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.

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.

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 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.

Context and Acknowledgements This dataset is inspired by and improves upon the City of New York's NYC Property Sales dataset. The dataset contains a record of every property sold in the New York City property market since 2003 (the first year sales data was first listed on the public record) and updates monthly to include rolling sales.

Please upvote if you found the dataset or additional resources helpful. 👍

Content This dataset contains the location, address, type, sale price, tax category, and sale date of properties sold.

• BOROUGH: Manhattan (1), Bronx (2), Brooklyn (3), Queens (4), and Staten Island (5).
• TAX CLASSES:
  • 1: Most Residential Properties up to Three Units, Vacant Lots Zoned for Residential, Condominiums Less Than Three Stories.
  • 2: All Other Residential Properties.
  • 3: Property with equipment owned by a gas, telephone, or electric company.
  • 4: All other Properties (Garages, Factories, Warehouses...)
• EASEMENT: A right that allows an entity to make limited use of another's real property.
• $0 Sales Prices: Indicates a transfer of ownership without a cash consideration.

For further reference on the fields in this dataset see the City of New York Department of Finance's Glossary of Terms and Building Codes.

<div></div>

Real Estate Sales 2001-2020 GL Metadata Updated: August 12, 2023

The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment.

Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Access & Use Information Public: This dataset is intended for public access and use. Non-Federal: This dataset is covered by different Terms of Use than Data.gov. License: No license information was provided.

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.

Description:

• This dataset contains information about real estate property transactions.
• Each row represents a single property transaction.
• The dataset includes various attributes such as transaction dates, property locations, estimated and sale prices, property characteristics (e.g., type, residential status, number of rooms and bathrooms, carpet area), and property tax rates.
• The dataset provides insights into the real estate market, including trends in property prices, property types, and tax rates across different localities and years.
• It can be used for various analytical purposes such as market analysis, predictive modeling, and decision-making in the real estate industry.
• Researchers, analysts, and stakeholders in the real estate sector can utilize this dataset to understand market dynamics, assess property values, and make informed investment decisions.

Features:

Date: The date when the property transaction occurred. Year: The year of the property transaction. Locality: The locality or area where the property is located. Estimated Value: The estimated value of the property. Sale Price: The actual sale price of the property. Property: The type of property (e.g., Single Family). Residential: Indicates whether the property is residential or not. Num_rooms: The number of rooms in the property. Num_bathrooms: The number of bathrooms in the property. Carpet Area: The carpet area of the property. Property Tax Rate: The property tax rate applicable to the property. Face: The facing direction of the property (e.g., North, South, East).

Gain a complete view of the real estate market with our Zillow datasets. Track price trends, rental/sale status, and price per square foot with the Zillow Price History dataset and explore detailed listings with prices, locations, and features using the Zillow Properties Listing dataset. Over 134M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Zpid
City
State
Home Status
Street Address
Zipcode
Home Type
Living Area Value
Bedrooms
Bathrooms
Price
Property Type
Date Sold
Annual Homeowners Insurance
Price Per Square Foot
Rent Zestimate
Tax Assessed Value
Zestimate
Home Values
Lot Area
Lot Area Unit
Living Area
Living Area Units
Property Tax Rate
Page View Count
Favorite Count
Time On Zillow
Time Zone
Abbreviated Address
Brokerage Name
And much more

Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

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.

The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment. Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Some municipalities may not report data for certain years because when a municipality implements a revaluation, they are not required to submit sales data for the twelve months following implementation.

VITAL SIGNS INDICATOR
Home Prices (EC7)

FULL MEASURE NAME
Home Prices

LAST UPDATED
December 2022

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: Zillow Home Value Index (ZHVI) - http://www.zillow.com/research/data/
2000-2021

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2000-2021

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2000-2021

Bureau of Labor Statistics: Consumer Price Index - http://data.bls.gov
2000-2021

US Census ZIP Code Tabulation Areas (ZCTAs) - https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html
2020 Census Blocks

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Housing price estimates at the regional-, county-, city- and zip code-level come from analysis of individual home sales by Zillow based upon transaction records. Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range. ZHVI is computed from public record transaction data as reported by counties. All standard real estate transactions are included in this metric, including REO sales and auctions. Zillow makes a substantial effort to remove transactions not typically considered a standard sale. Examples of these include bank takeovers of foreclosed properties, title transfers after a death or divorce and non arms-length transactions. 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 can be owned in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums in that 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. Data is adjusted for inflation using Bureau of Labor Statistics metropolitan statistical area (MSA)-specific series. 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 (CPI) 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 the CPI itself.

https://raw.githubusercontent.com/Masterx-AI/Project_Housing_Price_Prediction_/main/hs.jpg" alt="">

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.

