⚠️ NOTE⚠️ The condominium model (this repo) is nearly identical to the residential (single/multi-family) model, with a few key differences. Please read the documentation for the residential model first.
This repository contains code, data, and documentation for the Cook County Assessor’s condominium reassessment model. Information about prior year models can be found at the following links:
| Year(s) | Triad(s) | Method | Language / Framework | Link |
|---|---|---|---|---|
| 2015 | City | N/A | SPSS | Link |
| 2018 | City | N/A | N/A | Not available. Values provided by vendor |
| 2019 | North | Linear regression or GBM model per township | R (Base) | Link |
| 2020 | South | Linear regression or GBM model per township | R (Base) | Link |
| 2021 | City | County-wide LightGBM model | R (Tidyverse / Tidymodels) | Link |
| 2022 | North | County-wide LightGBM model | R (Tidyverse / Tidymodels) | Link |
| 2023 | South | County-wide LightGBM model | R (Tidyverse / Tidymodels) | Link |
The duty of the Cook County Assessor’s Office is to value property in a fair, accurate, and transparent way. The Assessor is committed to transparency throughout the assessment process. As such, this document contains:
- A description of the differences between the residential model and this (condominium) model
- An outline of ongoing issues specific to condominium assessments
The repository itself contains the code and data for the Automated Valuation Model (AVM) used to generate initial assessed values for all condominium properties in Cook County. This system is effectively an advanced machine learning model (hereafter referred to as “the model”). It uses previous sales to generate estimated sale values (assessments) for all properties.
The Cook County Assessor’s Office does not track characteristic data for condominiums. Like most assessors nationwide, our office staff cannot enter buildings to observe property characteristics. For condos, this means we cannot observe amenities, quality, or any other interior characteristics.
The only information our office has about individual condominium units is their age, location, sale date/price, and percentage of ownership. This makes modeling condos particularly challenging, as the number of usable features is quite small. Fortunately, condos have two qualities which make modeling a bit easier:
- Condos are more homogeneous than single/multi-family properties, i.e. the range of potential condo sale prices is much narrower.
- Condo are pre-grouped into clusters of like units (buildings), and units within the same building usually have similar sale prices.
We leverage these qualities to produce what we call strata, a feature unique to the condo model. See Condo Strata for more information about how strata is used and calculated.
⚠️ NOTE⚠️ Recently, the CCAO has started to manually collect high-level condominium data, including total building square footage and estimated unit square footage/number of bedrooms. This data is sourced from listings and a number of additional third-party sources and is available for the North and South triads only.
Because our office (mostly) cannot observe individual condo unit characteristics, we must rely on aggregate geospatial features, economic features, strata, and time of sale to determine condo assessed values. The features in the table below are the ones used in the 2023 assessment model.
| Feature Name | Category | Type | Unique to Condo Model |
|---|---|---|---|
| Condominium Building Year Built | Characteristic | numeric | X |
| Total Condominium Building Non-Livable Parcels | Characteristic | numeric | X |
| Total Condominium Building Livable Parcels | Characteristic | numeric | X |
| Total Condominium Building Square Footage | Characteristic | numeric | X |
| Condominium Unit Square Footage | Characteristic | numeric | X |
| Condominium Unit Bedrooms | Characteristic | numeric | X |
| Condominium Unit Half Baths | Characteristic | numeric | X |
| Condominium Unit Full Baths | Characteristic | numeric | X |
| Condominium Building Is Mixed Use | Characteristic | logical | X |
| Condominium % Ownership | Meta | numeric | X |
| Condominium Building Strata 1 | Meta | character | X |
| Condominium Building Strata 2 | Meta | character | X |
| Land Square Feet | Characteristic | numeric | |
| Township Code | Meta | character | |
| Neighborhood Code | Meta | character | |
| Sale Year | Time | numeric | |
| Sale Day | Time | numeric | |
| Sale Quarter of Year | Time | character | |
| Sale Month of Year | Time | character | |
| Sale Day of Year | Time | numeric | |
| Sale Day of Month | Time | numeric | |
| Sale Day of Week | Time | numeric | |
| Sale After COVID-19 | Time | logical | |
| Percent Population Age, Under 19 Years Old | acs5 | numeric | |
| Percent Population Age, Over 65 Years Old | acs5 | numeric | |
| Median Population Age | acs5 | numeric | |
| Percent Population Mobility, In Same House 1 Year Ago | acs5 | numeric | |
