In 2025, the average value of U.S. farm real estate was 4,350 U.S. dollars per acre. Compared to one decade earlier, the value has increased by 1,350 U.S. dollars. Generally, the value of U.S. farm real estate has had an upward trend since 1970. U.S. farms The number of farms in the United States has conversely been decreasing each year, reaching about 1.8 million farms as of 2024. Texas has more farms out of any other U.S. state by far, with about 230,000 farms as of 2024. Missouri and Iowa had the second and third most farms, though neither state exceeded 100,000 farms. Agricultural trade Agricultural products encompass any products from agricultural origin that are meant for human consumption or animal feed. Agricultural products can include livestock products or crops. In 2024, the U.S. exported about 170.5 billion U.S. dollars’ worth of agricultural goods worldwide, increasing from the previous several years. Mexico is a key destination for U.S. agricultural products and imported just over 28 billion dollars’ worth in 2023, more than Europe and Eurasia combined.

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

New Zealand's average farm sale prices showed significant regional variations in the three months to November 2024. The price of farm property in the country was the highest in the Nelson/Marlborough/Tasman region as of November 2024, with an average sale price of around ******* New Zealand dollars per hectare. In comparison, in the Auckland region, the average farm sales price came to just over ****** dollars per hectare. A farming nation The agriculture industry is a major economic pillar of the country. The contribution to the nation’s GDP is valued in the billions of New Zealand dollars. Horticulture, livestock, and dairying are all important segments, and the commodities produced within them are exported across the globe. While sheep livestock numbers have declined, they still make up a large share of the country’s livestock population. Horticultural farming While New Zealand exports various horticultural products, including wine grapes, potatoes, and apples, it is perhaps best known for its kiwi fruit. Accordingly, the land area dedicated to kiwi fruit farming has continued to increase over the years. New Zealand’s leading horticultural product export destinations include Asia, Europe, and Australia.

Romania Property Price Index: Residential: Houses: Rural data was reported at 88.040 2009=100 in Dec 2016. This records an increase from the previous number of 85.960 2009=100 for Sep 2016. Romania Property Price Index: Residential: Houses: Rural data is updated quarterly, averaging 81.630 2009=100 from Mar 2009 (Median) to Dec 2016, with 32 observations. The data reached an all-time high of 108.760 2009=100 in Dec 2009 and a record low of 73.440 2009=100 in Dec 2011. Romania Property Price Index: Residential: Houses: Rural data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Romania – Table RO.EB003: Property Price Index: 2009=100. Rebased from 2009=100 to 2015=100 Replacement series ID: 388999207

Key information about Philippines Real Residential Property Price Index Growth

• Philippines Real Residential Property Price Index Growth was reported at 6.111 % in Jun 2025.
• This records an increase from the previous number of 5.212 % for Mar 2025.
• Philippines Real Residential Property Price Index Growth data is updated quarterly, averaging 3.693 % from Mar 2009 to Jun 2025, with 66 observations.
• The data reached an all-time high of 12.061 % in Jun 2016 and a record low of -5.913 % in Dec 2009.
• Philippines Real Residential Property Price Index Growth data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Real Residential Property Price Index: 2010=100: Quarterly: YoY %.

The average price of Australian residential property has risen over the past ten years, and in June 2025, it reached over one million Australian dollars. Nonetheless, property experts in Australia have indicated that the country has been in a property bubble over the past decade, with some believing the market will collapse sometime in the near future. Property prices started declining in 2022; however, a gradual upward trend was witnessed throughout 2023, with minor fluctuations in 2024. Australian capital city price differences While the national average residential property price has exhibited growth, individual capital cities display diverse trends, highlighting the complexity of Australia’s property market. Sydney maintains its position as the most expensive residential property market across Australia's capital cities, with a median property value of approximately 1.19 million Australian dollars as of April 2025. Brisbane has emerged as an increasingly pricey capital city for residential property, surpassing both Canberra and Melbourne in median housing values. Notably, Perth experienced the most significant annual increase in its average residential property value, with a 10 percent increase from April 2024, despite being a comparably more affordable market. Hobart and Darwin remain the most affordable capital cities for residential properties in the country. Is the homeownership dream out of reach? The rise in property values coincides with the expansion of Australia's housing stock. In the June quarter of 2025, the number of residential dwellings reached around 11.37 million, representing an increase of about 53,600 dwellings from the previous quarter. However, this growth in housing supply does not necessarily translate to increased affordability or accessibility for many Australians. The country’s house prices remain largely disproportional to income, leaving the majority of low- and middle-income earners priced out of the market. Alongside this, elevated mortgage interest rates in recent years have made taking out a loan increasingly unappealing for many potential property owners, and the share of mortgage holders at risk of mortgage repayment stress has continued to climb.

