The Uncertainty
A real estate wholesale company needed a scalable way to identify viable prospects among hundreds of thousands of property listings, and to understand what traits distinguished potential clients from the broader pool.
The Clarity
A custom algorithm with strong classification performance was developed using a variety of data sources and analytical techniques. The result was a ranked list of properties and a comprehensive target profile allowing for deeper market insight.
Work Sample 5
A Lead Ranking Model for efficiently identifying top real estateinvestment opportunities
Case Study - Real Estate Lead Ranking Model
More Samples of Ranking & Classification Models
"Home Search" Survey, App, and Property Ranking Algorithm
Real Estate
Developed a customer-facing survey and corresponding ranking algorithm and app to select and rank real estate properties in Mexico City based on weighted customer preferences. Also built an internal-use companion app for analyzing and comparing real estate values and other metrics by neighborhood. Led the distributed data team tasked with building out a fully automated back-end system for the apps and integrated into existing company processes.
Predicting & Ranking Roof Repair Customers in Southern Florida
Manufacturing / Construction
Developed a ranking and classification model to identify the residential properties in two Florida counties most likely to require roof repair services due to damage from Hurricane Irma (2017). The model integrated selected and transformed features from two key data sources: nearly 700K property records from publicly available county datasets, and a purchased dataset containing ~1 million observations across more than 1,000 demographic and psychographic attributes.
SEM Based Classification Models for Product Usage
Consumer Goods / Retail
Built a suite of exploratory structural equation models (SEM) using data from highly detailed consumer surveys focused on personal care and baby care products sold by a major multinational corporation. The models aimed to uncover the latent (non-directly observable) drivers influencing product usage and preferences within the target market. Accompanying dendrograms and logistic regression models provided additional support for the relationships examined in the SEM path diagrams.