1. Linking administrative data to improve land governance: The example of Georgia Eka Meskhidze - Head of International Relations Unit, NAPR David Labadze - LGAF Data Expert David Egiashvili - LGAF Country Coordinator Denys Nizalov - LGAF Adviser, Kyiv School of Economics 1

2. Until 2000: Land Administration system development reform : simplification of registration procedures, terms, fees; improvement of geo database Mid 90s: Free market economy → privatization →property ownership rights established→ Property Market Development 2012-2015: Full coverage of the country → data accuracy and quality improved → Reliable Land Information System established → Confidence Increased Land Administration System Establishment in Georgia

3. Link to LGAF LGAF – 2011 LGAF Update 2012 Establishment of LGAF regular monitoring system – 2013 3

4. Indicators The following groups of indicators are considered: A.Number (and prices where possible) of registered transactions of different types - 24/4; B.Receipts of land tax revenue – 10/5; C.Share of communal, private, and state land mapped – 2/0; D.Cases of expropriation and privatization – 7/1; E.Number of land-related conflicts in the courts – 36/12; F.Share of agricultural/residential land registered and mapped in women’s name 1/1; 4

5. Data Sources NAPR Land Inventory (2006) Land Cadaster (31.12.2013) Registry of Real Estate and Immovable Property (31.12.2013) Supreme Court (from district level, appeal and supreme courts): Neighbor disputes Immovable property ownership Lease, agricultural land lease Mortgage Inheritance-related disputes Revenue Service Land and real estate tax revenues by municipalities Road Department (Ministry of Infrastructure and Regional Development); Tbilisi Municipality; Georgian Railway; At the moment Tbilisi Municipality and Georgian Railway cannot give any information about expropriation cases, but we received complete data from Roads Department (more than 600 cases). 5

6. Number Land and Real Estate Sales Name Agricultural (N) Non- Agricultural (N) Not Defined (N) Real Estate (N) Tbilisi231495542421771 Ajara A.R.2027315754946 Guria301161085164 Imereti55920629542483 Kakheti1926676272514 Mtskheta-Mtianeti464252403233 Racha-Lechkhumi and Kvemo Svaneti68526130 Samegrelo-Zemo Svaneti302771748828 Samtskhe-Javakheti3011091740711 Kvemo Kartli51210061522598 Shida Kartli833972374899 Apkhazeti A.R.0000 Total (Georgia)569912703198835177 6 Indicator A.

7. Average Price per Sq. M Name Agricultural (GEL per Sq.M) Non-Agricultural (GEL per Sq.M) Not Defined (GEL per Sq.M) Tbilisi33.39115.3586.78 Ajara A.R.20.8769.4678.04 Guria2.9643.242.64 Imereti8.3436.9510.56 Kakheti0.5718.201.08 Mtskheta-Mtianeti6.003.6815.83 Racha-Lechkhumi and Kvemo Svaneti1.7221.463.53 Samegrelo-Zemo Svaneti2.6291.604.99 Samtskhe-Javakheti5.8055.407.44 Kvemo Kartli4.0314.222.82 Shida Kartli0.7328.401.87 Apkhazeti A.R. Total (Georgia)10.4348.4225.38 7 Indicator A.

8. Agriculture and Non- Agriculture Land Sale Price 8 Indicator A.

9. Initial (first time) Registration Name Residential and Non-Residential Real Estate (N) Land (N)Land Area (Ha) Tbilisi259085641577 Ajara A.R.585549031070 Guria1903975812 Imereti322795286870 Kakheti4891276613600 Mtskheta-Mtianeti5264479747 Racha-Lechkhumi and Kvemo Svaneti42921117 Samegrelo-Zemo Svaneti89054551830 Samtskhe-Javakheti829534915300 Kvemo Kartli22546000 Shida Kartli80465361470 Apkhazeti A.R.000 Total (Georgia)410146555348400 9 Indicator A.

10. Number of mortgage transactions NameBuildings (N)Land (N) Tbilisi296316742 Ajara A.R.52122338 Guria128611 Imereti45343349 Kakheti3764999 Mtskheta-Mtianeti1831672 Racha-Lechkhumi and Kvemo Svaneti240 Samegrelo-Zemo Svaneti10432230 Samtskhe-Javakheti495910 Kvemo Kartli36523854 Shida Kartli7692014 Apkhazeti A.R.00 Total (Georgia)4602528759 10 Indicator A.

11. What we could not do… A.10.1. – A.10.3. Average price per sq. meter of residential real estate sales A.12.1. – A.12.3. Average price per sq. meter of non-residential real estate sales A.13.1. – A.13.3. Total number of residential real estate units A.14.1. – A.14.3. Total number of non-residential real estate units 11 Indicator A.

12. To be improved… NAPR data have to be cleaned; Discus adding fields to registration DB: Residential, Non-Residential; Sale transactions – standard currency have to set; Area for buildings to add; Etc. After cleaning reassessment have to be done; Historical data have to used for better understanding; 12 Indicator A.

