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DTSTART:20260308T030000
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DTSTAMP:20260519T000625Z
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DTSTART;TZID=America/Los_Angeles:20260528T173000
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DESCRIPTION:[]\nIEEE CIS SD is excited to host Ragul Shanmugam\, Co-founder
  of Rehouzd\, for a talk on the intersection of AI and real estate.\n\nAbs
 tract: Real estate looks like a clean ML problem\, you predict a price\, s
 core a deal\, match a buyer. In practice\, it's one of the messiest domain
 s in applied AI\; every property is unique\, ground truth lags\, photos ar
 e unstandardized\, and human judgment dominates the last mile. Even with f
 rontier foundation models freely available\, applying them to actual workf
 lows like underwriting and condition assessment is far harder than it look
 s — generic LLMs hallucinate on domain reasoning\, vision models don't u
 nderstand "needs rehab\," and single-model approaches rarely survive conta
 ct with real investor decisions. I'm planning to go through why this domai
 n breaks the standard AI playbook\, and share what we've shipped here at R
 ehouzd - a multi-agent LLM workflows for underwriting\, multimodal vision 
 for property condition assessment\, and ML valuation pipelines tested agai
 nst real money.\n\nSpeaker(s): Ragul Shanmugam\,Co-founder of Rehouzd\n\nV
 irtual: https://events.vtools.ieee.org/m/560120
LOCATION:Virtual: https://events.vtools.ieee.org/m/560120
ORGANIZER:pragathipremakumar.work@gmail.com
SEQUENCE:44
SUMMARY:IEEE CIS San Diego Talk: Why Real Estate Is the Hardest AI Problem 
 You've Never Heard Of?
URL;VALUE=URI:https://events.vtools.ieee.org/m/560120
X-ALT-DESC:Description: <br /><p><img style="float: right\; margin-left: 20
 px\; margin-bottom: 10px\;" src="https://events.vtools.ieee.org/vtools_ui/
 media/display/e3949d31-fb61-41fc-b2f1-e17fd7375bc6" alt="" width="396" hei
 ght="595"></p>\n<div style="text-align: justify\;">IEEE CIS SD is excited 
 to host Ragul Shanmugam\, Co-founder of Rehouzd\, for a talk on the inters
 ection of AI and real estate.</div>\n<div style="text-align: justify\;">&n
 bsp\;</div>\n<div style="text-align: justify\;">&nbsp\;</div>\n<div style=
 "text-align: justify\;"><strong>Abstract: </strong>Real estate looks like 
 a clean ML problem\, you predict a price\, score a deal\, match a buyer. I
 n practice\, it's one of the messiest domains in applied AI\; every proper
 ty is unique\, ground truth lags\, photos are unstandardized\, and human j
 udgment dominates the last mile. Even with frontier foundation models free
 ly available\, applying them to actual workflows like underwriting and con
 dition assessment is far harder than it looks &mdash\; generic LLMs halluc
 inate on domain reasoning\, vision models don't understand "needs rehab\,"
  and single-model approaches rarely survive contact with real investor dec
 isions. I'm planning to go through why this domain breaks the standard AI 
 playbook\, and share what we've shipped here at Rehouzd - a multi-agent LL
 M workflows for underwriting\, multimodal vision for property condition as
 sessment\, and ML valuation pipelines tested against real money.</div>
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