Difference between revisions of "FLATearthers proposal"

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[[FLATearthers  | <font face="Verdana" color="#040F3D" size=2><b>TEAM</b></font>]]
  
 
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[[Proposal | <font color="#040F3D" size=2><b>PROPOSAL</b></font>]]
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[[FLATearthers_proposal | <font face="Verdana" color="#040F3D" size=2><b>PROPOSAL</b></font>]]
  
 
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[[FLATearthers_poster | <font face="Verdana" color="#040F3D" size=2><b>POSTER</b></font>]]
  
 
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[[FLATearthers_application | <font face="Verdana" color="#040F3D" size=2><b>APPLICATION</b></font>]]
  
 
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| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #040F3D" width="9999px" | Project Description
 
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<div style= "text-align: left;  margin-left: 10em; margin-right: 10em; font-size: 15px"><font color=#000 face="Verdana">
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As Singapore government ramps up efforts to collect and share data sets to the public, various kinds of information are readily available. There are however, no readily available tools that could allow analysts who are well-versed in real estate, but do not have the coding know-how to perform analysis. Thus, our group hopes to bridge the gap and provide these analysts with a friendly user interface and experience.
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</div>
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<br>
  
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<div style="background: #C2CAD0; padding: 20px; line-height: 0.3em; letter-spacing:0.1em; font-size:150%; font-weight:bold; text-align: center; margin-left: 5em; margin-right: 5em;"><font color=#000 face="Verdana">PROJECT DESCRIPTION</font></div>
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<div style= "text-align: left;  margin-left: 10em; margin-right: 10em; font-size: 15px"><font color=#000 face="Verdana">
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Our project aims to find out how external and internal factors may affect resale housing prices, specifically for HDB resale flats. External factors are, though not limited to, shopping malls, hawker centers, MRTs and LRTs, supermarkets, primary schools, pre-schools and eldercare services. Internal factors are, though not limited to, remaining lease, floor area, storey, flat type and model.
  
  
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We will be using 2 different models:
  
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<!-- Project Motivation -->
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# Multiple linear regression
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# Geographically weighted regression
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to compare and explain how the factors stated above may, affect resale housing prices.
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</div>
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<br>
  
 
<!-- Data Sources -->
 
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<div style="background: #C2CAD0; padding: 20px; line-height: 0.3em; letter-spacing:0.1em; font-size:150%; font-weight:bold; text-align: center; margin-left: 5em; margin-right: 5em;"><font color=#000 face="Verdana">DATA SOURCES</font></div>
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| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #040F3D" width="9999px" | Data Sources
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{| class="wikitable" style="background-color:#FFF; margin: 1em auto;" width="80%; font-size: 15px;"
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|+
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|-style="font-size: 100%;
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! <b>Data Set</b>
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! <b>Source</b>
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! <b>Data Type/Method</b>
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|-style="font-size: 100%;
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| 2014 Master Plan Planning Subzone
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| [https://data.gov.sg/dataset/resale-flat-prices?resource_id=1b702208-44bf-4829-b620-4615ee19b57c data.gov.sg]
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| SHP
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|-style="font-size: 100%;
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| Resale flat prices
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| [https://data.gov.sg/dataset/resale-flat-prices?resource_id=1b702208-44bf-4829-b620-4615ee19b57c data.gov.sg]
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| CSV
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|-style="font-size: 100%;
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| Singapore Shopping malls
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| [https://en.wikipedia.org/wiki/List_of_shopping_malls_in_Singapore Wikipedia]
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| Text to SHP
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|-style="font-size: 100%;
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| Singapore Hawker Centers
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| Hawker Centres <br>
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# [https://data.gov.sg/dataset/hawker-centres?resource_id=c2e33097-4f46-4ef5-91db-64eef290ca85 data.gov.sg] <br><br>
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Others <br>
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# [http://www.kopitiam.biz/search-results/?keywords&zone=allzone&FC=yes&search=Search Kopitiam]
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# [https://www.koufu.com.sg/our-brands/food-halls/koufu Koufu]
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# [https://foodrepublic.com.sg/food-republic-outlets/ Food republic]
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| KML
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|-style="font-size: 100%;
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| MRT / LRT Stations
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| [https://www.mytransport.sg/content/mytransport/home/dataMall/static-data.html LTA Datamall]
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| SHP
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|-style="font-size: 100%;
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| Supermarkets
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| [https://en.wikipedia.org/wiki/List_of_shopping_malls_in_Singapore Wikipedia]
 +
| Text to SHP
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|-style="font-size: 100%;
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| Primary Schools
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| [https://data.gov.sg/dataset/school-directory-and-information Primary Schools]
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| CSV
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|-style="font-size: 100%;
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| Premium Primary Schools
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| [https://elite.com.sg/primary-schools Premium Primary Schools]
 +
| Text to SHP
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|-style="font-size: 100%;
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| Premium Pre-schools
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| [https://skoolopedia.com/preschool-singapore-infographic/ Pre-Schools]
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| Text to SHP
 
|}
 
|}
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</div>
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<br>
  
