This data set contains characteristic data points used by the Cook County Assessor in the 2021 Chicago reassessment to produce initial estimates of the current market value of most Chicago homes (single-family homes, small multi-family homes, and condo units). You can use the "Filter" option to search for a property's PIN or address, and see what data the Assessor’s Office had about a home’s characteristics at the time of modeling*. To learn more about how the 2021 model used this data, read about our public Residential Automated Valuation Model here. Chicago properties not listed here are reassessed using different modeling procedures.

*Important Note: This dataset is, at the time of publication, an early snapshot of data. Data about a home might change later in the assessment process this year as Assessor’s Office staff and analysts review these properties. After this review, updated characteristics and market values are mailed to homeowners. If the data listed on the assessment notice is incorrect, an appeal can be filed to provide the correct characteristics.

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).

Critical note: Building multipliers and models will be updated soon.

Archetype metadata, models, and multipliers are provided for 93 building archetypes located within the city of Chicago (United States):

Data (12KB *.csv) - minimalist list of each building (rows) for the following fields (columns)

ID - unique building ID

Area - estimate of total conditioned floor area (ft2)

CZ - ASHRAE Climate Zone designation

Height - building height (ft)

NumFloors - number of floors (above-grade) (IECC = Residential)

BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards

Standard - building vintage

WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)

Area2D - footprint area (ft2)

Num_build_per_zone - Number of this building type/vintage in WRF zone

Total_zone_area - Total area of this building type/vintage in WRF zone (ft2)

Area_multiplier - Scaling factor for building type/vintage for building in WRF zone

Models (7.69MB *.zip) - EnergyPlus building energy models named according to ID

Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus (v9.4) 2,784-page Input/Output Reference Guide for everything that can be retrieved or simulated from these models.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

REFI Chicago Atlantic Real Estate Finance Inc. Common Stock

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The DEM of Cook County was developed from the DEM tiles that were delivered after the 2017 LiDAR acquisition. This DEM assembles all the tiles into one raster. It displays the bare earth returns of the LiDAR as a raster.

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.