1. Property Tax in Kigali: Using Satellite Imagery to Assess Collection Potential
Daniel Ayalew Ali, Klaus Deininger
Annual World Bank Conference on Land and Poverty
23 March 2017

2. Background
More than 98% of the land in Rwanda is held under a leasehold system
agricultural parcels less than 2 ha are exempted from paying lease fees
But collection even in Kigali city is very low
60% of the parcels (99% of the agricultural parcels) in Kigali city are exempted from paying lease fees
only 30% of the residential parcels in Kigali city paid lease fees in 2015 ($2.2 million out of a potential of $8.5 million was collected in 2015
Rwanda has now prepared a draft property tax law to
broaden the tax base
promote efficient land use
Despite the apparent move towards a value based taxation system, property valuation methodology has yet to be developed

3. Objective
Explore the potential of combining land administration and remote sensing data for the purpose of mass valuation of properties in Kigali city
The LAIS contains spatial and textual data of all the parcels in Rwanda
Parcel (size, land use) and owner characteristics
Registration of sales transactions with prices (e.g., 24,894 residential sales were recorded in Kigali city during period)
Pleiades Tri-Stero Satellite imagery (0.5 m resolution) to generate building heights and floor area

4. Extent of area of interest (340 Sq KM)
Building heights extracted from high-resolution 3D satellite images – due to budget constraint look at only central Kigali (covering almost all of urban Kigali and a small area of rural Kigali); filtering out vegetation…
Important: most lease rent paying residential land; sales activities
Accuracy??? Digital surface model, Digital terrain model sq km (coverage)

5. The height information derived from this satellite dataset has some limitations (for instance in steep areas, small and low height buildings (e.g. might be covered by trees) as well as highly dense areas). 
Trees are filtered out using NDVI

7. Limitations
The accuracy of the derived product depends of the resolution of the imagery
Irrespective of the resolution, there are some other sources of errors
trees (can be filtered out using NDVI)
buildings located in steep areas
small and low height buildings (e.g., might be covered by trees) as well as highly dense areas)
Ground truthing would help to accurately estimate the margin of error

8. Sales price = f(parcel characteristics, building characteristics,
Estimate a hedonic property price function for residential parcels in Kigali city
Sales price = f(parcel characteristics, building characteristics,
community characteristics)
Actual sales prices (LAIS)
Parcel/building characteristics
parcel size (LAIS)
location of the parcel, i.e. latitude and longitude (LAIS)
distance to schools and tarred road (LAIS + NISR)
volume of buildings, i.e. height X floor area (imagery)
Community characteristics
total area of residential land at the village level (LAIS)
distance of the village from the CBD
number of workers hired by establishments in the cell (NISR est. census)
share of urban population at the sector level (RISR population census)
Density of built up area, mean and sd of building heights at the village level (imagery)

9. Hedonic price function: Dependent variable: log(property price in US$)
(1)
(2)
(3)
(4)
Log of parcel area in sqm
0.643***
0.588***
0.417***
0.401***
(0.091)
(0.090)
(0.110)
(0.109)
Log distance from the village to the CBD in km
-0.369***
-0.084
-0.354***
-0.207***
(0.103)
(0.083)
(0.072)
(0.074)
Log number of workers at the cell level in 2014
0.236***
0.126***
0.029
0.003
(0.037)
(0.035)
(0.036)
Ratio of urban population at the sector level
0.870***
0.480***
0.378***
0.251***
(0.140)
(0.104)
(0.117)
(0.095)
Log of residential land at the cell level
-0.184***
-0.078
0.024
0.045
(0.056)
(0.051)
(0.033)
(0.032)
Longitude, decimal degree
8.102***
6.044**
5.382***
4.662**
(2.693)
(2.504)
(2.072)
(2.081)
Latitude, decimal degree
0.046
1.191
1.659
2.044
(2.044)
(1.863)
(1.543)
(1.486)
Log distance to tarmac road in meters
-0.268***
-0.154***
(0.023)
(0.016)
Log distance to a primary school in meters
-0.119***
-0.058***
(0.028)
(0.019)
Log volume of buildings in m3
0.241***
(0.039)
Density of built up area of the village
2.624***
1.953***
(0.233)
Log average height of buildings at the village in meters
2.861***
2.637***
(0.306)
(0.334)
Log SD of building height at the village in meters
-0.470***
-0.494***
(0.106)
Year dummies included, but not reported
Constant
*** **
*** **
(77.951)
(71.702)
(59.346)
(59.542)
Number of observations
15,667 R2
0.204
0.262
0.358
0.374
note: *** p<0.01, ** p<0.05, * p<0.1
Is there an alternative to lease fees??
Three sets of variables: (1) parcel size, distance to CBD and community level variables (establishment census, population census, land registry) and location (XY coordinates; (2) distance of parcel to public services; (3) building heights extracted from high resolution 3D imagery – property level, and village level variables are constructed

10. Within sample prediction of property tax (1% of property value) by parcel size: residential land for the extent with building heights

11. Within sample prediction of property tax (1% of property value) by volume of buildings: residential land for the extent with building heights

12. Estimated total revenue
Property tax estimation: using sales price ( ) for residential land located in the area for which building heights are extracted
Amount (US$)
Only using residential parcels sold in 2013/16
Number of parcels = 13,837
Lease fee
587,301
Property tax sale price (1%)
2,730,596
Property tax using estimated price (1%)
2,345,628
Estimated total revenue
Number of parcels = 86,755
Total lease fee
4,908,390
PT (4*4.91 million)
19,633,560
PT (estimated price)
19,297,467
Average lease fee rate as percentage of property value was 0.73%
A flat rate of 1% increases tax revenue substantially
Estimated property tax will be 4 times revenue from lease fees
Back of the envelope calculation
4*4,908,390 = 19,633,560
What will happen if lease fees are replaced by property tax?

