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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.
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.
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.
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.
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A housing market prediction that many experts agree on is that it will be a seller’s market. Home prices are expected to rise for some time due to increased demand and limited supply. Millennials are at the age to start investing in the real estate market for the first time. Hence, the demand for residential and commercial projects is rising with every passing day. The future of real estate will witness a rise in demand and limited supply, resulting in it being a seller’s market.
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TwitterHouse 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.
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TwitterAfter 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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TwitterThe quarterly pulse monitor expects the Dutch house prices to climb by *** percent in 2025 due to the decline in purchasing power, higher cost of borrowing and worsening economic conditions. The price of Dutch residential property in 2025 was approximately ******* euros. These developments came on top of other issues that were already prevalent in the Dutch housing market, such as the discussion about nitrogen and its effect on housing construction. The effects of nitrogen on the price of a house At the end of 2019, months before the coronavirus, there was already a lot of uncertainty whether their predictions would hold true. This had to do with the so-called “nitrogen decision” (in Dutch: stikstofbesluit) in May 2019. Simply put, a Dutch advisory body found that the domestic policy for nitrogen emission (formally known as Programmatische Aanpak Stikstof or Programmatic Approach Nitrogen) went against European rules. As of August 2019, a sizable share of the Dutch population was not familiar with this nitrogen policy. However, the advisory body’s decision led to an immediate stop to all construction in the country (amongst other things). By the end of 2019, this stop was still in place. For 2020, newly to be constructed houses have to comply to new rules regarding nitrogen emission. This puts new pressure on a housing market that already had to keep with increasing demand. How about the housing market in Amsterdam? In the year 2022, Amsterdam ranked as the most expensive city in the Netherlands to acquire an apartment, with an average price per square meter that was ***** euros more expensive than in Utrecht. Amsterdam was also well above the average rents found in other cities. A house in Amsterdam had a rent of approximately ** euros per square meter in 2023, whereas rents in Rotterdam cost roughly ** euros per square meter. It should be noted, however, that rent changes in the Dutch capital are significantly lower than those found in Rotterdam and especially Utrecht.
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Housing Index in Sweden increased to 959 points in the third quarter of 2025 from 945 points in the second quarter of 2025. This dataset provides - Sweden House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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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
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Housing Index in the United Kingdom increased to 517.10 points in October from 514.20 points in September of 2025. This dataset provides - United Kingdom House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Housing Index in China remained unchanged at -2.20 percent in October. This dataset provides the latest reported value for - China Newly Built House Prices YoY Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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Key information about House Prices Growth
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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.
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Housing Index in Israel decreased to 595.40 points in September from 597.20 points in August of 2025. This dataset provides - Israel House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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TwitterThe average Canadian house price declined slightly in 2023, after four years of consecutive growth. The average house price stood at ******* Canadian dollars in 2023 and was forecast to reach ******* Canadian dollars by 2026. Home sales on the rise The number of housing units sold is also set to increase over the two-year period. From ******* units sold, the annual number of home sales in the country is expected to rise to ******* in 2025. British Columbia and Ontario have traditionally been housing markets with prices above the Canadian average, and both are set to witness an increase in sales in 2025. How did Canadians feel about the future development of house prices? When it comes to consumer confidence in the performance of the real estate market in the next six months, Canadian consumers in 2024 mostly expected that the market would go up. A slightly lower share of the respondents believed real estate prices would remain the same.
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Housing Index in Netherlands increased to 152.30 points in October from 151.60 points in September of 2025. This dataset provides - Netherlands House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Housing Index in Germany increased to 220.43 points in October from 219.91 points in September of 2025. This dataset provides the latest reported value for - Germany House Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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A dataset comprising the price, address, number of bathrooms, number of bedrooms, city, and province of real estate listings for Canada's top 45 most populous cities, according to the 2021 census.
Variables:
This dataset can be used for basic linear regression problems or for basic exploratory data analysis.
Data is currently representative of prices as of October 29th 2023. Future updates will occur monthly.
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Commercial Real Estate Market Size 2025-2029
The commercial real estate market size is valued to increase USD 427.3 billion, at a CAGR of 4.6% from 2024 to 2029. Growing commercial sector globally will drive the commercial real estate market.
Major Market Trends & Insights
APAC dominated the market and accounted for a 42% growth during the forecast period.
By End-user - Offices segment was valued at USD 476.50 billion in 2023
By Channel - Rental segment accounted for the largest market revenue share in 2023
Market Size & Forecast
Market Opportunities: USD 43.44 billion
Market Future Opportunities: USD 427.30 billion
CAGR : 4.6%
APAC: Largest market in 2023
Market Summary
The market is a dynamic and ever-evolving sector that continues to shape the global business landscape. Core technologies and applications, such as Building Information Modeling (BIM) and Real Estate Information Systems (REIS), are increasingly being adopted to streamline operations and enhance efficiency. According to a recent report, the BIM market in the real estate sector is projected to grow at a steady pace, reaching a market share of 30% by 2025. Service types and product categories, including property management, brokerage, and construction services, are also experiencing significant changes. For instance, the growing trend of remote work and online shopping is driving demand for flexible and adaptable commercial spaces.
