If you have ever tried to write a clean “Location” section for an IC memo, OM, or investor email, you know the drill. You open Google Maps. You bounce between satellite, Street View, and search results. You pull demographics from a separate tool. You copy business names into a scratchpad. Then you spend another twenty minutes turning scattered notes into two or three paragraphs that actually sound like something an investment committee will read. That 30–60 minute “location read” has been part of my own workflow for as long as I have been underwriting and packaging deals. So, we decided to teach a CRE Agents AI coworker how to do it. What We Built: Deep Location Analysis (with Demographics) Deep Location Analysis is a CRE Agents workflow that takes a single property address and returns what most of us would call a first-draft location section. You drop in the address. The workflow builds a location research URL, triggers an automated analysis, pulls the text and 1-mile Census demographics, and hands you back: A Deep Location URL you can click into and share A full, analyst-style location write-up A key facts block and 1-mile demographic table Simple next-step prompts (draft a narrative, list tenants, describe ingress/egress) What used to take 30–60 minutes now takes under 5, with more consistency and far less context switching. A Real Example: 100 North Riverside, Chicago To make this concrete, here is an excerpt from a run we did on 100 North Riverside Plaza in Chicago . The workflow starts with a short header: address, coordinates, the date we analyzed it, and quick actions to copy the link or export to PDF. Then it moves into a three-paragraph Location Summary that already sounds like a human analyst: The subject property occupies a premium riverfront position at the intersection of the West Loop and the central business district, characterized by high-density office towers and luxury residential units. Within a 1-mile radius, the median household income is $142,969 (77% above national average) with a highly educated population where 88% of residents hold a bachelor’s degree or higher. The demographic profile is exceptionally young and affluent, with a median age of 32.2 and per capita income 164% above the national average. Immediate adjacencies include the Ogilvie Transportation Center and the 150 North Riverside office tower, providing massive daily foot traffic from commuters and white-collar professionals. The site is anchored by high-end national and regional hospitality brands such as Beatrix, Small Cheval, and Bazaar Meat, alongside major corporate hubs for Bank of America and CoStar Group. Primary access is provided by the W Randolph St bridge and N Wacker Drive, with the I-90/94 expressway located less than a mile to the west. Strengths include unparalleled transit connectivity via Metra and ‘L’ lines, coupled with a high-income daytime population that supports premium retail and dining. The riverfront location offers significant aesthetic value and pedestrian engagement via the Chicago Riverwalk. The primary limitation is the low owner-occupancy rate (32%), reflecting a transient, renter-heavy market that may be sensitive to shifts in corporate office occupancy trends. In three paragraphs, you already have: Physical context: riverfront, West Loop + CBD edge Income, age, education, and renter profile Key adjacencies and anchors Access summary and a clear statement of strengths and limitations If you pasted this under “Location” in a memo, most readers would assume a human analyst spent time on it. Below that, the workflow breaks out Key Location Facts (median income, education, transit proximity, primary access, major anchors) and then a full Census Demographics section for the 1-mile radius: Population and density Median household income and per capita income vs national averages Median home value and rent Owner vs renter split Median age Unemployment rate All of this is standardized, labeled, and ready to drop into your materials. On the same page, you see Nearby Places within ¼ mile, broken into Retail & Shopping, Food & Dining, and Services, each with name and distance: Starbucks, Potbelly, Dunkin’ Small Cheval, Bazaar Meat, Beatnik On The River Bank of America, CoStar, Oak Street, JLL, various professional services You get an immediate feel for the tenant and customer ecosystem without hunting down every dot on the map. Finally, the Location Views section calls out four zoom levels that you can click through: Block, Neighborhood, City Context, and Metro Region. It is the same way most of us think about trade areas in our heads, but now it lives in a reusable workflow. What’s Under the Hood This is not a single monolithic agent trying to do everything at once. Under the hood, Deep Location Analysis is a two-agent workflow: Create and Visit URL Takes the address you provide Builds the CRE Agents Location Research URL for that property Visits it once to trigger the analysis