth is forecast to be stronger in 2025 and slow slightly until 2029. The rental market in London is expected to follow a similar trend, with Outer London slightly outperforming Central London.

Key information about House Prices Growth

• Canada house prices dropped 1.8% YoY in Oct 2025, following a decrease of 1.8% YoY in the previous month.
• YoY growth data is updated monthly, available from Jan 1982 to Oct 2025, with an average growth rate of 5.1%.
• House price data reached an all-time high of 16.5% in Mar 1989 and a record low of -9.7% in Apr 1991.

Turkey Residential Real Estate Market Outlook 2031

The Turkey residential real estate market size was USD 79.92 Billion in 2022 and is projected to reach USD 212.88 Billion by 2031 expand at a CAGR of 11.5% during the forecast period, 2023–2031. The growth of the market is attributed to the increasing surge in foreign buyer, rising population, affordable financing options.



Turkey is known for its diverse set of both oriental and European elements, country is a lucrative destination due to its tourism, infrastructure, transportation facilities, and ease of living. Turkey is at a historical combination of aspects where urban transformation meets green housing which is expected to improve affordability and quality of housing and community development. According to Housing Development Administration of Turkey (TOKi) a total of 500,000 residential units were constructed between 2003-2010 in 81 provinces and 830 townships across the country.

In 2002 Turkish property market was first opened to foreign buyers under the reciprocity clause. According to this clause only countries allowing Turkish citizens reciprocal rights, such as Britain, Germany, and Netherlands. The reciprocity clause was abolished in 2012, and since then nationals from 183 countries have been allowed to buy properties in Turkey.



The residential real estate market in Turkey was impacted negatively by the onset of Covid-19 in 2020, the market has since regained some of the momentum due to ease of restrictions worldwide. According to Turkish Statistical Institute Turkish Statistical Institute (TurkStat) in the first four months of 2020, the total number of home sales in Turkey rose by 8.9% to 383,821 units.

Turkey Residential Real Estate Market Trends, Drivers, Restraints, and Opportunities

• Rising population, increasing urbanization, disposable income growth, and affordable financing are major factors driving the market growth during the forecast period.


• Increasing demand for residential real estate among the foreign buyers due to factor such as

The Spain residential real estate market size was USD 145.18 Billion in 2022 and is likely to reach USD 264.67 Billion by 2031, expanding at a CAGR of 6.9% during 2023–2031. The growth of the market is attributed to the increase in construction as well as population.

Spain’s real estate market is posting a positive trend, especially in terms of demand. The revival in house sales was high in 2021. For instance, 468,000 transactions were completed by October 2021, a growth of 35.9% compared to 2020 and up by 8.3% on 2019. The activity in the residential sector was highest since 2008. A large part of this revival in demand has come from a reduction in pent-up demand and the forced savings accumulated during the months of lockdown and severely restricted travel, combined with highly favorable financing conditions, which make it more attractive to buy and invest in real estate assets. The residential sector is therefore on track to close 2021 with 545,000 sales in the year as a whole.

Before the pandemic began, the residential real estate market in Spain was growing at a healthy pace, which was then dented by Covid-19 as the construction of housing units came down. However, in 2021, the market was back on track with increase in construction.



As per the latest data from the Appraisal Society, it indicates that the price of new housing has remained stable, in a context of increased sales and improvement in economic indicators. The average price of new homes has grown 0.4% in Spain over the last 12 months to Euro 2,482 (approximately USD 2812) per square meter. This slight increase has been generalized and has been registered in 16 of the 17 autonomous communities.



The economic consequences of the Covid crisis made a dent in the real estate market, and has reflected in the 16.7% collapse of sales in Spain in 2020 to 419,898 transactions. As a result, experimental ways of life are introduced into the real estate market to compensate for the lack of social interaction between people.

Spain Residential Real Estate Market Trends, Drivers, Restraints, and Opportunities

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Key information about House Prices Growth

• Japan house prices grew 3.4% YoY in Aug 2025, following an increase of 4.7% YoY in the previous month.
• YoY growth data is updated monthly, available from Apr 2009 to Aug 2025, with an average growth rate of 1.3%.
• House price data reached an all-time high of 10.2% in Apr 2022 and a record low of -9.4% in Apr 2009.

