Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

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

This dataset comes from Zillow and provides a comprehensive look at U.S. housing market trends from 2018 to May 2024. It includes detailed data on median home values, average days outstanding for property sales, and their impact on reducing prices in several cities. This dataset is ideal for analyzing the correlation between home values, time to market, and price adjustments, offering valuable insights for real estate professionals, economists, and data analysts interested in the dynamics of the U.S. housing market.

About the license, taken from the Zillow website:

“For research and academic projects, we provide the following metrics that have more flexible Terms of Use regarding data storage and manipulation – https://www.zillow.com/research/data/”

The dataset contains year-, quarter- and city-wise data on the Housing Price Index in Indian and among its various cities such as Ahmedabad, Bangalore, Chennai, Delhi, Jaipur, Kanpur, Kochi, Kolkata, Lucknow, Mumbai, etc.

Mexico's housing market demonstrates significant regional price variations, with Mexico City emerging as the most expensive area for residential property in the third quarter of 2025. The capital city's average house price of 3.93 million Mexican pesos far exceeds the national average of 1.86 million pesos, highlighting the stark contrast in property values across the country. This disparity reflects broader economic and demographic trends shaping Mexico's real estate landscape. Sustained growth in housing prices The Mexican housing market has experienced substantial growth over the past decade, with home prices more than doubling since 2010. By the second quarter of 2025, the nominal house price index reached 287 points, representing a 187 percent increase from the baseline year. Even when adjusted for inflation, the real house price index showed a notable 50 percent growth, underscoring the market's resilience and attractiveness to investors. The mortgage market is dominated by three main player types: Infonavit, Fovissste, and commercial banks including Sofomes. In 2023, Infonavit, a scheme by Mexico's National Housing Fund Institute which provides lending to workers in the formal sector, was responsible for the majority of mortgages granted to individuals. Challenges in mortgage lending Despite the overall growth in housing prices, Mexico's mortgage market has faced challenges in recent years. The number of new mortgage loans granted has declined over the past decade, falling by approximately 200,000 loans between 2008 and 2023. This decrease in lending activity may be attributed to various factors, including economic uncertainties and changing consumer preferences. The state of Mexico, which is home to 13 percent of the country's population, likely plays a significant role in shaping these trends given its large demographic influence on the national housing market.

Task Description: Real Estate Price Prediction

This task involves predicting the price of real estate properties based on various features that influence the value of a property. The dataset contains several attributes of real estate properties such as square footage, the number of bedrooms, bathrooms, floors, the year the property was built, whether the property has a garden or pool, the size of the garage, the location score, and the distance from the city center.

The goal is to build a regression model that can predict the Price of a property based on the provided features.

Dataset Columns:

ID: A unique identifier for each property.

Square_Feet: The area of the property in square meters.

Num_Bedrooms: The number of bedrooms in the property.

Num_Bathrooms: The number of bathrooms in the property.

Num_Floors: The number of floors in the property.

Year_Built: The year the property was built.

Has_Garden: Indicates whether the property has a garden (1 for yes, 0 for no).

Has_Pool: Indicates whether the property has a pool (1 for yes, 0 for no).

Garage_Size: The size of the garage in square meters.

Location_Score: A score from 0 to 10 indicating the quality of the neighborhood (higher scores indicate better neighborhoods).

Distance_to_Center: The distance from the property to the city center in kilometers.

Price: The target variable that represents the price of the property. This is the value we aim to predict.

Objective: The goal of this task is to develop a regression model that predicts the Price of a real estate property using the other features as inputs. The model should be able to learn the relationship between these features and the price, providing an accurate prediction for unseen data.

Real Estate / Housing Dataset

This dataset is web scrapped from a real estate website, collecting all the necessary infos on the resale and new properties. It has around 14000+ rows of data having properties from various Indian cities like Chennai, Mumbai, Bangalore, Delhi, Pune, Kolkata and Hyderabad. Columns:

Name: Property Name, Property Title: Property Ad Title, Price: Property Price Location: Property Located Locality and Region Total Area: Total SQFT of the property Price Per SQFT: Price of Per SQFT of the property Description: Small paragraph about the property Baths: Number of baths in the property Balcony: Whether the Property has balcony or not

