HDB Resale Investment Potential Model: Project Summary
Objective
The objective of this model is to help users identify the best HDB resale flats for
investment in Singapore by analyzing appreciation trends. By examining variables
such as town, flat type, storey range, floor area, and remaining lease duration,
the model provides insights into which property types offer strong appreciation
potential while balancing growth with risk factors like lease decay.
The model leverages Excel for data calculations and Tableau for dynamic
visualization.
Project Structure and Workflow
1. Identifying Key Variables and Grouping Criteria
o
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Primary Variables:

Town: Location of the flat, which affects demand and value
appreciation.

Flat Type: Type of flat (e.g., 3-room, 4-room), as different types
attract different buyers and may appreciate differently.

Storey Range: Floor levels grouped as Low, Mid, and High to
understand the influence of floor height on appreciation.

Floor Area: Size of the flat (e.g., Small, Medium, Large), with
larger or smaller units potentially showing different appreciation
trends.

Remaining Lease: The number of years left on the lease,
affecting resale value and appreciation over time.
Grouping for Simplification:

Storey Range: Grouped into Low (1-6), Mid (7-15), and High
(16+).

Floor Area: Grouped into Small (<60 sqm), Medium (60-100
sqm), and Large (>100 sqm).

Remaining Lease: Grouped into Short (<56 years), Mid-Length
(56-75 years), and Long (76+ years) to assess lease decay risk.
2. Calculating Appreciation Rates Using a Trend-Based Approach
o
Trend Calculation: Rather than a simple average, we use a linear
trendline approach to capture appreciation trends over time, which
smooths out fluctuations and shows consistent growth patterns.
o
Slope Calculation: Calculate the slope of the trendline for each townflat type combination. The slope indicates the average annual
appreciation rate.
o
Standardized Comparison: Convert the slope to an Annual Trend %
by dividing it by the average resale price for the period, allowing a
standardized comparison across different towns and flat types.
3. Layered Analysis Based on Dynamic Filters
o
Base Level (Town): By default, the model shows appreciation rates
aggregated by town.
o
Layered Filtering:

When a user applies additional filters (e.g., flat type, storey
range), the model adjusts the calculations to show appreciation
rates specific to those selections.

This multi-level filtering allows users to view appreciation rates
at a high level (town-only) or in detail (town-flat type-storey
range-lease duration).
4. Implementing Dynamic Filtering in Tableau
o
Excel for Data Calculations: Excel is used for the initial data
structuring, grouping, and trend calculations.
o
Tableau for Interactive Dashboard:

Heat Map: Displays appreciation rates on a Singapore map,
color-coded to indicate high- and low-growth towns.

Interactive Filters: Dropdowns in Tableau allow users to filter by
town, flat type, storey range, and lease duration. Tableau
updates the heat map and all other visuals based on these
selections.

Trend and Comparison Charts: Includes line charts for
appreciation trends over time, bar charts for comparing Annual
Trend % across towns and flat types, and additional visuals for
trade-off and sensitivity analysis.
5. Trade-Off and Sensitivity Analysis
Trade-Off Analysis:
o
Objective: To help users evaluate potential gains relative to associated
risks, such as lease decay or lower appreciation for certain flat types or
storey ranges.
o
Key Scenarios:
1.
Lease Duration vs. Appreciation Rate: A scatter plot shows the trade-off
between appreciation and lease duration, helping users balance growth potential
with lease decay risk.
2.
Storey Range vs. Appreciation Rate: Bar charts or box plots allow
comparison of appreciation rates across low, mid, and high floors, showing if higher
floors warrant a premium.
3.
Flat Type vs. Floor Area: A stacked bar chart compares appreciation rates
across different flat types and sizes, helping users see if certain sizes are more
advantageous.
Sensitivity Analysis:
o
Objective: To assess how sensitive appreciation rates are to changes
in key variables, such as lease duration and floor area, revealing the
most impactful factors.
o
Key Scenarios:
1.
Lease Duration Sensitivity: A parameter control in Tableau allows users to
slide between lease scenarios to see how appreciation changes with shorter or
longer leases.
2.
Floor Area Sensitivity: Adjust floor area in percentage terms to see if larger
or smaller units are more responsive to demand.
3.
Flat Type and Storey Range Sensitivity: Dynamic filtering lets users switch
between flat types and storey ranges to observe how each influences appreciation,
allowing them to prioritize impactful variables.
Visualization and Dashboard Elements
1. Heat Map on Singapore Map:
o
Uses Tableau’s mapping features to show appreciation rates
geographically. The map is color-coded, with gradients to highlight high
and low appreciation rates by town.
o
As filters are applied, the heat map updates to display appreciation
rates specific to the selected criteria (e.g., town-flat type combination).
2. Trend and Comparison Charts:
o
Trend Comparison Line Chart: Displays appreciation trends over time
for each selected town and flat type, with trendlines for visual
comparison.
o
Bar Chart for Annual Trend %: Ranks towns by annual appreciation
rates, allowing users to see which towns and flat types perform best.
3. Visual Exploration of Trade-Offs and Sensitivity:
o
Trade-Off Matrix: Highlights the balance between appreciation
potential and variables like lease duration or storey range.
o
Sensitivity Analysis Section: Displays how appreciation rates
respond to changes in lease duration, floor area, and other key factors.
Summary of Outputs and User Benefits
1. High-Growth Town Identification:
o
Users can quickly identify which towns have shown the highest
appreciation rates over time.
2. Detailed Breakdown by Flat Type and Storey Range:
o
Drill-down filters allow users to refine results by flat type and storey
range, so they can assess specific property types in each town.
3. Lease-Based Risk Adjustment:
o
Users can filter by lease duration to understand the impact of lease
decay on appreciation rates, helping them make risk-adjusted choices.
4. Visual Exploration of Trade-Offs and Sensitivity:
o
Users can explore the trade-offs between lease length, storey range,
and appreciation potential, with sensitivity analysis to see how small
changes impact value.
5. Seamless Excel-Tableau Integration:
o
Excel is used for initial calculations and data structuring, while Tableau
provides dynamic, interactive visuals that adjust in real-time to user
inputs, making the data easily interpretable and visually engaging.