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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.

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.

Description:

This dataset contains prices of New York houses, providing valuable insights into the real estate market in the region. It includes information such as broker titles, house types, prices, number of bedrooms and bathrooms, property square footage, addresses, state, administrative and local areas, street names, and geographical coordinates.

Key Features:

• BROKERTITLE: Title of the broker
• TYPE: Type of the house
• PRICE: Price of the house
• BEDS: Number of bedrooms
• BATH: Number of bathrooms
• PROPERTYSQFT: Square footage of the property
• ADDRESS: Full address of the house
• STATE: State of the house
• MAIN_ADDRESS: Main address information
• ADMINISTRATIVE_AREA_LEVEL_2: Administrative area level 2 information
• LOCALITY: Locality information
• SUBLOCALITY: Sublocality information
• STREET_NAME: Street name
• LONG_NAME: Long name
• FORMATTED_ADDRESS: Formatted address
• LATITUDE: Latitude coordinate of the house
• LONGITUDE: Longitude coordinate of the house

Potential Use Cases:

• Price analysis: Analyze the distribution of house prices to understand market trends and identify potential investment opportunities.
• Property size analysis: Explore the relationship between property square footage and prices to assess the value of different-sized houses.
• Location-based analysis: Investigate geographical patterns to identify areas with higher or lower property prices.
• Bedroom and bathroom trends: Analyze the impact of the number of bedrooms and bathrooms on house prices.
• Broker performance analysis: Evaluate the influence of different brokers on the pricing of houses.

If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂 Thank you

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.

This dataset provides a comprehensive overview of new housing price indexes in Canada. The data is sourced from a reliable statistical survey, offering a detailed breakdown of housing prices across different components such as total house and land, house only, and land only. The dataset is structured to include key metrics such as geographical location, price index classification, and specific price values, providing a robust foundation for analyzing housing price dynamics within the country.

Key Features

• Price Index Metrics: The dataset includes price indexes for total house and land, house only, and land only, providing a complete picture of housing price dynamics across different components.
• Geographical Focus: Data is specific to Canada, providing insights into national housing price trends and patterns.
• Unit of Measurement: Information is presented in index units (201612=100), allowing for straightforward analysis and comparison.
• Temporal Precision: The data is time-stamped for January 1981, ensuring relevance and accuracy for temporal analysis.

Potential Uses

• Real Estate Market Analysis: Assist in understanding the housing price dynamics in Canada, which is crucial for real estate market forecasting and planning.
• Investment Decisions: Provide insights into optimal investment strategies for real estate in various regions.
• Economic Policy: Support policymakers in monitoring and ensuring compliance with housing market trends and economic standards.
• Market-Specific Insights: Evaluate the impact of housing price trends on specific regions and potential growth or decline areas.
• Strategic Planning: Inform strategic planning for real estate developers and policymakers by providing a clear snapshot of current housing price levels and trends.

Existing Home Sales in the United States increased to 4100 Thousand in October from 4050 Thousand in September of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - 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

• 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.

Median price paid for residential property in England and Wales, for all property types by lower layer super output area. Annual data..

This dataset uses data provided from Washington State’s Housing Market, a publication of the Washington Center for Real Estate Research (WCRER) at the University of Washington.

Median sales prices represent that price at which half the sales in a county (or the state) took place at higher prices, and half at lower prices. Since WCRER does not receive sales data on individual transactions (only aggregated statistics), the median is determined by the proportion of sales in a given range of prices required to reach the midway point in the distribution. While average prices are not reported, they tend to be 15-20 percent above the median.

Movements in sales prices should not be interpreted as appreciation rates. Prices are influenced by changes in cost and changes in the characteristics of homes actually sold. The table on prices by number of bedrooms provides a better measure of appreciation of types of homes than the overall median, but it is still subject to composition issues (such as square footage of home, quality of finishes and size of lot, among others).

There is a degree of seasonal variation in reported selling prices. Prices tend to hit a seasonal peak in summer, then decline through the winter before turning upward again, but home sales prices are not seasonally adjusted. Users are encouraged to limit price comparisons to the same time period in previous years.

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

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.

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.

