diff --git a/.gitignore b/.gitignore index 6ec62cc..3a4727e 100644 --- a/.gitignore +++ b/.gitignore @@ -20,6 +20,7 @@ cache/ *.zip *.csv *.xlsx +!condo_nonlivable_demo.xlsx *.xlsm *.html *.rmarkdown diff --git a/README.Rmd b/README.Rmd index 6bac107..500b362 100644 --- a/README.Rmd +++ b/README.Rmd @@ -34,6 +34,8 @@ This repository contains code, data, and documentation for the Cook County Asses | 2021 | City | County-wide LightGBM model | R (Tidyverse / Tidymodels) | [Link](https://github.com/ccao-data/model-condo-avm/tree/2021-assessment-year) | | 2022 | North | County-wide LightGBM model | R (Tidyverse / Tidymodels) | [Link](https://github.com/ccao-data/model-condo-avm/tree/2022-assessment-year) | | 2023 | South | County-wide LightGBM model | R (Tidyverse / Tidymodels) | [Link](https://github.com/ccao-data/model-condo-avm/tree/2023-assessment-year) | +| 2024 | City | County-wide LightGBM model | R (Tidyverse / Tidymodels) | [Link](https://github.com/ccao-data/model-condo-avm/tree/2024-assessment-year) + | # Model Overview @@ -42,52 +44,127 @@ The duty of the Cook County Assessor's Office is to value property in a fair, ac * [A description of the differences between the residential model and this (condominium) model](#differences-compared-to-the-residential-model) * [An outline of ongoing issues specific to condominium assessments](#ongoing-issues) -The repository itself contains the [code](./pipeline) and [data](./input) 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 repository itself contains the [code](./pipeline) 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. ## Differences Compared to the Residential Model -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 Cook County Assessor's Office has started to track a limited number of characteristics (building-level square footage, unit-level square footage, bedrooms, and bathrooms) for condominiums, but the data we have ***varies in both the characteristics available and their completeness*** between triads. Staffing limitations have forced the office to prioritize smaller condo buildings less likely to have recent unit sales in certain parts of the county. 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 which must instead be gathered from listings and a number of additional third-party sources. -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: +The only complete information our office currently 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: 1. Condos are more homogeneous than single/multi-family properties, i.e. the range of potential condo sale prices is much narrower. 2. 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](#condo-strata) for more information about how strata is used and calculated. -> :warning: **NOTE** :warning: -> -> 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. - ### Features Used -Because our office (mostly) cannot observe individual condo unit characteristics, we must rely on aggregate geospatial features, economic features, [strata](#condo-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. +Because our individual condo unit characteristics are sparse and incomplete, we primarily must rely on aggregate geospatial features, economic features, [strata](#condo-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. ```{r features_used, message=FALSE, echo=FALSE} library(dplyr) +library(glue) +library(jsonlite) +library(purrr) library(tidyr) library(yaml) condo_params <- read_yaml("params.yaml") -condo_preds <- condo_params$model$predictor$all +condo_preds <- as_tibble(condo_params$model$predictor$all) + +# Some values are derived in the model itself, so they are not documented +# in the dbt DAG and need to be documented here +# nolint start +hardcoded_descriptions <- tribble( + ~"column", ~"description", + "sale_year", "Sale year calculated as the number of years since 0 B.C.E", + "sale_day", + "Sale day calculated as the number of days since January 1st, 1997", + "sale_quarter_of_year", "Character encoding of quarter of year (Q1 - Q4)", + "sale_month_of_year", "Character encoding of month of year (Jan - Dec)", + "sale_day_of_year", "Numeric encoding of day of year (1 - 365)", + "sale_day_of_month", "Numeric encoding of day of month (1 - 31)", + "sale_day_of_week", "Numeric encoding of day of week (1 - 7)", + "sale_post_covid", "Indicator for whether sale occurred after COVID-19 was widely publicized (around March 15, 2020)", + "strata_1", + glue("Condominium Building Strata - {condo_params$input$strata$k_1} Levels"), + "strata_2", + glue("Condominium Building Strata - {condo_params$input$strata$k_2} Levels") +) +# nolint end + +# Load the dbt DAG from our prod docs site +dbt_manifest <- fromJSON( + "https://ccao-data.github.io/data-architecture/manifest.json" +) + +# nolint start: cyclomp_linter +get_column_description <- function(colname, dag_nodes, hardcoded_descriptions) { + # Retrieve the description for