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.

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.

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

Median house prices for California districts derived from the 1990 census.

About Dataset

Context This is the dataset used in the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome.

The data contains information from the 1990 California census. So although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning.

Content The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. Be warned the data aren't cleaned so there are some preprocessing steps required! The columns are as follows, their names are pretty self-explanatory: - longitude - latitude - housing_median_age - total_rooms - total_bedrooms - population - households - median_income - median_house_value - ocean_proximity

Acknowledgements This data was initially featured in the following paper: Pace, R. Kelley, and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33.3 (1997): 291-297.

and I encountered it in 'Hands-On Machine learning with Scikit-Learn and TensorFlow' by Aurélien Géron. Aurélien Géron wrote: This dataset is a modified version of the California Housing dataset available from: Luís Torgo's page (University of Porto)

Inspiration See my kernel on machine learning basics in R using this dataset, or venture over to the following link for a python based introductory tutorial: https://github.com/ageron/handson-ml/tree/master/datasets/housing

Housing Index in Hong Kong increased to 136.68 points in July 6 from 136.56 points in the previous week. This dataset provides - Hong Kong House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Nahb Housing Market Index in the United States decreased to 32 points in June from 34 points in May of 2025. This dataset provides the latest reported value for - United States Nahb Housing Market Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

House Price Index YoY in the United States decreased to 3 percent in April from 3.90 percent in March of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

Context This dataset was created by web-scraping the house-announcements website Immobiliare Beautiful Soup. It serves as a good exercise in data cleaning and prediction. The goal is to predict house prices using the variables available. It contains both cleaned and raw data (for data cleaning training). My GitHub repository also contains the scripts to rerun automatically scraping and adding the newly available announcements. The dataset will be updated over time so that time-series analysis will be possible in the future.

Context 1 rooms 2 m2 3 bathrooms 4 floor 5 condominium_expenses (in euros) 6 date --> date the announcement is uploaded online 7 contract --> type of contract 8 typology --> type of property 9 total_floors --> floor level 10 availability 11 other_features --> other features written in the announcement in Italian (to be processed) 12 price --> target variable 13 year_of_build 14 condition 15 air_conditioning 16 energy_efficiency --> check here 17 city 18 neighborhood 19 car_parking 20 energy_performance_building 21 housing units 22 start_end_works 23 current_building_use 24 energy_certification 25 co2_emissions 26 elevator 27 floor_level 28 heating_centralized 29 heating_radiator 30 heating_gas 31 air_conditiong_centralized 32 air_conditioning_heat 33 renewable_energy_performance_index_KWh/m2

About this Dataset

Class materials for REE 6315 in Fall 2017. We will be using this data as an ongoing example throughout the course. Students will need this data to complete in class quizzes and out of class assignments. Please also download the free real estate listing data also required for the course: https://www.dataandsons.com/categories/sales_&_transactions/u.s._real_estate_inventory

Data was sourced by combining open data sources with instructors original content.

Category

Classroom Datasets

Keywords

housing,equity,realestate,transactions,sales

Row Count

929

Price

$75.00

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

Housing Index in the United States decreased to 434.90 points in April from 436.70 points in March of 2025. This dataset provides the latest reported value for - United States House Price Index MoM Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Monthly single-family home sales in Connecticut, 2001 through the present. Data updated monthly by the Connecticut Housing Finance Authority and tracked in the following dashboard: https://www.chfa.org/about-us/ct-monthly-housing-market-dashboard/. CHFA has stopped maintaining the dashboard and associated datasets, and this dataset will no longer be updated as of 2022.

In 2017, the County Department of Economic Development, in conjunction with Reinvestment Fund, completed the 2016 Market Value Analysis (MVA) for Allegheny County. A similar MVA was completed with the Pittsburgh Urban Redevelopment Authority in 2016. The Market Value Analysis (MVA) offers an approach for community revitalization; it recommends applying interventions not only to where there is a need for development but also in places where public investment can stimulate private market activity and capitalize on larger public investment activities. The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional community boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies.

The 2016 Allegheny County MVA does not include the City of Pittsburgh, which was characterized at the same time in the fourth update of the City of Pittsburgh’s MVA. All calculations herein therefore do not include the City of Pittsburgh. While the methodology between the City and County MVA's are very similar, the classification of communities will differ, and so the data between the two should not be used interchangeably.

Allegheny County's MVA utilized data that helps to define the local real estate market. Most data used covers the 2013-2016 period, and data used in the analysis includes:

•Residential Real Estate Sales; • Mortgage Foreclosures; • Residential Vacancy; • Parcel Year Built; • Parcel Condition; • Owner Occupancy; and • Subsidized Housing Units.

The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources.

During the research process, staff from the County and Reinvestment Fund spent an extensive amount of effort ensuring the data and analysis was accurate. In addition to testing the data, staff physically examined different areas to verify the data sets being used were appropriate indicators and the resulting MVA categories accurately reflect the market.

Please refer to the report (included here as a pdf) for more information about the data, methodology, and findings.

Housing Index in China decreased by 3.20 percent in June from -3.50 percent in May 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.

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.

Analysis of ‘Housing Prices in Metropolitan Areas of India’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ruchi798/housing-prices-in-metropolitan-areas-of-india on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Content

This dataset comprises data that was scraped. It includes: - collection of prices of new and resale houses located in the metropolitan areas of India - the amenities provided for each house

Inspiration

With 40 explanatory variables describing various aspects of new and resale houses in the metropolitan areas of India, one can predict the final price of houses in these regions.

📌 Note : Since for a set of houses, nothing was mentioned about certain amenities, '9' was used to mark such values, which could indicate the absence of information about the apartment but these values don't ascertain the absence of such a feature in real life.

--- Original source retains full ownership of the source dataset ---

Housing Index in Spain increased to 2033 EUR/SQ. METRE in the first quarter of 2025 from 1972.10 EUR/SQ. METRE in the fourth quarter of 2024. This dataset provides the latest reported value for - Spain House Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Enrich your real estate strategies and market insights with our comprehensive Seattle housing dataset. Analyzing this dataset can aid in understanding housing market dynamics and trends, empowering organizations to refine their investment strategies and business decisions. Access the entire dataset or tailor a subset to fit your requirements.

Popular use cases include optimizing investment strategies based on neighborhood engagement and property popularity, performing detailed user behavior analysis and segmentation by housing type, price range, and location to tailor marketing and engagement efforts, and identifying and forecasting emerging trends in the Seattle housing market to stay ahead in the competitive real estate industry.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Measured in €