 
<!-- Project Timeline -->
 
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| style="font-family:Open Sans, Arial, sans-serif; font-size:24px; border-top:solid #ffffff; border-bottom:solid #040F3D" width="9999px" | Project Timeline
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[[File:Timeline flatearthers.png|frameless|center|1000px]]
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</div>
  
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<!-- Project Storyboard -->
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<div style="background: #C2CAD0; padding: 20px; line-height: 0.3em; letter-spacing:0.1em; font-size:150%; font-weight:bold; text-align: center; margin-left: 5em; margin-right: 5em;"><font color=#000 face="Verdana">PROJECT STORYBOARD</font></div>
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<div style= "text-align: left;  margin-left: 10em; margin-right: 10em; font-size: 15px"><font color=#000 face="Verdana">
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[[File:Storyboard flatearthers.PNG|frameless|center|1000px]]
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</div>
  
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<div style="background: #C2CAD0; padding: 20px; line-height: 0.3em; letter-spacing:0.1em; font-size:150%; font-weight:bold; text-align: center; margin-left: 5em; margin-right: 5em;"><font color=#000 face="Verdana">KEY CHALLENGES</font></div>
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{| class="wikitable" style="background-color:#FFF; margin: 1em auto;" width="80%; font-size: 15px;"
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|+
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|-style="font-size: 100%;
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! <b>Key Challenges</b>
 +
! <b>Mitigation</b>
 +
|-style="font-size: 100%;
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| Unfamiliarity with R and packages required for project
 +
|
 +
#Self-learning online through sites like Datacamp
 +
#Read up
 +
|-style="font-size: 100%;
 +
| Unfamiliarity with proper approach to use for project
 +
|
 +
#Read up on related projects found in research databases
 +
#View past projects with similar goals
 +
|-style="font-size: 100%;
 +
| Limitations of OneMap API to handle large amount of data
 +
|
 +
#Read up on related projects found in research databases
 +
#Split up large dataset across group members and across a few days
 
|}
 
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<!-- Tools and Technologies -->
 
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{| style="background-color:#ffffff ; margin: 3px 10px 3px 10px; width="80%"|
 
  
  

Latest revision as of 00:02, 15 April 2019

FLATearthers.jpeg

TEAM

 

PROPOSAL

 

POSTER

 

APPLICATION

 

RESEARCH PAPER


PROJECT MOTIVATION

As Singapore government ramps up efforts to collect and share data sets to the public, various kinds of information are readily available. There are however, no readily available tools that could allow analysts who are well-versed in real estate, but do not have the coding know-how to perform analysis. Thus, our group hopes to bridge the gap and provide these analysts with a friendly user interface and experience.


PROJECT DESCRIPTION

Our project aims to find out how external and internal factors may affect resale housing prices, specifically for HDB resale flats. External factors are, though not limited to, shopping malls, hawker centers, MRTs and LRTs, supermarkets, primary schools, pre-schools and eldercare services. Internal factors are, though not limited to, remaining lease, floor area, storey, flat type and model.


We will be using 2 different models:

  1. Multiple linear regression
  2. Geographically weighted regression

to compare and explain how the factors stated above may, affect resale housing prices.


DATA SOURCES
Data Set Source Data Type/Method
2014 Master Plan Planning Subzone data.gov.sg SHP
Resale flat prices data.gov.sg CSV
Singapore Shopping malls Wikipedia Text to SHP
Singapore Hawker Centers Hawker Centres
  1. data.gov.sg

Others

  1. Kopitiam
  2. Koufu
  3. Food republic
 KML
MRT / LRT Stations LTA Datamall SHP
Supermarkets Wikipedia Text to SHP
Primary Schools Primary Schools CSV
Premium Primary Schools Premium Primary Schools Text to SHP
Premium Pre-schools Pre-Schools Text to SHP


PROJECT TIMELINE
Timeline flatearthers.png
PROJECT STORYBOARD
Storyboard flatearthers.PNG


KEY CHALLENGES
Key Challenges Mitigation
Unfamiliarity with R and packages required for project
  1. Self-learning online through sites like Datacamp
  2. Read up
Unfamiliarity with proper approach to use for project
  1. Read up on related projects found in research databases
  2. View past projects with similar goals
Limitations of OneMap API to handle large amount of data
  1. Read up on related projects found in research databases
  2. Split up large dataset across group members and across a few days



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