IS602 Spreadsheets Modelling for Decision Making G2 T5

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IS602 Spreadsheets Modelling for Decision Making G2 T5
Project Proposal

Forecasting Public Nursing Home Demand in Singapore (2025-2035)
——A Demographic Analysis

1.Introduction

As one of the most dynamic countries in the world, Singapore is facing the serious challenge of a rapidly aging population. According to the latest statistics, the proportion of the elderly population aged 65 and above in Singapore has been steadily increasing. It is projected that by 2035, the elderly population will account for more than 25% of the total population, far exceeding the internationally recognized threshold for a deeply aging society (14%). With the rapid growth of the elderly population, the elderly dependency ratio (the ratio of the working-age population to the elderly population) has also been rising annually. This trend not only reflects the profound changes in Singapore's demographic structure but also places enormous pressure on the existing public nursing homes.

This study aims to forecast Singapore's demand for elderly care facilities over the next 10 years and assess whether the current expansion plans align with future needs. Unlike predictionmodels based on economic or policy factors, this study focuses on demographic data. The findings will help policymakers and the elderly care industry make informed decisions.

2.Data Sources

Categories
 Variables
 Sources
Demographics
 Resident Population Structure
 https://www.singstat.gov.sg
Birth Rate
Death Rate
Immigration Rate
 https://www.ceicdata.com
Elderly Care Institution
 Number of nursing home occupants in previous years
 https://www.ceicdata.com
Existing Nursing Home Number
 https://www.statista.com
Policy
 Government elderly care subsidies
 Singapore Parliament Reports
Expansion plan for nursing home(new bed for nursing home)
 https://www.straitstimes.com

3.Computational Methods and Analyses

3.1. Population Forecast

  • Predicting the future population pyramid by age group and gender using the resident population structure, birth rate, death rate, and immigration rate as inputs.
  • From the projected population, choose the senior population (65+) and examine its growth pattern.
Method: Linear Regression + Time Series Forecasting

3.2. Estimate the occupancy rate of nursing homes

Relevance analysis:
  • Calculate the relationship between the elderly dependency ratio and the occupancy rate of nursing homes based on the population structure to assess the impact of population aging on the demand for elderly care.
  • Evaluate the impact of government elderly care subsidies on the occupancy rate of nursing homes, and analyze how policy changes affect the demand for nursing homes.

Predict the future occupancy rate of nursing homes based on the elderly support ratio and government elderly care subsidies.

Method: Time Series Forecasting + Multiple Regression Analysis + Solver + Monte Carlo + Data Table

3.3. Predict the public's demand for nursing homes

  • Based on the historical occupancy rate of nursing homes (combined with the results of correlation analysis) and the predicted population(65+), to predict the demand for nursing home beds in the future.

Method: Demand Forecasting

3.4. Forecast nursing home supply
  • Taking the existing number of nursing home beds as the baseline and combining with the government's announced expansion plans for nursing homes, the total future supply capacity of nursing homes is predicted.
Method: Time Series Forecasting + Solver + Data Table

3.5. Calculate the shortage or surplus of beds in nursing homes

  • Compare the predicted public demand for nursing homes with the supply capacity to calculate the future gap or surplus of beds in nursing homes.
Method: Supply and demand Comparison + Solver + Goal Seek

4.Expected Results

Based on the proposed computational methods and analyses, we forecast the following key results:

4.1. The growth trend of the elderly population

If the current demographic trends continue, the population aged 65 and above is expected to continue to grow in the next 10 years, and the old-age dependency ratio will also rise further. This means that society's demand for elderly care services may gradually increase.

4.2. Changes in nursing home occupancy

As the population ages, nursing home occupancy is likely to rise steadily. Changes in government pension subsidy policies may have a direct impact on occupancy rates: 

Increased subsidies: More seniors may choose to stay in nursing homes.

Reduced subsidies: More seniors may prefer home care or community care.

4.3. Predicted public demand for nursing home beds

If trends in historical occupancy rates continue, combined with future data on the growth of the elderly population, the demand for nursing home beds is expected to gradually rise. If the growth rate of the elderly population aged 65 and above accelerates, the growth rate of demand for beds may be more significant.

4.4. Assessment of nursing home supply capacity

If the government goes ahead with the current expansion plan, the supply capacity of nursing home beds is expected to increase over the next 10 years. But if the expansion is slower, it may not be able to meet the growing demand for nursing homes.

4.5. Analysis of the supply and demand gap of nursing home beds

Demand is growing faster than supply: nursing homes may face a shortage of beds in the next 5-10 years. Supply grows faster than demand: If the government's expansion plans exceed actual demand growth, there may be an oversupply of beds in some years.

4.6. Policy recommendations

Addressing the bed shortage: The government can adjust expansion plans to accelerate the construction of nursing homes, or increase government subsidies to improve the affordability of nursing homes.
Optimize resource allocation:

If supply and demand are balanced, the government can further optimize the allocation of elderly care resources.

Explore more diversified ways of caring for the elderly, such as community care or home care, to provide more flexible choices.

5.Trade-off Analysis (analysing decisions and how they influence the outcomes)

Policy change impact on demand/supply balance
Scenario 1:
What if government subsidies are reduced?
Potential impacts:
-Higher out-of-pocket costs for seniors
-Potential decline in affordability and demand

Quantitative Methods:

1.Elasticity Analysis: Measure the relationship between subsidy reductions and nursing homeaffordability.

2.Regression Analysis: Examine historical trends to estimate the impact of subsidy cuts onoccupancy rates.

3. Scenario Modeling: Project occupancy rates under different subsidies reduction scenarios (e.g. 10%, 20%, 30% subsidy reduction).

Scenario 2:
What if the Expansion Plan is Reduced?
Potential impacts:
-Limited increase in public nursing home supply
-Higher demand for alternative care options, and lower demand for nursing homes
-Higher reliance on informal and home-based caregiving
Quantitative Methods：
1.Capacity Shortfall Estimation: Compare the original planned increase in bed capacity with different reduced expansion scenarios to estimate the gap in available beds.
2.Project occupancy rates under different expansion reduction scenarios: Simulate differentlevels of reduction in nursing home beds and analyze the impact on: Occupancy rates andassess whether demand will exceed supply. (e.g. 25%, 50%, 75% cut in planned capacity).

6.Sensitivity Analysis (analysing parameters and how they influence outcomes)

What if the aging population changes differently than expected?
Scenario 1. For Birth Rate
Increase: Lower demand (more family caregivers in the future)
Decrease: Higher demand (fewer caregivers, higher dependency ratio)
Quantitative Methods:
1.Evaluate the effects of 1%, 2% and 3% increases/decreases in birth rate.
2.Monte Carlo Simulation: Assess uncertainty in birth rate and its impact on nursing homedemand.

Scenario 2. For Death Rate

Increase: Lower demand (fewer elderly requiring care)
Decrease: Higher demand (people live longer which require more care)
Quantitative Methods:
1.Evaluate the effects of 1%, 2%, and 3% increases(decreases) in death rate.
2.Monte Carlo Simulation: Assess uncertainty in death rate and its impact on nursing homedemand.

Scenario 3. For Immigrant Rate

Increase: Lower demand (more young workers, better support)
Decrease: Higher demand (fewer young workers, more elderly dependency)
Quantitative Methods:
1.Evaluate the effects of different percentages increases(decreases) in immigrant rate.
2.Monte Carlo Simulation: Assess uncertainty in immigrant rate and its impact on nursing homedemand.

7.An Influence diagram and a Black-Box model



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