Hey everyone! I’ve got a big spreadsheet full of real estate listings that I want to map out visually. The problem is that all my data is stored as URLs from property websites, but I need the actual street addresses extracted from those links to create my map.
Most of these property URLs contain the address information directly in the web address itself. I’m looking for a way to create a formula that can automatically parse these URLs and extract just the address part, then display it in an adjacent column.
I’m pretty new to Google Sheets formulas and haven’t been able to figure this out on my own. Has anyone dealt with something similar before? Any suggestions would be really appreciated!
Manual formulas are a nightmare with thousands of URLs from different real estate sites. Every platform structures URLs differently, and you’ll waste hours writing regex patterns that barely work.
I solved this last month with Latenode. Instead of parsing URLs, I scrape the actual property pages for addresses. Much more reliable since the address is right there in the HTML.
It runs through your URL list, hits each page, grabs the address, and updates your Google Sheet. 30 minutes to set up vs hours of debugging formulas.
Plus you can pull other data like price, square footage, or listing dates. Way more useful than just addresses for your mapping project.
yea, it can be tricky! try using REGEXEXTRACT on the URL paths, but make sure to test different patterns. i had a similar issue before and it took a bit to find the right match. good luck!
depends on which sites you’re scraping. zillow and realtor.com have different patterns than smaller sites. use SPLIT function to break the url by forward slashes first, then find the part with numbers and street names. easier than regex if you’re just starting out
I tackled this same problem a few months back with rental properties. Each real estate site structures URLs differently, so you’ll need multiple approaches. Most sites embed the address between slashes or hyphens in the URL path. I had the best luck combining SUBSTITUTE and SPLIT functions to clean up URLs by replacing dashes and underscores with spaces, then using MID or RIGHT to grab the relevant section. You’ll have to play around with character positions since every site uses its own format. Watch out - some URLs abbreviate street types like ‘st’ instead of ‘street’ so plan on cleaning that up afterward.
Had this exact same problem last year with commercial property data. Here’s what worked for me: figure out the URL pattern first, then write your formulas. Most real estate URLs are pretty predictable - addresses usually come after words like ‘property’, ‘listing’, or state codes. I used nested SUBSTITUTE functions to swap out hyphens and plus signs for spaces, then wrapped everything in TRIM to kill the extra whitespace. Zip codes and state abbreviations in the URL path are the real pain points. I’d start by checking 10-15 URLs manually to see the patterns, then build your formula from there. You’ll probably need different formulas for different platforms if you’re pulling from multiple sources.
Had this exact problem when analyzing market trends across neighborhoods. The thing everyone’s missing is URL parameters and query strings that screw up address extraction. Real estate sites love adding tracking codes and search filters to URLs, which breaks your formulas. I got it working with LEFT and FIND functions to strip everything after question marks or hash symbols first, then used the SUBSTITUTE method others mentioned. Watch out for abbreviated directions too - URLs might have ‘N’ for North or ‘E’ for East while the actual listings spell them out. I’d make a lookup table for common abbreviations to standardize addresses before mapping. Also, URLs encode spaces as %20 so you’ll need extra SUBSTITUTE functions to clean those up.