Nico Lassaux
Co-Founder
Nico co-founded HelloData and led its machine learning, building the models that clean, reconcile and interpret property data from public sources. He has since left HelloData. He has applied data science to real estate since 2015, first as the second hire at Enodo, where he led the teams that studied what drives real estate markets, then as Vice President of Data Science after Walker & Dunlop acquired Enodo. He holds two master's degrees in Computer Science focused on statistics and data science, prefers elegant solutions to complicated ones, and spends his time off running, cycling and climbing.
Past Articles
What’s the Difference Between Black Box vs. Explainable AI (XAI) Models?
The lack of transparency in these models can lead to biased outcomes, lack of accountability, and even security risks. This is where the difference between black box and explainable AI (XAI) models comes into play.
The Best Sources of Real Estate Data in 2024
Access to high-quality data enables real estate professionals to make informed decisions by understanding market demands, performing accurate valuations, and driving smarter investments. Here are some of the best sources of real estate data in 2024.
Why your company needs an AI prompt library
Prompt libraries serve as centralized, categorized repositories of prompts that enhance the efficiency and reliability of AI models. They are particularly useful for tasks like image and text generation. This article highlights added functionalities like version control and data governance that make these libraries invaluable.
Detect Logo & Watermark in Real Estate using Computer Vision
The world of online real estate listings is plagued by a major issue - inappropriate use of images with logos or watermarks. Our proprietary technology at HelloData.ai effectively identifies, tags, and flags images with non-conforming watermarks and logos. Using the latest breakthroughs in computer vision, our unique watermark model spots images and videos containing artificially added watermarks, logos, and text overlays.
Automated Real Estate Data Extraction: Approaches and Benefits
Most real estate companies generate a ton of data through listings, purchase & sale agreements, market studies, site inspections, etc… the list goes on. Large volumes of data can be very powerful for analysis, but in real estate, this data tends to be locked in emails, PDF documents, and Excel models. It’s essentially unusable, unless the data can be extracted in a consistent way. With recent advancements in artificial intelligence (AI), however, it is becoming significantly easier for real estate companies to unlock the value of their data.