House prices in Spain are forecast to fall in 2024, after increasing by *** percent in 2023. Nevertheless, prices are expected to pick up in 2025, with an increase of ***********. The Portuguese housing market, on the other hand, grew by *** percent in 2023, but was forecast to contract in the next two years.

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.

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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.

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.

The number of U.S. home sales in the United States declined in 2024, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2024, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 413,000 U.S. dollars in 2024 and was forecast to increase slightly until 2026. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.

The United States real estate market was valued at USD 3.43 Trillion in 2024. The industry is expected to grow at a CAGR of 2.80% during the forecast period of 2025-2034 to reach a value of USD 4.52 Trillion by 2034. The market growth is mainly driven by the rising corporate investment, particularly in addressing the nation’s affordable housing shortage.

Major corporations are actively investing to integrate housing stability with social responsibility, supporting both new construction and the preservation of existing homes. In September 2024, UnitedHealth Group surpassed USD 1 billion in investments for affordable and mixed-income housing through direct capital and tax credits. These projects span 31 states and have delivered over 25,000 homes, simultaneously improved community health and providing secure housing for low- and moderate-income households.

Such corporate involvements are reshaping trends in United States real estate market by expanding the supply of affordable housing, reducing barriers for renters and homeowners, and stimulating development in high-demand urban and suburban areas. By aligning financial resources with strategic planning, corporations are enabling scalable solutions that meet social and economic objectives while enhancing overall market efficiency.

According to Cognitive Market Research, the global Real Estate Services market size was USD 100254.6 million in 2024. It will expand at a compound annual growth rate (CAGR) of 5.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 40101.84 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 30076.38 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 23058.56 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 5012.73 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 2005.09 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.2% from 2024 to 2031.
The Residential Type held the highest Real Estate Services market revenue share in 2024.

Market Dynamics of Real Estate Services Market

Key Drivers for Real Estate Services Market

Increasing focus on sustainability and environmentally-friendly buildings to Increase the Demand Globally: The increasing focus on sustainability and environmentally-friendly buildings is driving the Real Estate Services Market as businesses and consumers seek properties that reduce environmental impact and energy costs. Green buildings, which adhere to eco-friendly standards, are becoming more attractive due to their long-term cost savings, health benefits, and regulatory incentives. Real estate services must adapt to this trend by offering expertise in sustainable development, energy efficiency, and green certifications. Additionally, investors are prioritizing environmentally responsible properties to meet corporate social responsibility goals, further fueling demand for specialized real estate services. This shift is creating new opportunities and driving growth in the market as sustainability becomes a key consideration in real estate decisions.

Rising population levels to Propel Market Growth: Rising population levels are driving the Real Estate Services Market by increasing demand for housing, commercial spaces, and infrastructure. As populations grow, particularly in urban areas, the need for residential properties intensifies, leading to more real estate transactions, development projects, and property management needs. Additionally, growing populations stimulate economic activity, creating demand for offices, retail spaces, and industrial properties. This growth translates into higher demand for real estate services such as brokerage, property management, and valuation. Real estate companies also benefit from increased construction and development activity, as they provide essential services for planning, financing, and marketing new projects. Overall, population growth creates sustained demand across all segments of the real estate market, driving the need for professional services.

Restraint Factor for the Real Estate Services Market

High Initial Costs to Limit the Sales: High initial costs are restraining the Real Estate Services Market by making it difficult for potential buyers and investors to enter the market. Purchasing or developing real estate involves significant upfront expenses, including land acquisition, construction, legal fees, and financing costs. These high costs can be a barrier, especially for first-time buyers, small businesses, or developers with limited capital. Additionally, the requirement for substantial down payments and the rising costs of building materials and labor further exacerbate the financial burden. This financial strain reduces the number of transactions and developments, leading to lower demand for real estate services such as brokerage, consulting, and property management. Consequently, high initial costs limit market expansion and restrict the growth of service providers.

Trends for the Real Estate Services Market

Digital Transformation and PropTech Integration: The real estate services sector is swiftly embracing digital technologies and PropTech innovations to improve efficiency, tran...

Residential Real Estate Market is Segmented by Property Type (Apartments & Condominiums, and Landed Houses & Villas), by Price Band (Affordable, Mid-Market, and Luxury/Super-prime), by Business Model (Sales and Rental), by Mode of Sale (Primary and Secondary), and by Region (North America, South America, Europe, Asia-Pacific, and Middle East & Africa). The Market Forecasts are Provided in Terms of Value (USD).

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 ...