1. Customers File (customers.csv)

• Description: This file contains information about clients involved in real estate transactions. It includes personal details such as name, surname, birth date, gender, and country, along with transaction-specific information like the purpose of the deal and the satisfaction level.
• Key Columns:
  • customerid: Unique identifier for the customer.
  • entity: Type of client, whether an individual or a company.
  • name and surname: First and last name of the customer.
  • birth_date: Customer's date of birth.
  • sex: Gender of the customer (Male/Female).
  • country and state: The country and state the customer is associated with.
  • purpose: Purpose of the transaction (e.g., Home purchase or Investment).
  • deal_satisfaction: Customer's satisfaction level with the transaction, ranging from 1 to 5.
  • mortgage: Indicates whether the transaction involved a mortgage (Yes/No).
  • source: How the customer was acquired (e.g., Website or Agency).

2. Properties File (properties.csv)

• Description: This file contains information about the properties sold, including building details, property type, area, price, and sale status.
• Key Columns:
  • id: Unique identifier for the property.
  • building: Number of the building where the property is located.
  • date_sale: The date when the property was sold.
  • type: Type of property (e.g., Apartment).
  • property#: The property number within the building.
  • area: Area of the property in square feet.
  • price: Sale price of the property.
  • status: Status of the sale (e.g., Sold).
  • customerid: The unique identifier of the customer associated with the property.

Suggested Analysis and Tasks

1 Customer Insights: - Customer Segmentation: Group customers based on demographics, purpose, or deal satisfaction to understand different customer profiles. - Satisfaction Analysis: Investigate what factors (e.g., property price, area, or mortgage involvement) influence customer satisfaction levels. - Source Effectiveness: Analyze which acquisition sources (e.g., website or agency) yield the highest deal satisfaction.

2 Property Market Analysis: - Price Trends: Analyze how property prices vary over time or by location to identify market trends. - Demand Analysis: Determine which types of properties (e.g., apartments vs. houses) are most popular based on sales data. - Area vs. Price: Explore the relationship between property area and price to develop pricing models or evaluate property value.

3 Predictive Modeling: - Price Prediction: Build models to predict property prices based on features like area, type, and location. - Satisfaction Prediction: Create models to predict customer satisfaction using transaction details and demographics. - Likelihood of Sale: Develop a model to predict the likelihood of a property being sold based on its attributes and market conditions.

4 Geographical Analysis: - Heatmaps: Create heatmaps to visualize property sales and identify high-demand areas. - Country and State Trends: Examine how real estate trends differ between countries and states.

5 Mortgage Impact Study: - Mortgage vs. Non-Mortgage Analysis: Compare transactions that involved a mortgage to those that didn’t to study the impact on price, satisfaction, and deal closure speed.

6 Time Series Analysis: - Sales Over Time: Analyze property sales over different periods to identify seasonal trends or patterns. - Customer Birth Date Analysis: Study any correlations between customers’ birth years and their purchasing behavior.

In the one year period till the first quarter of 2021, the agricultural real estate price index in Saudi Arabia decreased by *** percent. In the same period, the general real estate price index increased by *** percent in Saudi Arabia.

In 2025, the average land price in Japan amounted to ******* Japanese yen per square meter. The average land price is based on land price surveys conducted by the Ministry of Land, Infrastructure, Transport, and Tourism and prefectural governments in January and July each year. Japan’s geography The Japanese archipelago consists of the five main islands of Honshu, Hokkaido, Kyushu, Shikoku, and Okinawa in addition to thousands of smaller islands. Together, they cover a surface area of around ******* square kilometers. ************** of the country’s land area is covered by mountains. Forestland and farmland constitute about ** percent of its landmass, while developed land accounts for **** percent. The population of *** million is concentrated in major cities like Tokyo, which is home to over **** million inhabitants. Urban-rural divide and land prices Owing to an overconcentration of economic activity in Tokyo and other major cities like Osaka and Nagoya, more than half of the population is located in ***** metropolitan areas. Tokyo and its surrounding prefectures that comprise the Tokyo metropolitan area attract many people from other parts of the country each year, often young individuals seeking jobs or starting university. In contrast, rural regions are confronted with depopulation and economic stagnation. Japan’s urban-rural divide is also reflected in land prices. Tokyo has by far the most expensive land prices. In terms of land price growth, the cities of Sapporo, Sendai, Hiroshima, and Fukuoka have outpaced the Greater Tokyo Area in the past decade.