| Percent Population Mobility, Moved From Other State in Past Year | acs5 | numeric | |
| Percent Households Family, Married | acs5 | numeric | |
| Percent Households Nonfamily, Living Alone | acs5 | numeric | |
| Percent Population Education, High School Degree | acs5 | numeric | |
| Percent Population Education, Bachelor Degree | acs5 | numeric | |
| Percent Population Education, Graduate Degree | acs5 | numeric | |
| Percent Population Income, Below Poverty Level | acs5 | numeric | |
| Median Income, Household in Past Year | acs5 | numeric | |
| Median Income, Per Capita in Past Year | acs5 | numeric | |
| Percent Population Income, Received SNAP in Past Year | acs5 | numeric | |
| Percent Population Employment, Unemployed | acs5 | numeric | |
| Median Occupied Household, Total, Year Built | acs5 | numeric | |
| Median Occupied Household, Renter, Gross Rent | acs5 | numeric | |
| Percent Occupied Households, Owner | acs5 | numeric | |
| Percent Occupied Households, Total, One or More Selected Conditions | acs5 | numeric | |
| Percent Population Mobility, Moved From Within Same County in Past Year | acs5 | numeric | |
| Longitude | loc | numeric | |
| Latitude | loc | numeric | |
| Municipality Name | loc | character | |
| FEMA Special Flood Hazard Area | loc | logical | |
| First Street Factor | loc | numeric | |
| First Street Risk Direction | loc | numeric | |
| School Elementary District GEOID | loc | character | |
| School Secondary District GEOID | loc | character | |
| CMAP Walkability Score (No Transit) | loc | numeric | |
| CMAP Walkability Total Score | loc | numeric | |
| Airport Noise DNL | loc | numeric | |
| Property Tax Bill Aggregate Rate | other | numeric | |
| Number of PINs in Half Mile | prox | numeric | |
| Number of Bus Stops in Half Mile | prox | numeric | |
| Number of Foreclosures Per 1000 PINs (Past 5 Years) | prox | numeric | |
| Number of Schools in Half Mile | prox | numeric | |
| Number of Schools with Rating in Half Mile | prox | numeric | |
| Average School Rating in Half Mile | prox | numeric | |
| Nearest Bike Trail Distance (Feet) | prox | numeric | |
| Nearest Cemetery Distance (Feet) | prox | numeric | |
| Nearest CTA Route Distance (Feet) | prox | numeric | |
| Nearest CTA Stop Distance (Feet) | prox | numeric | |
| Nearest Hospital Distance (Feet) | prox | numeric | |
| Lake Michigan Distance (Feet) | prox | numeric | |
| Nearest Major Road Distance (Feet) | prox | numeric | |
| Nearest Metra Route Distance (Feet) | prox | numeric | |
| Nearest Metra Stop Distance (Feet) | prox | numeric | |
| Nearest Park Distance (Feet) | prox | numeric | |
| Nearest Railroad Distance (Feet) | prox | numeric | |
| Nearest Water Distance (Feet) | prox | numeric |
For the most part, condos are valued the same way as single- and multi-family residential property. We train a model using individual condo unit sales, predict the value of all units, and then apply any post-modeling adjustment.
However, because the CCAO has so little information about individual units, we must rely on the condominium percentage of ownership to differentiate between units in a building. This feature is effectively the proportion of the building’s overall value held by a unit. It is created when a condominium declaration is filed with the County (usually by the developer of the building). The critical assumption underlying the condo valuation process is that percentage of ownership correlates with current market value.
Percentage of ownership is used in two ways:
- It is used directly as a predictor/feature in the regression model to estimate differing unit values within the same building.
- It is used to reapportion unit values directly i.e. the value of a
unit is ultimately equal to
% of ownership * total building value.
Visually, this looks like:
Percentage of ownership is the single most important feature in the condo model. It determines almost all intra-building differences in unit values.
The condo model uses an engineered feature called strata to deliver much of its predictive power. Strata is the binned, time-weighted, 5-year average sale price of the building. There are two strata features used in the model, one with 10 bins and one with 300 bins. Buildings are binned across each triad using either quantiles or 1-dimensional k-means. A visual representation of quantile-based strata binning looks like:
To put strata in more concrete terms, the table below shows a sample 5-level strata. Each condominium unit would be assigned a strata from this table (Strata 1, Strata 2, etc.) based on the 5-year weighted average sale price of its building. All units in a building will have the same strata.
| Strata | Range of 5-year Average Sale Price |
|---|---|
| Strata 1 | $0 - $121K |
| Strata 2 | $121K - $149K |
| Strata 3 | $149K - $199K |
| Strata 4 | $199K - $276K |
| Strata 5 | $276K+ |
Some additional notes on strata:
- Strata is calculated in the ingest stage of this repository.
