Use Zillow Research Home Value & Market Heat Scraper with Python

Get US state-level home value trends, active-listing inventory, and metro-level market heat index data from Zillow's published research files. Filter by state and cap the number of records returned.

1. Choose the data you need

Review the published README for supported collection modes, examples, and limits. The recipe below uses the current published input definition.

2. Prepare a source-based input

This JSON contains published prefills and required-field defaults. Review every value for your task before running it; it is not a promise of successful collection.

{
  "category": "all",
  "dataset": "market_temp_index_metro_uc_sfrcondo_month",
  "includeChanges": false,
  "maxItems": 3,
  "mode": "latest"
}

Download input.example.json and save your reviewed copy as input.json.

3. Start an Actor run

Set APIFY_TOKEN in your environment. Python 3.10 or newer is sufficient; this recipe uses no third-party packages.

Execution may incur charges. Review current pricing on Apify. The request asks for a 120-second timeout and a charge limit; these settings are subject to Apify’s pricing and do not guarantee an all-inclusive spending cap.

import json
import os
import urllib.request

# Save the schema-derived example as input.json; review and edit it first.
with open("input.json", encoding="utf-8") as stream:
    actor_input = json.load(stream)

token = os.environ["APIFY_TOKEN"]
request = urllib.request.Request(
    "https://api.apify.com/v2/acts/mhbqXuxSnwXKuO0uP/runs?timeout=120&maxTotalChargeUsd=0.50",
    data=json.dumps(actor_input).encode("utf-8"),
    headers={"Authorization": "Bearer " + token, "Content-Type": "application/json"},
    method="POST",
)
# This starts a potentially billable run. Do not automatically retry this POST.
with urllib.request.urlopen(request, timeout=40) as response:
    run = json.load(response)["data"]
print("Run ID:", run["id"])
print("Status:", run["status"])
# Inspect and download results in Apify Console once the run succeeds.

4. Inspect the actual results

Open the run in Apify Console and wait for a successful completion. Use the published output schema to locate results and the dataset schema to understand declared views. Missing or optional record fields must not be treated as guaranteed values.

Zillow Research observations: home values, rents, inventory, days to pending, price cuts, sales, market heat, forecasts, new construction and affordability by US region and date.

Questions before you run

Generated from source build LmI1eGACKbWePrgNH and its README and schemas. Source changes regenerate this guide; no third-party editorial claims are inferred.