Difference between revisions of "GeoEstate PROPOSAL"

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| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Project Description
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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.
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| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Project Motivation
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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.
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| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Data sources
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|}
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<table class="wikitable" style="background-color:#FFF; margin: 1em auto;" width="80%; font-size: 15px;">
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<tr>
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<th> Data </th>
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<th> Source </th>
 +
<th> Data Type/Method
 +
</th>
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</tr>
 +
<tr>
 +
<td> 2014 Master Plan Planning Subzone (Web) </td>
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<td> [https://data.gov.sg/dataset/master-plan-2014-subzone-boundary-web Data.gov.sg] </td>
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<td> SHP </td>
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</tr>
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<tr>
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<td> URA Private Residential Property Transactions </td>
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<td> [https://www.ura.gov.sg/realEstateIIWeb/transaction/search.action Ura.gov.sg] </td>
 +
<td>
 +
CSV <br/>Data was geocoded using [https://developers.google.com/maps/documentation/geocoding/start Google Geocoding API]
 +
<br/>Postal code was geocoded using [https://docs.onemap.sg/#search OneMap API]
 +
</td>
 +
</tr>
 +
<tr>
 +
<td> Pre-School Locations </td>
 +
<td> [https://data.gov.sg/dataset/pre-schools-location Data.gov.sg] </td>
 +
<td> KML <br/> Converted to Shapefile</td>
 +
</tr>
 +
<tr>
 +
<td> Primary/Secondary School Locations </td>
 +
<td> [https://data.gov.sg/dataset/school-directory-and-information Data.gov.sg] </td>
 +
<td> CSV<br/>Data was geocoded using
 +
[https://docs.onemap.sg/#search OneMap API] </td>
 +
</tr>
 +
<tr>
 +
<td> MRT/LRT Station Locations </td>
 +
<td> [https://www.mytransport.sg/content/dam/datamall/datasets/Geospatial/TrainStation.zip LTA Datamall] <br/>(Direct Download) </td>
 +
<td> SHP </td>
 +
</tr>
 +
<tr>
 +
<td> Supermarket Locations </td>
 +
<td> [https://data.gov.sg/dataset/supermarkets Data.gov.sg] </td>
 +
<td> KML <br/> Converted to Shapefile </td>
 +
</tr>
 +
<tr>
 +
<td> Shopping Mall Locations </td>
 +
<td> [https://en.wikipedia.org/wiki/List_of_shopping_malls_in_Singapore Wikipedia] </td>
 +
<td> Text <br/> Data was converted to Shapefile after geocoding using
 +
[https://docs.onemap.sg/#search OneMap API] </td>
 +
</tr>
 +
<tr>
 +
<td> Park Locations </td>
 +
<td> [https://data.gov.sg/dataset/nparks-parks Data.gov.sg] </td>
 +
<td> KML <br/> Converted to Shapefile </td>
 +
</tr>
 +
<tr>
 +
<td> Sports Facilities Locations </td>
 +
<td> [https://data.gov.sg/dataset/sportsg-sport-facilities Data.gov.sg] </td>
 +
<td> KML <br/> Converted to Shapefile </td>
 +
</tr>
 +
<tr>
 +
<td> Hawker Centre Locations </td>
 +
<td>
 +
Public Food Centres: <br>
 +
1. [https://data.gov.sg/dataset/hawker-centres Data.gov.sg]<br><br>
 +
Private Food Centres: <br>
 +
2. [http://www.kopitiam.biz/search-results/?keywords&zone=allzone&FC=yes&search=Search Kopitam]<br>
 +
3. [https://www.koufu.com.sg/our-brands/food-halls/koufu/ Koufu]<br>
 +
4. [https://www.foodjunction.com/outlets/ Food Junction]<br>
 +
5. [https://foodrepublic.com.sg/food-republic-outlets/ Food Republic]
 +
</td>
 +
<td>
 +
1: KML - Converted to Shapefile <br>
 +
2 - 5: Text - Data scraped from sites and geocoded using [https://docs.onemap.sg/#search OneMap API]
 +
</td>
 +
</tr>
 +
</table>
 +
 +
{| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"|
 +
| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Data Transformation
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|}
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 +
<br>
 +
<br>
 +
 +
{| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"|
