# **5. Analyzing Infrastructure Valuation and Variation in Access**

In this section, we use regression analysis to examine: a) the value placed by households on infrastructure services; and b) correlates of access to a given service. First, we use a hedonic analysis of rental data to gain insights into the implicit valuation of different services. Then, we apply a logistic regression to each of five selected infrastructure services (electricity, private piped water, private toilet, organized garbage collection and sewage disposal) to identify factors that systematically affect the likelihood of a household having access to that service. Further analysis of the housing dimensions of this data set can be found in a paper by Gulyani, Talukdar and Bassett (July 2018), titled “Housing choices in urban Kenya: Shared housing, living conditions, and the rental market in 15 cities.”

# **5.1 Revealed valuations for services:  Insights from housing rents**

To properly guide and inform decisions regarding the financing and prioritization of basic public infrastructure services, it is important to understand how such services are valued by recipients. In section 3.3 we compiled data on household spending on different infrastructure services. Household spending can serve as a crude but generally incomplete proxy for relative demand between services. Another more accurate way to develop insights into implicit valuation of any services is to examine a functioning ‘product market’ where the product’s market value (thus, price) is expected to be driven by product attributes that include the service in question. For infrastructure services, the rental housing market with observations on characteristics of housing units versus rents (“products versus prices”) represents such a functioning product market. Accordingly, we undertook a statistical analysis of the rental housing market in urban Kenya to examine the following questions: To what extent do various infrastructure services explain the observed variations in rents paid across urban Kenya? What do the observed variations in rents paid across rental housing units reveal about the implicit valuations of various infrastructure services by tenants?

## **_Revealed valuation approach_**

The statistical approach that we use is the hedonic form of regression analysis (Follain and Jimenez 1985; Gulyani, Bassett, and Talukdar 2012), with the log of "monthly rent" as the dependent variable. The hedonic form of regression analysis is a well-established approach in the economics literature to estimate implicit valuations of product attributes by analyzing observed market prices of products as a function of their attributes (Nelson 1970; Rosen 1974).

In keeping with previous theoretical and empirical studies utilizing the “Living Conditions Diamond” framework (see Gulyani and Bassett 2010; Gulyani and Talukdar 2008), we use four sets of explanatory variables in the rent regression analysis—the unit, neighborhood, tenure, and infrastructure. In analyzing and discussing the results, we focus on: (a) the aggregate impact of infrastructure variables on rent; (b) the impact of specific infrastructure variables; and (c) whether higher quality infrastructure services are, indeed, valued more and translate into higher rent.

A complete list of explanatory variables and their subcomponents are listed below.

_Housing (unit size and quality):_

- Number of rooms in the unit

- Enumerators’ assessment of unit quality

- Does it have permanent walls?

21