**Table 5.2 – Tenure transition matrix – all plots (in %)**

|**Tenure at time of survey**<br>**Tenure at time of transaction **|**No administrative**<br>**document**|**Administrative**<br>**document**|**Precarious**<br>**title**|**Property**<br>**title**|**Unknown**|**Total**|
|---|---|---|---|---|---|---|
|**No administrative document**|13.4|11.1|3.5%|0.7|0.4|29.1|
|**Administrative document**|0.0|44.4|8.0%|0.5|0.0|53.0|
|**Precarious title**|0.0|0.0|12.0%|0.7|0.1|12.7|
|**Property title**|0.0|0.0|0.0%|5.0|0.0|5.0|
|**Unknown **|0.0|0.0|0.0%|0.0|0.1|0.1|
|**Total **|13.4|55.5|23.5%|7.0|0.5|100.0|

_Source: authors’ survey, 2012_ The Table in Annex 5.8 represents tenure transitions for the subsample of plots destined for residential use. The figures are very similar to those for the sample as a whole and confirm that tenure upgrading strategies are not implemented to the full extent and that many households stop short of obtaining a property title.<sup>65</sup> No specific spatial patterns seem to determine where conversions occur.

# **5.5. Land tenure and land prices**

Having collected data on transaction prices (both at the time of transaction and at the time of the survey), we can explore the determinants of land prices in Bamako. Our main objectives are to assess both the values of the different land tenure situations and the spatial patterns that prevail in terms of land prices. In theory, land prices are expected to be related to physical characteristics such as plot size, location (physical accessibility of the city center) and availability of services (water or electricity), as well as to tenure status and associated security and embedded rights (e.g. transferability). We thus resort to hedonic price analysis to investigate how prices are correlated with tenure and location, while controlling for other characteristics. We do this for all plot transactions with a “price”, thus excluding the non-monetary land access modes from the pooled sample of observations and considering public allocation as if it were a land market.<sup>66</sup> To address possible outlier issues, we remove observations for which the price of land per square meter was in the top or bottom 1% of the price distribution. The equation we estimate is of the form: In this equation _pi_ is the price per square meter of plot _i_ (which can be either the price paid at the date of the transaction or the estimated price at the date of the survey), _ti_ is a set of dummy variables describing the tenure on the land, _di_ is the Euclidean distance to the city center. The _Xij_ are various covariates present in the database such as the distance to the nearest paved road, the area of the plot, whether the land is serviced (water and/or electricity) and the bank of the river on which the land is located. We include the area of the plot (even though the dependent variable is price per square meter in order to capture non-linear effects).

> 65 Explanations could be that tenure upgrading to property title is not always desired, it takes too much time to be observed within the retrospective 3-year time frame of the survey, and is most likely too costly for many households (see Section 4.2 for details on the costs of land tenure conversion).

> 66 It is legitimate to do so.  In all likelihood, since no specific indications were given to respondents, declared prices should be inclusive of bribes and should thus approximate the market value of land under the assumption that the land administration is able to fully extract rents from allottees (See Selod and Tobin, 2013 for a spatial theory of land price determination with the coexistence of diverse land tenure situations in a West African city).

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