https://raw.githubusercontent.com/Masterx-AI/Project_Housing_Price_Prediction_/main/hs.jpg" alt="">

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.

The housing price dataset provides a comprehensive collection of property listings, encompassing various attributes such as the number of bedrooms, bathrooms, living area size, lot size, and location details. This dataset is invaluable for a wide range of data analysis and machine learning applications. For instance, it can be utilized in predictive modeling to forecast property prices based on features such as location, amenities, and condition. Additionally, it can aid in identifying trends and patterns in the real estate market, assisting investors, real estate agents, and policymakers in making informed decisions. Moreover, the dataset can serve as a foundation for developing recommendation systems for homebuyers, guiding them towards properties that align with their preferences and requirements. Overall, the housing price dataset offers a wealth of insights and opportunities for leveraging data-driven approaches to understand and navigate the housing market effectively.

This dataset contains the US residential house prices. Data comes from S&P (Standard and Poors) Case-Shiller data and includes both the national index and the indices for 20 metropolitan regions. The indices are created using a repeat-sales methodology.

Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q4 2024 about appraisers, HPI, housing, price index, indexes, price, and USA.

ttd22/house-price dataset hosted on Hugging Face and contributed by the HF Datasets community

House Price Index YoY in the United States remained unchanged at 4.80 percent in January. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q3 2024 about residential, HPI, housing, real, price index, indexes, price, and USA.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Sep 2024, following an increase of 6.2% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2024, with an average growth rate of 5.5%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

Annual house price data based on a sub-sample of the Regulated Mortgage Survey.

The financial crisis in 2008 led to steep declines in the hose price in France, followed by some large increases. In 2022, the French property market contracted again, with the inflation-adjusted home price declining by 1.28 percent in the fourth quarter of the year. Despite home prices continuing to increase in real terms, in the second quarter of 2022, the inflation-adjusted price change reached a negative 4.28 percent.

Average House Prices in the United States increased to 510000 USD in January from 509700 USD in December of 2024. This dataset includes a chart with historical data for the United States New Home Average Sales Price.

Global house prices experienced a significant shift in 2022, with advanced economies seeing a notable decline after a prolonged period of growth. The real house price index (adjusted for inflation) for advanced economies peaked at nearly 140 index points in early 2022 before falling to around 132 points by the first quarter of 2024. This represents a reversal of the upward trend that had characterized the housing market for roughly a decade. Conversely, real house prices in emerging economies resumed growing, after a brief correction in the second half of 2022. What is behind the slowdown? Inflation and slow economic growth have been the primary drivers for the cooling of the housing market. Secondly, the growing gap between incomes and house prices since 2012 has decreased the affordability of homeownership. Last but not least, homebuyers in 2024 faced dramatically higher mortgage interest rates, further contributing to worsening sentiment and declining transactions. Some markets continue to grow While many countries witnessed a deceleration in house price growth in 2022, some markets continued to see substantial increases. Turkey, in particular, stood out with a nominal increase in house prices of over 55 percent in the 1st quarter of 2024. Other countries that recorded a two-digit growth include Russia and the United Arab Emirates. When accounting for inflation, the three countries with the fastest growing residential prices in early 2024 were the United Arab Emirates, Poland, and Bulgaria.

Graph and download economic data for All-Transactions House Price Index for Wisconsin (WISTHPI) from Q1 1975 to Q4 2024 about appraisers, WI, HPI, housing, price index, indexes, price, and USA.

Housing Index in Portugal increased to 228.89 points in the third quarter of 2024 from 220.74 points in the second quarter of 2024. This dataset provides - Portugal House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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].

Median Home Sale Price: All Residential: Burlington, VT data was reported at 319.000 USD th in Jul 2020. This records an increase from the previous number of 318.000 USD th for Jun 2020. Median Home Sale Price: All Residential: Burlington, VT data is updated monthly, averaging 260.500 USD th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 319.000 USD th in Jul 2020 and a record low of 204.000 USD th in Feb 2012. Median Home Sale Price: All Residential: Burlington, VT data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB056: Median Home Sale Price: by Metropolitan Areas.

Mexico's housing market demonstrates significant regional price variations, with Mexico City emerging as the most expensive area for residential property in 2024. The capital city's average house price of 3.91 million Mexican pesos far exceeds the national average of 1.73 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 third quarter of 2023, the nominal house price index reached 255.54 points, representing a 146 percent increase from the baseline year. Even when adjusted for inflation, the real house price index showed a notable 40 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.

Housing Index in Colombia increased to 142.37 points in the fourth quarter of 2024 from 139.93 points in the third quarter of 2024. This dataset provides - Colombia House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Median Home Sale Price: All Residential: Connersville, IN data was reported at 179.000 USD th in Jul 2020. This records an increase from the previous number of 112.000 USD th for Jun 2020. Median Home Sale Price: All Residential: Connersville, IN data is updated monthly, averaging 66.000 USD th from Feb 2012 (Median) to Jul 2020, with 95 observations. The data reached an all-time high of 26,500.000 USD th in Jul 2014 and a record low of 10.000 USD th in Sep 2015. Median Home Sale Price: All Residential: Connersville, IN data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB056: Median Home Sale Price: by Metropolitan Areas.

The U.S. housing market has seen significant price growth since 2011, with the median sales price of existing single-family homes reaching a record high of 408,000 U.S. dollars in 2024. This represents a substantial increase of 133,000 over the past five years, highlighting the rapid appreciation of home values across the country. The trend of rising prices can also be observed in the new homes sold. Regional variations and housing shortage While the national median price provides a broad overview, regional differences in home prices are notable. The West remains the most expensive region, with prices twice higher than in the more affordable Midwest. This disparity persists despite efforts to increase housing supply. In 2024, approximately 982,000 building permits for single-family housing units were granted, showing a slight increase from previous years but still well below the 2005 peak of 1.68 million permits. The ongoing housing shortage continues to drive prices upward across all regions. Market dynamics and future outlook The number of existing home sales has plummeted since 2020, reflecting the growing cost of homeownership. Factors such as high home prices, unfavorable economic conditions, and aggressive increases in mortgage rates have contributed to affordability challenges for many potential homebuyers. Despite these challenges, forecasts suggest a potential recovery in the housing market by 2025, though transaction volumes are expected to remain below long-term averages.