Case-Shiller Index of U.S. Residential House Prices

This dataset provides comprehensive insights into U.S. residential house prices through the S&P Case-Shiller Home Price Index, which includes both the national index and indices for 20 metropolitan regions. The data is derived from the S&P Case-Shiller Index, a widely recognized and reliable measure of U.S. housing price movements. It is updated monthly and utilizes the "repeat sales method" to track the price changes of the same properties over time, making it an accurate reflection of housing appreciation.

Data Overview

The dataset includes: - S&P/Case-Shiller U.S. National Home Price Index: A composite index of single-family home prices across nine U.S. Census divisions. - Indices for 20 Metropolitan Regions: Regional indices that highlight housing price trends in major U.S. cities.

Key Methodology

The index uses a "repeat sales" approach, which tracks properties that have been sold at least twice to capture changes in their value over time. This method minimizes biases from changes in housing stock or individual property characteristics. The index originated in the 1980s through the work of Karl E. Case and Robert J. Shiller, pioneers in developing the repeat sales technique. It remains one of the most trusted tools for measuring U.S. housing market trends.

The indices are used widely by policymakers, economists, and analysts to gauge housing market conditions and make informed decisions.

Use Cases

This dataset can be used for: - Housing Market Analysis: Track trends in national and metropolitan housing prices. - Econometric Modeling: Analyze the relationship between housing prices and macroeconomic factors. - Forecasting: Build models to predict future housing market movements.

Source

Data sourced from: https://github.com/datasets/house-prices-us Original source: https://datahub.io/core/house-prices-us

The Scandinavian residential real estate market size is anticipated to expand at significant CAGR during forecast period 2021–2028. Growth of the market is attributed to rapid urbanization, rapid development in Scandinavian countries, strict regulation by government on zoning, and rising immigration.

For groups of people, individuals, and families the houses are built under residential real estate. The residential type contains townhouses, single-family homes, condominiums, apartments, and other types of living arrangements. The permanent improvements such as bridges, water, fences, trees, homes, minerals, and buildings attached to the land, made by naturally & humans including real estate. Raw land, commercial, residential, industrial, and special uses are five main categories of real estate.



The covid-19 pandemic impacted the Scandinavian residential real estate market. Decreasing supply of raw materials, lockdown across the globe, and supply chain disorders forced companies to close down production leading to unfortunate decline in market growth. Launch of vaccines to combat the Covid-19 pandemic is expected to contribute to the market growth over the forecast period.

Scandinavian Residential Real Estate Market Trends, Drivers, Restraints, and Opportunities

• Increasing number of household development project is estimated to boost the market.


• Rapid innovations in construction sector are the major factor fueling the market.


• Lack of awareness about residential real estate is projected to restrict the market expansion during the forecast period.


• High price of services is anti

Key information about House Prices Growth

• Portugal house prices grew 17.2% YoY in Jun 2025, following an increase of 16.3% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2010 to Jun 2025, with an average growth rate of 10.4%.
• House price data reached an all-time high of 17.2% in Jun 2025 and a record low of -8.3% in Jun 2012.

Key information about House Prices Growth

• Netherlands house prices grew 11.5% YoY in Jan 2025, following an increase of 10.9% YoY in the previous month.
• YoY growth data is updated monthly, available from Jan 1996 to Jan 2025, with an average growth rate of 4.7%.
• House price data reached an all-time high of 20.9% in Jan 2022 and a record low of -9.6% in Jan 2013.

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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.

Graph and download economic data for All-Transactions House Price Index for Philadelphia, PA (MSAD) (ATNHPIUS37964Q) from Q3 1976 to Q3 2025 about Philadelphia, PA, appraisers, HPI, housing, price index, indexes, price, and USA.

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 ...

