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

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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.

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.

In the United States, interest rates for all mortgage types started to increase in 2021. This was due to the Federal Reserve introducing a series of hikes in the federal funds rate to contain the rising inflation. In the second quarter of 2025, the 30-year fixed rate dropped slightly, to **** percent. The rate remained below the peak of **** percent in the fourth quarter of 2023. Why have U.S. home sales decreased? Cheaper mortgages normally encourage consumers to buy homes, while higher borrowing costs have the opposite effect. As interest rates increased in 2022, the number of existing homes sold plummeted. Soaring house prices over the past 10 years have further affected housing affordability. Between 2014 and 2024, the median price of an existing single-family home risen by about ** percent. On the other hand, the median weekly earnings have risen much slower. Comparing mortgage terms and rates Between 2008 and 2024, the average rate on a 15-year fixed-rate mortgage in the United States stood between **** and **** percent. Over the same period, a 30-year mortgage term averaged a fixed-rate of between **** and **** percent. Rates on 15-year loan terms are lower to encourage a quicker repayment, which helps to improve a homeowner’s equity.

The housing market has been booming, with mortgage lending growing, but the recent hike interest rate rise threatens to stop the residential property market in its tracks.

Global house prices experienced a significant shift in 2022, with advanced economies seeing a notable decline after a prolonged period of growth. The real house price index (adjusted for inflation) for advanced economies peaked at nearly *** index points in early 2022 before falling to around ***** points by the second quarter of 2023. In the second quarter of 2025, the index reached ***** points. This represents a reversal of the upward trend that had characterized the housing market for roughly a decade. Likewise, real house prices in emerging economies declined after reaching a high of ***** points in the third quarter of 2021. What is behind the slowdown? Inflation and slow economic growth have been the primary drivers for the cooling of the housing market. Secondly, the growing gap between incomes and house prices since 2012 has decreased the affordability of homeownership. Last but not least, homebuyers in 2024 faced dramatically higher mortgage interest rates, further contributing to worsening sentiment and declining transactions. Some markets continue to grow While many countries witnessed a deceleration in house price growth in 2022, some markets continued to see substantial increases. Turkey, in particular, stood out with a nominal increase in house prices of over ** percent in the first quarter of 2025. Other countries that recorded a two-digit growth include North Macedonia and Russia. When accounting for inflation, the three countries with the fastest growing residential prices in early 2025 were North Macedonia, Portugal, and Bulgaria.

Factors Affecting USA National Home Prices:

Overview:

This dataset contains a comprehensive collection of indicators which dictate the housing prices in the United States.

1. US Mortgage Rates:

• The average interest rates on mortgage loans in the United States.
• Used to track the cost of borrowing for housing and its impact on the real estate market.

2. Gross Domestic Product (GDP):

• The total monetary value of all goods and services produced within the United States during a specified period.
• A fundamental measure of economic performance, reflecting the overall economic health and growth trends of the country.

3. Unemployment Rates:

• The percentage of the labor force that is currently unemployed and actively seeking employment.
• A crucial indicator of labor market health and economic stability, influencing government policies and social welfare programs.

4. FED Funds Rate:

• The interest rate at which depository institutions lend reserve balances to other depository institutions overnight, as set by the Federal Reserve.
• This rate is a primary tool for monetary policy, influencing borrowing costs and, subsequently, overall economic activity.

5. Population Growth:

• The annual rate at which the U.S. population is changing, reflecting births, deaths, and migration.
• Offers insights into demographic trends, which have implications for labor force, consumer markets, and social services planning.

6. Consumer Price Index (CPI):

• A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.
• A key indicator for assessing inflation or deflation, influencing consumer spending behavior and economic policy decisions.

S&P Case-Shiller Housing Price Index (USA):

• Measures changes in the prices of residential real estate properties over time, offering insight into the health and trends of the housing market in the United States.
• Crucial for assessing the state of the housing market, including property values, trends, and their impact on the broader economy.

The USA home loan market is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. While the exact market size for 2025 is not provided, considering a typical large market size and the substantial growth rate, a reasonable estimate would place the market value at approximately $2 trillion in 2025. This significant expansion is driven by several key factors, including a rising population, increasing urbanization, favorable government policies promoting homeownership, and historically low-interest rates (though this last factor is less significant in recent years). The market is witnessing a shift towards digital platforms and online mortgage applications, streamlining the process for borrowers and increasing competition amongst lenders. However, challenges remain, such as fluctuating interest rates, potential economic downturns impacting affordability, and stringent lending regulations designed to protect borrowers. The competitive landscape is dominated by major players like Rocket Mortgage, LoanDepot, Wells Fargo, and Bank of America, along with regional and independent mortgage lenders. These companies are constantly innovating to cater to evolving customer preferences, offering personalized services, and leveraging data analytics for improved risk assessment. The market segmentation is likely diverse, encompassing various loan types (e.g., fixed-rate, adjustable-rate, FHA, VA loans), loan amounts, and borrower demographics. Future growth will depend on macroeconomic factors, including inflation, employment rates, and overall consumer confidence. Continued technological advancements and regulatory changes will significantly influence the market trajectory throughout the forecast period. Key drivers for this market are: Increase in digitization in mortgage lending market, Increase in innovations in software designs to speed up the mortgage-application process. Potential restraints include: Increase in digitization in mortgage lending market, Increase in innovations in software designs to speed up the mortgage-application process. Notable trends are: Growth in Nonbank Lenders is Expected to Drive the Market.

