Land information is one of the basic requirements for land management activities such as land consolidation. However, the dearth of land information on customary lands limits the development and application of land consolidation. This paper presents and discusses the results of an experiment carried out to test the potential of participatory land administration applied on customary lands in support of land consolidation. A brief overview of the evolution of crowdsourced, voluntary, and participatory approaches is provided alongside newly related insights into neogeography and neo-cadastre, and fit-for-purpose and pro-poor land administration. The concept of participatory land administration is then developed in this context. The area of the experiment is in Northern Ghana where the process was developed together with the local farming community. The study involved collecting land information relating to farms over a two-week period, using a mobile app and a satellite image, based on participatory land administration. The results show that Participatory Land Administration can potentially support land consolidation, though further investigation is needed on how it can be integrated into the formal land registration system, into an actual land consolidation project.

In this paper, we develop a framework for an approach that assigns values to customary rural farmland parcels based on the local people’s view of land value to support land consolidation. In a case study of Nanton, Ghana, key land value factors were identified and weighted by the local community. The weights were integrated into the framework that produced a Land Value Index for each farmland parcel. Though the strength of the approach is found in scenario and sensitivity analyses. However, the prime weakness of this framework is that it is more expensive to use than automatic valuation models.

Resettlement resulting from dam construction has raised several concerns due to the negative aftermath impacts. In Ghana, the construction of three hydroelectric dams resulted in large-scale resettlements. Given the little experience that Ghana has in resettlements, it is necessary for a robust monitoring structure for resettlements. However, this was not available in the last resettlement undertaken for the Bui Dam Project. This paper aims at developing an assessment framework for monitoring resettlement activities on customary lands from a good governance perspective. Based on four good governance principles, transparency, public participation and inclusiveness, equity and rule of law and accountability, a good governance assessment framework is built and applied to the Bui Dam Project using a case study approach. Data were collected through interviews and focus group discussion with the key actors of the resettlement project. It was first found that the planning stage of the resettlement came out with a robust plan that was to prevent the impoverishment of the affected persons. However, in the implementation of the resettlement, not all good governance principles were adhered to. In conclusion, it was found that by deconstructing the resettlement process with a good governance framework, the problematic areas of the resettlement can be effectively differentiated between the planning and implementation phases.

Recent rapid population growth and increasing urbanisation have led to fast vertical developments in urban areas. Therefore, in the context of the dynamic property market, factors related to the third dimension (3D) need to be considered. Current hedonic price modelling (HPM) studies have little explicit consideration for the third dimension, which may have a significant influence on modelling property values in complex urban environments. Therefore, our research aims to narrow the cognitive gap of the missing third dimension by assessing both 2D and 3D HPM and identifying important 3D factors for spatial analysis and visualisation in the selected study area, Xi’an, China. The statistical methods we used for 2D HPM are ordinary least squares (OLS) and geographically weighted regression (GWR). In 2D HPM, they both have very low R² (0.111 in OLS and 0.217 in GWR), showing a very limited generalisation potential. However, a significant improvement is observed when adding 3D factors, namely view quality, sky view factor (SVF), sunlight and property orientation. The obtained higher R² (0.414) shows the importance of the third dimension or—3D factors for HPM. Our findings demonstrate the necessity to include such factors into HPM and to develop 3D models with a higher level of details (LoD) to serve more purposes such as fair property taxation.