13. Receipts of land tax revenue Name enterprise property tax (1000Gel) enterprise land tax (1000Gel) individual property tax (1000Gel) individual land tax (1000Gel) natural resource use fee (1000Gel) Tbilisi99356193461048453722851 Ajara A.R.108771557830400294 Guria124075157379875 Imereti825133454978391911 Kakheti3074202115466371403 Mtskheta-Mtianeti55711589270403435 Racha-Lechkhumi and Kvemo Svaneti420901120297 Samegrelo-Zemo Svaneti8410695018311652399 Samtskhe-Javakheti200114261365572717 Kvemo Kartli13096434826549359673 Shida Kartli286013651041172554 Apkhazeti A.R.1900103616 Total (Georgia)15534642788130012191523425 13 Indicator B.

14. Revenue from Land and Property 14 Indicator B.

15. What we could not do… B.1.1- B.1.3. Number of enterprise property tax payers B.3.1- B.3.3. Number of enterprise land tax payers B.5.1- B.5.3. Number of individual property tax payers B.7.1- B.7.3. Number of individual land tax payers B.9.1- B.9.3. Number of natural resource use fee payers 15 Indicator B.

16. To be improved… Number of taxpayers have to changed to number of taxable Property; Develop yearly plan of reporting; Adding historical data; Analyzing opportunities of linking revenue service individual level data with NAPR data; 16 Indicator B.

17. Land area by category CategoryArea (Ha)% Residential332001% Industry920002% Agriculture160000039% Pasture89900022% Wood and forestry89000021% Public120000% Infrastructure586001% Water703002% Other42200010% No visible use646002% Total (Georgia)4141700100% 17 Indicator C.

18. Share of Land Registered Name% Tbilisi65.0% Ajara A.R.18.9% Guria9.9% Imereti9.8% Kakheti27.7% Mtskheta-Mtianeti4.9% Racha-Lechkhumi and Kvemo Svaneti0.6% Samegrelo-Zemo Svaneti10.4% Samtskhe-Javakheti17.8% Kvemo Kartli40.4% Shida Kartli14.6% Apkhazeti A.R.0.0% Georgia15.2% 18 Indicator C.

19. Share of Inventoried and Registered Land 19 Indicator C. 62% 15% To be improved…

20. Cases of Expropriation NameNumber Rate (GEL per Sq.M) Area (Ha) Ajara A.R.24315.6132.17 Imereti2543.1416.25 Shida Kartli1783.5911.69 Total (Georgia)6755.4160.11 20 Indicator D.

21. Cases of Privatization Name Number of Land Parcels Area (Ha) Number of Real Estate Tbilisi35533.49733 Ajara A.R.10075.4356 Guria121.241 Imereti10922.7110 Kakheti74380.576 Mtskheta-Mtianeti10210.7856 Racha-Lechkhumi and Kvemo Svaneti122.671 Samegrelo-Zemo Svaneti3419.705 Samtskhe-Javakheti92255.6719 Kvemo Kartli124232.7024 Shida Kartli2624.6722 Apkhazeti A.R.00.000 Georgia10401059.62933 21 Indicator D.

22. What we could not do… D.5.1. – D.5.2. Average compensation per hectare received by state or local government for privatized land Full Info on Expropriation… 22 Indicator D.

23. To be improved… More stake holders have to involved: Municipalities; Georgian Railway; BP; Ministry of Economics and Sustainable Development; Other ministries; Deeper study on impact to fined additional dimensions; Discus with NAPR adding price where transaction type is “Privatization”; 23 Indicator D.

24. Dispute Cases in District (City) Courts Name Filed Rejected Decision Made Among them granting the claim Terminated Unexamined completed Tbilisi1630693587492891861555 Ajara A.R.105333521151396 Guria8065006 Imereti2287989662126215 Kakheti1334732211226117 Mtskheta-Mtianeti33131080629 Racha-Lechkhumi and Kvemo Svaneti0000303 Samegrelo-Zemo Svaneti892046377679 Samtskhe-Javakheti682036258973 Kvemo Kartli9839201371581 Shida Kartli8132301871483 Apkhazeti A.R.2100012 Total (Georgia)24759778917061693022339 24 Indicator E.

25. Cases filed in the Court of Appeal and Supreme Court Appeal Court - 479 Cases Supreme Court - 268 cases 25 Indicator E.

