CA property tax map

Property tax scrapers and a map. Online at https://www.officialdata.org/ca-property-tax.

The code is a work in progress and still contains references to large datafiles on my local filesystem. This should not be a blocker for most contributions, especially if you want to help with adding data for more counties.

Please feel free to contact me or open an issue if you have a question or need help. I am working on making this project more contributor-friendly!

How to add a county

Every county has a different property tax interface. The process usually looks like this:

1. Find a GIS file containing all Parcels (aka properties) in the county. Most counties have a data pages. Examples: Stanislaus, San Francisco. Parcels are uniquely identified by a string called an APN or AIN. If you're lucky, the file will also have zoning information. If you're really lucky, maybe it has tax info all in one place - but I haven't found that yet.

2. Investigate the county's property tax lookup page. Usually this is an enterprisey/legacy system. Example: San Francisco has one of the nicest systems.

3. Figure out how to automate lookup for property tax for a given APN. Best case scenario is that it's plain HTML returned by a GET request (e.g. SF). Or, the page might be Javascript dependent and requires a headless browser like puppeteer (e.g. Alameda). Even worse is if it's behind a captcha (like LA, but fortunately there is a workaround there if you manually obtain a valid session cookie).

4. Write the scraper and parser (see below).

Crawling

This project crawls and saves data from county property tax sites. The data is processed into a CSV that is loaded by the web app. Crawlers are written in Python. Set up your Python environment using Poetry.

There are two steps to the crawl process:

1. scrape.py - Downloads the HTML of each property tax page.
2. parse.py - Parses the HTML of each property tax page into CSV output: Street address,APN,Centroid Lat,Centroid Lng,Annual Tax,County code. This gzipped CSV is served by the app.

Here's a sample from San Mateo:

101 SAN DIEGO AVE,003011260,-122.4663697567297,37.708206018415844,9173.9,SM
125 SHAKESPEARE ST,003031010,-122.46300058163514,37.708164756800194,4494.82,SM
101 SANTA CRUZ AVE,003013320,-122.46550298776202,37.70817709008342,9383.36,SM
102 SANTA CRUZ AVE,003011010,-122.4660214643012,37.70820149758747,3518.94,SM
860 BRUNSWICK ST,004261220,-122.45122997419291,37.70810413461794,9187.42,SM
425 W MAPLE WAY,068100240,-122.2796584694945,37.45555043044969,19058.62,SM
100 SANTA BARBARA AVE,003013390,-122.465166112976,37.70817368941809,11951.38,SM
55 RICE ST,004031010,-122.45759671865413,37.70822035145098,11856.58,SM
16 RAVILLA CT,004340490,-122.44807089917549,37.70835813680627,3148.0,SM
34 RICE ST,004032010,-122.45677712332471,37.70823557691507,2218.78,SM

I separate the parse and scrape steps so that we can improve the parser without having to do a very costly and slow scrape of tax pages, and so I can share raw scraped datasets with you for validation and other purposes.

Crawl/scrape/parse code lives in the scrapers/ directory. Each county has scrape.py and parse.py. The "scrape" step downloads the HTML tax page for every Assessor Parcel Number (APN). The "parse" step pulls out tax and address information.

Note that some counties struggle to serve traffic or will rate limit. It's important that crawling is respectful to the technical limitations of the county, otherwise this data will become even harder to get.

Serving

A simple node app hosts the map and a /lookup endpoint. On start it loads all parsed data into a geospatial index. Install node dependencies with yarn install and then run the app in server/index.js. Then access via http://localhost:13000

TODO

• Add support for more counties
• Expose a toggle for commercial/residential properties only (where data is available)
• Obtain Redfin or Zillow or other property value estimates so that we can display property tax as a % of property value
• Automatically surface interesting places?