VITAL SIGNS INDICATOR
Home Prices (EC7)

FULL MEASURE NAME
Home Prices

LAST UPDATED
December 2022

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: Zillow Home Value Index (ZHVI) - http://www.zillow.com/research/data/
2000-2021

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2000-2021

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2000-2021

Bureau of Labor Statistics: Consumer Price Index - http://data.bls.gov
2000-2021

US Census ZIP Code Tabulation Areas (ZCTAs) - https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html
2020 Census Blocks

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Housing price estimates at the regional-, county-, city- and zip code-level come from analysis of individual home sales by Zillow based upon transaction records. Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range. ZHVI is computed from public record transaction data as reported by counties. All standard real estate transactions are included in this metric, including REO sales and auctions. Zillow makes a substantial effort to remove transactions not typically considered a standard sale. Examples of these include bank takeovers of foreclosed properties, title transfers after a death or divorce and non arms-length transactions. 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 can be owned in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums in that 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. Data is adjusted for inflation using Bureau of Labor Statistics metropolitan statistical area (MSA)-specific series. 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 (CPI) 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 the CPI itself.

Datasys: Property Ownership Data – 100M+ Records, Weekly Updates, Verified Every 30 Days

Our Property file offers highly accurate data on real estate owners, updated weekly and verified against the National Change of Address (NCOA) registries every 30 days—four times more frequently than competitors. Perfect for postal, email, and phone outreach, this data guarantees improved responsiveness.

With data sourced from public records, opt-in sources, and new movers’ files, each entry includes full name, postal address, company name, title, and industry. Datasys ensures top-quality, standardized records, with email and phone data updated quarterly, catering to both direct marketers and agencies.

Context

Zillow's Economic Research Team collects, cleans and publishes housing and economic data from a variety of public and proprietary sources. Public property record data filed with local municipalities -- including deeds, property facts, parcel information and transactional histories -- forms the backbone of our data products, and is fleshed out with proprietary data derived from property listings and user behavior on Zillow.

The large majority of Zillow's aggregated housing market and economic data is made available for free download at zillow.com/data.

Content

Variable Availability:

Zillow Home Value Index (ZHVI): A smoothed seasonally adjusted measure of the median estimated home value across a given region and housing type. A dollar denominated alternative to repeat-sales indices. Find a more detailed methodology here: http://www.zillow.com/research/zhvi-methodology-6032/

Zillow Rent Index (ZRI): A smoothed seasonally adjusted measure of the median estimated market rate rent across a given region and housing type. A dollar denominated alternative to repeat-rent indices. Find a more detailed methodology here: http://www.zillow.com/research/zillow-rent-index-methodology-2393/

For-Sale Listing/Inventory Metrics: Zillow provides many variables capturing current and historical for-sale listings availability, generally from 2012 to current. These variables include median list prices and inventory counts, both by various property types. Variables capturing for-sale market competitiveness including share of listings with a price cut, median price cut size, age of inventory, and the days a listing spend on Zillow before the sale is final.

Home Sales Metrics: Zillow provides data on sold homes including median sale price by various housing types, sale counts (methodology here: http://www.zillow.com/research/home-sales-methodology-7733/), and a normalized view of sale volume referred to as turnover. The prevalence of foreclosures is also provided as ratio of the housing stock and the share of all sales in which the home was previously foreclosed upon.

For-Rent Listing Metrics: Zillow provides median rents prices and median rent price per square foot by property type and bedroom count.

Housing type definitions:

All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.

Condo/Co-op: Condominium and co-operative homes.

Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.

Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Tiers: By metro, we determine price tier cutoffs that divide the all homes housing stock into thirds using the full distribution of estimated home values. We then estimate real estate metrics within the property sets, Bottom, Middle, and Top, defined by these cutoffs. When reported at the national level, all Bottom Tier homes defined at the metro level are pooled together to form the national bottom tier. The same holds for Middle and Top Tier homes.

Regional Availability:

Zillow metrics are reported for common US geographies including Nation, State, Metro (2013 Census Defined CBSAs), County, City, ZIP code, and Neighborhood.

We provide a crosswalk between colloquial Zillow region names and federally defined region names and linking variables such as County FIPS codes and CBSA codes. Cities and Neighborhoods do not match standard jurisdictional boundaries. Zillow city boundaries reflect mailing address conventions and so are often visually similar to collections of ZIP codes. Zillow neighborhood boundaries can be found here.

Suppression Rules: To ensure reliability of reported values the Zillow Economic Research team applies suppression rules triggered by low sample sizes and excessive volatility. These rules are customized to the metric and region type and explain most missingness found in the provided datasets.

Additional Data Products

The following data products and more are available for free download exclusively at [Zillow.com/Data][1]:

• Zillow Home Value Forecast
• Zillow Rent Forecast
• Negative Equity (the share of mortgaged properties worth less than mortgage balance)
• Zillow Home Price Expectations Survey
• Zillow Housing Aspirations Report
• Zillow Rising Sea Levels Research
• Cash Buyers Time Series
• Buy vs. Rent Breakeven Horizon
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio
• Conventional 30-year Fixed Mortgage Rate, Weekly Time Series
• Jumbo 30-year Fixed Mortgage Rates, Weekly Time Series

Acknowledgements

The mission of the Zillow Economic Research Team is to be the most open, authoritative source for timely and accurate housing data and unbiased insight. We...

Prices & Characteristics of Spanish Homes

Uncovering Market Trends in Spain

By [source]

About this dataset

This dataset provides a wealth of information about the current Spanish housing market for potential buyers. This comprehensive data set includes research-level information about region, number of rooms, size, price, photos and more for different available properties across the country. This data can help researchers understand the wide pricing range and characteristics associated with these homes in great detail. For example, it allows us to uncover average price per square meter as well as differences in prices between larger and smaller locations. Further exploration also reveals correlations between price and surface area as well as number of rooms and pricing models - all immensely helpful to those wishing to purchase or rent properties in Spain! By further investigating this rich set of information provided by this dataset, prospective property buyers can be more informed when making decisions regarding their next home or investment opportunities within the Spanish housing market

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Welcome to the Prices and Characteristics of Spanish Houses for Sale dataset! This data set contains comprehensive information about Spanish houses for sale, including location, price, size, and number of rooms. Here’s a guide to help you get started.

• Explore the columns included in this dataset: the summary column provides an overview of the property while description provides more in-depth details. The location column offers geographical details about each house; photo displays a picture of each property; recomendado indicates whether or not it has been recommended; price gives you an idea of how much each house costs; size determines how large or small it is; rooms tells you how many bedrooms it has to offer; price/m2 states the Square Meter Price for each home; bathrooms lets you know how many bathrooms it has on the premises; Num Photos shows you the exact number of images available for that home and type directs which type it is (apartment); region helps pinpoint exactly where these homes are located.

• Analyze relationships between variables: use this dataset to uncover interesting correlations between pricing and other characteristics such as size and number of rooms, or between prices in different regions within Spain. You can also gain insight into average pricing by square meter across various locations - this data might be useful if you're looking at making a real estate investment decision based on market trends around Spain's housing sector!

• Research current market trends: review historical data points from within this dataset with regards to pricing changes over time, as well as differences in supply/demand dynamics across distinct locations within Spain's housing market - all these insights can be used when deciding whether or not now would be an ideal time to purchase property in certain areas!
  Overall, we hope that with this information at hand your research into Spain's current housing market will provide useful results and lend insight that may assist your purchase decision process when considering buying S[anish homes!