a column `colname` either from a set of + # dbt DAG nodes (`dag_nodes`) or a set of hardcoded descriptions + # (`hardcoded_descriptions`). Column descriptions that come from dbt DAG nodes + # will be truncated starting from the first period to reflect the fact that + # we use periods in our dbt documentation to separate high-level column + # summaries from their detailed notes + # + # Prefer the hardcoded descriptions, if they exist + if (colname %in% hardcoded_descriptions$column) { + return( + hardcoded_descriptions[ + match(colname, hardcoded_descriptions$column), + ]$description + ) + } + # If no hardcoded description exists, fall back to checking the dbt DAG + for (node_name in ls(dag_nodes)) { + node <- dag_nodes[[node_name]] + for (column_name in ls(node$columns)) { + if (column_name == colname) { + description <- node$columns[[column_name]]$description + if (!is.null(description) && trimws(description) != "") { + # Strip everything after the first period, since we use the first + # period as a delimiter separating a column's high-level summary from + # its detailed notes in our dbt docs + summary_description <- strsplit(description, ".", fixed = TRUE)[[1]][1] + return(gsub("\n", " ", summary_description)) + } + } + } + } + # No match in either the hardcoded descriptions or the dbt DAG, so fall + # back to an empty string + return("") +} +# nolint end + +# Make a vector of column descriptions that we can add to the param tibble +# as a new column +param_notes <- condo_preds$value %>% + ccao::vars_rename(names_from = "model", names_to = "athena") %>% + map(~ get_column_description( + .x, dbt_manifest$nodes, hardcoded_descriptions + )) %>% + unlist() res_params <- read_yaml( "https://raw.githubusercontent.com/ccao-data/model-res-avm/master/params.yaml" ) res_preds <- res_params$model$predictor$all -condo_unique_preds <- setdiff(condo_preds, res_preds) +condo_unique_preds <- setdiff(condo_preds$value, res_preds) -ccao::vars_dict %>% - inner_join( - as_tibble(condo_preds), - by = c("var_name_model" = "value") +condo_preds %>% + mutate(description = param_notes) %>% + left_join( + ccao::vars_dict, + by = c("value" = "var_name_model") ) %>% distinct( - var_name_model, `Feature Name` = var_name_pretty, Category = var_type, Type = var_data_type, + Notes = description, + value, ) %>% mutate( Category = recode( @@ -106,13 +183,13 @@ ccao::vars_dict %>% ) ) %>% mutate(`Unique to Condo Model` = ifelse( - var_name_model %in% condo_unique_preds | + value %in% condo_unique_preds | `Feature Name` %in% c("Condominium Building Year Built", "Condominium % Ownership"), "X", "" )) %>% arrange(desc(`Unique to Condo Model`), Category) %>% - select(-var_name_model) %>% + select(-value) %>% knitr::kable(format = "markdown") ``` @@ -131,8 +208,16 @@ Visually, this looks like: ![](docs/figures/valuation_perc_owner.png) +For what the office terms "nonlivable" spaces — parking spaces, storage space, and common area — the breakout of value works differently. See [this excel sheet](docs/spreadsheets/condo_nonlivable_demo.xlsx) for an interactive example of how nonlivable spaces are valued based on the total value of a building's livable space. + Percentage of ownership is the single most important feature in the condo model. It determines almost all intra-building differences in unit values. +### Multi-sales + +The condo model is trained on a select number of "multi-sales" in addition to single-parcel sales. Multi-sales are sales that include more than one parcel. In the case of condominiums, many units are sold bundled with deeded parking spaces that are separate parcels. These two-parcel sales are highly reflective of the unit's actual market price. We split the total value of these two-parcel sales according to their relative percent of ownership before using them for training. For a \$100,000 sale of a unit (4% ownership) and a parking space (1% ownership), the sale would be adjusted to \$80,000: + +$$\frac{0.04}{0.04 + 0.01} * \$100,000 = \$80,000$$ + ## Condo Strata 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: @@ -247,6 +332,14 @@ Public users can download data for each assessment year using the links below. E - [land_nbhd_rate_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2023/land_nbhd_rate_data.parquet) - [training_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2023/training_data.parquet) +#### 2024 + +- [assessment_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2024/assessment_data.parquet) +- [char_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2024/char_data.parquet) +- [condo_strata_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2024/condo_strata_data.parquet) +- [land_nbhd_rate_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2024/land_nbhd_rate_data.parquet) +- [training_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2024/training_data.parquet) + For other data from the CCAO, please visit the [Cook County Data Portal](https://datacatalog.cookcountyil.gov/). # License diff --git a/README.md b/README.md index 81e7aed..13c0261 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,7 @@ Table of Contents Model](#differences-compared-to-the-residential-model) - [Features Used](#features-used) - [Valuation](#valuation) + - [Multi-sales](#multi-sales) - [Condo Strata](#condo-strata) - [Ongoing Issues](#ongoing-issues) - [Unit Heterogeneity](#unit-heterogeneity) @@ -45,6 +46,8 @@ prior year models can be found at the following links: | 2021 | City | County-wide LightGBM model | R (Tidyverse / Tidymodels) | [Link](https://github.com/ccao-data/model-condo-avm/tree/2021-assessment-year) | | 2022 | North | County-wide LightGBM model | R (Tidyverse / Tidymodels) | [Link](https://github.com/ccao-data/model-condo-avm/tree/2022-assessment-year) | | 2023 | South | County-wide LightGBM model | R (Tidyverse / Tidymodels) | [Link](https://github.com/ccao-data/model-condo-avm/tree/2023-assessment-year) | +| 2024 | City | County-wide LightGBM model | R (Tidyverse / Tidymodels) | [Link](https://github.com/ccao-data/model-condo-avm/tree/2024-assessment-year) | +| | | | | | # Model Overview @@ -59,26 +62,32 @@ contains: - [An outline of ongoing issues specific to condominium assessments](#ongoing-issues) -The repository itself contains the [code](./pipeline) and -[data](./input) 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 repository itself contains the [code](./pipeline) 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. ## Differences Compared to the Residential Model -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: +The Cook County Assessor’s Office has started to track a limited number +of characteristics (building-level square footage, unit-level square +footage, bedrooms, and bathrooms) for condominiums, but the data we have +***varies in both the characteristics available and their +completeness*** between triads. Staffing limitations have forced the +office to prioritize smaller condo buildings less likely to have recent +unit sales in certain parts of the county. 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 which must instead be +gathered from listings and a number of additional third-party sources. + +The only complete information our office currently 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: 1. Condos are more homogeneous than single/multi-family properties, i.e. the range of potential condo sale prices is much narrower. @@ -89,97 +98,97 @@ We leverage these qualities to produce what we call ***strata***, a feature unique to the condo model. See [Condo Strata](#condo-strata) for more information about how strata is used and calculated. -> :warning: **NOTE** :warning: -> -> 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. - ### Features Used -Because our office (mostly) cannot observe individual condo unit -characteristics, we must rely on aggregate geospatial features, economic -features, [strata](#condo-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 | | +Because our individual condo unit characteristics are sparse and +incomplete, we primarily must rely on aggregate geospatial features, +economic features, [strata](#condo-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 | Notes | Unique to Condo Model | +|:------------------------------------------------------------------------|:---------------|:----------|:------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------| +| Condominium Building Year Built | Characteristic | numeric | Year the property was constructed | X | +| Total Condominium Building Livable Parcels | Characteristic | numeric | Count of livable 14-digit PINs (AKA condo units) | X | +| Total Condominium Building Non-Livable Parcels | Characteristic | numeric | Count of non-livable 14-digit PINs | X | +| Condominium Building Is Mixed Use | Characteristic | logical | The 10-digit PIN (building) contains a 14-digit PIN that is neither class 299 nor 399 | X | +| Total Condominium Building Square Footage | Characteristic | numeric | Square footage of the *building* (PIN10) containing this unit | X | +| Building Square Footage | Characteristic | numeric | Square footage of the *building* (PIN10) containing this unit | X | +| Condominium Unit Square Footage | Characteristic | numeric | Square footage of the condominium unit associated with this PIN | X | +| Unit Square Footage | Characteristic | numeric | Square footage of the condominium unit associated with this PIN | X | +| Condominium Unit Bedrooms | Characteristic | numeric | Number of bedrooms in the building | X | +| Bedrooms | Characteristic | numeric | Number of bedrooms in the building | X | +| Condominium Unit Half Baths | Characteristic | numeric | Number of half baths | X | +| Half Baths | Characteristic | numeric | Number of half baths | X | +| Condominium Unit Full Baths | Characteristic | numeric | Number of full bathrooms | X | +| Full Baths | Characteristic | numeric | Number of full bathrooms | X | +| Condominium % Ownership | Meta | numeric | Proration rate applied to the PIN | X | +| Condominium Building Strata 1 | Meta | character | Condominium Building Strata - 10 Levels | X | +| Condominium Building Strata 2 | Meta | character | Condominium Building Strata - 100 Levels | X | +| Land Square Feet | Characteristic | numeric | Square footage of the land (not just the building) of the property | | +| Township Code | Meta | character | Cook County township code | | +| Neighborhood Code | Meta | character | Assessor neighborhood code | | +| Sale Year | Time | numeric | Sale year calculated as the number of years since 0 B.C.E | | +| Sale Day | Time | numeric | Sale day calculated as the number of days since January 1st, 1997 | | +| Sale Quarter of Year | Time | character | Character encoding of quarter of year (Q1 - Q4) | | +| Sale Month of Year | Time | character | Character encoding of month of year (Jan - Dec) | | +| Sale Day of Year | Time | numeric | Numeric encoding of day of year (1 - 365) | | +| Sale Day of Month | Time | numeric | Numeric encoding of day of month (1 - 31) | | +| Sale Day of Week | Time | numeric | Numeric encoding of day of week (1 - 7) | | +| Sale After COVID-19 | Time | logical | Indicator for whether sale occurred after COVID-19 was widely publicized (around March 15, 2020) | | +| Percent Population Age, Under 19 Years Old | acs5 | numeric | Percent of the people 17 years or younger | | +| Percent Population Age, Over 65 Years Old | acs5 | numeric | Percent of the people 65 years or older | | +| Median Population Age | acs5 | numeric | Median age for whole population | | +| Percent Population Mobility, Moved From Other State in Past Year | acs5 | numeric | Percent of people (older than 1 year) who moved from another state in the past 12 months | | +| Percent Households Family, Married | acs5 | numeric | Percent of households that are family, married | | +| Percent Households Nonfamily, Living Alone | acs5 | numeric | Percent of households that are non-family, alone (single) | | +| Percent Population Education, High School Degree | acs5 | numeric | Percent of people older than 25 who attained a high school degree | | +| Percent Population Education, Bachelor Degree | acs5 | numeric | Percent of people older than 25 who attained a bachelor’s degree | | +| Percent Population Education, Graduate Degree | acs5 | numeric | Percent of people older than 25 who attained a graduate degree | | +| Percent Population Income, Below Poverty Level | acs5 | numeric | Percent of people above the poverty level in the last 12 months | | +| Median Income, Household in Past Year | acs5 | numeric | Median income per household in the past 12 months | | +| Median Income, Per Capita in Past Year | acs5 | numeric | Median income per capita in the past 12 months | | +| Percent Population Income, Received SNAP in Past Year | acs5 | numeric | Percent of households that received SNAP in the past 12 months | | +| Percent Population Employment, Unemployed | acs5 | numeric | Percent of people 16 years and older unemployed | | +| Median Occupied Household, Total, Year Built | acs5 | numeric | Median year built for all occupied households | | +| Median Occupied Household, Renter, Gross Rent | acs5 | numeric | Median gross rent for only renter-occupied units | | +| Percent Occupied Households, Owner | acs5 | numeric | Percent of households that are owner-occupied | | +| Percent Occupied Households, Total, One or More Selected Conditions | acs5 | numeric | Percent of occupied households with selected conditions | | +| Percent Population Mobility, Moved From Within Same County in Past Year | acs5 | numeric | Percent of people (older than 1 year) who moved in county in the past 12 months | | +| Active Homeowner Exemption | ccao | logical | Parcel has an active homeowner exemption | | +| Corner Lot | ccao | logical | Corner lot indicator | | +| Number of Years Active Homeowner Exemption | ccao | numeric | Number of years parcel has had an active homeowner exemption | | +| Longitude | loc | numeric | X coordinate in degrees (global longitude) | | +| Latitude | loc | numeric | Y coordinate in degrees (global latitude) | | +| Census Tract GEOID | loc | character | 11-digit ACS/Census tract GEOID | | +| First Street Factor | loc | numeric | First Street flood factor The flood factor is a risk score, where 10 is the highest risk and 1 is the lowest risk | | +| School Elementary District GEOID | loc | character | School district (elementary) GEOID | | +| School Secondary District GEOID | loc | character | School district (secondary) GEOID | | +| CMAP Walkability Score (No Transit) | loc | numeric | CMAP walkability score for a given PIN, excluding transit walkability | | +| CMAP Walkability Total Score | loc | numeric | CMAP walkability score