Homiere: Personalized Home Search

Home Sweet Homiere

Problem & Motivation

Despite technological innovations transforming many industries, real estate has been slow to embrace advancements, with the home search process remaining one of its most under-optimized areas. The core functions of finding a home continue to rely heavily on human effort, as buyers manually sift through countless listings with basic filters. Moreover, the 2024 National Association of Realtors (NAR) settlement introduced regulatory changes, empowering buyers to negotiate their agent's fees, ultimately enhancing their autonomy. These shifts are already having a tangible impact on the industry, with buyer's agent fees dropping from an average of 2.62% in early 2024 to 2.55% by August 2024¹. This signals a trend towards greater buyer control and a need for more efficient tools in the home search process.

Solution

There's no place like Homiere

The combination of a technological gap and regulatory shifts creates a unique opportunity for Homiere, an AI-powered proptech solution that offers a personalized home search experience. Homiere is designed to provide a more transparent, customized, and efficient process for buyers, allowing them to input their specific preferences and receive an optimized list of homes that match their criteria. This is achieved by preprocessing property listing data—textual, tabular, and image-based—while integrating external data such as crime rates, inflation, air quality, location details, and more. By leveraging NLP, LLMs, multimodal models, and other advanced data science techniques, Homiere delivers a comprehensive, data-driven home search experience.

Data Source

Homiere's core data is California Regional Multiple Listing Service, which is a  a database of properties for sale in the Southern California region. For our MVP, we are starting with over 10,000 listings in this region. Supplemntal to this data are:

• Air quality index
• Crime rate
• Inflation rate
• Schools and their ratings
• Nearby locations
• Walk, transit and bike scores
• Property photos

Data Pipeline & Technical Architecture

@pavan

Data Science Approach

@spencer and bao

Evaluation

@zane

Key Learnings & Impact

@zane

Future Works

@addy

Acknowledgements

@spencer and bao

¹The Typical Buyer’s Agent Earns 2.55% in Commission, a Rate That Has Declined Since the NAR Settlement Was Announced in March