Difference between revisions of "FLATearthers proposal"

Revision as of 00:34, 8 April 2019

PROJECT MOTIVATION

As Singapore government ramps up effortsto 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:

Multiple linear regression
Geographically weighted regression

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

DATA SOURCES


Data Set	Format	Attribute
HDB Resale Value	CSV	Month Town Flat Type Block Street Name Storey Range Floor Area Remaining Lease (Years) Resale Price
Shopping malls	csv	-
MRT/LRT	csv	-
Primary Schools	csv (General-information-of-schools.csv Hands-On Ex 3)	school_name postal_code
Secondary schools	csv (General-information-of-schools.csv Hands-On Ex 3)	school_name postal_code
JCs and Polytechnics	csv (General-information-of-schools.csv Hands-On Ex 3)	school_name postal_code
Hospitals	csv	-
Hawker Centres	csv	-

PROJECT TIMELINE

PROJECT STORYBOARD

KEY CHALLENGES


Key Challenges	Mitigation
Unfamiliarity with R and packages required for project	Self-learning online through sites like Datacamp Read up
Unfamiliarity with proper approach to use for project	Read up on related projects found in research databases View past projects with similar goals
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

@@ Line 33: / Line 33: @@
 <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= "text-align: left;  margin-left: 10em; margin-right: 10em; font-size: 15px"><font color=#000 face="Verdana">
-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.
+As Singapore government ramps up effortsto 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.
 </div>
 <br>
@@ Line 42: / Line 40: @@
 <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>
 <div style= "text-align: left;  margin-left: 10em; margin-right: 10em; font-size: 15px"><font color=#000 face="Verdana">
-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.
+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:
-We will be using 3 different models:
+# Multiple linear regression
+# Geographically weighted regression
-# Hedonic pricing model
+to compare and explain how the factors stated above may, affect resale housing prices.
-# Geographically weighted regression
-# Geographically and temporally weighted regression to compare and explain how the factors stated above may, affect resale housing prices.
 </div>
 <br>

Difference between revisions of "FLATearthers proposal"

Revision as of 00:34, 8 April 2019

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