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

Europe Commercial Real Estate Market Size 2025-2029

Europe commercial real estate market size is forecast to increase by USD 91.4 billion at a CAGR of 5.7% between 2024 and 2029. European commercial real estate market is experiencing significant growth, with increasing private investment pouring into the sector. The primary catalyst fueling market growth is the increasing aggregate private investment.This trend is driven by a robust economic environment, favorable demographic shifts, and the ongoing recovery from the COVID-19 pandemic.

Market Size & Forecast

  Market Opportunities: USD 31.78 billion
  Future Opportunities: USD 91.4 billion 
  CAGR : 5.7%  

However, this growth comes with challenges,rising interest rates pose a threat to affordability and profitability, potentially dampening investor enthusiasm and increasing borrowing costs. As a result, companies must navigate this complex landscape by carefully assessing potential investment opportunities, considering alternative financing options, and adapting to changing market conditions. In order to capitalize on the market's potential and mitigate risks, strategic planning and agility will be essential for success.

What will be the size of Europe Commercial Real Estate Market during the forecast period?

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European commercial real estate market continues to evolve, presenting dynamic opportunities across various sectors. Property risk assessment and building inspection reports play crucial roles in mitigating potential hazards, ensuring compliance with safety standards. Property tax appeals and portfolio diversification help investors minimize risk and maximize returns. Facility management services, property valuation techniques, and property value metrics enable effective asset management. Data-driven investment strategies, including transaction closing costs, space planning solutions, and development approval processes, facilitate informed decision-making. Capital expenditure planning, portfolio optimization, operating expense control, lease contract review, energy consumption audits, and commercial lease terms are essential for maintaining profitability.
For instance, the adoption of energy management systems in commercial buildings has led to a 10% average reduction in energy consumption, contributing to cost savings and environmental sustainability. Commercial real estate market is expected to grow by 3% annually, driven by these evolving trends and the ongoing demand for efficient, sustainable, and compliant properties.

How is this Europe Commercial Real Estate Market segmented?

Europe commercial real estate market market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029,for the following segments.

Type

  Rental
  Lease
  Sales


End-user

  Offices
  Retail
  Leisure
  Others


End-User

  Corporate
  Investment
  Government


Location

  Urban
  Suburban


Geography

  Europe

    France
    Germany
    Italy
    UK

By Type Insights

The rental segment is estimated to witness significant growth during the forecast period. European commercial real estate market is characterized by dynamic lease renewal negotiations, construction project management, and insurance considerations for green building certification and property refurbishment costs. Zoning regulations compliance and vacancy loss calculations are crucial elements in property acquisition strategy, while property tax optimization and valuation models inform building lifecycle cost analyses. Property management software and tenant occupancy rates are essential for portfolio performance metrics, and market rent surveys guide tenant retention strategies. Portfolio risk management, building code compliance, property data analytics, and rental income projections are integral to asset management strategies. Due diligence processes and capitalization rate analysis are vital during urban planning regulations and space utilization analysis.

In the rental segment, growth is expected to reach over 5% annually, with office rents in the UK, Benelux markets, and peripheral Europe experiencing the highest quarterly growth of 1.8%. However, investment markets remain cautious due to economic uncertainties and rising inflation and finance rates, despite the leasing market's strength and increasing rents. For instance, rental income in the office sector in Paris grew by 3.5% in 2021, reaching €1,122 per square meter per year.

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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.

European commercial real estate market continues to be a significant global investment destina

Existing Home Sales in the United States increased to 4100 Thousand in October from 4050 Thousand in September of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Single Family Home Prices in the United States increased to 415200 USD in October from 412300 USD in September of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

India Real Estate Market is projected to reach USD 1044.43 Billion by 2030 at a CAGR of 16.6% from 2025-2030

The India Real Estate Market Report is Segmented by Business Model (Sales and Rental), by Property Type (Residential and Commercial), by End-User (Individuals/Households, Corporates & SMEs and Others), and by City (Mumbai Metropolitan Region, Delhi NCR, Pune, Bengaluru, Hyderabad, Chennai, Kolkata, Ahmedabad, and the Rest of India). The Market Forecasts are Provided in Terms of Value (USD).

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

Housing Index in Saudi Arabia decreased to 103.90 points in the third quarter of 2025 from 105 points in the second quarter of 2025. This dataset provides - Saudi Arabia Housing Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q2 2025 about sales, housing, and USA.

This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.

Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].

Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].

Real Estate Market in Saudi Arabia is Segmented by Residential Estate (Apartments, Villas) and Commercial Real Estate (Offices, Retail, Hospitality, Others). The Report Offers Market Size and Forecasts for the Real Estate Market in Saudi Arabia in Value (USD) for the Above Segments.