13. Can this exercise be replicated for cases with no cadaster information?
The only missing information will be parcel area
All the other variables can be reconstructed using high resolution satellite imagery, but requires generation of building footprints (but at a cost)
So, do the results significantly change if we drop parcel area and total area of residential land from the model?

14. Hedonic price function: Dependent variable: log(property price in US$)
(1)
(2)
(3)
(4)
Log distance from the village to the CBD in km
-0.353**
0.093
-0.263***
-0.085
(0.149)
(0.138)
(0.091)
(0.100)
Log number of workers at the cell level in 2014
0.210***
0.104***
0.030
0.008
(0.043)
(0.039)
(0.037)
(0.036)
Ratio of urban population at the sector level
0.533***
0.265**
0.300***
0.202**
(0.154)
(0.134)
(0.104)
(0.088)
Longitude, decimal degree
8.509***
5.605**
5.029**
4.171**
(2.793)
(2.661)
(2.054)
(2.102)
Latitude, decimal degree
-3.818*
-1.024
0.752
1.385
(2.128)
(1.896)
(1.621)
(1.567)
Log distance to tarmac road in meters
-0.319***
-0.170***
(0.026)
(0.020)
Log distance to a primary school in meters
-0.090**
-0.049**
(0.022)
Log volume of buildings in m3
0.318***
0.304***
(0.023)
Density of built up area of the village
1.903***
1.145***
(0.352)
(0.389)
Log average height of buildings at the village in meters
3.490***
3.215***
(0.310)
(0.346)
Log SD of building height at the village in meters
-0.586***
-0.606***
(0.124)
(0.125)
Year dummies included, but not reported
Constant
*** ** ** *
(82.259)
(77.722)
(59.219)
(60.652)
Number of observations
15,667 R2
0.068
0.150
0.308
0.328
note: *** p<0.01, ** p<0.05, * p<0.1
Is there an alternative to lease fees??
Three sets of variables: (1) parcel size, distance to CBD and community level variables (establishment census, population census, land registry) and location (XY coordinates; (2) distance of parcel to public services; (3) building heights extracted from high resolution 3D imagery – property level, and village level variables are constructed

15. Within sample prediction of property tax (1% of property value): residential land for the extent with building heights
With cadaster information
With no cadaster

16. Within sample prediction of property tax (1% of property value): residential land for the extent with building heights
With cadaster information
With no cadaster

17. Estimated total revenue
Property tax estimation: using sales price ( ) for residential land located in the area for which building heights are extracted
Amount (US$)
Only using residential parcels sold in 2013/16
Number of parcels = 13,837
Lease fee
587,301
Property tax sale price (1%)
2,730,596
Property tax using estimated price (1%)
2,210,146
Estimated total revenue
Number of parcels = 86,755
Total lease fee
4,908,390
PT (3.76*4.91 million)
18,455,546
PT (estimated price)
16,744,537
Average lease fee rate as percentage of property value was 0.73%
A flat rate of 1% increases tax revenue substantially
Estimated property tax will be times revenue from lease fees
Back of the envelope calculation
4*4,908,390 = 18,455,546
What will happen if lease fees are replaced by property tax?

18. Costing for NDSM generation (in Euro): from GAF AG estimate
Cost item
Per square km
Kigali city (730 sq km)
Min.
Max.
Minimum
Maximum
Satellite data (e.g., Airbus Pleiades Triplet dataset) 30 50
21,900
36,500
NDSM generation incl. filtering, tree elimination, quality enhancement, calculation of relative heights
100
31,390
73,000
If no cadaster data (generation of building footprints)
300
350
219,000
255,500
Total cost with cadaster info
53,290
109,500
Total cost with no cadaster info
272,290
365,000
The cost is if building footprints are generated in Europe (using automated

19. Conclusions
There was a significant gap in the collection of land lease fees
Conversion of prime land in urban Kigali could contribute in increasing revenue from lease fees. However, total conversion is needed for it to make meaningful impact.
Good enough prices can be generated in a cost effective way to substantially increase tax revenue
Building heights extracted from high resolution satellite imagery substantially increase the predictive power of the hedonic price model
Possible to extract additional attributes from high resolution satellite imagery such as roof type, level of informality, etc. to improve model prediction
Can be replicated even for cases with no cadaster information
Cost of the exercise is marginal compared to the potential revenue that will be generated from property tax

20. Descriptive statistics
Mean
Std. Err.
Parcel level
Parcel area in square meters
609.94
28.78
Property price in USD
42841
12851
Property price per sqm in USD
73.07
17.23
Distance to tarred road in meters
475.00
4.08
Distance to primary school in meters
704.28
4.17
Volume of buildings in cubic meter
484.12
4.84
Number of parcels
15667
Village level
Distance from the village to the CVD in km
5.10
0.12
Density of built up area of the village
0.18
0.00
Average height of buildings at the village in meters
3.66
0.03
Standard devation of building heights at the village
1.56
0.04
Number of villages
744
Cell level
Total resedential area of the cell in sqm
829361
72789
Number of people hired by establishments in the cell
1111
186
Number of cells
107
Sector level
Ration of urban population
0.76
0.07
Number of sectors 32