for a given PIN, including transit walkability | | +| Municipality Name | loc | character | Taxing district name, as seen on Cook County tax bills | | +| Property Tax Bill Aggregate Rate | other | numeric | Tax bill rate for the taxing district containing a given PIN | | +| Number of PINs in Half Mile | prox | numeric | Number of PINs within half mile | | +| Number of Bus Stops in Half Mile | prox | numeric | Number of bus stops within half mile | | +| Number of Foreclosures Per 1000 PINs (Past 5 Years) | prox | numeric | Number of foreclosures per 1000 PINs, within half mile (past 5 years) | | +| Number of Schools in Half Mile | prox | numeric | Number of schools (any kind) within half mile | | +| Total Airport Noise DNL | prox | numeric | Estimated DNL for a PIN, assuming a baseline DNL of 50 (“quiet suburban”) and adding predicted noise from O’Hare and Midway airports to that baseline | | +| Nearest Bike Trail Distance (Feet) | prox | numeric | Nearest bike trail distance (feet) | | +| Nearest Cemetery Distance (Feet) | prox | numeric | Nearest cemetery distance (feet) | | +| Nearest CTA Route Distance (Feet) | prox | numeric | Nearest CTA route distance (feet) | | +| Nearest CTA Stop Distance (Feet) | prox | numeric | Nearest CTA stop distance (feet) | | +| Nearest Hospital Distance (Feet) | prox | numeric | Nearest hospital distance (feet) | | +| Lake Michigan Distance (Feet) | prox | numeric | Distance to Lake Michigan shoreline (feet) | | +| Nearest Major Road Distance (Feet) | prox | numeric | Nearest major road distance (feet) | | +| Nearest Metra Route Distance (Feet) | prox | numeric | Nearest Metra route distance (feet) | | +| Nearest Metra Stop Distance (Feet) | prox | numeric | Nearest Metra stop distance (feet) | | +| Nearest Park Distance (Feet) | prox | numeric | Nearest park distance (feet) | | +| Nearest Railroad Distance (Feet) | prox | numeric | Nearest railroad distance (feet) | | +| Nearest Secondary Road Distance (Feet) | prox | numeric | Nearest secondary road distance (feet) | | +| Nearest University Distance (Feet) | prox | numeric | Nearest university distance (feet) | | +| Nearest Vacant Land Parcel Distance (Feet) | prox | numeric | Nearest vacant land (class 100) parcel distance (feet) | | +| Nearest Water Distance (Feet) | prox | numeric | Nearest water distance (feet) | | +| Nearest Golf Course Distance (Feet) | prox | numeric | Nearest golf course distance (feet) | | ### Valuation @@ -211,10 +220,30 @@ Visually, this looks like: ![](docs/figures/valuation_perc_owner.png) +For what the office terms “nonlivable” spaces — parking spaces, storage +space, and common area — the breakout of value works differently. See +[this excel sheet](docs/spreadsheets/condo_nonlivable_demo.xlsx) for an +interactive example of how nonlivable spaces are valued based on the +total value of a building’s livable space. + Percentage of ownership is the single most important feature in the condo model. It determines almost all intra-building differences in unit values. +### Multi-sales + +The condo model is trained on a select number of “multi-sales” in +addition to single-parcel sales. Multi-sales are sales that include more +than one parcel. In the case of condominiums, many units are sold +bundled with deeded parking spaces that are separate parcels. These +two-parcel sales are highly reflective of the unit’s actual market +price. We split the total value of these two-parcel sales according to +their relative percent of ownership before using them for training. For +a \$100,000 sale of a unit (4% ownership) and a parking space (1% +ownership), the sale would be adjusted to \$80,000: + +$$\frac{0.04}{0.04 + 0.01} * \$100,000 = \$80,000$$ + ## Condo Strata The condo model uses an engineered feature called *strata* to deliver @@ -398,6 +427,14 @@ running the model pipeline. - [land_nbhd_rate_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2023/land_nbhd_rate_data.parquet) - [training_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2023/training_data.parquet) +#### 2024 + +- [assessment_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2024/assessment_data.parquet) +- [char_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2024/char_data.parquet) +- [condo_strata_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2024/condo_strata_data.parquet) +- [land_nbhd_rate_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2024/land_nbhd_rate_data.parquet) +- [training_data.parquet](https://ccao-data-public-us-east-1.s3.amazonaws.com/models/inputs/condo/2024/training_data.parquet) + For other data from the CCAO, please visit the [Cook County Data Portal](https://datacatalog.cookcountyil.gov/). diff --git a/docs/spreadsheets/condo_nonlivable_demo.xlsx b/docs/spreadsheets/condo_nonlivable_demo.xlsx new file mode 100644 index 0000000..9ed8428 Binary files /dev/null and b/docs/spreadsheets/condo_nonlivable_demo.xlsx differ