# Property Tax Disparity — Assessed Value vs Sale Price (`omegapointsolutions/property-tax-disparity`) Actor

Measures property tax assessment fairness from official county and state open data. Compares assessed values against actual recorded sale prices using the IAAO sales-ratio method, returning median ratio, COD, PRD and PRB with pass/fail thresholds. Covers 252 jurisdictions across IL, MD, CT and NY.

- **URL**: https://apify.com/omegapointsolutions/property-tax-disparity.md
- **Developed by:** [Omega Point Solutions](https://apify.com/omegapointsolutions) (community)
- **Categories:** Real estate, Business, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $20.00 / 1,000 record delivereds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

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In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

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- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
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For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Property Tax Disparity — Assessed Value vs Sale Price

Measures whether lower-value homes are assessed at a higher share of what they
actually sell for than higher-value homes — the property tax disparity — using the
**IAAO sales-ratio method** over official county and state open data.

The "market value" side of every ratio is a **recorded sale price from the
jurisdiction's own records**. No automated valuation model, no Zestimate, no
commercial valuation service. That matters twice: nothing here depends on another
company's terms of service, and a recorded sale price is evidence an assessment
appeal or a newsroom can actually stand behind. An AVM estimate is not.

### What you get

Per county, an IAAO ratio study:

| Measure | What it means | IAAO standard |
|---|---|---|
| **Median ratio** | Level of assessment | 0.90 – 1.10 |
| **COD** | Horizontal equity — are similar homes treated alike? | 5–15 homogeneous, 5–20 heterogeneous |
| **PRD** | Vertical equity — > 1.03 is regressive | 0.98 – 1.03 |
| **PRB** | Vertical equity by regression, robust to outliers | −0.05 – +0.05 |

Plus sale-price quintiles with the median ratio in each (the disparity, shown
directly), pass/fail on every threshold, and a plain-language verdict.

Set `output` to `parcels` to get every individual sale with its own ratio.

### Coverage — stated honestly

There are about **3,144 county-equivalents** in the US, and property tax is levied
locally in **all 50 states**. This Actor covers **252 jurisdictions across 4 states**:

| Source | Level | Jurisdictions | Notes |
|---|---|---|---|
| `maryland` | parcel | 24 | All 23 counties + Baltimore City, SDAT |
| `connecticut` | parcel | 169 | All towns, 1.14M sales 2001–2023 |
| `new-york` | jurisdiction | 58 | State's own municipality equity survey |
| `cook-county-il` | parcel | 1 | Chicago; assessment + sales joined on PIN |

**Why not all 3,144?** Because the data does not exist in machine-readable form for
most of them. A sales-ratio study needs two things published together: assessed
values *and* actual sale prices. A sweep of the Socrata open-data network across 12
query terms found only 15 domains publishing both, and most were a single city. The
majority of US counties publish assessments behind a parcel-lookup web form with no
bulk export and no sale prices at all, and many states treat sale prices as
non-public entirely (non-disclosure states such as TX, UT and MO).

Coverage grows by adding verified entries to `src/registry.py`. Every entry there
was queried live before being listed — a promising dataset *name* in a catalog is
not evidence it carries a sale price.

### Why a registry and not a loop

Three things differ per jurisdiction, and getting any of them wrong produces a
confidently wrong number:

1. **Level of assessment.** Cook County assesses class-2 residential at 10% of
   market value, Maryland at 100%, Connecticut at 70%. Comparing a raw assessed
   value to a sale price without dividing by the LOA is off by an order of magnitude.
2. **The arm's-length screen.** Ratio studies are only valid over open-market sales.
   Cook ships boolean filter flags; Maryland encodes it as English prose in
   `how_conveyed` (902k of its transfers are explicitly non-arm's-length); Connecticut
   ships neither. Leave quitclaims and $1 family transfers in and you manufacture
   fake regressivity at the low end.
3. **Shape.** Cook needs a PIN join across two datasets; MD and CT carry assessment
   and sale price on the same row; NY publishes no parcel sale price at all, so it
   returns the state's own municipality-level equity survey instead.

