There are limitations on the viability of including housing costs in the budget standard. Not only do housing costs vary greatly by location and between household types, even those at the same or a similar standard of living, they also constitute a relatively large share of a household’s total budget. It would be conceptually flawed to “standardize” this cost. Unlike housing costs, given the relatively low pricevariance and high supply elasticity of non-housing costs, it is possible to derive a workable non-housing standard within a particular geographical region. This is used to calculate housing affordability using the RIM approach (Stone, Burke, and Ralston 2011).

Budget standards are not widely implemented in the developing country context for several reasons. First, creating a budget standard is a complex, time-consuming, and specialized task that can take several years to implement, depending on how many household types are being considered, and at how many different points in the income distribution. Second, households of different compositions and incomes differ enormously in terms of their levels and patterns of spending, the degree to which they can engage in cost substitution, and how they allocate their time (Saunders and Bedford 2017; Heylen 2018).

One way to get around this challenge is to arrive at a ‘non-housing expense threshold’ from household income or expenditure survey (HIES) data that are more easily available. For example, Kutty (2005) operationalized a residual income standard for the United States with the non-housing standard set at two-thirds of the federal poverty threshold and applied it to compute what she calls “housing-induced poverty.” Another challenge in the application of the RIM in many developing countries is the difficulty in obtaining accurate income data. A way around this is to explore the substitution of income data for expenditure data, in particular for the lowest income quintiles who presumably have low or no savings accumulation. An adaptation of the RIM methodology is used by micro finance institutions (MFIs) and affordable housing finance companies (AHFCs) in India as they work with the low-income segment in providing micro-finance loans, including for housing. Loan officers work with community groups and individuals, often leveraging technology, to collect housing and non-housing expenditure data that is used to assess a household’s capacity to pay (Bhanot, et al. 2019).

# 3.3 House Price-to-Income Ratio (PIR)

Another simple and commonly used indicator of affordability is the price-to-income ratio (PIR), which is used to measure the affordability of for-sale housing at both the _household_ and _aggregate levels_ . Decades ago, a common rule of thumb used by real estate agents and financial advisors in the United States was that a family could afford to purchase a home priced at about 3 times its gross annual income. A 2019 study of first-time homebuyers in 50 metropolitan areas, however, found that this ratio was actually closer to 3.4 on average, and as high as 5.2 in some markets (AEI Housing Center 2019). Also referred to as the _median multiple_ , at the aggregate level it is the ratio of median house prices to median annual household incomes. It is widely used by the real estate and lending industry and allows for comparison across markets at various geographical scales.

That said, the PIR can produce some conflicting results as compared to the EIR. Consider, for example, the case of two households who plan to purchase a USD 50,000 house. A 30-year loan with a 5 percent annual interest rate requires a monthly loan repayment of USD 215. For a household with an annual income of USD 10,000, the PIR would be 5 (typically considered unaffordable), but the EIR ratio is 26 percent (typically considered affordable). However, if the annual interest rate is 20 percent and the repayment period is 15 years (which is closer to the reality in many developing countries, particularly in Africa), then 11