Project Portfolio
Lead Ranking & Scoring Model
Sample Case Study: Real Estate Lead Ranking Model for identifying the <1% ‘needle-in-a-haystack’ high potential targets
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.
Real Estate Lead Scoring Model Case Study Details
Problem Description
How can we identify and target only the most promising properties?
A leader in real estate wholesaling in the U.S. Northeast was spending a lot of money mailing marketing materials to hundreds of thousands of property owners, despite knowing only a small fraction had any potential of becoming clients. To reduce costs and boost conversion rates, they needed a way to intelligently score or “grade” the properties, so they could focus their marketing efforts on the most viable prospects.
Lots of data but no information
Although the firm had access to an extensive amount of public property records and proprietary datasets, extracting actionable insights proved elusive. Without a structured analytical approach, they were unable to detect the patterns that could reveal which properties were more likely to respond positively to outreach.
Approach
A meticulously designed scoring algorithm
Nearly 200 public and private datasets were joined using a variety of matching techniques
The joined datasets were extensively cleaned, and variables were analyzed and transformed
Cluster analysis was applied to identify natural variable groupings
The analysis culminated in the development of a scoring algorithm consisting of 66 binary and numeric features, weighted according to predictive significance.
Gain and lift analysis demonstrated strong classification performance in the top deciles
Outcomes / Impact
Scored and ranked property lists and a ‘target profile’
Using the custom scoring model, 469,065 properties were evaluated and assigned a grade from A to F. Fewer than 1% of the properties received an “A” grade, while 7% were classified as “B” or “C.” The remaining 92% were given grades “D” or “F,” indicating they were significantly less likely to convert and could be deprioritized in marketing campaigns. This enabled the client to focus resources on high-probability leads and reduce unnecessary marketing expenditures.
Alongside the ranked property list, the client received a comprehensive ‘target profile’ outlining the key characteristics of a “Grade A” property, based on the 66 input variables used to build the model. This profile enhanced the client’s understanding of their target clients, supporting better informed, data-driven outreach strategies.
Real Estate Lead Scoring Model Case Study Images
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.