09 global financial crisis in Metropolitan Lima, but not in rural areas. Both self-assessed rental values and reported market rents increased by around 3 percent a year in Metropolitan Lima and by about 7 percent a year in rural areas. The unconditional difference varies year-by-year and ranges between S/. −696 in 2017 and S/. 2,287 in 2007 in Metropolitan Lima, which means that, in some years, homeowners seem to have underestimated the rental market values of their dwellings, while, in other years, they seem to have overestimated the values. Meanwhile, in rural areas, homeowners seem to have always underestimated the rental values of their dwellings relative to the rents paid by market tenants. In rural areas, the average unconditional inaccuracy seem to have expanded during the most recent years.

Table 1. Average self-assessed monthly rents and unconditional difference with market rents paid by tenants, 2003–17

||_Metrop _<br>|_olitan Lima_<br>||_Rur_<br>|_al areas_<br>||
|---|---|---|---|---|---|---|
||_Self-assessed rent_<br>_(1)_|_Delta_<br>_(2)_|_P-value_<br>_(3)_|_Self-assessed rent_<br>_(4)_|_Delta_<br>_(5)_|_P-value_<br>_(6)_|
|2003|5,081|−489|0.378|480|−218|0.000|
|2004|7,125|211|0.717|719|−316|0.000|
|2005|7,456|2,229|0.001|701|−254|0.000|
|2006|7,589|1,717|0.005|725|−285|0.000|
|2007|7,634|2,287|0.000|915|−262|0.000|
|2008|6,469|1,546|0.000|985|−209|0.021|
|2009|6,720|1,699|0.000|1,037|−177|0.012|
|2010|6,496|673|0.111|1,042|−309|0.000|
|2011|7,012|1,191|0.003|1,180|−125|0.184|
|2012|7,190|1,201|0.003|1,203|−298|0.001|
|2013|7,013|−17|0.962|1,223|−369|0.000|
|2014|7,512|319|0.370|1,286|−347|0.000|
|2015|7,969|924|0.014|1,256|−432|0.000|
|2016|8,170|188|0.606|1,293|−476|0.000|
|2017|7,956|−696|0.072|1,302|−545|0.000|

_Source:_ Based on National Household Survey data. _Note:_ Monetary values are expressed in Peruvian sol in 2017 prices using CPIs of the WDI database.

# **Propensity Score Matching Results**

The conditional independence assumption requires that the selection be solely based on observables. Any differences in outcomes between treated and control individuals with the same values of observable characteristics are thus ascribable to treatment. The analysis is guided by the hedonic theory of consumption, and the set of covariates includes the following: quartiles of the total number of rooms; a dummy for dwellings with external walls made of low-quality materials, including clay, canes and mud, stones and mud, wood, or rug; a dummy for dwellings with floors made of low-quality materials, including dirt; a dummy for dwellings with roofs made of low-quality materials, including cane and rug with mud, rug, or straw and palm leaves; a dummy for public water network access inside the dwelling or outside the dwelling, but inside the building; a dummy for access to electricity; a dummy for public sewerage access inside the dwelling or outside the dwelling, but inside the building; a set of five dummies for dwelling type, that is, detached house, apartment in a building, apartment in a detached house with independent water access and sewerage, apartment in a detached house with shared water services, and a hut or other type of dwelling; and a set of three dummies capturing geographical location outside Metropolitan Lima (the Costa, Selva, or Sierra). A logit regression of the probability of being a homeowner is estimated over the covariates listed above separately for each year and for Metropolitan Lima and rural areas.

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