GeoEstate

GeoEstate

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The Team

Project Description

Our project aims to shine light on future prices of landed property 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.

Project Motivation

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.

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

Data Transformation

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: to analyse the spatial variations in house price adjustments due to economic conditions, and to quantify and describe patterns in the variations of house prices in the study area of Fife, Scotland

Methodology:

Spatial Interpolation Technique - using points with known values to estimate values at other unknown points. There were 3 main methods being used:

• Diffusion Interpolation with Boundaries
• Inverse-distance weighting
• Deterministic ordinary Kriging (Most accurate)

Learning Points:

• House price hot spot will migrate from year to year and multiple models is required if the study duration spans over multiple years
• Economic downturn actually leads to increase of property prices despite more supply from unemployed people

Areas for Improvement:

• Data lacked information such as the size and type of real estates which while could be approximated via interpolation, overall still hurts the accuracy of the model
• Using a different model such Geographically Weighted Regression (GWR) to identify spatial patterns apparent in the study area.

2. Statistical analysis of the relationship between public transport accessibility and flat prices in Riga

(By: Dmitry Pavlyuk)

Aim of Study: to examine the relationship between public transport accessibility and residential land value in Riga, Latvia

Methodology:

• Geographically Weighted Regression (GWR)
• Global Regression Model

Learning Points:

• Within city centre, accessibility has no significant relationship on flat prices as the city centres are already rich in transport route and new routes have a diminishing impact
• For the population with higher income, higher public transport accessibility will possibility lead to lower property prices
• Overall GWR performed significantly better than global regression
  • Variable that have no significant relation in one model might be significant in another. For example, the influence of the first floor on the price was insignificant in the global regression model, it was a local dependency in GWR.

Areas for Improvement:

• Overall limited impact by transport which was the main focus of the study
• Possibility of using Manhattan distance to compute the actual distance travelled rather than straight line distance

Approach

• Geographically Weighted Regression (GWR)

Project Prototype

Tools & Technology

Project Timeline

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