GeoEstate

GeoEstate

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APPLICATION

RESEARCH PAPER

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 Data was geocoded using Google Geocoding API Postal code was geocoded using OneMap API
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. Data.gov.sg Private Food Centres: 2. Kopitam 3. Koufu 4. Food Junction 5. Food Republic	1: KML - Converted to Shapefile 2 - 5: Text - Data scraped from sites and geocoded using OneMap API

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	Self-directed learning with online resources such as Datacamp, Browsing community forum (Stackoverflow / discuss.onemap) for help Looking at official documentation for various packages
2.	Limited oneMap API call for standard account	Creation of R script to catch timeout & wait Filtering out distinct records to query oneMap to reduce the quantity of duplicated request