Difference between revisions of "FLATearthers proposal"

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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 MOTIVATION</font></div>
 
<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 MOTIVATION</font></div>
 
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In Singapore, the large majority of the population live in HDB flats. Given the scarcity of land in Singapore, housing prices tend to hold a large price tag as with HDB flats. Buying a HDB flat represents a huge financial commitment that many young adults face as they search for the ideal home with the appropriate price tag to match.
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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.
 
 
As prospective buyers of HDB flats, we would like to investigate the intrinsic value of HDB flats and explain why they are priced as such. We intend to take a geospatial approach to analyze and quantify the different factors affecting the resale prices of HDB flats by both internal and external factors.
 
 
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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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Our project aims to find out how external and internal factors may affect resale housing prices. External factors are, though not limited to, noise levels, proximity to hawker centers, MRT stations, shopping malls, schools and eldercare services. Internal factors are, though not limited to, remaining lease, number of rooms, floor, flat type and age of the flat.
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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.
  
  
We will be using 3 different models:
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We will be using 2 different models:
  
# Hedonic pricing model
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# Multiple linear regression
# Geographically weighted regression
+
# Geographically weighted regression  
# Geographically and temporally weighted regression to compare and explain how the factors stated above may, affect resale housing prices.
+
 
 +
to compare and explain how the factors stated above may, affect resale housing prices.
 
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|-style="font-size: 100%;
 
|-style="font-size: 100%;
 
! <b>Data Set</b>
 
! <b>Data Set</b>
! <b>Format</b>
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! <b>Source</b>
! <b>Attribute</b>
+
! <b>Data Type/Method</b>
 +
|-style="font-size: 100%;
 +
| 2014 Master Plan Planning Subzone
 +
| [https://data.gov.sg/dataset/resale-flat-prices?resource_id=1b702208-44bf-4829-b620-4615ee19b57c data.gov.sg]
 +
| SHP
 
|-style="font-size: 100%;
 
|-style="font-size: 100%;
| [https://data.gov.sg/dataset/resale-flat-prices?resource_id=1b702208-44bf-4829-b620-4615ee19b57c HDB Resale Value]
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| Resale flat prices
 +
| [https://data.gov.sg/dataset/resale-flat-prices?resource_id=1b702208-44bf-4829-b620-4615ee19b57c data.gov.sg]
 
| CSV
 
| CSV
|
 
*  Month
 
*  Town
 
*  Flat Type
 
*  Block
 
*  Street Name
 
*  Storey Range
 
*  Floor Area
 
*  Remaining Lease (Years)
 
*  Resale Price
 
 
|-style="font-size: 100%;
 
|-style="font-size: 100%;
| [https://en.wikipedia.org/wiki/List_of_shopping_malls_in_Singapore Shopping malls]
+
| Singapore Shopping malls
| csv
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| [https://en.wikipedia.org/wiki/List_of_shopping_malls_in_Singapore Wikipedia]
| -
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| Text to SHP
 
|-style="font-size: 100%;
 
|-style="font-size: 100%;
| [https://en.wikipedia.org/wiki/List_of_Singapore_MRT_stations MRT/LRT]
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| Singapore Hawker Centers
| csv
+
| 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>
 +
Others <br>
 +
# [http://www.kopitiam.biz/search-results/?keywords&zone=allzone&FC=yes&search=Search Kopitiam]
 +
# [https://www.koufu.com.sg/our-brands/food-halls/koufu Koufu]
 +
# [https://foodrepublic.com.sg/food-republic-outlets/ Food republic]
 +
| KML
 
|-style="font-size: 100%;
 
|-style="font-size: 100%;
| Primary Schools
+
| MRT / LRT Stations
| csv (General-information-of-schools.csv Hands-On Ex 3)
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| [https://www.mytransport.sg/content/mytransport/home/dataMall/static-data.html LTA Datamall]
|  
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| SHP
* school_name
 
* postal_code
 
 
|-style="font-size: 100%;
 
|-style="font-size: 100%;
| [https://docs.onemap.sg/#authentication-service-post Secondary schools]
+
| Supermarkets
| csv (General-information-of-schools.csv Hands-On Ex 3)
+
| [https://en.wikipedia.org/wiki/List_of_shopping_malls_in_Singapore Wikipedia]
|
+
| Text to SHP
* school_name
 
* postal_code
 
 
|-style="font-size: 100%;
 
|-style="font-size: 100%;
| [http://www.polytechnic.edu.sg JCs and Polytechnics]
+
| Primary Schools
| csv (General-information-of-schools.csv Hands-On Ex 3)
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| [https://data.gov.sg/dataset/school-directory-and-information Primary Schools]
|  
+
| CSV
* school_name
 
* postal_code
 
 
|-style="font-size: 100%;
 
|-style="font-size: 100%;
| [https://en.wikipedia.org/wiki/List_of_hospitals_in_Singapore Hospitals]
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| Premium Primary Schools
| csv
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| [https://elite.com.sg/primary-schools Premium Primary Schools]
| -
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| Text to SHP
 
|-style="font-size: 100%;
 
|-style="font-size: 100%;
| [https://data.gov.sg/dataset/hawker-centres Hawker Centres]
+
| Premium Pre-schools
| csv
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| [https://skoolopedia.com/preschool-singapore-infographic/ Pre-Schools]
| -
+
| Text to SHP
 
|}
 
|}
 
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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