error that favors a spatial error model (see appendix table 3) over spatial lag (appendix table 2) or OLS (appendix table 1) models. The spatial error model is specified as: where _Piv_ is the log of the market price of residential property _i_ located in village _v_ ; **_S_** _iv_ is a vector of continuous structural variables in log terms, i.e., parcel or lot size in square meters and volume of buildings in cubic meters; **_L_** _iv_ is a vector of locational variables consisting of distance from the parcel to the CBD and the nearest primary school or tarmac road; **_N_** _v_ is a vector of neighborhood attributes in log terms (except the density measure) including density of built-up area and average and standard deviation of building heights at village level, total residential land per cell and number of workers hired by business establishments per cell; ��� is an error that follows a spatial autoregressive process with ��� a vector of i.i.d. error terms; the �<sup>�</sup> s are parameters to be estimated; λ is a scalar autoregressive parameter measuring spatial correlation in the errors; and _W_ is a distance-based spatial weight matrix.<sup>21</sup> The results from four specifications are reported in table 6 with successive columns for a basic regression with only parcel area and locational and time factors (col. 1), adding access to public amenities such as primary schools and tarred roads (col. 2), remote sensing data from satellite imagery (col. 3), and all of the above (col. 4). Although all variables have the signs expected, incorporating image-based attributes clearly and significantly improves goodness of fit.<sup>22</sup> Also, the spatial autoregressive coefficient, λ, is highly significant and, with an estimated coefficient of 0.79 for the preferred model, suggests a positive correlation between error terms of nearby properties.

With estimated elasticities of 0.56 and 0.17, parcel size and volume of buildings on a parcel are important predictors of the market prices of residential properties in Kigali even after locational and neighborhood factors are accounted for. Signs of coefficients of locational attributes are consistent with expectation. Size (-0.39 and -0.20) and significance of the coefficient on the property’s distance to the CBD is consistent with a model of a monocentric city (Epple et al 2010), though it becomes insignificant once other locational variables are controlled for (cols. 2 and 4). Weak access to public amenities proxied by distance to primary schools (estimated coefficient of -0.05) and tarred roads (-0.14) is estimated to have a negative relationship to property prices.

Beyond the variables noted, neighborhood characteristics affect residential property prices in urban Kigali even after controlling for the spatial dependence of error terms. Coefficients of 1.62 on mean and -0.37 on

> 21 The spatial weights are based on threshold distance of 435m at which each property has at least one neighbor. Alternative distance band weights

> (750m, 1,500m and 5,000m) were used, but the one of 435m produced better goodness of fit using the likelihood ratio and AIC criteria.

> 22 The log likelihood for models (4) and (3) is -13,835 and -13,983 as compared to -14,494 and -14,673 for models (2) and (1).

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