26. What we could not do… E.26.1. – E.26.3. Number of Real estate related cases resolved by the Court of Appeal E.8.1. – E.8.3. Number of Land related cases resolved in the Court of Appeal E.27.1. – E.27.3. Number of Real estate related cases resolved by the Supreme Court E.9.1. – E.9.3. Number of Land related cases resolved in the Supreme Court E.4.1. – E.4.3. Number of Land related cases filed in the City (District) Court that involve state as a party E.5.1. – E.5.3. Number of Land related cases filed in the Court of Appeal that involve state as a party E.6.1. – E.6.3. Number of Land related cases filed in the Supreme Court that involve state as a party E.10.1. – E.10.3. Number of Land related cases resolved in the City (District) Court that involve state as a party E.11.1. – E.11.3. Number of Land related cases resolved in the Court of Appeal that involve state as a party E.12.1. – E.12.3. Number of Land related cases resolved in the Supreme Court that involve state as a party E.13.1. – E.13.3. Number of Land related cases pending in the City (District) Court for less than 1 year E.14.1. – E.14.3. Number of Land related cases pending in the City (District) Court for more than 1 year but less than 5 years E.15.1. – E.15.3. Number of Land related cases pending in the City (District) Court for more than 5 years E.16.1. – E.16.3. Number of Land related cases pending in the Court of Appeal for less than 1 year E.17.1. – E.17.3. Number of Land related cases pending in the Court of Appeal for more than 1 year but less than 5 years E.18.1. – E.18.3. Number of Land related cases pending in the Court of Appeal for more than 5 years E.19.1. – E.19.3. Number of Land related cases pending in the Supreme Court for less than 1 year E.20.1. – E.20.3. Number of Land related cases pending in the Supreme Court for more than 1 year but less than 5 years E.21.1. – E.21.3. Number of Land related cases pending in the Supreme Court for more than 5 years E.28.1. – E.28.3. Number of Real estate related cases pending in the City (District) Court for less than 1 year E.29.1. – E.29.3. Number of Real estate related cases pending in the City (District) Court for more than 1 year but less than 5 years E.30.1. – E.30.3. Number of Real estate related cases pending in the City (District) Court for more than 5 years E.31.1. – E.31.3. Number of Real estate related cases pending in the Court of Appeal for less than 1 year E.32.1. – E.32.3. Number of Real estate related cases pending in the Court of Appeal for more than 1 year but less than 5 years E.33.1. – E.33.3. Number of Real estate related cases pending in the Court of Appeal for more than 5 years E.34.1. – E.34.3. Number of Real estate related cases pending in the Supreme Court for less than 1 year E.35.1. – E.35.3. Number of Real estate related cases pending in the Supreme Court for more than 1 year but less than 5 years E.36.1. – E.36.3. Number of Real estate related cases pending in the Supreme Court for more than 5 years 26 Indicator E.

27. To be improved… Land – Property classification; Pending Period for cases; Have to work with Supreme court on: Classification changes; Row data structure; New specifications for LGAF monitoring data; Identification of immovable property related criminal cases; Expropriation cases; 27 Indicator E.

28. Share of agricultural/residential land registered and mapped in women’s name What we could not do… Have no data about owner’s gender…. Gender field have to add to NAPR DB; Have to link NAPR and CRA DB’s and import gender information; 28 Indicator F.

29. Stakeholders Workshop 19 march, 2014 Tbilisi World Bank Regional Office 29

30. Next Steps More Stakeholders and providers of information Quality of Data Additional Fields New technical specifications Memorandum between stakeholders Involving decision makers Discussing Indicators Short and long term development of monitoring system 30

31. Use of the Monitoring System Descriptive analysis (trend, level of indicators, cross group comparison) – transparency of the system, supporting private investors’ decisions Support design of reforms and innovations (statistical analysis of responses and elastisities, establishing of relationships among various factors) Reform monitoring (tracking the changes in the target/pilot areas) Reform impact evaluation (assessment of reform’s impact on target indicators) Investigation of outliers, targeting inspections and problem identification

32. Sample Analysis Land Market Characteristics Log(Individ. land tax per reg. parcel) Log(Enterprise land tax per reg. parcel) Land Transactions per hectare Log(Individ. land tax per hectare) Log(Enterprise land tax per hectare) (1)(2)(3)(4)(5) Share of land registered, % 0.167*** (0.032) 0.087** (0.037) -0.002 (0.003) 0.200*** (0.029) 0.120*** (0.038) Share of land square -0.002*** (0.001) -0.001 (0.001) 0.000 (0.000) -0.002*** (0.001) -0.001* (0.001) Log(Agricultura l land price) -0.284 (0.193) -0.127 (0.227) -0.017 (0.019) -0.224 (0.178) -0.067 (0.230) Log(Other land price) 0.048 (0.156) 0.270 (0.183) 0.027* (0.015) 0.066 (0.144) 0.288 (0.185) City 0.683 (0.949) 0.708 (1.115) 0.918*** (0.091) 2.979*** (0.874) 3.004** (1.127) Constant 2.368*** (0.378) 3.585*** (0.443) 0.013 (0.036) -11.548*** (0.348) -10.331*** (0.448) Observations63 R-squared0.6170.3530.8550.7280.524

33. Targeted investigation RankResidualName 63-.693Rustavi 62-.101Kazbegi ……… 2.273Tbilisi 1.315Batumi Rank of residuals for transactions per hectare model Use: problem detection; inspection targeting

34. Thank you for your attention Eka Meskhidze Head of International Relations Unit, NAPR - emeskhidze@napr.gov.geemeskhidze@napr.gov.ge David Labadze LGAF Data Expert – david.Labadze@gmail.comdavid.Labadze@gmail.com David Egiashvili LGAF Country Coordinator - david.egiashvili@pcgroup.gedavid.egiashvili@pcgroup.ge Denys Nizalov LGAF Adviser, Kyiv School of Economics – Nizalov@kse.org.uaNizalov@kse.org.ua 34