This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

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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.

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.

The dataset originates from the book "Practical Statistics for Data Scientists" by Peter Bruce, Andrew Bruce, and Peter Gedeck.

Context:

You work for a real estate agency in the King County area, USA, and the company aims to develop a prediction model to estimate house prices based on various characteristics. The goal is to provide accurate estimates that help clients set the right sale or purchase price.

To achieve this, you use a detailed dataset that includes information about past sales, such as sale price, property size, number of bedrooms and bathrooms, as well as specific variables like the year of construction and real estate value indices. You use this data to create a predictive model that analyzes the impact of these factors on house prices in the region.

The objective is to provide a powerful tool for the agency’s real estate agents, allowing them to quickly and accurately estimate house prices and thus help clients make informed decisions.

Content:

The dataset has 22 variables and 22 688 sales.

The number of U.S. home sales in the United States declined in 2024, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2024, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 413,000 U.S. dollars in 2024 and was forecast to increase slightly until 2026. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.

During the COVID-19 pandemic, the number of house sales in the UK spiked, followed by a period of decline. In 2023 and 2024, the housing market slowed notably, and in January 2025, transaction volumes fell to 46,774. House sales volumes are impacted by a number of factors, including mortgage rates, house prices, supply, demand, as well as the overall health of the market. The economic uncertainty and rising unemployment rates has also affected the homebuyer sentiment of Brits. How have UK house prices developed over the past 10 years? House prices in the UK have increased year-on-year since 2015, except for a brief period of decline in the second half of 2023 and the beginning of 2024. That is based on the 12-month percentage change of the UK house price index. At the peak of the housing boom in 2022, prices soared by nearly 14 percent. The decline that followed was mild, at under three percent. The cooling in the market was more pronounced in England and Wales, where the average house price declined in 2023. Conversely, growth in Scotland and Northern Ireland continued. What is the impact of mortgage rates on house sales? For a long period, mortgage rates were at record-low, allowing prospective homebuyers to take out a 10-year loan at a mortgage rate of less than three percent. In the last quarter of 2021, this period came to an end as the Bank of England rose the bank lending rate to contain the spike in inflation. Naturally, the higher borrowing costs affected consumer sentiment, urging many homebuyers to place their plans on hold and leading to a decline in sales.

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.

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.

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.

Update 10/31/2023: Sales are no longer filtered out of this data set based on deed type, sale price, or recency of sale for a given PIN with the same price. If users wish to recreate the former filtering schema they should set sale_filter_same_sale_within_365, sale_filter_less_than_10k, and sale_filter_deed_type to False.

Parcel sales for real property in Cook County, from 1999 to present. The Assessor's Office uses this data in its modeling to estimate the fair market value of unsold properties.

When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded.

Sale document numbers correspond to those of the Cook County Clerk, and can be used on the Clerk's website to find more information about each sale.

NOTE: These sales are filtered, but likely include non-arms-length transactions - sales less than $10,000 along with quit claims, executor deeds, beneficial interests are excluded. While the Data Department will upload what it has access to monthly, sales are reported on a lag, with many records not populating until months after their official recording date.

Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time.

For more information on the sourcing of attached data and the preparation of this dataset, see the Assessor's Standard Operating Procedures for Open Data on GitHub.

Read about the Assessor's 2025 Open Data Refresh.

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Same dataset as "House Sales in King County, USA", but with treated content and with a split version (train-test) allowing direct use in machine learning models.

We have 14 columns in the dataset, as it follows:

• date: Date of the home sale
• price: Price of each home sold
• bedrooms: Number of bedrooms
• bathrooms: Number of bathrooms
• living_in_m2: Square meters of the apartments interior living space
• nice_view: A flag that indicates the view's quality of a property
• perfect_condition: A flag that indicates the maximum index of the apartment condition
• grade: An index from 1 to 5, where 1 falls short of quality level and 5 have a high quality level of construction and design
• has_basement: A flag indicating whether or not a property has a basement
• renovated: A flag if the property was renovated
• has_lavatory: Check for the presence of these incomplete/secondary bathrooms (bathtub, sink, toilet)
• single_floor: A flag indicating whether the property had only one floor
• month: The month of the home sale
• quartile_zone: A quartile distribution index of the most expensive zip codes, where 1 means less expansive and 4 most expansive.

