SG Neighbourhood Recommendation

Project Name

Singapore Neighbourhood Recommendation System

Project Date

2022

Affiliation

Yale-NUS College

Team

Joshua VARGAS, Student
Elizabeth STEPTON, Student
NING Xinran, Student
Sean LIM, Student

Joshua's Contribution

Software: Python development, Streamlit dashboard
Data: data processing
  • Project Brief
  • Software Development
  • UX Research
  • Geospatial Analysis
  • Data Scripting
  • Software
  • Interactive Visualisation
  • Python
  • GDAL
  • Streamlit

Singapore Neighbourhood Recommendation System is a final project for YSC2244 Programming for Data Science. The aim of this project is to deploy techniques learned in YSC2244, as well as self-taught technologies, to address a real-world issue that can be addressed through a data-driven application.

context

Singapore is one of the world's most expensive property markets. Prospective homebuyers are faced with information asymmetry, and it is difficult to narrow one's property search. This inspired us, a group of Yale-NUS students (including three seniors and two internationals), to develop a prototype for a tool we would have loved to use through utilising open-source GIS solutions and publicly-available data.

Singapore is now the 5th most expensive city in the world for property prices, according to the 2022 Knight Frank Wealth Report. It is closing the gap with 4th-placer New York and is already ahead of Shanghai, Paris, and Tokyo. Amid a general global downturn, property prices are soaring in Singapore, leading the government to announce unprecedented property cooling measures to prevent the housing market from becoming a bubble.

One oft-cited reason for the rise in property prices and rent has been information asymmetry. Tenants and homebuyers are left at a disadvantage compared to real estate agencies and landlords, allowing the latter to charge higher rates.

Our project aims to help address this information asymmetry by giving prospective homebuyers insights into neighbourhoods so that they can narrow down their choices. Our team implemented a website for prospective homebuyers to identify the most suitable subzone zones in Singapore based on their priorities and preferences. Our final deliverable was an interactive website that allows the user to indicate their preferences via a series of sliders, and the results are generated via a filtered database and a map that visualizes these results.

Future extensions could make the project useful for renters; enable real-time property price scraping and prediction; and take into account other factors such as BTO launches for Singaporean citizens and PRs.

gallery

Tap on a photo to expand it.

A screenshot of the Singapore Neighbourhood Recommendation System

related projects

code + design + sound © 2026 joshua vargas