Using Python in CRE
Hi - does anyone uses Python in CRE, perhaps real estate research and analytics? If yes, would love to hear some practical applications, thanks
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I don't work at this company anymore, but I used python, powerbi, and sql when I was in corporate real estate in addition to GIS tools like placer.ai
We were a large retailer with over 1000 stores and 300 in the pipeline, over $20 billion in revenue.
We developed in house, so you can imagine how python and sql were useful to us with current store operating data and applying that to underperforming stores and our pipeline.
Sounds like a ton of good data and interesting job. Could you expand a little more on a few examples of Python application at your previous job? Thanks in advance
What sort of projects did you work on in that role? What did the day to day look like? Ive been curious about this part of the industry.
Yep - Web scraping data comps!
Bump on this thread - anybody find any use outside of "data analytics" (might not be the best all encompassing term)? Does anyone see any relevance in an investment/development/acquisition style role?
Wondering if the integration of python into excel is a valid enough reason to pursue the skill (with potentially parlaying that into a more data scientist role).
In acquisitions the use would be maybe saving yourself some time if you could set it up to feed the existing excel format.
Your bosses aren’t wasting time figuring out a new format though. CRE is super simple analysis relative to other finance fields. It’s relationship driven and imo the best analysis comes from market/product experts that already know the replacement costs, barriers to entry, and supply in their neighborhoods. Good acquisition guys know that off the top of their head.
I bet 90% of people on here or more could value an investment opportunity at a fair price. That’s the easy part to me. But how do you actually buy it at that right price?
How many could win the deal, line up partners, finance it, properly identify risks, run DD, potentially retrade it to the right price and still close? How many could get the deal before it hits the market?
I don’t want to discount using newer better tech to be more efficient because CRE is definitely archaic, and I can see it potentially identifying a trend here or there. But in a acquisition role I’m not sure your long term value is coming through becoming a data/modeling expert.
Maybe in more of a research, economist type role?
Double post - delete
Definitely agree with this sentiment - think there could be some application as the above with a large scale co looking at a lot of data, but local knowledge for sure trumps data that looks pretty. Also agree with your statement that CRE is simple enough to run values and anything more than excel could be overkill.
Will be interesting as products become more and more commoditized if there is more use of tech in this space.
Ding ding ding. Very much this.
In my seat (research/economist role), I'm using SQL/Python/R/GIS platforms to merge disparate data sets and compile analytics/reporting across every US market. It helps inform the buy/hold/sell strategy given the local macro environments as well as provide site-level analytics and risk assessments for any given deal. This type of role only makes sense for bigger shops with a larger investment footprint - if you're local to say, Los Angeles, you should know your market inside and out without some data nerd telling you what's what. If you're covering ~50+ markets on the other hand...
But at the end of the day, it's still just data. You need boots on the ground to actually understand the minutiae impacting a potential deal. A person in an acquistions seat would be much better served focusing on that rather than writing a Python script to rip together income growth, permits, and the like for everywhere under the sun. Most major shops have a dedicated research function to cover that.
In terms of something like underwriting, I haven't seen anyone do it before. So many input factors aren't provided in a standardized format - how many different rent roll formats have you all seen? - which would make 'operationalizing' that task incredibly difficult from a programming language perspective.
Organizing and cleaning data efficiently, automating reports, scraping publicly available data. There are so many options and it's kind of incredible that more people in the industry don't have a baseline proficiency. Teaching myself on nights and weekends was one of the best things that I did professionally, and now that I have a baseline proficiency to build things that I can use I really enjoy it and find it interesting. Pretty amateur coder in the grand scheme of things, for the record. But cre boomers are still pretty impressed lol. Great for speeding up analytical drudge work
As others have said, this industry is stuck in the stone ages in a lot of ways and I say that with love
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