30 Year Mortgage Rate in the United States decreased to 6.23 percent in November 26 from 6.26 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Compare UK interest rates and mortgage rates alongside house prices. Interactive charts showing the Bank of England base rate versus 2-year, 5-year, and SVR mortgage rates, with historical HPI trends.

Spain Housing Market Indicators: Interest Rate for Loans to Households for House Purchase data was reported at 2.180 % pa in May 2018. This records a decrease from the previous number of 2.210 % pa for Apr 2018. Spain Housing Market Indicators: Interest Rate for Loans to Households for House Purchase data is updated monthly, averaging 4.920 % pa from Jan 1989 (Median) to May 2018, with 353 observations. The data reached an all-time high of 17.050 % pa in Jan 1991 and a record low of 2.050 % pa in Dec 2017. Spain Housing Market Indicators: Interest Rate for Loans to Households for House Purchase data remains active status in CEIC and is reported by Bank of Spain. The data is categorized under Global Database’s Spain – Table ES.EB003: Housing Market Indicators.

Based on panel error correction models for a sample of up to 21 countries this paper analyses the macroeconomic determinants of house prices and rents. In accordance with the existing literature I find significantly positive effects of per capita income and bank lending on house prices, whereas the housing stock per capita and interest rates have negative effects. For rents the results are somewhat more remarkable, indicating that both the housing stock and interest rates have a negative effect. While contradicting conventional economic theory the latter finding might be explained by real estate investors exploiting their pricing power with varying degree depending on the level of real interest rates. Moreover, the estimated impact of interest rates on both house prices and rents varies with structural housing market characteristics. For instance, while interest rates have a more pronounced effect on house prices in countries with more developed mortgage markets, the same does not hold for the effect of interest rates on rents.

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Residential Real Estate Market Size 2025-2029

The residential real estate market size is valued to increase USD 485.2 billion, at a CAGR of 4.5% from 2024 to 2029. Growing residential sector globally will drive the residential real estate market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 55% growth during the forecast period.
By Mode Of Booking - Sales segment was valued at USD 926.50 billion in 2023
By Type - Apartments and condominiums segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 41.01 billion
Market Future Opportunities: USD 485.20 billion
CAGR : 4.5%
APAC: Largest market in 2023

Market Summary

The market is a dynamic and ever-evolving sector that continues to shape the global economy. With increasing marketing initiatives and the growing residential sector globally, the market presents significant opportunities for growth. However, regulatory uncertainty looms large, posing challenges for stakeholders. According to recent reports, technology adoption in residential real estate has surged, with virtual tours and digital listings becoming increasingly popular. In fact, over 40% of homebuyers in the US prefer virtual property viewings. Core technologies such as artificial intelligence and blockchain are revolutionizing the industry, offering enhanced customer experiences and streamlined processes.
Despite these advancements, regulatory compliance remains a major concern, with varying regulations across regions adding complexity to market operations. The market is a complex and intriguing space, with ongoing activities and evolving patterns shaping its future trajectory.

What will be the Size of the Residential Real Estate Market during the forecast period?

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How is the Residential Real Estate Market Segmented and what are the key trends of market segmentation?

The residential real estate industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Mode Of Booking

  Sales
  Rental or lease


Type

  Apartments and condominiums
  Landed houses and villas


Location

  Urban
  Suburban
  Rural


End-user

  Mid-range housing
  Affordable housing
  Luxury housing


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    Australia
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Mode Of Booking Insights

The sales segment is estimated to witness significant growth during the forecast period.

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The Sales segment was valued at USD 926.50 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

APAC is estimated to contribute 55% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How Residential Real Estate Market Demand is Rising in APAC Request Free Sample

The market in the Asia Pacific (APAC) region holds a significant share and is projected to lead the global market growth. Factors fueling this expansion include the region's rapid urbanization and increasing consumer spending power. Notably, residential and commercial projects in countries like India and China are experiencing robust development. The residential real estate sector in China plays a pivotal role in the economy and serves as a major growth driver for the market.