In 2022, house price growth in the UK slowed, after a period of decade-long increase. Nevertheless, in June 2025, prices reached a new peak, with the average home costing ******* British pounds. This figure refers to all property types, including detached, semi-detached, terraced houses, and flats and maisonettes. Compared to other European countries, the UK had some of the highest house prices. How have UK house prices increased over the last 10 years? Property prices have risen dramatically over the past decade. According to the UK house price index, the average house price has grown by over ** percent since 2015. This price development has led to the gap between the cost of buying and renting a property to close. In 2023, buying a three-bedroom house in the UK was no longer more affordable than renting one. Consequently, Brits have become more likely to rent longer and push off making a house purchase until they have saved up enough for a down payment and achieved the financial stability required to make the step. What caused the recent fluctuations in house prices? House prices are affected by multiple factors, such as mortgage rates, supply, and demand on the market. For nearly a decade, the UK experienced uninterrupted house price growth as a result of strong demand and a chronic undersupply. Homebuyers who purchased a property at the peak of the housing boom in July 2022 paid ** percent more compared to what they would have paid a year before. Additionally, 2022 saw the most dramatic increase in mortgage rates in recent history. Between December 2021 and December 2022, the **-year fixed mortgage rate doubled, adding further strain to prospective homebuyers. As a result, the market cooled, leading to a correction in pricing.

View monthly updates and historical trends for US Existing Home Median Sales Price. from United States. Source: National Association of Realtors. Track ec…

Prior to 1974 the data was based on surveys of existing house sales in Dublin carried out by the Valuation Office on behalf of the D. O. E. Since 1974 the data has been based on information supplied by all lending agencies on the average price of mortgage financed existing house transactions. 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. Data for 1969/1970 is not available for Cork, Limerick, Galway, Waterford and Other areas 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. National and Other Areas figure changed for 2015 on 27/6/15 as revised data received from Local Authorities Prices includes houses and apartments measured in EUR

Purpose and brief description The house price index measures the inflation in the residential property market. The house price index reflects price developments for all residential properties purchased by households (apartments, terraced houses, detached houses), regardless of whether they are new or existing. Only market prices are taken into account, so self-build homes are excluded. The price of the land is included in the price of the properties. Population Real estate transactions involving residential properties Periodicity Quarterly. Release calendar Results available 3 months after the reference period Definitions House price index: The house price index measures changes in the prices of new or existing dwellings, regardless of their use or previous owner. Inflation - house price index: Inflation is defined as the ratio between the value of a given quarter and that of the same quarter of the previous year. Weighting - house price index: Weighting based on the national accounts (gross fixed capital formation in housing) and the total number of real estate transactions involving residential properties. Type of dwelling according to the classification set out in Regulation (EU) No 93/2013 on housing price indices. Technical information The house price index measures the price evolution of real estate prices on the market of private property. The index follows price changes of new or existing residential real estate purchased by households, irrespective of their purpose (letting or owner-occupying). Only market prices are taken into account. Houses built by their owners are therefore not included. The price of the building plot is included in the house price. The house price index is based on real estate transaction data from the General Administration of the Patrimonial Documentation of the FPS Finances. The prices used are those included in the deeds of sale. Given the time between the date on which the preliminary sales agreement is signed and the date on which the deed is executed (between three and four months), this index measures the price evolution with a delay compared to the actual date on which the sales price is set. This delay is inherent to the data source. The house price index is calculated by the European Union Member States, Norway and Iceland. Eurostat calculates the index for the Euro area (as well as for the European Union as a whole) using the harmonised indices of the Member States. Given the role of the housing market in the economic and financial crisis of 2008, the house price index was included in the indicators used in the procedure to prevent and correct macroeconomic imbalances in the European Union. The house price index is calculated under the European Regulation 2016/792 on harmonised indices of consumer prices and the house price index and 2023/1470 laying down the methodological and technical specifications as regards the house price index and the owner-occupied housing price index. Data are available from 2005 onward for Belgium as well as for the European Union and the majority of European countries. The house price index can be broken down by new houses and existing houses. The weights of these two items in the overall index are determined by the gross fixed capital formation in houses (for the new houses) and the total value of transactions of the previous year (for the existing houses). Until 2013, the house price index of new houses was roughly estimated based on the output price index in the construction sector. Since 2014, it is also based on real estate transaction data. House price index for existing houses is available per region since 2010. The data have therefore been completely reviewed when the results for the fourth quarter of 2023 were published in March 2024. Since the houses that are put up for sale differ from one quarter to another, the changes in characteristics are processed with hedonic regression models to eliminate price fluctuations due to changes in characteristics of the properties sold. These models aim to estimate the theoretical price based on the characteristics and location of the houses sold. The index is then calculated based on changes in the average prices observed and adjusted by a factor depending on the differences in quality observed between dwellings sold during the different periods.

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.

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.

This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.

Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].

Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].