Table 3. Summary of findings based on the literature reviewed.

Approach/Method		Strengths	Weaknesses	Key Variables Used	Common Application Areas
Conventional (Normative) Approaches	IRM	Ease of application. Ease of affordability comparisons across regions. Based on a small number of regularly available variables. Enjoys global acceptance. Applicable in a range of areas to study the differences across households and affordability trends over time. Measures the actual household expenditure relative to their actual income.	Arbitrary benchmark, no clear rationale behind affordability thresholds. Does not account for household structure and level of income of various families, unless modified. Does not address housing quality and adequacy. Concentrates on economic aspects only. Erroneously assumes that every family and individual has equal capacity to pay for housing. Fails to address non-housing expenditure. Could over and under estimate affordability problems.	House price. Monthly gross income. Monthly rent.	Ascertaining the market realities in housing and income. Comparison of same type of household affordability over different areas in a given time frame. Describing household expenditure. Eligibility criteria for public housing subsidy allocation. Predicting household ability for housing payment.
	RIM	Effective where the economic realities are similar for all chosen samples. Considers household structure and different levels of income among different households. Sensitive to housing market realities in income and housing. Acknowledges that people have different spending needs. Addresses equity concerns. Incorporates housing quality. The correlation flanked by housing and non-housing expenditure is well articulated. Considers household spending pattern and the leverage effect.	Its confusion with poverty measurement. Insensitive to the living cost of different areas. Does not account for the influence of housing quality on location preference on housing cost (no account of location tradeoffs). Creates a certain threshold above which affordability becomes increasingly subjective. Focuses on the economic dimension of affordability. Requires an element of generalization and judgment about household type.	Commuting cost. Non-housing related expenditure. Geographic location. Household size. Loan amount. Household Composition. Spending Pattern.	Discerning norms for mortgage loans and housing allowances. For comparing HA situations of two households. Valuable in forecasting the expenditure patterns of low and medium income families. Effective in affordability studies of small areas.
	Composite Method	More sensitive to the ability of households to confront their housing and non-housing expenses, as compared to both ratio and residual income approaches. Addresses the concept of opportunity cost, that is perceived as the fundamental nature of HA, i.e., the tradeoffs that are made to acquire housing and if such tradeoffs are rational or extreme.	‘Costs of living’ are determined by some form of normative assessment. Fails to address other important issues, such as; the return on investment for housing expenditure, in terms of neighborhood and housing quality. Insensitive to the tradeoffs between cheap and affordable housing. Excludes household savings, wealth and other financial aids while depending entirely on household income. Superficially defines the point at which individuals’ acquires the right to live independently.	Living Standard, neighborhood quality. Running costs Maintenance costs, and commuting costs. Rental or Mortgage payments. Externalities: Cost saving on transport, living styles	In measuring rental housing affordability. Affordability trends over time of a region.
Scarcely Used Approaches	Behavioral	Considered to be more accurate in demonstrating the households’ expenditure pattern. Integration into the normative approach is possible, for determining the benchmark affordability ratios.	Difficult to access data required for its assessment. Sometimes shows a vague proof on the behavioral pattern of people’s housing consumption.	Household income. Household characteristics. Housing Choice.	Forecasting household consumption patterns and choice behavior.
	Subjective	Enables respondents to reveal various scenarios and issues that shape their housing stability, which are rarely measured. Accounts for differences of what informs housing choice, like taste and experience.	Fluctuates over time more often than objective measures. These indicators are often derived from samples that do not represent the entire population. Therefore, the generalizability of findings could be limited.	Household income. Household Composition. Household Perception. Housing Quality. Housing Type.	Assessing housing consumption patterns. Forecasting real estate prices. Mortgage/loan repayment ability of home owners and rental housing.
Emerging Innovative Approaches	MCDM	Aids in network and complex decision making Offers a means of problem structuring and working through information. Without all measures being converted into the same unit; social, cultural, economic and environmental considerations can be traded off.	Time consuming with large numbers. Ignores the different effects among clusters. Perfect consistency is very difficult. Some forms of MCDM may be deterministic. Does not take into account the uncertainty in weightings. Different models of MCDM can provide dissimilar outcome if used for the same problem.	Housing Price. Household Income. Externalities: Cost saving on transport, living styles, etc.	Assessing sustainable housing affordability.
	Gini Coefficient	Easy interpretability, since it is founded on ratio analysis. Considers segmentation and examines inequality in HA. Allows the comparison of income distribution in different countries, regions over time.	As a relative measure, its use and interpretation is controversial. Neglects the causes of inequality. Data on absolute regional and individual income is lost.	Household net income. Monthly rent	Estimating the relationship between income inequality and housing affordability of a given population.
	MPP	Permits a better exploration of housing price dynamics. Can offer salient information on future evolution trends of housing prices. Enables the comparison of the effectiveness of housing policy on price trends in various housing units’ sizes across cities over time. It is absolute data-driven, and no assumptions are imposed on the model.	The three-dimensional plot and the contour map provide a lot to important information on the distribution dynamics, however, they are difficult to interpret. The three-dimensional plot and the contour map provides a lot of important information on the distribution dynamics, however, they are difficult to interpret. The contour maps and three-dimensional plot provide much salient information on distribution dynamics, but the interpretation is very difficult and challenging.	House Price. Household Income.	Regional comparison of housing affordability trends.