Zillow Tax History Scraper

Zillow shows a property's tax bills and past sales in a tidy little widget, but try pulling that into a model and you are stuck copying numbers cell by cell. This actor does the lifting. Hand it a Zillow property ID, a listing link, or a plain street address, and it gives you the full tax and price history for that home as one flat row. Every assessed value, every annual tax bill, and every sale lands in numbered columns you can drop straight into a spreadsheet.

What you get

One row per property, with two kinds of history laid out side by side so nothing needs reshaping after export:

• Sale and price events — for each of up to 10 events: sale1Date, sale1Price, sale1PricePerSqft, sale1ChangePct, sale1EventType, sale1ListingSource, sale1BuyerAgent, sale1SellerAgent, running through sale10*.
• Tax records — for each of up to 10 years: levy1Year, levy1TaxPaid, levy1TaxChangePct, levy1AssessedValue, levy1AssessedChangePct, running through levy10*.
• Lookup context — queryInput, zillowPropertyId, collectedAt, and errorMessage so you can trace every row back to what you asked for.

Every rate from Zillow arrives as a plain percentage, so there are no decimal fractions to convert on your end.

Quick start

1. Click Try for free and open the input form.
2. Fill in at least one of Zillow property IDs, Listing page URLs, or Property addresses — mix all three in a single run if you like.
3. Set Run timeout (seconds) to fit the size of your list (about 30 seconds per property).
4. Press Start, then export the dataset as JSON, CSV, Excel, or XML when the run finishes.

Use cases

• Deal screening — pull tax and price history for a shortlist of homes and rank them on tax burden or recent price moves
• Housing market research — build a dataset of assessed values and tax trends across a neighborhood or ZIP
• Comparative market analysis — line up past sale prices and price-per-square-foot for nearby properties
• Property tax appeals — gather year-over-year assessed value and tax changes to support a case
• Investment underwriting — feed historical tax bills into cash-flow models without manual entry
• Data enrichment — turn a list of addresses or Zillow links you already have into a clean history table

Input

Provide at least one of the three lookup fields. You can combine IDs, URLs, and addresses freely in one run.

Field	Type	Required	Description
propertyIds	array of strings	One of propertyIds, listingPageUrls, or propertyAddresses	Zillow ZPIDs to look up, one per line. Prefilled with 29489015, 32345923.
listingPageUrls	array of strings	One of propertyIds, listingPageUrls, or propertyAddresses	Zillow home detail links, one per line. Works whether or not the ID sits in the path.
propertyAddresses	array of strings	One of propertyIds, listingPageUrls, or propertyAddresses	Full street addresses, one per line. The actor resolves the matching ID first.
timeoutSeconds	integer	No	Total run budget in seconds. Raise it for longer lists, around 30 seconds per property. Default 360.

Example input

{
    "propertyIds": ["29489015", "32345923"],
    "listingPageUrls": [
        "https://www.zillow.com/homedetails/1234-Maple-Grove-Denver-CO-80205/29489015_zpid/"
    ],
    "propertyAddresses": ["1234 Maple Grove Dr, Denver, CO 80205"],
    "timeoutSeconds": 360
}

Output

Each property becomes a single wide row. Up to 10 sale events sit in sale1* through sale10* columns and up to 10 tax years in levy1* through levy10*. Columns for events a property does not have are simply left out, so a run of 50 properties gives you 50 rows.