The average price of detached and duplex houses in the biggest cities in Germany varied between approximately ***** euros and 10,000 euros per square meter in 2024. Housing was most expensive in Munich, where the square meter price of houses amounted to ***** euros. Conversely, Berlin was most affordable, with the square meter price at ***** euros. How have German house prices evolved? House prices maintained an upward trend for more than a decade, with 2020 and 2021 experiencing exceptionally high growth rates. In 2021, the nominal year-on-year change exceeded 10 percent. Nevertheless, the second half of 2022 saw the market slowing, with the annual percentage change turning negative for the first time in 12 years. Another way to examine the price growth is through the house price index, which uses 2015 as a base. At its peak in 2022, the German house price index measured about *** percent, which means that a house bought in 2015 would have appreciated by ** percent. Is housing affordable in Germany? Housing affordability depends greatly on income: High-income areas often tend to have more expensive housing, which does not necessarily make them unaffordable. The house price to income index measures the development of the cost of housing relative to income. In the first quarter of 2024, the index value stood at ***, meaning that since 2015, house price growth has outpaced income growth by about ** percent. Compared with the average for the euro area, this value was lower.

Graph and download economic data for All-Transactions House Price Index for Baltimore city, MD (ATNHPIUS24510A) from 1975 to 2024 about Baltimore City, MD; Baltimore; MD; HPI; housing; price index; indexes; price; and USA.

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.

This is the dataset of average housing prices in Ho Chi Minh City, Vietnam.

Data for districts 1 to 9.

Data collected from 2017 to 2022

Data in the form of Time Series Data for predicting house prices, holidays and weekends is not included in this dataset.

The Date column represents the transaction date Columns from District 1 to District 9 show the average transaction value per square meter.

With this data, data scientists can: - Compare house prices between districts by year - Forecast house prices in the future, can use singular spectrum analysis to analyze - Evaluate model quality with RMSE or equivalent measures

Update: HousePricingHCM_v2.csv is including holidays and weekends, We use replacing missing data by average transactions of the days before and after the holiday to fill in the missing data

Dataset Description: Hyderabad City House Prices

Overview

The Hyderabad City House Prices dataset is a detailed collection of real estate data for residential properties across various localities in Hyderabad. This dataset is aimed at real estate analysts, data scientists, urban planners, and researchers who are interested in studying the housing market, price trends, and neighborhood dynamics within Hyderabad, one of India's rapidly growing metropolitan cities.

Features

The dataset includes the following features:

1. Title: The headline or main title of the property listing.
2. Location: Specific address or locality details within Hyderabad.
3. Price (L): The listed price of the property in Indian Lakhs.
4. Rate per Sqft: The cost per square foot of the property.
5. Area in Sqft: The total area of the property in square feet.
6. Building Status: The construction status of the property (e.g., Under Construction, Ready to Move).

Usage

This dataset can be utilized for various purposes, including: - Market Analysis: Understanding pricing trends, supply and demand, and market conditions in different localities of Hyderabad. - Price Prediction Models: Developing machine learning models to predict property prices based on the given features. - Investment Analysis: Identifying potential investment opportunities by analyzing location, property type, and price data. - Urban Planning: Assisting urban planners in understanding housing distribution and development trends across the city.

Data Collection

The data has been scraped from popular real estate websites such as Magicbricks, 99acres, and Housing.com using the Scrapy framework. The data was collected in [insert month/year] and represents a snapshot of the real estate market in Hyderabad at that time.

Sample Data

Contact

For more information or to access the dataset, please contact [Your Name] at [Your Email Address].

This dataset provides valuable insights into Hyderabad's diverse real estate market, helping stakeholders make informed decisions based on accurate and up-to-date data.

Graph and download economic data for All-Transactions House Price Index for San Francisco-San Mateo-Redwood City, CA (MSAD) (ATNHPIUS41884Q) from Q3 1975 to Q3 2025 about San Francisco, appraisers, CA, HPI, housing, price index, indexes, price, and USA.

Montevideo, Uruguay's capital, leads Latin American cities with the highest apartment sale prices in 2025, averaging ***** U.S. dollars per square meter. This figure surpasses other major metropolitan areas like Mexico City and Buenos Aires, highlighting significant disparities in real estate markets across the region. The data underscores the varying economic conditions and housing demand in different Latin American urban centers. Regional housing market trends While Montevideo tops the list for apartment prices, other countries in Latin America have experienced notable changes in their housing markets. Chile, for instance, saw the most substantial increase in house prices since 2010, with its nominal house price index surpassing *** points in early 2025. However, when adjusted for inflation, Mexico showed the highest inflation-adjusted percentage increase in house prices, growing by nearly *** percent in the first quarter of 2025, contrasting with a global decline of one percent. Home financing in Mexico The methods of home financing vary across Latin America. A breakdown of homeownership by financing method in Mexico reveals that about two-thirds of owner-occupied housing units were financed through personal resources in 2022. Nevertheless, government-backed loans such as Infonavit (Mexico’s National Housing Fund Institute), Fovissste (Housing Fund of the Institute for Social Security and Services for State Workers), and Fonhapo (National Fund for Popular Housing), play an important role for homebuyers, with just over ** percent of home purchases relying on such finance. Bank credit, which offers mortgage loans with interest rates ranging between **** and ** percent, appeared as a less popular option.

