Difference between revisions of "Group10 proposal"
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= Project Objectives = | = Project Objectives = | ||
The objectives of the project are as follows:<br/> | The objectives of the project are as follows:<br/> | ||
| − | 1. To find out how different planning area behave with regards to price and number of transaction;<br/> | + | <p style="margin-left: 40px">1. To find out how different planning area behave with regards to price and number of transaction;<br/> |
2. To find out the difference between property type, type of sale and between the 2 years;<br/> | 2. To find out the difference between property type, type of sale and between the 2 years;<br/> | ||
3. To understand the extent to which area has on the price;<br/> | 3. To understand the extent to which area has on the price;<br/> | ||
Revision as of 15:50, 25 April 2020
Contents
Background
Having an accurate housing price model is important for the government or policymakers as properties plays a significant role in household wealth and national economy. To date, the hedonic pricing model is still the commonly used method by researchers to study housing prices. Hedonic pricing models are often computed using an ordinary least squares (OLS) estimator, with the assumption that the observations are independent and identically distributed. This indicates that there is no spatial auto-correlation among the observations but in reality, that might not be true and thus, a hedonic pricing model could lead to biased, inconsistent and inefficient results. Moreover, when new developments are launched, the developers dictate the prices. Our team attempts to understand the price variation for new launches.
Our team attempts to explore the use of various statistically methods to provide insights to the property transnational data.
Motivation
Our research and development efforts were motivated by the general lack of effective and easy to use web-enabled client-based analytics tool for discovering Singapore property price trends. It aims to provide
users with an analytical tool for discovering insights to Singapore property prices through easy-to-understand visual analytics charts. Specifically, it attempts to support the following analysis requirements:
1) To visualise price changes in different areas of Singapore over time;
2) To compare the price range and transaction volume of different areas of Singapore;
3) To identify if the average unit price of a private property project is above or below the average unit price of the planning area and its extent of differences;
4) To enable users to perform statistical analysis on the price distribution of different property types and regions in Singapore.
Project Objectives
The objectives of the project are as follows:
1. To find out how different planning area behave with regards to price and number of transaction;
2. To find out the difference between property type, type of sale and between the 2 years;
3. To understand the extent to which area has on the price;
4. To visually represent our findings.
Proposed Scope and Methodology
We will confine our work to the private housing market in Singapore. This is because HDBs has many restrictions imposed and therefore to some extent, its prices are controlled. In contrast, the private housing market is open to all buyers and therefore can be more accurately determined by the attributes of the property and its surrounding.
The surrounding amenities we will consider in our project are Schools, MRT, Bus Stops etc.
Methodology will be linear regression and geographically weighted regression
Project Timeline
Features
Our visualisation will use the Singapore Map with the following features:
- Breakdown into small areas according to ???
- Colour coded according to the degree of R-squared value
- User option to select which attributes to look at?
An example of our visualisation:
Data Source & Preparation
We will use REALIS data, 2018 & 2019, for our project.
| Data Source | Documentation |
|---|---|
| Realis | Transaction Data of Private Property in Singapore |
Software
Packages
| Package Name | Description |
|---|---|
| Shiny | Building of interactive web applications |
| Shinydashboard | Dashboard Creation |
| ShinyThemes | Themes for Shiny |
| ShinyWidgets | Themes for Widgets |
| RColorBrewer | Provide color schemes |
| Tidyverse | Collection of R packages for data analysis, packages includes ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats |
| Lubridate | Working with date and time |
| Geofacet | Visualize data by geographical regions |
| Treemap | Visualize data as nested rectangles |
| ggstatplot | ggplot with detailed statistical test |
| Ridgeplot | Visualize data as ridgeplot |
