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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.

Dataset

This dataset was created by hassan mohamed

Released under MIT

Contents

House Price Index YoY in the United States increased to 2.60 percent in July from 2.30 percent in June of 2026. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

Graph and download economic data for All-Transactions House Price Index for Phoenix-Mesa-Chandler, AZ (MSA) (ATNHPIUS38060Q) from Q2 1977 to Q2 2026 about Phoenix, AZ, appraisers, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for All-Transactions House Price Index for Idaho (IDSTHPI) from Q1 1975 to Q2 2026 about ID, appraisers, HPI, housing, price index, indexes, price, and USA.

Analyzing Property Values: Insights into Housing Market Trends and Pricing Dynam

Dataset

This dataset was created by Kat Hernandez

Contents

Graph and download economic data for All-Transactions House Price Index for Colorado Springs, CO (MSA) (ATNHPIUS17820Q) from Q1 1979 to Q2 2026 about Colorado Springs, CO, appraisers, HPI, housing, price index, indexes, price, and USA.

This dataset provides values for AVERAGE HOUSE PRICES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Quarterly historical dataset of the FHFA All-Transactions House Price Index for the United States, adjusted for CPI inflation. While nominal house prices tend to rise over time with the general price level, the real housing price index isolates the inflation-adjusted appreciation or depreciation of residential real estate. The series is used to date housing cycles, to compare affordability across decades, and to model real wealth effects.

Interactive monthly chart and 17 years of historical data from 2009 to 2026.

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, parking etc. Regression Techniques used to predict price of the house

Dataset

This dataset was created by Hassan Raof

Released under Apache 2.0

Contents

Housing Index in Norway decreased to 367.05 points in September from 369.79 points in August of 2026. This dataset provides the latest reported value for - Norway House Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

California Housing Prices: 1990 Census Data

This dataset contains information derived from the 1990 census in California, focusing on housing characteristics and median house values at the block group level. Each row in the dataset represents a block group, which is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a cluster of typically 600 to 3,000 people).

About the Features:

The dataset includes the following features:

1. longitude: Numerical. Represents the longitudinal coordinate of the block group. A higher value indicates a location farther west in California.

2. latitude: Numerical. Represents the latitudinal coordinate of the block group. A higher value indicates a location farther north in California.

3. housingMedianAge: Numerical. Represents the median age of the houses within a block group, measured in years. A lower value indicates newer buildings.

4. totalRooms: Numerical. Represents the total number of rooms within all the housing units in the block group.

5. totalBedrooms: Numerical. Represents the total number of bedrooms within all the housing units in the block group.

6. population: Numerical. Represents the total number of people residing within the block group.

7. households: Numerical. Represents the total number of households within the block group. A household is defined as a group of people residing within a home unit.

8. medianIncome: Numerical. Represents the median income for households within the block group, measured in tens of thousands of US Dollars. For example, a value of 5.0 corresponds to an income of $50,000.

9. medianHouseValue: Target Variable. Numerical. Represents the median house value for households within the block group, measured in US Dollars. This is the variable you would typically aim to predict in a regression task.

10. oceanProximity: Categorical. Indicates the location of the house with respect to the ocean or sea. Possible values might include:

    • NEAR BAY
    • <1H OCEAN (less than 1 hour to the ocean)
    • INLAND
    • NEAR OCEAN
    • ISLAND

Potential Uses:

This dataset is well-suited for various machine learning tasks, including:

• Regression: Predicting the medianHouseValue based on the other features.
• Exploratory Data Analysis (EDA): Understanding the relationships between different housing characteristics and their geographical distribution.
• Feature Engineering: Creating new features from the existing ones to potentially improve prediction accuracy.

Considerations:

• The data is from the 1990 census, so housing market conditions and prices have likely changed significantly since then.
• The oceanProximity feature is categorical and will need to be handled appropriately (e.g., through one-hot encoding or other categorical encoding techniques) for many machine learning models.
• Missing values might exist in some of the numerical features and would need to be addressed during data preprocessing.

This dataset provides a valuable snapshot of housing characteristics in California from 1990 and serves as a good starting point for practicing regression and data analysis techniques.

Have home prices outpaced what a typical household earns? S&P CoreLogic Case-Shiller U.S. National Home Price Index (CSUSHPISA, seasonally adjusted, nominal, index Jan 2000 = 100) versus median household income (MEHOINUSA646N, current dollars), both rebased to 100 at their shared start. Both series are nominal so the comparison is like-for-like: a house-price line above the income line means housing costs have grown faster than typical household income over the window. House prices are monthly; household income is annual, so income is shown as a step at each year and the within-year comparison is approximate.

The quarterly Nationwide house price index for all houses in the United Kingdom (UK) exceeded 14,000 index points in the second quarter of 2025. The index shows the development of housing prices, with 1952 used as a baseline year. An index value of 14,425.6 implies a price increase of 14,000 percent between 1952 and 2025.

Graph and download economic data for All-Transactions House Price Index for Tacoma-Lakewood, WA (MSAD) (ATNHPIUS45104Q) from Q1 1977 to Q2 2026 about Tacoma, WA, appraisers, HPI, housing, price index, indexes, price, and USA.

Home prices in the U.S. reach new heights The American housing market continues to show remarkable resilience, with the S&P/Case Shiller U.S. National Home Price Index reaching an all-time high of 336.66 in June 2026. This figure represents a significant increase from the index value of 166.23 recorded in January 2015, highlighting the substantial growth in home prices over the past decade. The S&P Case Shiller National Home Price Index is based on the prices of single-family homes and is the leading indicator of the American housing market and one of the indicators of the state of the broader economy. The S&P Case Shiller National Home Price Index series also includes S&P/Case Shiller 20-City Composite Home Price Index and S&P/Case Shiller 10-City Composite Home Price Index – measuring the home price changes in the major U.S. metropolitan areas, as well as twenty composite indices for the leading U.S. cities. Market fluctuations and recovery Despite the overall upward trend, the housing market has experienced some fluctuations in recent years. During the housing boom in 2021, the number of existing home sales reached the highest level since 2006. However, transaction volumes quickly plummeted, as the soaring interest rates and out-of-reach prices led to housing sentiment deteriorating. Factors influencing home prices Several factors have contributed to the rise in home prices, including a chronic supply shortage, the gradual decline in interest rates, and the spike in demand during the COVID-19 pandemic. During the subprime mortgage crisis (2007-2010), the construction of new homes declined dramatically. Although it has gradually increased since then, the number of new building permits, home starts, and completions is still shy from the levels before the crisis. With demand outweighing supply, competition for homes can be fierce, leading to bidding wars and soaring prices. The supply of existing homes is further constrained, as homeowners are less likely to sell and move homes due to the worsened lending conditions.

Dataset

This dataset was created by Amisha0528

Released under Apache 2.0

Contents