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 2023, the average price of real estate in China was approximately ****** yuan per square meter, representing a decrease from the previous year. Rising prices in the real estate market Since the 1998 housing reform, property prices in China have been rising continuously. Housing in the country is now often unaffordable, especially considering the modest per capita income of Chinese households. Shanghai and Beijing even have some of the most competitive real estate markets in the world. The rapid growth in housing prices has increased wealth among homeowners, while it also led to a culture of speculation among buyers and real estate developers. Housing was treated as investments, with owners expecting the prices to grow further every year. Risk factors The expectation of a steadily growing real estate market has created a property bubble and a potential debt crisis. As Chinese real estate giants, such as China Evergrande and Country Garden, operate by continuously acquiring land plots and initiating new projects, which often require substantial loans and investments, a slowdown in property demands or a decline in home prices can significantly affect the financial situation of these companies, putting China’s banks in a vulnerable position. In addition, due to a lack of regulations and monetary constraints, the long-term maintenance issues of high-rise apartments are also a concern to the sustainable development of China’s cities.

Key information about US Nominal Residential Property Price Index

• United States Nominal Residential Property Price Index was reported at 232.573 2010=100 in Sep 2024.
• This records an increase from the previous number of 230.393 2010=100 for Jun 2024.
• US Nominal Residential Property Price Index data is updated quarterly, averaging 61.010 2010=100 from Mar 1970 to Sep 2024, with 219 observations.
• The data reached an all-time high of 232.573 2010=100 in Sep 2024 and a record low of 10.610 2010=100 in Mar 1970.
• US Nominal Residential Property Price Index data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Nominal Residential Property Price Index: 2010=100: Quarterly.

Hungary, Portugal, Czechia, and Poland were the countries in Europe where house prices increased the most between 2015 and 2024. The EMF house price index for all four countries measured more than *** index points, indicating that home prices more than doubled since 2015 — the base year. Property prices are tightly connected with the supply of new homes. France, Poland, and Denmark are some of the countries with the most dwellings completed per 1,000 citizens in Europe.

The average bid price of new housing in Europe was the highest in Luxembourg, at 8,760 euros per square meter. Since there is no central body that collects and tracks transaction activity or house prices across the whole continent or the European Union, only bid prices were considered. House prices have been soaring, with Sweden topping the ranking Considering the RHPI of houses in Europe (the price index in real terms, which measures price changes of single-family properties adjusted for the impact of inflation), however, the picture changes. Sweden, Luxembourg and Norway top this ranking, meaning residential property prices have surged the most in these countries. Real values were calculated using the so-called Personal Consumption Expenditure Deflator (PCE), This PCE uses both consumer prices as well as consumer expenditures, like medical and health care expenses paid by employers. It is meant to show how expensive housing is compared to the way of living in a country. Home ownership highest in Eastern Europe The home ownership rate in Europe varied from country to country. In 2020, roughly half of all homes in Germany were owner-occupied whereas home ownership was at nearly ** percent in Romania or around ** percent in Slovakia and Lithuania. These numbers were considerably higher than in France or Italy, where homeowners made up ** percent and ** percent of their respective populations.For more information on the topic of property in Europe, visit the following pages as a starting point for your research: real estate investments in Europe and residential real estate in Europe.

Value of farmland and buildings per acre, for Canada and the provinces at July 1 (in dollars).

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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.

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.

Snapshot img

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

Real estate markets are of great importance for both local and international investors. Sydney and Melbourne are two dynamic markets where economic and social factors have significant impacts on property prices. Below is a detailed description of each feature:

1. Date: The date when the property was sold. This feature helps in understanding the temporal trends in property prices.
2. Price:The sale price of the property in USD. This is the target variable we aim to predict.
3. Bedrooms:The number of bedrooms in the property. Generally, properties with more bedrooms tend to have higher prices.
4. Bathrooms: The number of bathrooms in the property. Similar to bedrooms, more bathrooms can increase a property’s value.
5. Sqft Living: The size of the living area in square feet. Larger living areas are typically associated with higher property values.
6. Sqft Lot:The size of the lot in square feet. Larger lots may increase a property’s desirability and value.
7. Floors: The number of floors in the property. Properties with multiple floors may offer more living space and appeal.
8. Waterfront: A binary indicator (1 if the property has a waterfront view, 0 other- wise). Properties with waterfront views are often valued higher.
9. View: An index from 0 to 4 indicating the quality of the property’s view. Better views are likely to enhance a property’s value.
10. Condition: An index from 1 to 5 rating the condition of the property. Properties in better condition are typically worth more.
11. Sqft Above: The square footage of the property above the basement. This can help isolate the value contribution of above-ground space.
12. Sqft Basement: The square footage of the basement. Basements may add value depending on their usability.
13. Yr Built: The year the property was built. Older properties may have historical value, while newer ones may offer modern amenities.
14. Yr Renovated: The year the property was last renovated. Recent renovations can increase a property’s appeal and value.
15. Street: The street address of the property. This feature can be used to analyze location-specific price trends.
16. City: The city where the property is located. Different cities have distinct market dynamics.
17. Statezip: The state and zip code of the property. This feature provides regional context for the property.
18. Country: The country where the property is located. While this dataset focuses on properties in Australia, this feature is included for completeness.

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