Difference between revisions of "IS428 2016-17 Term1 Assign1 Nguyen Duy Loc"
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- The residential projects data quarter by quarter of 2015 | - The residential projects data quarter by quarter of 2015 | ||
+ | |||
+ | [[File: quarterdataofresidents.PNG |Width 800px|frame|center]] | ||
+ | |||
+ | The data inside these quarterly records will be the number of units available in the market during the period of time in 2015. It might be unsold or sold. | ||
+ | |||
+ | The data doesn't have a date field so it was edited to have a quarter field for each of the records. | ||
+ | |||
- The residential transactions data monthly of 2015. | - The residential transactions data monthly of 2015. | ||
+ | |||
+ | [[File: monthlydataoftransactions.PNG |800px|frame|center]] | ||
+ | |||
+ | The data was downloaded monthly and added into the a combined list of property transactions of 2015. These transactions are defined as demand data since buying activities denote market demand. | ||
==Tools Utilized== | ==Tools Utilized== | ||
Tablue 10 | Tablue 10 | ||
− | QGIS | + | QGIS 2.12 |
==Results== | ==Results== | ||
− | [[File: unitsbytype.PNG |800px| | + | ===Supply=== |
+ | |||
+ | As seen below, the majority of the of the units supply come from condominium category, followed secondly by apartments. | ||
+ | |||
+ | |||
+ | [[File: relativesballon.PNG |800px|frame|center]] | ||
+ | |||
+ | However, this dominant category is dropping in term of supply. Clearly, the supply is following the decreasing demand trend for condominiums. | ||
+ | |||
+ | [[File: unitsbytype.PNG |800px|frame|center]] | ||
+ | |||
+ | Among the residentials units supplied in 2015, some are planned, some are under-construction and some are complete. | ||
+ | |||
+ | [[File: statusofunits.PNG |800px|frame|center]] | ||
+ | |||
+ | To clearly see the trend in demand, we look at how many units are launched during 2015 but unsold. Even though the trend in the number of units is downward, the number of units unsold is upward. | ||
+ | |||
+ | [[File: unitsunsold.PNG |800px|frame|center]] | ||
+ | |||
+ | ===Demand & Prices=== | ||
+ | |||
+ | The below picture show us the sales throughout all the months of 2015 by planning regions. For North East Region, there is a huge surge during the summer period. | ||
+ | |||
+ | Overall, sales tends to decreases toward the end of the year from November and starts to pick up around March. During the period between March and November, sales fluctuate wildly. | ||
+ | |||
+ | [[File: monthlysales.PNG |400px|frame|center]] | ||
+ | |||
+ | Its not always the case where the pricing dictates demand. Still, it is true for North East Region sales during summer when the price plummeted and the sales rocketed up. | ||
+ | |||
+ | [[File: pricingdemand.PNG |400px|frame|center]] | ||
+ | |||
+ | One common question always comes to our minds is when to buy and where to buy a house? For instance, I want to buy a condo. | ||
+ | |||
+ | [[File: whenwheretobuy.PNG |400px|frame|center]] | ||
+ | |||
+ | However, is it good enough if a person takes the median pricing and gauge the future price of the house? It depends on the distribution of the pricing in that area as well as the property type. | ||
+ | |||
+ | [[File: distributebyregions.PNG |400px|frame|center]] | ||
+ | |||
+ | Based on the transactions data, we observe the following distribution of selling/buying activities. | ||
+ | |||
+ | |||
+ | [[File: activenesssellingbuying.PNG |200px|frame|center]] | ||
+ | |||
+ | Last but not least, the postal code from transaction data was used to trace the coordinates of the properties sold. Thus, we have a gauge on how the price varies according to the location's proximity to the CBD of Singapore. | ||
+ | |||
+ | However, the transaction data in 2015 didn't contain all the sub regions of Singapore, some regions doesn't have a $psm price. Still, it is clear that the price is high in the central area of the island. | ||
+ | |||
+ | |||
+ | [[File: priceinmap.PNG |200px|frame|center]] | ||
+ | |||
+ | ==Infographics== | ||
− | + | [[File: infographicsnguyenduyloc.PNG |800px|frame|center]] | |
− | + | [[File: infographicsnguyenduyloc1.PNG |800px|frame|center]] | |
− | [[File: |
Latest revision as of 18:35, 29 August 2016
Contents
Abstract
The market of private properties in 2015 continued to show decline further in term of supply and prices. Would this trend go further into 2016?
The prices on the market is usually determined by supply and demand. From this analysis, we will see how the supply react to the decreasing demand and how responsive is this industry to the market forces.
Problem and Motivation
What are the three patterns of the share of the private properties supply in 2015?
What are the three patterns of the distribution of the private properties prices in 2015?
With reference to the findings, what policy should we implement for 2016.
Approaches
Data Preparation
The data is collected from REALIS by quarter:
- The residential projects data quarter by quarter of 2015
The data inside these quarterly records will be the number of units available in the market during the period of time in 2015. It might be unsold or sold.
The data doesn't have a date field so it was edited to have a quarter field for each of the records.
- The residential transactions data monthly of 2015.
The data was downloaded monthly and added into the a combined list of property transactions of 2015. These transactions are defined as demand data since buying activities denote market demand.
Tools Utilized
Tablue 10
QGIS 2.12
Results
Supply
As seen below, the majority of the of the units supply come from condominium category, followed secondly by apartments.
However, this dominant category is dropping in term of supply. Clearly, the supply is following the decreasing demand trend for condominiums.
Among the residentials units supplied in 2015, some are planned, some are under-construction and some are complete.
To clearly see the trend in demand, we look at how many units are launched during 2015 but unsold. Even though the trend in the number of units is downward, the number of units unsold is upward.
Demand & Prices
The below picture show us the sales throughout all the months of 2015 by planning regions. For North East Region, there is a huge surge during the summer period.
Overall, sales tends to decreases toward the end of the year from November and starts to pick up around March. During the period between March and November, sales fluctuate wildly.
Its not always the case where the pricing dictates demand. Still, it is true for North East Region sales during summer when the price plummeted and the sales rocketed up.
One common question always comes to our minds is when to buy and where to buy a house? For instance, I want to buy a condo.
However, is it good enough if a person takes the median pricing and gauge the future price of the house? It depends on the distribution of the pricing in that area as well as the property type.
Based on the transactions data, we observe the following distribution of selling/buying activities.
Last but not least, the postal code from transaction data was used to trace the coordinates of the properties sold. Thus, we have a gauge on how the price varies according to the location's proximity to the CBD of Singapore.
However, the transaction data in 2015 didn't contain all the sub regions of Singapore, some regions doesn't have a $psm price. Still, it is clear that the price is high in the central area of the island.