FLATearthers proposal
In Singapore, the large majority of the population live in HDB flats. Given the scarcity of land in Singapore, housing prices tend to hold a large price tag as with HDB flats. Buying a HDB flat represents a huge financial commitment that many young adults face as they search for the ideal home with the appropriate price tag to match.
As prospective buyers of HDB flats, we would like to investigate the intrinsic value of HDB flats and explain why they are priced as such. We intend to take a geospatial approach to analyze and quantify the different factors affecting the resale prices of HDB flats by both internal and external factors.
Our project aims to find out how external and internal factors may affect resale housing prices. External factors are, though not limited to, noise levels, proximity to hawker centers, MRT stations, shopping malls, schools and eldercare services. Internal factors are, though not limited to, remaining lease, number of rooms, floor, flat type and age of the flat.
We will be using 3 different models:
- Hedonic pricing model
- Geographically weighted regression
- Geographically and temporally 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 |
|
