# **4 The Price of a Fully Equipped House – How high can it be?**

The analysis in the previous sections suggest that given the low stage of development of most African countries, households are still struggling to secure their daily meals as they spend over 60 percent of their income in food. Low household expenditures on housing are accompanied by poor housing quality evidence by large shelter shortages. And governments have also failed in delivering basic services as is reflected in the widespread infrastructure shortages. But how expensive can a fully equipped house be?

Using the principles of hedonic estimates (see Box 4), this paper estimates the average price of a nonovercrowded home that contains all shelter and housing infrastructure amenities outlined in Table 2. Table 3 outlines the predicted price derived from a first-stage hedonic regression, along with the upper and lower bounds of the 95 percent confidence interval.

## **—** **<u>Box 4. Hedonic Regression Analysis City Selection and Estimation</u>**

Hedonic methods are an approach to estimating housing demand by uncovering the implicit prices for housing characteristics. Rosen (1974) defined implicit prices as those revealed by analyzing how much households pay for a good given its composition of characteristics. An earlier study by Kain and Quigley (1970) used hedonics to estimate the implicit price of housing in St. Louis, Missouri based on a bundle of housing characteristics and factor analysis of housing quality. Kain and Quigley found they could identify the housing characteristics, both qualitative and quantitative, that most influenced market prices. Jimenez (1982) was the first to estimate housing prices in a squatter settlement in a developing country using hedonic pricing. His study of the Tondo area in Manila, Philippines found that housing conditions were most influential in determining value. Later Follain and Jimenez, estimated willingness to pay for disaggregated housing characteristics in developing country cities in Colombia, Korea, and the Philippines (1985). In SSA only a few studies of specific markets exist such as Megbolugbe’s analysis of the housing market in Jos, Nigeria (1986) and Arimah’s analysis of Ibadan, Nigeria (1992). Very few studies in SSA have been undertaken since then, largely because of the lack of quality data (Arimah 1992). Using the rich SHIP data, this paper employs a first-stage hedonic regression to estimate how the existence or lack of housing amenities affect the price households pay for housing in select Sub-Saharan African cities.

This analysis focuses on housing markets in the countries’ largest urban areas, represented by its capital or primary city. To narrow down the field of cities, countries where the data are from before 2007 are eliminated in order to capture the most recent prices possible. Because the SHIP data are nationally representative samples, the largest cities with relatively large samples were selected. Finally, cities that had an excessive number of missing variables were eliminated since they prevented the estimation for more than two housing amenities. As a result, the cities that fit all of the aforementioned criteria are Kigali, Rwanda; Abidjan, Cote d’Ivoire; Dar es Salaam, Tanzania; and Maputo, Mozambique.

Using actual and imputed (an estimate of rents that homeowners estimate they would receive for their home) rents, the hedonic regression employs the following log-linear equation: 0 1 2 (1) Where: ln(PP) = ββ + ββ (RR) + ββ (XX) + εε _P_ = rent (actual) _R_ = number of habitable rooms **_X_** = vector of housing amenity dummies In cases where respondents are homeowners or otherwise live rent free, rents are reported as imputed, meaning that respondents estimate the value they would receive in rent. The equation is similar to equation (1), but includes a homeownership dummy.

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