FLATearthers proposal

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FLATearthers.jpeg

TEAM

 

PROPOSAL

 

POSTER

 

APPLICATION

 

RESEARCH PAPER


PROJECT MOTIVATION

As Singapore government ramps up effortsto collect and share data sets to the public, various kinds of information are readily available. There are however, no readily available tools that could allow analysts who are well-versed in real estate, but do not have the coding know-how to perform analysis. Thus, our group hopes to bridge the gap and provide these analysts with a friendly user interface and experience.


PROJECT DESCRIPTION

Our project aims to find out how external and internal factors may affect resale housing prices, specifically for HDB resale flats. External factors are, though not limited to, shopping malls, hawker centers, MRTs and LRTs, supermarkets, primary schools, pre-schools and eldercare services. Internal factors are, though not limited to, remaining lease, floor area, storey, flat type and model.


We will be using 2 different models:

  1. Multiple linear regression
  2. Geographically weighted regression

to compare and explain how the factors stated above may, affect resale housing prices.


DATA SOURCES
Data Set Format Attribute
HDB Resale Value CSV
  • Month
  • Town
  • Flat Type
  • Block
  • Street Name
  • Storey Range
  • Floor Area
  • Remaining Lease (Years)
  • Resale Price
Shopping malls csv -
MRT/LRT csv -
Primary Schools csv (General-information-of-schools.csv Hands-On Ex 3)
  • school_name
  • postal_code
Secondary schools csv (General-information-of-schools.csv Hands-On Ex 3)
  • school_name
  • postal_code
JCs and Polytechnics csv (General-information-of-schools.csv Hands-On Ex 3)
  • school_name
  • postal_code
Hospitals csv -
Hawker Centres csv -


PROJECT TIMELINE
Timeline flatearthers.png
PROJECT STORYBOARD
Storyboard flatearthers.PNG


KEY CHALLENGES
Key Challenges Mitigation
Unfamiliarity with R and packages required for project
  1. Self-learning online through sites like Datacamp
  2. Read up
Unfamiliarity with proper approach to use for project
  1. Read up on related projects found in research databases
  2. View past projects with similar goals
Limitations of OneMap API to handle large amount of data
  1. Read up on related projects found in research databases
  2. Split up large dataset across group members and across a few days