Example output

{
    "queryInput": "1234 Maple Grove Dr, Denver, CO 80205",
    "zillowPropertyId": "29489015",
    "sale1Date": "2026-01-18",
    "sale1Price": 845000,
    "sale1PricePerSqft": 312,
    "sale1ChangePct": -2.31,
    "sale1EventType": "Listed for sale",
    "sale1ListingSource": "REcolorado",
    "sale1BuyerAgent": null,
    "sale1SellerAgent": null,
    "sale2Date": "2021-06-09",
    "sale2Price": 612000,
    "sale2PricePerSqft": 226,
    "sale2ChangePct": 9.82,
    "sale2EventType": "Sold",
    "sale2ListingSource": "REcolorado",
    "sale2BuyerAgent": "Maria Delgado",
    "sale2SellerAgent": "Tom Hargrove",
    "sale3Date": "2016-04-22",
    "sale3Price": 478000,
    "sale3PricePerSqft": 177,
    "sale3ChangePct": 0,
    "sale3EventType": "Sold",
    "sale3ListingSource": "REcolorado",
    "sale3BuyerAgent": null,
    "sale3SellerAgent": "Tom Hargrove",
    "levy1Year": 2025,
    "levy1TaxPaid": 6480,
    "levy1TaxChangePct": 4.12,
    "levy1AssessedValue": 689000,
    "levy1AssessedChangePct": 3.05,
    "levy2Year": 2024,
    "levy2TaxPaid": 6224,
    "levy2TaxChangePct": 2.88,
    "levy2AssessedValue": 668600,
    "levy2AssessedChangePct": 6.41,
    "levy3Year": 2023,
    "levy3TaxPaid": 6050,
    "levy3TaxChangePct": -1.07,
    "levy3AssessedValue": 628300,
    "levy3AssessedChangePct": 5.18,
    "collectedAt": "2026-06-30T10:32:00+00:00",
    "errorMessage": null
}

Output fields

Field	Type	Description
queryInput	string	The exact value you submitted — ID, URL, or address
zillowPropertyId	string	The Zillow property ID the row resolved to
sale{n}Date	string	Date of sale event n
sale{n}Price	integer	Sale amount in USD
sale{n}PricePerSqft	integer	Price per square foot
sale{n}ChangePct	number	Percent change from the previous event; negative means a drop
sale{n}EventType	string	Event label such as Sold, Listed for sale, or Price change
sale{n}ListingSource	string	MLS or listing source name
sale{n}BuyerAgent	string	Buyer agent name when shown
sale{n}SellerAgent	string	Seller agent name when shown
levy{n}Year	integer	Tax year
levy{n}TaxPaid	number	Annual tax billed in USD
levy{n}TaxChangePct	number	Year-over-year change in tax billed as a percent
levy{n}AssessedValue	integer	Assessed value in USD
levy{n}AssessedChangePct	number	Year-over-year change in assessed value as a percent
collectedAt	string	ISO 8601 time the row was captured
errorMessage	string	Present only when the lookup failed after every retry

Tips for best results

• Start with a handful. Run 5 to 10 properties first to confirm the columns line up with your pipeline before sending a big list.
• Match the timeout to the batch. The 360-second default suits short runs. Budget about 30 seconds per property for longer lists, and set the same value under Timeout in your Apify run settings.
• Mix input types freely. IDs, URLs, and addresses can all sit in the same run; the actor resolves each to a property ID before pulling history.
• Skip the ID lookup when you can. Passing a property ID or a URL with the ID in the path goes straight to history, while a bare address or slug needs one extra resolve step.
• Filter failed rows by errorMessage. When a lookup fails after all retries, the actor still writes a row with errorMessage filled in so you can re-run just those inputs.

How can I use Zillow tax history data?

How can I use the Zillow Tax History Scraper to pull property tax records in bulk? Paste a list of Zillow property IDs, listing URLs, or addresses, and each one comes back as a row holding up to 10 years of tax history — levy{n}Year, levy{n}TaxPaid, levy{n}AssessedValue, and the year-over-year change percentages. It is a quick way to assemble a tax dataset for a whole street or ZIP without copying numbers by hand.

How can I get Zillow price history for comparative market analysis? Each property row carries up to 10 sale and listing events in sale{n}Date, sale{n}Price, sale{n}PricePerSqft, and sale{n}EventType. Pull the same data for a set of nearby homes, line up the price-per-square-foot, and you have the backbone of a CMA ready for a spreadsheet or BI tool.

How can I track assessed value changes for a property tax appeal? Look up a single address and read the levy{n}AssessedValue and levy{n}AssessedChangePct columns across the years on record. Seeing how the assessment moved relative to the actual tax billed gives you the figures to build an appeal around.

How can I export Zillow property history to CSV or Excel? Run the actor, then use Apify's export options to download the dataset as CSV, Excel, JSON, or XML. Because every property is already a flat row of numbered columns, the file opens cleanly in any spreadsheet with no reshaping needed.

Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses or private contact information. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

Support

Questions, feature requests, or a field you'd like added? Reach out at data.apify@proton.me and we'll get back to you.