With these trends continuing, the APAC the market is poised for continued expansion during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

In the Residential Real Estate Market, understanding the impact property tax rates home values and effect interest rates mortgage affordability is essential for buyers and investors. Key factors affecting home price appreciation and factors influencing housing affordability shape market trends, while the importance property due diligence process and requirements environmental site assessment ensure informed decisions. Investors benefit from methods calculating rental property roi, process home equity loan application, and benefits real estate portfolio diversification. Tools like property management software efficiency and techniques effective property marketing help tackle challenges managing rental properties. Additionally, strategies successf

The global home loan market is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) exceeding 7% from 2025 to 2033. While the exact 2025 market size ("XX Million") is unspecified, considering a typical market size for such sectors and the provided CAGR, a reasonable estimation would place it in the range of several hundred billion to a trillion dollars depending on the geographical scope of the report (global, regional etc.). This substantial market value underscores the significant demand for home financing globally. The growth is propelled by several key factors, including increasing urbanization, rising disposable incomes in developing economies, supportive government policies promoting homeownership, and the ongoing expansion of the mortgage lending sector itself. Technological advancements, such as online lending platforms and streamlined application processes, also contribute to market expansion by increasing accessibility and efficiency. Conversely, factors like fluctuating interest rates, stringent lending regulations intended to mitigate risk, and economic downturns impacting consumer confidence can act as market restraints. However, the consistent growth trajectory suggests that the positive drivers outweigh these challenges in the long term. The home loan market is segmented based on various criteria such as loan type (fixed-rate, adjustable-rate, etc.), borrower profile (first-time homebuyers, repeat buyers), and loan amount (high-value, low-value). Key players in this dynamic market include established financial institutions like Bank of America, Charles Schwab, Citigroup, Goldman Sachs (Marcus), HSBC, JPMorgan Chase, Morgan Stanley, and Wells Fargo, alongside specialized mortgage lenders such as Dewan Housing Finance Corporation and LIC Housing Finance. Competition among these entities is intense, with each striving to innovate and offer competitive products and services to capture market share within their respective segments. This competition benefits consumers through improved terms and offerings, further driving market growth. The market's future growth trajectory will largely be influenced by the interplay between these driving forces, the potential impact of unforeseen economic events, and ongoing regulatory changes. Key drivers for this market are: Real Estate Market Trends, Government Policies. Potential restraints include: Real Estate Market Trends, Government Policies. Notable trends are: Turkey has the Highest Mortgage Interest Rate.

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 ...

View weekly updates and historical trends for 30 Year Mortgage Rate. from United States. Source: Freddie Mac. Track economic data with YCharts analytics.

Mortgage interest rates in Czechia have experienced significant fluctuations over the past few years, reaching a peak of nearly *** percent in December 2022 before gradually declining. As of March 2025, the interest rate on new mortgages in the country amounted to **** percent, showing a slight decrease from the previous month. This trend in mortgage rates has occurred alongside substantial increases in housing prices. Housing market dynamics The changes in mortgage rates have gone hand in hand with notable shifts in the Czech housing market. Despite the high-interest rates, new mortgage lending reached over 18 million Czech koruna in December 2024, marking a significant increase from the same month in the previous year. This growth in lending has continued despite the steady rise in housing prices, with the house price index reaching ***** in the third quarter of 2024. This marks a significant increase from the 2015 baseline, reflecting the ongoing upward trend. The average purchase price per square meter for family houses increasing across the country. In 2023, Prague recorded the highest average price at ******* Czech koruna per square meter. Construction sector trends The construction sector in Czechia has shown its response to these market conditions. The index of multi-dwelling building construction fluctuated recently, with 2024 showing a slight decrease to **** index points compared to the previous year. However, regarding non-residential buildings, the construction has been continuously growing since 2018 with hotels and industrial buildings accounting for the majority of new non-residential constructions.

This dataset contains synthetic data representing key economic indicators and housing market trends in the UK from 2002 to 2023. The dataset includes quarterly data points for the following variables:

Date: Quarterly timestamps from Q1 2002 to Q4 2023. Housing Cost Index: An index representing the general trend in UK housing prices over time. The values are generated to simulate a typical upward trend observed in real estate markets. Interest Rate (%): The Bank of England's base interest rate, represented as a percentage. The values range from 0.5% to 6%, reflecting typical interest rate fluctuations. Inflation Rate (%): The Consumer Price Index (CPI) values, represented as a percentage, ranging from 1% to 5%, simulating typical inflation trends. Employment Levels (000s): The number of employed individuals in the UK, represented in thousands. The data simulates employment levels ranging from 25 million to 35 million. Growth in Wage (%): The average wage growth rate per quarter, represented as a percentage, ranging from 2% to 7%. GDP Growth Rate (%): The quarterly growth rate of the UK's Gross Domestic Product (GDP), represented as a percentage, with values ranging from -2% to 5%, simulating economic growth and contraction periods. This dataset can be used for educational purposes, including time series analysis, regression modeling, and economic research. Please note that the data is synthetic and not derived from actual historical records. It aims to replicate realistic patterns and trends observed in the UK economy and housing market during the specified period.

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.

Discover the booming global housing mortgage market! This comprehensive analysis reveals a $15 trillion market in 2025, projected to reach $23 trillion by 2033, driven by urbanization, rising incomes, and Fintech innovation. Explore regional trends, key players, and future growth forecasts.