The data thus generated are illustrated in table 5, highlighting that, with median parcel area of 400 m<sup>2</sup> , building height of 3.7 m, and built-up volume of 304 m<sup>3</sup> , about 20% of the area in the urban part of our study area is built up.<sup>18</sup> Complementing these data with the 2012 population and housing census, the 2014 establishment census, shapefiles for the location of schools from the National Institute of Statistics, and a map of road networks from the Rwanda Transport Development Agency made it possible to generate additional property attributes for inclusion in a computerized mass appraisal model.

Descriptive statistics suggest that the average property is 5.6 km from the CBD and 600 m from the closest primary school. Data on establishments, available only at cell level to protect respondent confidentiality, suggest better job opportunities in urban Kigali, where some 1,330 people per cell were employed by local establishments, compared to 360 in the city’s rural cells. With a per km<sup>2</sup> cost of US$34 for imagery and $112 for processing (and ample scope for further reductions, e.g., by linking to open street map data or using local analysts), the total cost for the study area was about US$40,000.<sup>19</sup>

# **3.2 Factors Affecting Property Values and Model Suitability**

CAMA models for property taxation typically involve estimation of a hedonic model on a set of properties for which prices are observed and then using the model to impute the values of properties for which price data are not available (IIAO 2013). Assuming that properties sold are a random sample, and following Rosen (1974), property prices are expressed as �� ����, �, ��, where _P_ i is the sale price of property _i_ ; _S_ denotes structural attributes such as parcel or lot size, building size, and quality; _L_ is a vector of observable location-specific factors such as access to social services or jobs; and _N_ denotes other factors capturing neighborhood quality. Estimating this function makes it possible to derive implicit market prices for specific attributes. Assuming that properties being sold are representative of the larger universe, prices can be imputed as a basis for mass valuation.

Testing for the presence of spatial autocorrelation using Moran’s _I_ statistic for residuals from OLS estimates (see appendix table 1) rejects the assumption of _iid_ error terms. To identify a suitable alternative model, we used the Lagrange Multiplier test for model specification to identify the most important reason (omitted spatial lag or spatially autoregressive errors) for spatial dependence (Anselin et al. 1996).<sup>20</sup> As our objective was to predict prices to assess the potential for property tax, we focused on the squared mean prediction

> 18 It should be noted that because here we consider only building structures, the total built-up area (including roads and other infrastructure) will be much higher in urban villages that include the central business district.

> 19 The cost is as estimated by a team from GAF AG that includes the cost of imagery for the study area (340 km2) and processing of the Normalized Digital Surface Model (NDSM) including filtering, tree elimination, quality enhancement, and calculation of relative building heights. The cost of imagery will be as high as US$56 if archived data are not available, and the cost of processing will be as low as US$56 if street map or cadaster data are available.

> 20 Estimation results are highly significant for error and lag dependence models as well as their robust counterparts (appendix table 1).

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