Research Ideas

• Comparing the average Spanish house price in different regions to determine if prices are more expensive in certain regions.
• Examining the correlation between size and number of rooms to understand which properties would be a better investment given their size.
• Analyzing the relationship between number of photos uploaded for a property and its price, to determine if there is any correlation between them or not

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: pisos.csv | Column name | Description | |:----------------|:------------------------------------------------------------| | summary | A brief description of the property. (Text) | | location | The geographical area or postcode of the property. (Text) | | photo...

Dataset SummaryPlease note: this data is live (updated nightly) to reflect the latest changes in the City's systems of record.About this data:The operational purpose of the vacant land dataset is to facilitate the tracking and mapping of vacant land for the purposes of promoting redevelopment of lots to increase the City's tax base and spur increased economic activity. These properties are both City owned and privately owned. The vast majority of vacant lots are the result of a demolition of a structure that once stood on the property. Vacant lots are noted in the official tax parcel assessment records with a class code beginning with 3, which denotes the category vacant land.Related Resources:For a searchable interactive mapping application, please visit the City of Rochester's Property Information explorer tool. For further information about the city's property tax assessments, please contact the City of Rochester Assessment Bureau. To access the City's zoning code, please click here.Data Dictionary: SBL: The twenty-digit unique identifier assigned to a tax parcel. PRINTKEY: A unique identifier for a tax parcel, typically in the format of “Tax map section – Block – Lot". Street Number: The street number where the tax parcel is located. Street Name: The street name where the tax parcel is located. NAME: The street number and street name for the tax parcel. City: The city where the tax parcel is located. Property Class Code: The standardized code to identify the type and/or use of the tax parcel. For a full list of codes, view the NYS Real Property System (RPS) property classification codes guide. Property Class: The name of the property class associated with the property class code. Property Type: The type of property associated with the property class code. There are nine different types of property according to RPS: 100: Agricultural 200: Residential 300: Vacant Land 400: Commercial 500: Recreation & Entertainment 600: Community Services 700: Industrial 800: Public Services 900: Wild, forested, conservation lands and public parks First Owner Name: The name of the property owner of the vacant tax parcel. If there are multiple owners, then the first one is displayed. Postal Address: The USPS postal address for the vacant landowner. Postal City: The USPS postal city, state, and zip code for the vacant landowner. Lot Frontage: The length (in feet) of how wide the lot is across the street. Lot Depth: The length (in feet) of how far the lot goes back from the street. Stated Area: The area of the vacant tax parcel. Current Land Value: The current value (in USD) of the tax parcel. Current Total Assessed Value: The current value (in USD) assigned by a tax assessor, which takes into consideration both the land value, buildings on the land, etc. Current Taxable Value: The amount (in USD) of the assessed value that can be taxed. Tentative Land Value: The current value (in USD) of the land on the tax parcel, subject to change based on appeals, reassessments, and public review. Tentative Total Assessed Value: The preliminary estimate (in USD) of the tax parcel’s assessed value, which includes tentative land value and tentative improvement value. Tentative Taxable Value: The preliminary estimate (in USD) of the tax parcel’s value used to calculate property taxes. Sale Date: The date (MM/DD/YYYY) of when the vacant tax parcel was sold. Sale Price: The price (in USD) of what the vacant tax parcel was sold for. Book: The record book that the property deed or sale is recorded in. Page: The page in the record book where the property deed or sale is recorded in. Deed Type: The type of deed associated with the vacant tax parcel sale. RESCOM: Notes whether the vacant tax parcel is zoned for residential or commercial use. R: Residential C: Commercial BISZONING: Notes the zoning district the vacant tax parcel is in. For more information on zoning, visit the City’s Zoning District map. OWNERSHIPCODE: Code to note type of ownership (if applicable). Number of Residential Units: Notes how many residential units are available on the tax parcel (if applicable). LOW_STREET_NUM: The street number of the vacant tax parcel. HIGH_STREET_NUM: The street number of the vacant tax parcel. GISEXTDATE: The date and time when the data was last updated. SALE_DATE_datefield: The recorded date of sale of the vacant tax parcel (if available). Source: This data comes from the department of Neighborhood and Business Development, Bureau of Business and Zoning.