College - Author 1

College of Engineering

Department - Author 1

Computer Engineering Department

Degree Name - Author 1

BS in Computer Engineering

College - Author 2

College of Engineering

Department - Author 2

Computer Science Department

Degree - Author 2

BS in Computer Science



Primary Advisor

Christopher Siu, College of Engineering, Computer Science and Software Engineering Department


Real estate agents are often tasked with finding their clients’ ideal properties. This can be difficult because multiple clients may have varying preferences, such as number of bedrooms, square footage, or price. Furthermore, different clients may weight their individual preferences differently. Existing applications do not consider multiple clients’ satisfaction, nor do they allow clients to weigh their preferences, potentially leading to less-than-ideal matchings between clients and properties.

In this project, we design and implement an iOS application whereby real estate agents can match multiple clients with individually weighted preferences to properties scraped from web listings. We model this client-property matching problem as a weighted graph, and apply the Kuhn–Munkres algorithm to find matchings that lead to the greatest overall client satisfaction.