Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

Problem Statement 👉 Download the case studies here Investors and buyers in the real estate market faced challenges in accurately assessing property values and market trends. Traditional valuation methods were time-consuming and lacked precision, making it difficult to make informed investment decisions. A real estate firm sought a predictive analytics solution to provide accurate property price forecasts and market insights. Challenge Developing a real estate price prediction system involved… See the full description on the dataset page: https://huggingface.co/datasets/globosetechnology12/Real-Estate-Price-Prediction.

House prices in Spain are forecast to fall in 2024, after increasing by 1.2 percent in 2023. Nevertheless, prices are expected to pick up in 2025, with an increase of one percent. The Portuguese housing market, on the other hand, grew by 5.5 percent in 2023, but was forecast to contract in the next two years.

The US residential real estate market, a cornerstone of the American economy, is projected to experience steady growth over the next decade. While the provided CAGR of 2.04% is a modest figure, it reflects a market maturing after a period of significant expansion. This sustained growth is driven by several key factors. Firstly, population growth and urbanization continue to fuel demand for housing, particularly in densely populated areas and emerging suburban markets. Secondly, low interest rates (historically, though this can fluctuate) have made mortgages more accessible, stimulating buyer activity. Thirdly, a robust construction sector, though facing challenges in material costs and labor shortages, is gradually increasing the housing supply, mitigating some of the upward pressure on prices. However, challenges remain. Rising inflation and potential interest rate hikes pose a risk to affordability, potentially dampening demand. Furthermore, the ongoing evolution of remote work is reshaping residential preferences, with a shift toward larger homes in suburban or exurban locations. This trend impacts the relative demand for various property types, potentially increasing the appeal of landed houses and villas compared to apartments and condominiums in certain regions. The segmentation of the market into apartments/condominiums and landed houses/villas provides crucial insights into consumer preferences and investment strategies. High-density urban areas will continue to see strong demand for apartments and condos, while suburban and rural areas are likely to experience a greater increase in landed property sales. Major players like Simon Property Group, Mill Creek Residential, and others are strategically adapting to these trends, focusing on both development and management across various property types and geographic locations. Analyzing regional data within the US (e.g., comparing growth in the Northeast versus the Southwest) will highlight market nuances and potential investment opportunities. While the global data provided is valuable for understanding broader market forces, focusing the analysis on the US market allows for a more granular understanding of the specific drivers, trends, and challenges within this significant segment of the real estate sector. The forecast period (2025-2033) suggests continued, albeit measured, expansion. Recent developments include: May 2022: Resource REIT Inc. completed the sale of all of its outstanding shares of common stock to Blackstone Real Estate Income Trust Inc. for USD 14.75 per share in an all-cash deal valued at USD 3.7 billion, including the assumption of the REIT's debt., February 2022: The largest owner of commercial real estate in the world and private equity company Blackstone is growing its portfolio of residential rentals and commercial properties in the United States. The company revealed that it would shell out about USD 6 billion to buy Preferred Apartment Communities, an Atlanta-based real estate investment trust that owns 44 multifamily communities and roughly 12,000 homes in the Southeast, mostly in Atlanta, Nashville, Charlotte, North Carolina, and the Florida cities of Jacksonville, Orlando, and Tampa.. Key drivers for this market are: Investment Plan Towards Urban Rail Development. Potential restraints include: Italy’s Fragmented Approach to Tenders. Notable trends are: Existing Home Sales Witnessing Strong Growth.

Single Family Home Prices in the United States increased to 422800 USD in May from 414000 USD in April of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Dec 2024, following an increase of 5.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Dec 2024, with an average growth rate of 5.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

CEIC calculates House Prices Growth from quarterly House Price Index. Federal Housing Finance Agency provides House Price Index with base January 1991=100.

Just as in many other countries, the housing market in the UK grew substantially during the coronavirus pandemic, fueled by robust demand and low borrowing costs. Nevertheless, high inflation and the increase in mortgage rates has led to house price growth slowing down. According to the forecast, 2024 is expected to see house prices decrease by ***** percent. Between 2024 and 2028, the average house price growth is projected at *** percent. A contraction after a period of continuous growth In June 2022, the UK's house price index exceeded *** index points, meaning that since 2015 which was the base year for the index, house prices had increased by ** percent. In just two years, between 2020 and 2022, the index surged by ** index points. As the market stood in December 2023, 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 years. Growth is forecast to be stronger in 2024 and slow down in the period between 2025 and 2028. The rental market in London is expected to follow a similar trend, with Central London slightly outperforming Greater London.

Housing Index in Saudi Arabia increased to 104.90 points in the first quarter of 2025 from 104.20 points in the fourth quarter of 2024. This dataset provides - Saudi Arabia Housing Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

Once there lived an atrocious King with the finest sword a man could bear at that time. Alzar, the record keeper, lost papers that had prices for houses in the kingdom. As he trembled with mortal fear, he went to Elric the sorcerer seeking for help. "King is very specific and rather precise with numbers!" exclaimed Elric seeing the records.

