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.

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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.

House prices grew year-on-year in most states in the U.S. in the first quarter of 2025. Hawaii was the only exception, with a decline of **** percent. The annual appreciation for single-family housing in the U.S. was **** percent, while in Rhode Island—the state where homes appreciated the most—the increase was ******percent. How have home prices developed in recent years? House price growth in the U.S. has been going strong for years. In 2025, the median sales price of a single-family home exceeded ******* U.S. dollars, up from ******* U.S. dollars five years ago. One of the factors driving house prices was the cost of credit. The record-low federal funds effective rate allowed mortgage lenders to set mortgage interest rates as low as *** percent. With interest rates on the rise, home buying has also slowed, causing fluctuations in house prices. Why are house prices growing? Many markets in the U.S. are overheated because supply has not been able to keep up with demand. How many homes enter the housing market depends on the construction output, whereas the availability of existing homes for purchase depends on many other factors, such as the willingness of owners to sell. Furthermore, growing investor appetite in the housing sector means that prospective homebuyers have some extra competition to worry about. In certain metros, for example, the share of homes bought by investors exceeded ** percent in 2025.

This data set is a listing of all properties sold by NORA through the following disposition channels. -Auction: Properties put up for auction and sold to the highest bidder. -Development: Properties offered via request for proposals to create affordable housing. -Lot Next Door: Properties sold to adjacent owners. -Alternate Land Use: Properties sold for purposes of creating green space and used for activities such as community gardens.

The average square meter price of new residential real estate in Spain was the highest in Catalonia and the Community of Madrid in 2025. In the second quarter of the year, both regions boasted home prices of over 4,800 euros per square meter, with Catalonia at 4,893 euros and the Community of Madrid at 5,037 euros. That was substantially higher than the average for the country, which amounted to 3,151 euros per square meter. Overall, house prices in Spain have been on the rise since 2016.

The travel time data on this map is modeled from a 2005 transit network. The home values are as of 2000 and are expressed in year 2000 dollars. The home value estimates were created by the Association of Bay Area Governements by combining ParcelQuest real estate transaction data and real estate tax assessment data. This information can be generated for any address in the region using an interactive mapping tool available under Maps at onebayarea.org/maps.htm (Note - this tool is no longer available).

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

These datasets contain comprehensive information on current real estate listings in Washington, D.C., obtained from Zillow, and offer a detailed overview of the Washington, D.C. housing market as of 5th June 2024.

The data was extracted from Zillow using a combination of two scraping tools from Apify: Zillow ZIP Code Scraper 🔗 https://apify.com/maxcopell/zillow-zip-search and Zillow Details Scraper 🔗 https://apify.com/maxcopell/zillow-detail-scraper.

The full dataset includes all details for each listing for sale, such as:

• 📍 Complete address, city, state, zip code, latitude/longitude coordinates
• 🏡 Property type (single family, condo, apartment, etc.)
• 💵 Listing price
• 🛏️ Number of bedrooms and bathrooms
• 📐 Square footage
• 🌳 Lot size in acres (if applicable)
• 🏗️ Year of construction
• 🏘️ HOA fees (if applicable)
• 💸 Property tax history
• ✨ Amenities such as rooftop terraces, concierge services, etc.
• 🏫 Nearby schools and their GreatSchools ratings
• 🧑‍💼 Property and listing agents, brokers, and their contact information
• 🕒 Availability for tours and open houses
• 🖼️ Links to listing photos

With over 5,000 current listings, this dataset is perfect for in-depth analysis of the Washington, D.C. housing market and the Washington, D.C. real estate scene. Potential applications include:

• Comparing listing prices and price per square foot across various neighborhoods and property types
• Mapping listings to visualize the spatial distribution of available inventory
• Analyzing the age of available housing stock using year-of-construction data
• Assessing typical HOA fees and property taxes for listings
• Identifying listings with desirable amenities
• Evaluating school quality near listings using GreatSchools ratings
• Contacting listing agents programmatically using the provided agent information

Whether you're a real estate professional, market analyst, data scientist, or simply interested in the Washington, D.C., housing market, this dataset offers a wealth of information to explore. You can begin investigating and discovering insights into Washington, D.C. real estate today.

Graph and download economic data for Real Residential Property Prices for Mexico (QMXR628BIS) from Q1 2005 to Q2 2025 about Mexico, residential, HPI, housing, real, price index, indexes, and price.