In 2023, the average price for residential real estate in Shenzhen ****** yuan per square meter. This was the highest price among all major cities in China, with the average price across the country amounting to ****** yuan per square meter. A pillar of the Chinese economy China gradually abolished its welfare housing allocation system and liberalized its real estate market in the 1990s. In 2003, the government declared the real estate sector as one of the pillars of the Chinese economy. Thanks to the country's rapid economic development and urbanization, China's real estate market expanded significantly in the last two decades, with the sector accounting for about seven percent of China's GDP in 2022. Unaffordable in major urban centers While the real estate industry greatly contributed to the growth of China's economy, the housing market boom also created social issues and financial risks. In comparison to household income, property prices in major cities, most notably Shanghai, Beijing, Guangzhou, and Shenzhen, are extraordinarily expensive for average citizens. Soaring housing prices have also led to a rapid division of wealth between homeowners and renters. At the same time, debt problems created by the rapid expansion of real estate companies and the high levels of debt accumulated by Chinese citizens have created serious potential hazards for China's financial system.

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.

Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.

Key Features:

• Comprehensive Property Data: Includes essential details such as listing prices, property types, square footage, and the number of bedrooms and bathrooms.
• Geographic Coverage: Encompasses a wide range of U.S. states and cities, providing a broad view of the national real estate market.
• Historical Trends: Analyze past market data to understand price movements, regional differences, and market trends over time.
• Geo-Location Details: Enables spatial analysis and mapping by including precise geographical coordinates of properties.

Who Can Benefit From This Dataset:

• Real Estate Investors: Identify lucrative opportunities by analyzing property values, market trends, and regional price variations.
• Market Analysts: Gain a deeper understanding of the U.S. housing market dynamics to inform research and reporting.
• Data Scientists and Researchers: Leverage detailed real estate data for modeling, urban studies, or economic analysis.
• Financial Analysts: Utilize the dataset for financial modeling, helping to predict market behavior and assess investment risks.

Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data

Dataset Overview

This dataset comprises detailed real estate listings scraped from Realtor.com, providing a snapshot of various property types across Chicago. It includes 2,000 entries with information on property characteristics such as type, size, age, price, and features. This dataset was ethically collected using an API provided by Apify, ensuring all data scraping adhered to ethical standards.

Data Science Applications

This dataset is ideal for a variety of data science applications, including but not limited to: - Predictive Modeling: Forecast property prices based on various features like location, size, and age. - Market Analysis: Understand trends in real estate, including the types of properties being sold, pricing trends, and the influence of property features on market value. - Natural Language Processing: Analyze the textual descriptions provided for each listing to extract additional features or perform sentiment analysis. - Anomaly Detection: Identify unusual listings or potential outliers in the data, which could indicate errors in data collection or unique investment opportunities.

Column Descriptors

1. type: The type of property (e.g., single-family home, condo).
2. text: A textual description of the property.
3. year_built: The year in which the property was constructed.
4. beds: The number of bedrooms.
5. baths: Total number of bathrooms (including full and half).
6. baths_full: Number of full bathrooms.
7. baths_half: Number of half bathrooms.
8. garage: Garage capacity (number of cars).
9. lot_sqft: Size of the lot in square feet.
10. sqft: Living area size in square feet.
11. stories: Number of stories/floors in the property.
12. lastSoldPrice: The price at which the property was last sold.
13. soldOn: The date on which the property was last sold.
14. listPrice: The listing price of the property at the time of data collection.
15. status: The current status of the listing (e.g., for sale, sold).

Ethically Mined Data

This dataset was responsibly and ethically mined, adhering to all legal standards of data collection. The use of Apify's API ensures that the data collection process respects privacy and the platform's terms of service.

Acknowledgements

We thank Realtor.com for maintaining a comprehensive and accessible database, and Apify for providing the tools necessary for ethical data scraping. Their contributions have been invaluable in the creation of this dataset. Credits to Dall E3 for thumbnail image.

Usage Policy

This dataset is provided for non-commercial and educational purposes only. Users are encouraged to use this data to enhance learning, contribute to academic or personal projects, and develop skills in data science and real estate market analysis.

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.