GeoEstate
| The Team |
| Project Description |
Landed Property has always been seen as something for the wealthy, with only the top 5% of Singaporean earners being able to afford it. For Singaporeans who aspire to own such Property, there are currently many available options – Terrace Houses, Semi-Detached Houses, Corner Terrace Houses, Detached Houses and many more. Due to fluctuating property prices, it may be difficult for an aspiring landed property owner to properly plan and budget to get their dream house. Furthermore, existing owners may be stressed about when the best time to sell is.
Our project aims to shine light on this by providing an accurate geographically weighted regression model using factors such as location, tenure and type of sale to predict future landed property prices. Now, aspiring owners can filter by location and type of house, and easily see what their dream house would be worth in several years. Alternatively, owners of landed property who plan to sell can see if they should do so sooner or later.
Landed Property purchase and sale is a huge financial commitment and we at GeoEstate are committed to ensure that you make the best financial decision for you and your family.
| Project Motivation |
| Data sources |
| Data | Source | Data Type/Method |
|---|---|---|
| 2014 Master Plan Planning Subzone (Web) | Data.gov.sg | SHP |
| URA Private Residential Property Transactions | Ura.gov.sg |
CSV |
| Pre-School Locations | Data.gov.sg | KML Converted to Shapefile |
| Primary/Secondary School Locations | Data.gov.sg | CSV Data was geocoded using OneMap API |
| MRT/LRT Station Locations | LTA Datamall (Direct Download) |
SHP |
| Supermarket Locations | Data.gov.sg | KML Converted to Shapefile |
| Shopping Mall Locations | Wikipedia | Text Data was converted to Shapefile after geocoding using OneMap API |
| Park Locations | Data.gov.sg | KML Converted to Shapefile |
| Sports Facilities Locations | Data.gov.sg | KML Converted to Shapefile |
| Hawker Centre Locations |
Public Food Centres: |
1: KML - Converted to Shapefile |
| Approach |
| Project Timeline |
| Project Prototype |
| Tools & Technology |
| Literature Review |
1. A Spatial Analysis of House Prices in the Kingdom of Fife, Scotland
(By: Julia Zmölnig, Melanie N Tomintz, Stewart A Fotheringham)
Aim of Study:
Methodology:
Learning Points:
Areas for Improvement:
2. Statistical analysis of the relationship between public transport accessibility and flat prices in Riga
(By: Dmitry Pavlyuk)
Aim of Study:
Methodology:
Learning Points:
Areas for Improvement:
3. Using Geographic Information Systems to Improve Real Estate Analysis
(By: Mauricio Rodriguez, C. F. Sirmans, Allen P. Marks)
Aim of Study:
Methodology:
Learning Points:
Areas for Improvement:
| Challenges |
| No. | Key Challenges | Mitigation |
|---|---|---|
| 1. | Unfamiliarity with R, its packages and R Shiny |
|
| 2. | Limited oneMap API call for standard account |
|