Additionally, regulations and policies are evolving to accommodate these changes, with many governments investing in smart city initiatives and green building standards. Despite these opportunities, the market faces challenges such as economic uncertainty, changing demographics, and increasing competition. However, these challenges also present new opportunities for innovation and growth. For instance, the adoption of proptech solutions and the integration of artificial intelligence and machine learning are transforming the way commercial real estate is bought, sold, and managed. Overall, the market is a complex and dynamic ecosystem that requires constant monitoring and adaptation to stay ahead of the curve.
What will be the Size of the Commercial Real Estate Market during the forecast period?
Get Key Insights on Market Forecast (PDF) Request Free Sample
How is the Commercial Real Estate Market Segmented and what are the key trends of market segmentation?
The commercial 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.
End-user
Offices
Retail
Leisure
Others
Channel
Rental
Lease
Sales
Transaction Type
Commercial Leasing
Property Sales
Property Management
Service Type
Brokerage Services
Property Development
Valuation Consulting
Facilities Management
Geography
North America
US
Canada
Europe
France
Germany
Italy
UK
Middle East and Africa
Egypt
KSA
Oman
UAE
APAC
China
India
Japan
South America
Argentina
Brazil
Rest of World (ROW)
By End-user Insights
The offices segment is estimated to witness significant growth during the forecast period.
In the ever-evolving market, the offices segment is experiencing significant growth, driven by shifting work trends and corporate demands. Flexible work arrangements, hybrid models, and technological integration are transforming the need for office space. Businesses prioritize contemporary, adaptable, and technologically advanced workspaces to attract and retain talent. Co-working spaces like Regus and WeWork, which offer flexible office solutions, are gaining popularity. Major corporations, such as Google and Amazon, invest in innovative office designs that foster collaboration and employee satisfaction. According to recent market data, the offices end-user segment is projected to expand by 15% between 2024 and 2028, underscoring the continuous adaptation of workspaces to modern business practices.
Meanwhile, tenant occupancy rates remain a critical concern for commercial property owners. Lease agreement terms, negotiation strategies, and rent collection efficiency are essential factors in maintaining a healthy portfolio. Building lifecycle costs, code compliance, and investment return metrics are other essential considerations for property managers. Environmental impact assessments, construction cost estimating, and property tax appeals are also crucial elements in the market. Property value depreciation, commercial property insurance, and portfolio risk management are essential aspects of property management. Property management software, energy efficiency upgrades, and property tax assessments are key tools for optimizing o
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This dataset consists of scraped data that includes the following information:
House Prices: The dataset contains the prices of houses both for sale and rent, located in metropolitan areas of India. These prices serve as the target variable that can be predicted using the other variables in the dataset. This variable is in exactPrice column
Amenities: The dataset provides information about the amenities or features available for each house. These amenities may include facilities such as parking, swimming pool, gymnasium, garden, etc.
📌 Note :
Please note that for certain houses, information about specific amenities may be missing; in those cases, the value '9' indicates the absence of information. However, it's important to note that the presence or absence of '9' for an amenity does not necessarily imply its actual presence or absence in real life.
The dataset comprises 91 explanatory variables that describe various aspects of the houses in the metropolitan areas of India. By utilizing these variables, one can attempt to predict the final price of houses in these regions.
It's worth mentioning that the dataset has been scraped from relevant website.
This dataset can serve as a valuable resource for tasks such as house price prediction, analyzing the impact of different amenities on house prices, or studying the housing market trends in metropolitan areas of India.
Although this dataset may not be a high-class dataset for high-standard projects. It's a good dataset to start with regression problems and help in learning projects
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Discover the booming global residential real estate market! Our in-depth analysis reveals a $11.14B market in 2025, projected to grow at a 6.07% CAGR through 2033. Learn about key drivers, trends, regional insights, and leading companies shaping this dynamic industry. Get the data-driven insights you need to succeed. Recent developments include: December 2023: The Ashwin Sheth group is planning to expand its residential and commercial portfolio in the MMR (Mumbai Metropolitan Area) region, India., November 2023: Tata Realty and Infrastructure, a wholly-owned subsidiary of Tata Sons, plans to grow its business with more than 50 projects in major cities in India, Sri Lanka and the Maldives. The projects have a development potential of more than 51 million square feet.. Key drivers for this market are: Rapid urbanization, Government initiatives. Potential restraints include: High property prices, Regulatory challenges. Notable trends are: Increased urbanization and homeownership by elderly.
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United Kingdom Residential Real Estate Market is Segmented by Property Type (Apartments and Condominiums, and Villas and Landed Houses), by Price Band (Affordable, Mid-Market and Luxury), by Business Model (Sales and Rental), by Mode of Sale (Primary and Secondary), and by Region (England, Scotland, Wales and Northern Ireland). The Market Forecasts are Provided in Terms of Value (USD)
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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.
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.
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.
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.