### Methodological caveats

These are surfaced rather than buried, because a buyer who misreads them publishes
something wrong:

- **Connecticut median ratios run low (~0.6–0.8).** CT towns revalue on a 5-year
  cycle, so an assessment can be 4 years stale against a current sale. In a rising
  market that depresses the median for reasons that are not inequity. COD, PRD and
  PRB are computed *within* a town and remain valid; read the median as a
  revaluation-lag signal.
- **Cook County's county-wide COD runs high (~29).** Cook is one county spanning 38
  townships with very different markets, and mixing them inflates dispersion.
  Township is carried on every parcel record so you can stratify.
- **Minimum sample.** Counties with fewer than 30 arm's-length sales in the year are
  skipped, not reported thin. IAAO sets 30 as the floor.
- **PRD and PRB can disagree.** PRD is a ratio of two means that a few extreme sales
  can swing; PRB fits the trend across the distribution. When they disagree the
  verdict says so and defers to PRB rather than picking the louder number.

### Network routing

Portals disagree about which IPs they trust, and not consistently with each other:
Cook County serves a datacenter IP fine but **403s the residential proxy**, while
Maryland does the exact opposite. Pinning either route breaks whichever source
disagrees. Every request therefore tries **direct first and falls back to the proxy
on 403/429**. Supply a free `socrataAppToken` to lift the anonymous rate limit on
large runs.

### Sources

All public, all official:

- Cook County Assessor — `datacatalog.cookcountyil.gov` (`uzyt-m557`, `wvhk-k5uv`)
- Maryland SDAT / MD Planning — `opendata.maryland.gov` (`ed4q-f8tm`)
- Connecticut OPM — `data.ct.gov` (`5mzw-sjtu`)
- NY Dept. of Taxation and Finance — `data.ny.gov` (`4sut-q3dt`)

Schemas verified live 2026-08-08.

### Who buys this

Assessment appeal firms and property tax consultants, civil-rights and housing
policy researchers, investigative newsrooms, county boards reviewing their own
assessor, and law firms building equal-protection claims on assessment uniformity.

# Actor input Schema

## `jurisdictions` (type: `array`):

Which sources to analyze. Use "all" for every verified jurisdiction. Options: cook-county-il (1 county, parcel level), maryland (24 jurisdictions, parcel level), connecticut (169 towns, parcel level), new-york (58 counties, state equity survey).

## `year` (type: `string`):

Calendar year of sales to analyze. Cook County and Maryland carry 2019-2026; Connecticut list years run 2001-2023. Defaults to 2023, the most recent year present in every parcel-level source.

## `output` (type: `string`):

"summary" returns one IAAO ratio study per county (the headline product). "parcels" returns every individual sale with its ratio. "both" returns both.

## `maxRecords` (type: `integer`):

Hard cap on delivered (and billed) records. Summary output is small — a few hundred records covers every jurisdiction. Raise this only when requesting parcel-level output.

## `maxParcelsPerJurisdiction` (type: `integer`):

How many arm's-length sales to pull per source before computing statistics. Larger samples give tighter COD and PRB estimates but take longer. IAAO requires at least 30 sales per county for a valid study. 5000 is enough for a stable statewide read; raise it for per-county precision.

## `socrataAppToken` (type: `string`):

Optional free Socrata application token. Lifts the strict anonymous rate limit on the open-data portals and reduces throttling on large runs. Get one at any Socrata portal under Developer Settings.

## `proxyGroups` (type: `array`):

Apify Proxy groups to route requests through. Maryland's portal returns 403 to datacenter IPs, so RESIDENTIAL is the default. Set to \["DATACENTER"] to run cheaper if your account lacks residential proxy access.

## `homogeneousMarket` (type: `boolean`):

Sets the IAAO COD acceptance ceiling: 15 for homogeneous single-family areas, 20 for heterogeneous or rural ones. Only affects the pass/fail flag, never the computed statistics.

## Actor input object example

```json
{
  "jurisdictions": [
    "all"
  ],
  "year": "2023",
  "output": "summary",
  "maxRecords": 5000,
  "maxParcelsPerJurisdiction": 5000,
  "proxyGroups": [
    "RESIDENTIAL"
  ],
  "homogeneousMarket": true
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "jurisdictions": [
        "all"
    ],
    "year": "2023"
};

// Run the Actor and wait for it to finish
const run = await client.actor("omegapointsolutions/property-tax-disparity").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "jurisdictions": ["all"],
    "year": "2023",
}

# Run the Actor and wait for it to finish
run = client.actor("omegapointsolutions/property-tax-disparity").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "jurisdictions": [
    "all"
  ],
  "year": "2023"
}' |
apify call omegapointsolutions/property-tax-disparity --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,omegapointsolutions/property-tax-disparity"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/9SezvAIV1aneLJbSe/builds/KMPGgMHFasbP8nSyS/openapi.json