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| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Literature Review
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|}
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=== 1. [https://www.researchgate.net/publication/282354934_A_Spatial_Analysis_of_House_Prices_in_the_Kingdom_of_Fife_Scotland A Spatial Analysis of House Prices in the Kingdom of Fife, Scotland] ===
 +
''(By: Julia Zmölnig, Melanie N Tomintz, Stewart A Fotheringham)''
 +
 +
[[File:GeoEstate_interpolation.jpg|center]]
 +
 +
<b>Aim of Study</b>: 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
 +
 +
<b>Methodology</b>:
 +
 +
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)
 +
 +
<b>Learning Points</b>:
 +
 +
* 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
 +
 +
<b>Areas for Improvement</b>:
 +
 +
* 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. [https://www.researchgate.net/publication/41573053_Statistical_analysis_of_the_relationship_between_public_transport_accessibility_and_flat_prices_in_Riga Statistical analysis of the relationship between public transport accessibility and flat prices in Riga] ===
 +
''(By: Dmitry Pavlyuk)''
 +
 +
[[File:GeoEstate_public_transport_accessibility.JPG|center]]
 +
 +
<b>Aim of Study</b>: to examine the relationship between public transport accessibility and residential land value in Riga, Latvia
 +
 +
<b>Methodology</b>:
 +
 +
* Geographically Weighted Regression (GWR)
 +
* Global Regression Model
 +
 +
<b>Learning Points</b>:
 +
 +
* 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.
 +
 +
<b>Areas for Improvement</b>:
 +
* 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
 +
 +
 +
{| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"|
 +
| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Approach
 +
|}
 +
 +
* Geographically Weighted Regression (GWR)
 +
<br>
 +
 +
{| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"|
 +
| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Project Prototype
 +
|}
 +
 +
<br>
 +
<br>
 +
 +
{| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"|
 +
| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Tools & Technology
 +
|}
 +
 +
[[File:GeoEstate_tech_stack.png|center|1000px]]
 +
 +
{| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"|
 +
| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Project Timeline
 +
|}
 +
 +
[[File:GeoEstate_timeline.jpg|center|1600px]]
 +
 +
{| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"|
 +
| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #2DB0AF" width="9999px" | Challenges
 +
|}
 +
 +
 +
<table class="wikitable" style="background-color:#FFF; margin: 1em auto;" width="80%; font-size: 15px;">
 +
<tr>
 +
<th> No. </th>
 +
<th> Key Challenges </th>
 +
<th> Mitigation </th>
 +
</tr>
 +
 +
<tr>
 +
<td> 1. </td>
 +
<td> Unfamiliarity with R, its packages and R Shiny </td>
 +
<td>
 +
# Self-directed learning with online resources such as Datacamp,
 +
# Browsing community forum (Stackoverflow / discuss.onemap) for help
 +
# Looking at official documentation for various packages
 +
</td>
 +
</tr>
 +
 +
<tr>
 +
<td> 2. </td>
 +
<td> Limited oneMap API call for standard account </td>
 +
<td>
 +
# Creation of R script to catch timeout & wait
 +
# Filtering out distinct records to query oneMap to reduce the quantity of duplicated request
 +
</td>
 +
</tr>
 +
</table>
  
 
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Revision as of 01:09, 22 March 2019

GeoEstate logo.png
GeoEstate

HOME

 

PROPOSAL

 

POSTER

 

APPLICATION

 

RESEARCH PAPER

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)

GeoEstate interpolation.jpg

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)

GeoEstate public transport accessibility.JPG

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
GeoEstate tech stack.png
Project Timeline
GeoEstate timeline.jpg
Challenges


No. Key Challenges Mitigation
1. Unfamiliarity with R, its packages and R Shiny
  1. Self-directed learning with online resources such as Datacamp,
  2. Browsing community forum (Stackoverflow / discuss.onemap) for help
  3. Looking at official documentation for various packages
2. Limited oneMap API call for standard account
  1. Creation of R script to catch timeout & wait
  2. Filtering out distinct records to query oneMap to reduce the quantity of duplicated request


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