Massachusetts Zillow: For-Sale & Rentals Data Scraped - Refreshed Daily

The client needed continuous visibility into real estate in Massachusetts on Zillow.com: all property types, both for sale and for rent, refreshed every day. The task was not a one-time export but a standing feed showing what is on the market right now, described down to the individual unit.

Sources: zillow.com
Coverage: all property types for sale and for rent in the state of Massachusetts, at unit level
Frequency: daily
Volume: approximately 22,000 rows per day
Setup and launch: 5 days of development and testing, 1 day for a full collection run

One property page can hold many listings

The trickiest structural part of Zillow is not the listing page itself. It is buildings that contain multiple units. Behind a single property page there can be a set of unit cards, each one a separate offer with its own data. A scraper that stops at the building level looks like it is working perfectly well: it returns rows, nothing errors out, the totals look plausible. It is simply missing the units inside those buildings. Our crawler steps through every unit card inside such properties, so a multi-unit building enters the dataset as the number of listings it actually represents instead of a single line.

Zillow's Press&Hold check had to be solved before anything else

Zillow does not rely on an off-the-shelf captcha. The site runs its own Press&Hold challenge, and it is genuinely effective at cutting off automated traffic. That made it a question to answer at the start rather than a detail to patch at the end, because it decided whether a daily pipeline was feasible at all. We treated it as a separate engineering task and handled it on our side. For the client this is invisible: the run completes on schedule.

Five days to build, one day to collect

Development and testing took 5 days. A complete pass across Massachusetts takes 1 day. That ratio is what turns "daily" into something we can commit to rather than a word in a proposal: the collection window fits inside the refresh interval, so one run finishes before the next one is due, and the schedule does not drift as volumes move. It also means the project was producing data within days of the brief, not a quarter later.

What arrives every day

Roughly 22,000 rows per day, covering for-sale and rental properties of every type across the state, at unit level rather than building level, with the full listing data from each unit card. Because the dataset is rebuilt daily, consecutive snapshots are directly comparable, so what entered, changed or left the market is visible in the data itself instead of being reconstructed afterwards.

The same approach carries over to another state, another portal or a different refresh interval: solve the structural traps and the access questions first, then hold the schedule.