- Calculating the 5-year average sale price of a building requires at least 1 sale. Buildings with no sales have their strata imputed via KNN (using year built, number of units, and location as features).
- Number of bins (10 and 100) was chosen based on model performance. These numbers yielded the lowest root mean-squared error (RMSE).
The CCAO faces a number of ongoing issues specific to condominium modeling. We are currently working on processes to fix these issues. We list the issues here for the sake of transparency and to provide a sense of the challenges we face.
The current modeling methodology for condominiums makes two assumptions:
- Condos units within the same building are similar and will sell for similar amounts.
- If units are not similar, the percentage of ownership will accurately reflect and be proportional to any difference in value between units.
The model process works even in heterogeneous buildings as long as assumption 2 is met. For example, imagine a building with 8 identical units and 1 penthouse unit. This building violates assumption 1 because the penthouse unit is likely larger and worth more than the other 10. However, if the percentage of ownership of each unit is roughly proportional to its value, then each unit will still receive a fair assessment.
However, the model can produce poor results when both of these assumptions are violated. For example, if a building has an extreme mix of different units, each with the same percentage of ownership, then smaller, less expensive units will be overvalued and larger, more expensive units will be undervalued.
This problem is rare, but does occur in certain buildings with many heterogeneous units. Such buildings typically go through a process of secondary review to ensure the accuracy of the individual unit values.
The condo model relies on sales within the same building to calculate strata. This method works well for large buildings with many sales, but can break down when there are only 1 or 2 sales in a building. The primary danger here is unrepresentative sales, i.e. sales that deviate significantly from the real average value of a building’s units. When this happens, buildings can have their average unit sale value pegged too high or low.
Fortunately, buildings without any recent sales are relatively rare, as condos have a higher turnover rate than single and multi-family property. Smaller buildings with low turnover are the most likely to not have recent sales.
When no sales have occurred in a building in the 5 years prior to assessment, the building’s strata features are imputed. The model will look at nearby buildings that have similar unit counts/age and then try to assign an appropriate strata to the target building.
Most of the time, this technique produces reasonable results. However, buildings without sales still go through an additional round of review to ensure the accuracy of individual unit values.
Note: The FAQs listed here are for condo-specific questions. See the residential model documentation for more general FAQs.
Q: What are the most important features in the condo model?
As with the residential model, the importance of individual features varies by location and time. However, generally speaking, the most important features are:
- Location, location, location. Location is the largest driver of county-wide variation in condo value. We account for location using geospatial features like neighborhood.
- Condo percentage of ownership, which determines the intra-building variation in unit price.
- Condo building strata. Strata provides us with a good estimate of the average sale price of a building’s units.
Q: How do I see my condo building’s strata?
Individual building strata are not included with
assessment notices or shown on the CCAO’s website. However, strata are
stored in the sample data included in this repository. You can load the
data
(input/condo_strata_data.parquet)
using R and the read_parquet() function from the arrow library.
Q: How do I see the assessed value of other units in my building?
You can use the CCAO’s Address
Search to
see all the PINs and values associated with a specific condominium
building, simply leave the Unit Number field blank when submitting a
search.
Q: How do I view my unit’s percentage of ownership?
The percentage of ownership for individual units is printed on assessment notices. You may also be able to find it via your building’s board or condo declaration.
Installation and usage of this model is identical to the installation and usage of the residential model. Please follow the instructions listed there.
The data required to run these scripts is produced by the ingest stage, which uses SQL pulls from the CCAO’s Athena database as a primary data source. CCAO employees can run the ingest stage or pull the latest version of the input data from our internal DVC store using:
dvc pullPublic users can download data for each assessment year using the links
below. Each file should be placed in the input/ directory prior to
running the model pipeline.
For other data from the CCAO, please visit the Cook County Data Portal.
Distributed under the AGPL-3 License. See LICENSE for more information.
We welcome pull requests, comments, and other feedback via GitHub. For more involved collaboration or projects, please see the Developer Engagement Program documentation on our group wiki.
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