This dataset includes the listing prices for the sale of properties (mostly houses) in Ontario. They are obtained for a short period of time in July 2016 and include the following fields: - Price in dollars - Address of the property - Latitude and Longitude of the address obtained by using Google Geocoding service - Area Name of the property obtained by using Google Geocoding service

This dataset will provide a good starting point for analyzing the inflated housing market in Canada although it does not include time related information. Initially, it is intended to draw an enhanced interactive heatmap of the house prices for different neighborhoods (areas)

However, if there is enough interest, there will be more information added as newer versions to this dataset. Some of those information will include more details on the property as well as time related information on the price (changes).

This is a somehow related articles about the real estate prices in Ontario: http://www.canadianbusiness.com/blogs-and-comment/check-out-this-heat-map-of-toronto-real-estate-prices/

I am also inspired by this dataset which was provided for King County https://www.kaggle.com/harlfoxem/housesalesprediction

The percent of homes and condominiums sold for cash out of all residential properties sold in a calendar year. These types of sales tend to signify investor-based purchases as homes purchased for cash either become rental properties or later sold again in an effort to generate a profit. Source: RBIntelYears Available: 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023

Housing Index in Poland increased to 215.66 points in the second quarter of 2025 from 213.20 points in the first quarter of 2025. This dataset provides the latest reported value for - Poland Housing Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Interactive map of USA showing 16 housing market factors such as Median home value,Median family income, First-time home buyer share, etc

🇬🇧 United Kingdom English This table shows the average House Price/Earnings ratio, which is an important indicator of housing affordability. Ratios are calculated by dividing house price by the median earnings of a borough. The Annual Survey of Hours and Earnings (ASHE) is based on a 1 per cent sample of employee jobs. Information on earnings and hours is obtained in confidence from employers. It does not cover the self-employed nor does it cover employees not paid during the reference period. Information is as at April each year. The statistics used are workplace based full-time individual earnings. Pre-2013 Land Registry housing data are for the first half of the year only, so that they are comparable to the ASHE data which are as at April. This is no longer the case from 2013 onwards as this data uses house price data from the ONS House Price Statistics for Small Areas statistical release. Prior to 2006 data are not available for Inner and Outer London. The lowest 25 per cent of prices are below the lower quartile; the highest 75 per cent are above the lower quartile. The "lower quartile" property price/income is determined by ranking all property prices/incomes in ascending order. The 'median' property price/income is determined by ranking all property prices/incomes in ascending order. The point at which one half of the values are above and one half are below is the median. Regional data has not been published by DCLG since 2012. Data for regions has been calculated by the GLA. Data since 2014 has been calculated by the GLA using Land Registry house prices and ONS Earnings data. Link to DCLG Live Tables An interactive map showing the affordability ratios by local authority for 2013, 2014 and 2015 is also available.

Context

I'm trying to make a Choropleth map over time of home sale prices by block in Brooklyn for the last 15 years to visualize gentrification. I have the entire dataset for all 5 boroughs of New York, but am starting with Brooklyn.

Content and Acknowledgements

Primary dataset is the NYC Housing Sales Data Found in this Link: http://www1.nyc.gov/site/finance/taxes/property-rolling-sales-data.page

The data in all the separate excel spreadsheets for 2003-2017 was merged via VBA scripting in Excel and further cleaned & de-duped in R

Additionally, in my hunt for shapefiles I discovered these wonderful shapefiles from NYCPluto: https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page

I left joined it by "Block" & "Lot" onto the primary data frame, but 25% of the block/lot combo's ended up not having a corresponding entry in the Pluto shapefile and are NAs.

Note that as in other uploaded datasets of NYC housing on Kaggle, many of these transactions have a sale_price of $0 or only a nominal amount far less than market value. These are likely property transfers to relatives and should be excluded from any analysis of market prices.

Inspiration

Can you model Brooklyn home prices accurately?

Mapping spatial processes at a small scale is a challenge when observed data are not abundant. The article examines the residential housing market in Fort Worth, Texas, and builds price indices at the inter- and intra-neighborhood levels. To accomplish our objectives, we initially model price variability in the joint space–time continuum. We then use geostatistics to predict and map monthly housing prices across the area of interest over a period of 4 years. For this analysis, we introduce the Bayesian maximum entropy (BME) method into real estate research. We use BME because it rigorously integrates uncertain or secondary soft data, which are needed to build the price indices. The soft data in our analysis are property tax values, which are plentiful, publicly available, and highly correlated with transaction prices. The results demonstrate how the use of the soft data provides the ability to map house prices within a small areal unit such as a subdivision or neighborhood.

