In addition to the unconditional mean values illustrated above in this section, another simple way to compare homeowner self-assessments with the market rents paid by tenants is to estimate an ordinary least squares regression that provides the average size of the inaccuracy, conditional on a set of covariates. The analysis estimates this regression separately for each year and in Metropolitan Lima and in rural areas using the same set of covariates used in the matching exercise without the imposition of the overlap and common support conditions (Annex A, Table A.1). In Metropolitan Lima, a statistically significant difference is found between homeowner self-assessed values and the market rents paid by tenants in 6 of 15 years, and these years do not overlap with the survey years in which a statistically significant inaccuracy was detected through matching. Similarly, in the case of rural areas, ordinary least squares estimates find a statically significant difference between what estimated by homeowners and what tenants pay in 7 out of 15 years and only in 2012 and 2017 both ordinary least squares and matching estimators identified a statistically significant difference.

# **7. Conclusions**

This paper proposes the use of matching estimators to test the accuracy of the value reports of homeowners by making use of information from a typical household budget survey. The lack of supplemental sources, such as administrative data from national cadastres, is a common issue faced by researchers and practitioners working in developing countries.

The findings indicate that, in 2003–17, homeowners in Metropolitan Lima, on average, provided, with the exception of the two most recent years, accurate estimates of the housing services values of the dwellings they were living in, compared with tenants living in dwellings with observably similar characteristics. In rural areas, two matching algorithms, namely, kernel and nearest neighbor, do not achieve a good balance of the matched sample in the majority of the years considered. Homeowners did not provide accurate market rental values of their dwellings between 2014 and 2017 and underestimated the rental value on average by about −24 percent, a magnitude similar to that detected in Metropolitan Lima in 2016 and 107 (−20 percent).

As a note of caution, matching algorithms differ in the number of tenant observations used and in the way they are used to estimate the counterfactual. For these reasons, they also differ in terms of the capacity to achieve a balance in terms of dwelling characteristics. This is reflected in the comparability of the matched sample and ultimately in the reliability of the accuracy test. Hence, should practitioners resort to matching algorithms to assess the accuracy of homeowner self-assessed rental values, as the paper argues, attention should be paid to the choice of the algorithm as well as to the satisfaction of the overlap and common support conditions. This is key to obtaining a balanced matched sample and to proceed to a comparison using comparable observations.

This paper offers a cautionary tale about the suitability of self-assessed rental values in obtaining a good approximation of market rental values and argues that adequate methods, such as matching estimators, should be used to avoid comparing the incomparable given that considerable differences exist in terms of dwelling characteristics between homeowners and tenants in many developing countries. Homeowners may or may not provide accurate estimates of the rental market prices of the dwellings they live in for several reasons, including the way questions during the collection of such information are phrased, sentimental attachment to the properties, and a lack of information because of spatially clustered or thin rental markets.