Fortunately, some records were still present, but they were too scattered! King has commanded Alzar to present to him the complete record with price (in golden grains) of each house against its unique ID. Now Elric invites you through time travel to help poor Alzar lest he should lose his life to sword. Alzar will present to you the information that he has. 1) Each paper is specific to one builder family with details of houses that they built. 2) Alzar has sorted for you the house details with builder family name and ,,Not Known" where builder's information was lost. "But certainly, there are only ten builder families" he remarks.

"Careful! Black Magic has scraped off some more data from the records" says Elric as you begin to think upon...

Inspiration

• Analyse the data and find out what factors affects the housing prices?
• Create machine learning model to estimate housing price?

Housing Index in Germany increased to 218.58 points in May from 217.43 points in April 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.

The quarterly pulse monitor expects the Dutch house prices to fall by five percent in 2023 due to the decline in purchasing power, higher cost of borrowing and worsening economic conditions. The price of Dutch residential property in 2022 was approximately 489,000 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 2,000 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 26 euros per square meter in 2023, whereas rents in Rotterdam cost roughly 18 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.

Nigerian House Price Dataset This dataset provides a comprehensive look at housing prices across various towns and states in Nigeria. It contains key features that influence property values. The variable in the dataset are:

bedrooms: Number of bedrooms in the property bathrooms: Number of bathrooms available toilets: Number of toilets available parking_space: Availability of parking spaces (measured in number of cars accommodated) title: This variable represent the house type town: The town where the property is located state: The state in Nigeria where the property is located ****price:**** The listed price of the property in Nigerian Naira (₦)

This dataset is valuable for analyzing real estate trends, predicting housing prices, and understanding the factors that drive property valuation in Nigeria. It offers insights into the housing market across different regions, making it a useful resource for data scientists, analysts, and real estate professionals.

House prices in Norway fell by 1.4 percent and, according to the forecast, are expected to continue to fall until 2024. In 2023, properties were forecast to experience a decline in prices of 12 percent. In 2025, growth is projected to recover, rising to five percent.

Housing Index in China decreased by 3.50 percent in May from -4 percent in April of 2025. 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.

Key information about House Prices Growth

• Kazakhstan house prices grew 5.9% YoY in Feb 2025, following an increase of 6.7% YoY in the previous month.
• YoY growth data is updated monthly, available from Nov 2013 to Feb 2025, with an average growth rate of 7.4%.
• House price data reached an all-time high of 75.6% in May 2022 and a record low of -4.2% in Jan 2024.

CEIC calculates House Prices Growth from Average Price of Existing Dwellings per Square Meter. The Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan provides Average Price of Existing Dwellings per Square Meter in local currency.

Steps Throughout the Full Project:

1- Initial Data Exploration: Introduction to the dataset and its variables. Identification of potential relationships between variables. Examination of data quality issues such as missing values and outliers.

2- Correlation Analysis: Utilization of correlation matrices and heatmaps to identify relationships between variables. Focus on variables highly correlated with the target variable, 'SalePrice'.

3- Handling Missing Data: Analysis of missing data prevalence and patterns. Deletion of variables with high percentages of missing data. Treatment of missing observations for remaining variables based on their importance.

4- Dealing with Outliers: Identification and handling of outliers using data visualization and statistical methods. Removal of outliers that significantly deviate from the overall pattern.

5- Testing Statistical Assumptions: Assessment of normality, homoscedasticity, linearity, and absence of correlated errors. Application of data transformations to meet statistical assumptions.

6- Conversion of Categorical Variables: Conversion of categorical variables into dummy variables to prepare for modeling.

Summary: The project undertook a comprehensive analysis of housing price data, encompassing data exploration, correlation analysis, missing data handling, outlier detection, and testing of statistical assumptions. Through visualization and statistical methods, the project identified key relationships between variables and prepared the data for predictive modeling.

Recommendations: Further exploration of advanced modeling techniques such as regularized linear regression and ensemble methods for predicting housing prices. Consideration of additional variables or feature engineering to improve model performance. Evaluation of model performance using cross-validation and other validation techniques. Documentation and communication of findings and recommendations for stakeholders or further research.

A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price. The company is looking at prospective properties to buy to enter the market. You are required to build a regression model using regularization in order to predict the actual value of the prospective properties and decide whether to invest in them or not. The company wants to know the following things about the prospective properties: 1) Which variables are significant in predicting the price of a house, and 2)How well those variables describe the price of a house.

Context

Insurance companies collect multiple features of a House and select which houses can be insured and what amount they can charge the Premium from them. So here I have collected data from multiple insurance companies in USA where features with house prices are given

Content

This data set has many property details from address to their location co ordinates nad many other features, use them to predict the House price

Inspiration

Multiple regression datasets have been published every one unique in their own way, Use of location coordinates and some other co-ordinates are new here.

Housing Index in Portugal increased to 235.68 points in the fourth quarter of 2024 from 228.89 points in the third quarter of 2024. This dataset provides - Portugal House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

We construct daily house price indices for 10 major US metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the methodology of the popular monthly Case-Shiller house price indices. Our new daily house price indices exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity of the corresponding daily returns. A relatively simple multivariate time series model for the daily house price index returns, explicitly allowing for commonalities across cities and GARCH effects, produces forecasts of longer-run monthly house price changes that are superior to various alternative forecast procedures based on lower-frequency data.