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to inform development of the City's new Comprehensive Plan, Rochester 2034, and retained czb, LLC – a firm with national expertise based in Alexandria, VA – to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. Importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long recognized that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/Dictionary: STATEFP10: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state in the 2010 census. New York State is 36. COUNTYFP10: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county in the 2010 census. Monroe County is 055. TRACTCE10: The six-digit number assigned to each census tract in a US county in the 2010 census. BLKGRPCE10: The single-digit number assigned to each block group within a census tract. The number does not indicate ranking or quality, simply the label used to organize the data. GEOID10: A unique geographic identifier based on 2010 Census geography, typically as a concatenation of State FIPS code, County FIPS code, Census tract code, and Block group number. NAMELSAD10: Stands for Name, Legal/Statistical Area Description 2010. A human-readable field for BLKGRPCE10 (Block Groups). MTFCC10: Stands for MAF/TIGER Feature Class Code 2010. For this dataset, G5030 represents the Census Block Group. BLKGRP: The GEOID that identifies a specific block group in each census tract. TYPOLOGYFi: The point system for Block Groups. Lower scores indicate higher levels of demand – including housing values and value appreciation that are above the Rochester average and vulnerabilities to distress that are below average. Higher scores indicate lower levels of demand – including housing values and value appreciation that are below the Rochester average and above presence of distressed or vulnerable properties. Points range from 1.0 to 3.0. For more information on how the points are calculated, view page 16 on the Rochester Citywide Housing Study 2018. Shape_Leng: The built-in geometry field that holds the length of the shape. Shape_Area: The built-in geometry field that holds the area of the shape. Shape_Length: The built-in geometry field that holds the length of the shape. Source: This data comes from the City of Rochester Department of Neighborhood and Business Development.

New Home Sales in the United States increased to 800 Thousand units in August from 664 Thousand units in July of 2025. This dataset provides the latest reported value for - United States New Home Sales - 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

• Mexico house prices grew 8.9% YoY in Sep 2025, following an increase of 8.7% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2006 to Sep 2025, with an average growth rate of 10.2%.
• House price data reached an all-time high of 11.7% in Mar 2023 and a record low of 2.2% in Jun 2010.

Context

The dataset consists of lists of unique objects of popular portals for the sale of real estate in Russia. More than 540 thousand objects. The dataset contains 540000 real estate objects in Russia.

Content

The Russian real estate market has a relatively short history. In the Soviet era, all properties were state-owned; people only had the right to use them with apartments allocated based on one's place of work. As a result, options for moving were fairly limited. However, after the fall of the Soviet Union, the Russian real estate market emerged and Muscovites could privatize and subsequently sell and buy properties for the first time. Today, Russian real estate is booming. It offers many exciting opportunities and high returns for lifestyle and investment. The real estate market has been in a growth phase for several years, which means that you can still find properties at very attractive prices, but with good chances of increasing their value in the future.

Dataset

The dataset has 13 fields. - date - date of publication of the announcement; - time - the time when the ad was published; - geo_lat - Latitude - geo_lon - Longitude - region - Region of Russia. There are 85 subjects in the country in total. - building_type - Facade type. 0 - Other. 1 - Panel. 2 - Monolithic. 3 - Brick. 4 - Blocky. 5 - Wooden - object_type - Apartment type. 1 - Secondary real estate market; 2 - New building; - level - Apartment floor - levels - Number of storeys - rooms - the number of living rooms. If the value is "-1", then it means "studio apartment" - area - the total area of ​​the apartment - kitchen_area - Kitchen area - price - Price. in rubles

Attention.

The dataset may contain erroneous data due to input errors on services, as well as outliers, and so on.

:)

Using this dataset, we offer Kagglers algorithms that use a wide range of functions to predict real estate prices. Competitors will rely on a vast dataset that includes housing data and macroeconomic models. An accurate forecasting model provides more confidence to its clients in a volatile economy.

