HDB Sales and Interest Rates 2023 Report

Project Details:

In this project, I've analyzed the interplay between interest rates and HDB resale prices in Singapore. Utilizing SQL queries, I extracted relevant data from databases, focusing on high-value transactions in 2023. Leveraging Python and geopandas, I processed and visualized this data on a map, revealing spatial trends in median resale prices and their fluctuations between 2022 and 2023. By integrating financial data with geographical insights, this project provides valuable insights into the dynamics of Singapore's real estate market, aiding in informed decision-making and deeper understanding of economic influences on property values.

Software / programming language used:
Python
SQL
Hex Tech
MAS API

Highlights

MAS SORA API

This API is a useful tool for retrieving SORA (Singapore Overnight Rate Average) interest rates from MAS (Monetary Authority of Singapore). It provides a wide range of data fields including interbank rates, commercial bills, USD SIBOR, SGS repo rates, and more. The ability to specify date ranges adds flexibility for historical analysis or real-time monitoring of interest rate trends in Singapore's financial market.

SQL Query

In my project, I've been analyzing the dynamics between interest rates and HDB (Housing Development Board) resale prices in Singapore for the year 2023. To delve into this, I utilized SQL queries to gather relevant data.

Initially, I retrieved interest rate data from 2018 onwards using a specialized database. Then, I focused on HDB resale transactions, targeting those from 2021, 2022, and 2023. Subsequently, I narrowed down the scope to 2023 transactions where the resale prices exceeded $999,999.

Finally, to gain a deeper understanding, I aggregated the count of these high-value transactions by HDB town for the year 2023. These queries help shed light on the relationship between interest rates and high-value property transactions, offering insights that could be valuable for understanding market trends and making informed decisions in real estate investments.

Python Code

In my project, I've employed Python to clean and prepare data for visualization on a map, enhancing our understanding of HDB (Housing Development Board) resale price trends across different towns in Singapore.

Firstly, I utilized the geopandas library to read in a GeoJSON file containing spatial data of Singapore HDB towns. After ensuring consistency in naming conventions, I merged this spatial data with our main dataset using the HDB town as a common key.

Subsequently, I calculated the median resale prices for both 2022 and 2023, grouping the data by HDB town. This facilitated a comparison between median resale prices across these two years, aiding in identifying trends.

Further, I computed the percentage increase in median resale prices from 2022 to 2023, allowing for a deeper analysis of price fluctuations. These processed data points were then aggregated by HDB town once more, readying them for mapping.

By merging these refined datasets with the spatial information, we're now equipped to visualize the distribution and changes in median resale prices across different HDB towns in Singapore, offering valuable insights into the real estate landscape.