70,000 ha are in Kigali city. While nationally 64% of parcels (66% of area) are used for agriculture and 12% (8% of area) for residential purposes, in Kigali 49% (56% of land area) are used for agriculture and 35% (17% of area) for residences. For 2013–16, sales were registered for some 85,000 properties, almost half in Kigali and some 20,000 in our study area. Property prices averaged US$28 per m<sup>2</sup> for residential and US$4 per m<sup>2</sup> for agricultural land nationwide and US$40 (US$ 7.5) or US$ 51 (US$ 11) per m<sup>2</sup> for residential (agricultural) land in Kigali and our study area, respectively.

To compare potential to actual revenue from land and more effective collection of current lease fees, we draw on 2015 parcel-level data for land tax revenue from the Rwanda Revenue Authority (RRA). Although RRA and RNRA use the same UPI to identify properties, so that in principle RRA could use the cadastral map to automatically bill owners for land lease fees owed (as assessed at the time of registration), this has not been done. Instead lease fee charges are still based on self-declaration. Table 4, based on using the UPI to link RRA and LAIS data, illustrates that only about 40% (45,000) of the 112,000 residential parcels in Kigali were subject to land lease fees and 37,000 did so. In urban Kigali in 2015, only US$1.95 million of US$6.74 million owed on residential properties was collected. Thus, in urban Kigali alone, closing the collection gap could more than triple revenue from land use fees.<sup>15</sup> As the property registry does not contain data on many building characteristics needed for a mass valuation exercise, we drew on other data sources. Most importantly, to generate information on built-up area, we used 0.5m-resolution Pleaides Tri-Stereo imagery (acquired in August 2015) for a 340 km<sup>2</sup> study area that covers the central business district (CBD) and most of urban Kigali (see figure 1).<sup>16</sup> Applying a semi-global matching algorithm (Hirschmueller 2011) to this imagery made it possible to elaborate a Digital Surface and a Digital Terrain Model. After filtering out trees using the Normalized Differenced Vegetation Index (NDVI), building heights for each of the imagery’s 0.5 m/pixels could then be recovered as the difference between the surface and the terrain model (Bärisch et al. 2017). We overlaid the result with boundaries of cadastral parcels obtained from LAIS, considered only heights above 2 m to exclude vehicles, and used the area covered by pixels with the same height as an estimate of building footprint. Building volume was then computed as the product of building footprint and height. Figures 2 and 3 illustrate the results for a lowand a higher-density neighborhood in central Kigali. While they illustrate some issues that might have been addressed by further processing, cost considerations led us not to proceed.<sup>17</sup>

> 15 Comparing the potential revenue gain to the total cost of about US$50 million for the national land registration program suggests that the program did help to generate significant benefits.

> 16 The size of the study area was dictated by data availability and cost considerations.

> 17 The main issue that could be addressed by further processing would be to obtain more precise estimates of building footprints. This would also make it possible to address some sources of error in estimating the height of structures in steep areas and small and low buildings that might be hidden by trees or located in highly dense areas. Exploring ways to automate this process is an important area for future research.

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