Property Listings from the Maryland Department of Transportation's Office of Real Estate and Economic Development office. These properties are state-owned properties that are currently for sale, will be for sale, have a sale pending, or have recently sold.This map is updated when properties change categories or new properties become available. Use the interactive pop-up menus within the map for each property to view more information about the selected properties and to view the property in different maps and contexts. The state of Maryland is able to sell state-owned land periodically. This can involve public auctions as well. Please visit the Maryland Department of Transportation's Real Estate and Economic Development website for additional information: https://mdotrealestate.maryland.gov/Pages/default.aspx and check with their current tabular list of properties for the inventory.

House prices decreased in all Swedish counties in 2023. The highest average purchase price for one- and two-residential property buildings in Sweden was in Stockholm, where the average price amounted to 6.7 million Swedish kroner in 2023, approximately twice the nation average house price. The lowest average purchase price that year was in Västernorrland, which was around 1.7 million Swedish kroner.

The price of residential property in New Zealand was the highest in the Auckland region in October 2025, with an average sale price of over *** million New Zealand dollars. The most populated city in the country, Auckland, has consistently reported higher house prices compared to most other regions. Buying property in New Zealand, particularly in its major cities, is expensive. The nation has one of the highest house-price-to-income ratios in the world. Auckland residential market The residential housing market in Auckland is competitive. Prices have been slowly decreasing although the Auckland region experienced an annual increase in the average residential house price in October 2025 compared to the same month in the previous year. The price of residential property in Auckland was the highest in the Auckland City district, with an average sale price of around **** million New Zealand dollars. Home financing Due to the rising cost of real estate, an increasing number of New Zealanders who want to own their own property are taking on mortgages. Most residential mortgage lending in New Zealand went to owner-occupier borrowers, followed by first home buyers. In addition to mortgage lending, previously under the KiwiSaver HomeStart initiative, first-home buyers in New Zealand were able to apply to withdraw all or part of their KiwiSaver retirement savings to assist with purchasing a first home. Nonetheless, the scheme was discontinued in May 2024. Furthermore, even with a large initial deposit, it may take decades for many borrowers to pay off their mortgage.

Electrical resistivity results from two regional airborne electromagnetic (AEM) surveys (Minsley et al. 2021, and Burton et al. 2021) over the Mississippi Alluvial Plain (MAP) were combined by the U.S. Geological Survey to produce three-dimensional (3D) gridded models and derivative hydrogeologic products. First, the base of the Mississippi River Valley Alluvial aquifer (MRVA) was updated using the AEM resistivity data, both borehole and manual picks, and a supervised machine learning algorithm. The 3D resistivity elevation grid was then intersected with the 2018 potentiometric surface and the new base of MRVA surface to isolate the saturated MRVA extent and generate estimates of the hydrogeologic framework and properties. The saturated aquifer thickness was calculated as the difference between the potentiometric surface elevation and the MRVA base elevation. The average electrical resistivity and facies classification of the saturated aquifer material were calculated for each 1 kilometer (km) x 1 km grid cell. See child item “Mississippi Alluvial Plain: Electrical Resistivity & Facies Classification Grids” for more details on the facies classes. Lastly, the degree of connectivity across the base of the MRVA, i.e. how likely the MRVA is hydraulically connected to deeper subcropping Tertiary units, was estimated through the vertically integrated electrical conductance (VIC) between different vertical offsets (+/- 5 meter (m), 10 m, 25 m) from the aquifer base. For example, for every 1 km x 1 km cell, the VIC for +/- 25 m is the result of integrating the electrical conductance values from all 5 m elevation layers between 25 above the MRVA base and 25 m below the MRVA base. Areas with high VIC values suggest there is low or minimal hydraulic connection across the MRVA base, while low VIC values indicate areas of high potential connection. All products were exported as raster images in Georeferenced Tagged Image File Format (GeoTIFF) files. Burton, B.L., Minsley, B.J., Bloss, B.R., and Kress, W.H., 2021, Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2018 - February 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9XBBBUU. Minsley, B.J., James, S.R., Bedrosian, P.A., Pace, M.D., Hoogenboom, B.E., and Burton, B.L., 2021, Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2019 - March 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9E44CTQ.