• Objective: To predict the prices of houses in the City of Melbourne
• Approach: Using Decision Tree and Random Forest https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Ffc6fb7d0bd8e854daf7a6f033937a397%2FPicture1.png?generation=1693489996707941&alt=media" alt="">
• Data Cleaning:
• Date column is shown as a character vector which is converted into a date vector using the library ‘lubridate’
• We create a new column called age to understand the age of the house as it can be a factor in the pricing of the house. We extract the year from column ‘Date’ and subtract it from the column ‘Year Built’
• We remove 11566 records which have missing values
• We drop columns which are not significant such as ‘X’, ‘suburb’, ‘address’, (we have kept zipcode as it serves the purpose in place of suburb and address), ‘type’, ‘method’, ‘SellerG’, ‘date’, ‘Car’, ‘year built’, ‘Council Area’, ‘Region Name’
• We split the data into ‘train’ and ‘test’ in 80/20 ratio using the sample function
• Run libraries ‘rpart’, ‘rpart.plot’, ‘rattle’, ‘RcolorBrewer’
• Run decision tree using the rpart function. ‘Price’ is the dependent variable https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F6065322d19b1376c4a341a4f22933a51%2FPicture2.png?generation=1693490067579017&alt=media" alt="">
• Average price for 5464 houses is $1084349
• Where building area is less than 200.5, the average price for 4582 houses is $931445. Where building area is less than 200.5 & age of the building is less than 67.5 years, the avg price for 3385 houses is $799299.6.
• $4801538 is the Highest average prices of 13 houses where distance is lower than 5.35 & building are is >280.5
  https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F136542b7afb6f03c1890bae9b07dc464%2FDecision%20Tree%20Plot.jpeg?generation=1693490124083168&alt=media" alt="">
• We use the caret package for tuning the parameter and the optimal complexity parameter found is 0.01 with RMSE 445197.9 https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Feb1633df9dd61ba3a51574873b055fd0%2FPicture3.png?generation=1693490163033658&alt=media" alt="">
• We use library (Metrics) to find out the RMSE ($392107), MAPE (0.297) which means an accuracy of 99.70% and MAE ($272015.4)
• Variables ‘postcode’, longitude and building are the most important variables
• Test$Price indicates the actual price and test$predicted indicates the predicted price for particular 6 houses. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F620b1aad968c9aee169d0e7371bf3818%2FPicture4.png?generation=1693490211728176&alt=media" alt="">
• We use the default parameters of random forest on the train data https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fe9a3c3f8776ee055e4a1bb92d782e19c%2FPicture5.png?generation=1693490244695668&alt=media" alt="">
• The below image indicates that ‘Building Area’, ‘Age of the house’ and ‘Distance’ are the most important variables that affect the price of the house. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fc14d6266184db8f30290c528d72b9f6b%2FRandom%20Forest%20Variables%20Importance.jpeg?generation=1693490284920037&alt=media" alt="">
• Based on the default parameters, RMSE is $250426.2, MAPE is 0.147 (accuracy is 99.853%) and MAE is $151657.7
• Error starts to remain constant between 100 to 200 trees and thereafter there is almost minimal reduction. We can choose N tree=200. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F365f9e8587d3a65805330889d22f9e60%2FNtree%20Plot.jpeg?generation=1693490308734539&alt=media" alt="">
• We tune the model and find mtry = 3 has the lowest out of bag error
• We use the caret package and use 5 fold cross validation technique
• RMSE is $252216.10 , MAPE is 0.146 (accuracy is 99.854%) , MAE is $151669.4
• We can conclude that Random Forest give us more accurate results as compared to Decision Tree
• In Random Forest , the default parameters (N tree = 500) give us lower RMSE and MAPE as compared to N tree = 200. So we can proceed with those parameters.

Context

This dataset was actually made to check the correlations between a housing price index and its crime rate. Rise and fall of housing prices can be due to various factors with obvious reasons being the facilities of the house and its neighborhood. Think of a place like Detroit where there are hoodlums and you don't want to end up buying a house in the wrong place. This data set will serve as historical data for crime rate data and this in turn can be used to predict whether the housing price will rise or fall. Rise in housing price will suggest decrease in crime rate over the years and vice versa.

Content

The headers are self explanatory. index_nsa is the housing price non seasonal index.

Acknowledgements

Thank you to my team who helped in achieving this.

Inspiration

https://www.kaggle.com/marshallproject/crime-rates https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis Data was collected from these 2 sources and merged to get the resulting dataset.

VITAL SIGNS INDICATOR Home Prices (EC7)

FULL MEASURE NAME Home Prices

LAST UPDATED August 2019

DESCRIPTION Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE Zillow Median Sale Price (1997-2018) http://www.zillow.com/research/data/

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1997-2018; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Median housing price estimates for the region, counties, cities, and zip code come from analysis of individual home sales by Zillow. The median sale price is the price separating the higher half of the sales from the lower half. In other words, 50 percent of home sales are below or above the median value. Zillow defines all homes as single-family residential, condominium, and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that you own in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums where the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Home sales prices are not reliably available for Houston, because Texas is a non-disclosure state. For more information on non-disclosure states, see: http://www.zillow.com/blog/chronicles-of-data-collection-ii-non-disclosure-states-3783/

Inflation-adjusted data are presented to illustrate how home prices have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself.

Average House Prices in the United States increased to 534100 USD in August from 478200 USD in July of 2025. This dataset includes a chart with historical data for the United States New Home Average Sales Price.