In 2022, house price growth in the UK slowed, after a period of decade-long increase. Nevertheless, in June 2025, prices reached a new peak, with the average home costing ******* British pounds. This figure refers to all property types, including detached, semi-detached, terraced houses, and flats and maisonettes. Compared to other European countries, the UK had some of the highest house prices. How have UK house prices increased over the last 10 years? Property prices have risen dramatically over the past decade. According to the UK house price index, the average house price has grown by over ** percent since 2015. This price development has led to the gap between the cost of buying and renting a property to close. In 2023, buying a three-bedroom house in the UK was no longer more affordable than renting one. Consequently, Brits have become more likely to rent longer and push off making a house purchase until they have saved up enough for a down payment and achieved the financial stability required to make the step. What caused the recent fluctuations in house prices? House prices are affected by multiple factors, such as mortgage rates, supply, and demand on the market. For nearly a decade, the UK experienced uninterrupted house price growth as a result of strong demand and a chronic undersupply. Homebuyers who purchased a property at the peak of the housing boom in July 2022 paid ** percent more compared to what they would have paid a year before. Additionally, 2022 saw the most dramatic increase in mortgage rates in recent history. Between December 2021 and December 2022, the **-year fixed mortgage rate doubled, adding further strain to prospective homebuyers. As a result, the market cooled, leading to a correction in pricing.

The Australian luxury residential property market, valued at $23.88 billion in 2025, is poised for robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 5.75% from 2025 to 2033. This expansion is fueled by several key drivers. Strong economic performance in key cities like Sydney, Melbourne, and Brisbane, coupled with a burgeoning high-net-worth individual (HNWI) population, continues to underpin demand for premium properties. Furthermore, a limited supply of luxury housing stock in prime locations, combined with increasing preference for spacious, high-amenity homes, particularly villas and landed houses, contributes to sustained price appreciation. While rising interest rates present a potential restraint, the resilience of the luxury market segment, driven by wealthier buyers less susceptible to interest rate fluctuations, is expected to mitigate this effect. The market is segmented by property type (apartments/condominiums versus villas/landed houses) and location, with Sydney, Melbourne, and Brisbane dominating market share, reflecting their established luxury real estate markets and strong economic activity. Prominent developers like Metricon Homes, James Michael Homes, and others cater to this discerning clientele, offering bespoke designs and high-end finishes. The sustained growth trajectory indicates a promising outlook for investors and developers alike, although careful consideration of macroeconomic factors and regulatory changes will remain crucial. The forecast period (2025-2033) anticipates consistent market expansion, driven by ongoing demand from both domestic and international high-net-worth individuals. While the "Other Cities" segment demonstrates potential for growth, Sydney, Melbourne, and Brisbane are likely to maintain their dominant positions due to existing infrastructure, established luxury markets, and lifestyle appeal. The preference for villas and landed houses is expected to remain strong, reflecting a shift towards larger properties with increased privacy and outdoor space. However, the market will likely see some adjustments in response to economic conditions, including potential shifts in buyer preferences and developer strategies to meet evolving market demands. Maintaining a keen understanding of these dynamics will be critical for navigating the complexities of this dynamic market. Recent developments include: August 2023: Sydney-based boutique developer Made Property laid plans for a new apartment project along Sydney Harbour amid sustained demand for luxury waterfront properties. The Corsa Mortlake development, positioned on Majors Bay in the harbor city’s inner west, will deliver 20 three-bedroom apartments offering house-sized living spaces and ready access to a 23-berth marina accommodating yachts up to 20 meters. With development approval secured for the project, the company is moving quickly to construction. Made Property expects construction to be completed in late 2025., September 2023: A luxurious collection of private apartment residences planned for a prime double beachfront site in North Burleigh was released to the market for the first time with the official launch of ultra-premium apartment development Burly Residences, being delivered by leading Australian developer David Devine and his team at DD Living. The first stage of Burly Residences released to the market includes prestigious two and three-bedroom apartments – with or without multipurpose rooms – and four-bedroom plus multipurpose room apartments that deliver luxury and space with expansive ocean and beach views.. Key drivers for this market are: 4., Increasing Number of High Net-Worth Individuals (HNWIs). Potential restraints include: 4., Increasing Number of High Net-Worth Individuals (HNWIs). Notable trends are: Ultra High Net Worth Population Driving the Demand for Prime Properties.