Among the group of studies which combine different household surveys to estimate the flow of the service, Marquier (2003) merges the French housing survey ( _enquête logement_ ) with the survey on income and living condition (SILC) and Ceriani et al (2013) match two household budget surveys produced for Italy (one by the Statistical Office, the other by the Bank of Italy) to maximize information about households’ real estate properties. Garner et al. (2006) suggest using the rental equivalence reported in the United States’ Consumer Expenditure Survey published by the Bureau of Labor Statistics, and apply the coefficient of a hedonic regression on housing unit characteristics of the American Housing Survey. It is relevant to mention, most studies found in the literature are focused on developed countries. This is a consequence of the existence and easily availability of alternative datasets which are atypical in developing countries. Another possible source of information could be the rental surveys conducted by the different Statistical Offices for constructing the 2011 PPPs (ICP, 2013).

As mentioned, a second option would be using administrative registers such as cadasters, or transaction and assessment data. These type of data sources may be the most comprehensive about dwellings’ characteristics in a country. However, there are several drawbacks of using these datasets. Usually, municipalities collect this type of information and it is not generally centralized. In other words, a huge effort might be done in gathering it for the whole country. Additionally, dwellings’ values reported in cadasters may be outdated. On the other hand, transaction data have information only on houses which have been exchanged on the market, which may be a small share of the total stock. In countries where the value of the dwelling is taxed, fiscal records may represent a good option for the analysis (see Pollakiwsky 1995).

Kholodilin (2012) analyzes the level of flats’ prices in 48 large European cities, by collecting data from internet websites. Similar approach is implemented by the Bank of Albania for calculating the House Price Index and the Rent Price Index for Tirana (Kristo and Bollano, 2012). The dataset is obtained from sale and rent announcements published in a newspaper.<sup>23</sup> One limitation of this approach is that newspaper announcements may not be a good approximation of the stock of housing in a country. For instance previous examples only consider the capital and large cities in their samples. Moreover, even if we could virtually find sale or rent announcements of dwellings in all locations in one country, the sample of dwellings may likely suffer from selection bias. Similarly, data collected from multiple listing agencies and mortgages transactions suffer from lack of representativeness. As suggested in Pollakowsky (1995), this type of information may be more appropriated for studies concentrated in a specific location or metropolitan area.

Finally, key informants could be asked to assess the value of the dwellings.<sup>24</sup> These could be for instance enumerators while conducting the households’ survey, as in the case of Zimbawe (Zimbawe National Statistical Agency, 2013, p.127). Someone knowledgeable about a specific location might have some insights on non-observable characteristics of the dwelling, and can be more objective in her evaluation than home-owners. When no other information for rents or home values is available, using external sources could provide a good approximation of the value of housing services.

# **3 Importance of rent imputation in other economic disciplines**

Other economic disciplines have also acknowledged the importance of the imputed rent. This section provides an overview of how practitioners in the fields of National Accounts, Consumer Price Index,

> 23 The sample includes 90 apartments in each quarter (Kristo and Bollano, 2012).

> 24 This is similar to the problem faced when collecting food prices (see Gibson 2007)

15