0 1 2 3 (2) Where: ln(PP) = ββ + ββ (RR) + ββ (OO) + ββ (XX) + εε _P_ = rent (imputed) _R_ = number of habitable rooms _O_ = homeownership dummy **_X_** = vector of housing amenity dummies Since the hedonic equations are log-linear, the estimates can be interpreted as the percent increase in rent from each additional room, controlling for all other amenities. For dummy variables, the estimates can be interpreted as the percent increase in rent associated with having that amenity, controlling for all other amenities and number of rooms.

For all four cities, in terms of estimates for actual rent, the number of rooms and having a permanent roof significantly affects the rental price of housing (see Annex 5, Tables 5 and 6 for full regression tables). All the signs for the significant coefficients appeared as expected, except for the number of rooms in Maputo, which was negative. Electricity, water, and having an improved toilet also significantly affect the rental price of housing for at least two cities examined.<sup>24</sup> The final column of Table 3 provides an annualized ratio of total expenditure to rent price, which can be interpreted as analogous to rent-to-income ratio. These ratios are significantly higher than what households currently spend on rent for the mean house size, which is around 12 percent of total expenditures for Kigali, but is as low as 2.3 percent in Maputo. These higher rent-to-income ratios are more typical of developed countries. The Housing Indicators Program found this ratio to be 18 percent for industrialized countries compared to 10 percent for Southern Africa in 1994 (Angel 2000: 235), and the Global Urban Indicators found this ratio to be 19.1 percent in 1998 for high-income countries<sup>25</sup> (UNHabitat 2002). While these estimated rent-to-income ratios are closer to those of developed countries, they are likely beyond the affordability of households in SSA who allocate upwards of 60 percent of their expenditures on food (Figure 2).

Moreover, these estimates assume that the dwelling unit is fully equipped with amenities such as permanent building materials, a toilet, an electricity connection, etc., which this paper found are severely lacking in urban SSA. In fact, among the homes included in the sample, only one house in Kigali, 32 in Abidjan and none in Dar es Salaam and Maputo are fully equipped.<sup>26</sup> However, evidence from the regression tables in Annex 5 shows households in these cities assign significant value to the presence of these amenities in their homes. Using the coefficients estimated by log-linear equations regressing the price (rent) of houses against indicators for the presence of specific amenities (see Box 4, equations (1) and (2)) and multiplying them by average rents allows us to assign an estimated premium to the presence of amenities.<sup>27</sup> These estimates suggest that in Kigali, for example, having a permanent roof is associated

> 24 Two of the four cities allowed for hedonic analysis from imputed rents – Kigali and Maputo. Imputed rent figures were not available in Cote d’Ivoire, and the large number of missing variables for imputed rent in Dar es Salaam prevented the ability to arrive at consistent estimates.

> 25 Interestingly, according to the Global Urban Indicators the average rent-to-income ratio is 39.5 percent in Africa, significantly higher than that found in the Housing Indicators Program and this analysis.

> 26 For a more detailed representation of predicted prices over several dwelling sizes, see Annex 6, which presents graphs of the annualized predicted prices of homes over the number of habitable rooms comprising the dwelling unit. The average house size for each city is denoted by a vertical blue line. While some of the data appears noisy as a result of small sample sizes for some dwelling sizes as well as large variations in actual and imputed rent, the price estimates at the room means have relatively tight confidence intervals.

> 27 These price premiums are deflated by local Consumer Price Index (2005) and valued at constant US$ using 2005 purchasing power parity in order to make them comparable to one another.

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