Condo Prices Near MRT Stations

Problem

Location is one of the strongest drivers of residential pricing in Singapore, but “near an MRT” is usually treated as a vague selling point rather than something that can be measured properly.

I wanted to turn that into a structured question: how do condo prices differ across MRT lines, stations, and tenure types when you measure actual proximity instead of relying on anecdotal market language?

Approach

I built a geospatial workflow that joined property transactions to MRT stations using distance-based logic.

SQL was used to construct geographic points for stations and condo transactions.
Properties within a 1000-meter radius of each station were grouped and compared.
Transactions were segmented by tenure so leasehold and freehold stock could be read separately.
Python was used to clean, format, and prepare the outputs for interactive presentation.

To make the analysis easier to interpret, I also created GeoJSON layers for MRT lines so the results could be read spatially instead of only as a table.

What the Build Did

The project calculated average transacted prices and average price per square foot for properties near each station. That made it possible to compare:

station-by-station pricing patterns
differences across MRT lines
the spread between leasehold and freehold stock
how transport access interacts with local submarket structure

This was useful because proximity alone does not explain value. A station sits inside a broader neighbourhood context, and the line, surrounding condo stock, and tenure mix all matter.

Why It Mattered

The project turned a common real-estate talking point into something testable.

Instead of saying a property was “close to transport”, the workflow made it possible to ask:

close to which station?
on which line?
with what nearby transaction pattern?
and with what tenure profile?

That created a more practical decision-support tool for investors, homebuyers, and anyone trying to compare location value in a more disciplined way.