FLATearthers proposal
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.
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:
- Multiple linear regression
- Geographically weighted regression
to compare and explain how the factors stated above may, affect resale housing prices.
| Data Set | Format | Attribute |
|---|---|---|
| HDB Resale Value | CSV |
|
| Shopping malls | csv | - |
| MRT/LRT | csv | - |
| Primary Schools | csv (General-information-of-schools.csv Hands-On Ex 3) |
|
| Secondary schools | csv (General-information-of-schools.csv Hands-On Ex 3) |
|
| JCs and Polytechnics | csv (General-information-of-schools.csv Hands-On Ex 3) |
|
| Hospitals | csv | - |
| Hawker Centres | csv | - |
| Key Challenges | Mitigation |
|---|---|
| Unfamiliarity with R and packages required for project |
|
| Unfamiliarity with proper approach to use for project |
|
| Limitations of OneMap API to handle large amount of data |
|
