Group21 Proposal

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Project Overview

The Housing & Development Board (HDB) Market has faced high volatility in Singapore over the years. This dynamic and ever-changing market has observed drastic price fluctuations from 1990 to 2018. While looking at the resale price trends in Singapore, it is observed that the market has seen a slightly upward bias in resale prices for 2018. Through the scope of this project, we intend to understand the current market trend of HDB Flats.

With a fluctuation in prices of HDBs in the markets, the purchase decision for HDBs becomes a major commitment for any prospective buyer. Hence, it becomes very important to understand the resale price trends of HDBs across different locations in Singapore. This would enable the users of our application to gain insights and understand the market overview of HDB flats.

Through the scope of this project, we intend provide the real estate agents, brokers, investors and other enthusiasts with interactive visualizations to understand market trends and facilitate investment decision.

Motivation

Based on our research, we observed that the existing visual representations on resale price trends fail to provide comprehensive insights to the users. Looking at the existing visualizations, we realized the need for an interactive visual dashboard that would provide the users of the application with a holistic view of the HDB Resale Price trends. We intend to address the pressing issue stated above by employing data visualizations techniques to provide a visual summary of the resale price trends in Singapore for the most recent data, a 12-month period (from June 2017 to May 2018).

Our desire to work on this project is encouraged by the interesting price trends of HDB flats in the market and data from Singapore Government website (https://data.gov.sg), where data is made readily available to the public for in-depth analysis. The software R is used as the code base. R enables us to deploy powerful visualizations and easily prepare the data. This allows us to design complex visualizations easily and gain insights. The functions are available in different packages and are used to develop the application.

Review and Critic of Past Works

Visualization and Source	Critique
Treemap of Average HDB Resale Prices (SG Young Investment Blog)	The SG Young investment blog provides a treemap containing the average resale prices of HDBs across different towns in singapore. The main issue with this visualization is that it showcases the overall average resale price of each town in Singapore but it does not give the user the opportunity to drill down and compare the resale prices across different streets that belong to a particular town. By ensuring that the user can drill down, we can ensure that the user can get a holistic and actual picture of the price difference across different streets in a town. The visualization also does not give us the average resale prices in propotion to the flat area. By incorporating this in the visualization, we can more accurately understand if larger floor area is one of the factors that contribute to an higher resale price trends.
Bar Chart showing average resale price for a Room Type in a town (SG Young Investment)	The barchart shows the average resale price for different room types in a particular tows. However, the problem with this visualization is that it fails to show all the values of resale prices across different towns. We believe that if the visualization would showcase a box plot that show us all the resale values of different flat types, we would be in a better position to examine the outliers in the dataset and gain insights.
HDB Resale Prices for different flat types (Medium Corporation)	The visualization by Jiefu Wang (Medium Corporation) showcases the trend of HDB Resale prices across different towns in Singapore. The visualization shows the overall trend in resale prices from 2001 to 2015 for each town. The line on the graphs show the trend in resale prices for each flat type. The visualization helps in comparing resale prices across different towns to identify the towns with higher or fluctuating resale prices. However, it is very difficult to deduce insights from this graph. The visualization could be improved further and could be made more understandable and easier to interpret.

About the Dataset

The data was taken from https://data.gov.sg. The data from June 2017 to May 2018 was taken for analysis as it represents the current market trend of HDB Flats. The data for the 12 months period contained Month, Town, Flat type, block and street name, Storey range, flat model, floor area, Lease commence date and remaining lease and Resale Prices

Analytical Framework

Expected Challenges

1. Extracting the latitude and longitude coordinates of each block without using the Postal district.
2. Data manipulation (summarizing, aggregating, grouping and creating subsets of the data) based on the visualization.
3. Building an interactive leaflet plot.