Ukraine Land Price Data

Information on market prices for land is essential for decision-making by participants and mortgage lending as it allows banks to value collateral and the Central Bank to adequately reflect the value of such collateral. While the country’s reliance on administratively set ‘normative values’ for agricultural land likely limited transparency and price discovery, public availability of market prices will be essential to support lending to the agricultural sector for longer-term productivity-enhancing investment.

In principle, price data should be readily available from registry records and, indeed, Law 340-IX mandates recording of prices in the process of registration. To allow quick translation of legal provisions into practice, the WB/EU project ‘Supporting transparent land governance in Ukraine’ collaborated with NAIS to develop and implement software that would record prices for land transfers on a pilot basis. This note summarizes evidence from some 43,000 agricultural lease contracts, close to 4,000 emphyteutic leases, and about 3,000 mortgages for non-agricultural land registered over the period from January 2019 to June 2020 inclusive.

  1. Differences in land price levels and contract length

Agricultural land lease prices vary across the country: While there is very little variation in contract length which is about 10 years everywhere, with a median of UAH 2,000/ha (see table 1), annual leases for agricultural land vary considerably: the highest price/ha (UAH 4,346) in Poltava while less than UAH 1,000/ha is fetched in Luhansk, Zhytomyr, and Mykolaiv.

Rights of emphyteusis trade at a steep discount: Prices for emphyteutic leases (i.e., a long-term lease where payment for the entire contract period is made in a lumpsum upfront), most for the legal maximum of 49 years, are low with a median of less than UAH 15,000. The median annuity that the one-time payment could generate (at an interest rate of 8%) amounts to 1,176 is less than 60% of the mean rental, suggesting that landowners opting for emphyteusis are either ill-informed, cash constrained, or extremely myopic.

Mortgages are rare and short term: With about 2,000 residential mortgages annually with a median duration of less than 3 years and a value of some UAH 1,000/m2 or less than UAH 1 mn./loan), mortgage lending remains limited. As data is available since 2004, we note that the number of mortgages reached a peak of about 5,000/annum in 2008 with valuations of some UAH 18,000/m2 during the boom in 2010.

  1. Factors associated with agricultural land price differences

Larger parcels fetch higher prices: Regressions of lease prices on land size and contract length (with oblast or rayon fixed effects) suggest that after controlling for location-specific factors such as soil quality or infrastructure access, parcels size shows a U-shaped relationship to lease prices (with a minimum at about 2 ha) while contract length shows an inverted U.

Weak land governance and high concentration depress land prices: To assess the effect of structural factors on land prices, we regress the predicted rayon fixed effects from the above regression on proxies for the quality of land governance and market power in land lease markets. The first is approximated by computing land area cultivated in 2019 (based on interpretation of satellite imagery) but not registered,[1] whereas the Hirschman-Herfindahl index of land concentration using state statistics data (form 29) is used as a proxy for the latter.[2] Highly significant coefficients for both variables in our regressions suggest that both of these variables reduce rayon level land prices. To illustrate the magnitudes involved, we note that moving from the oblast with the highest to that with the lowest level of land concentration and land governance would increase the level of land prices by 40% and 28%, respectively.

  1. Caveats and suggested next steps

Systematic collection & publication of lease prices would be key to increase transparency in land markets and allow for more rational decision-making. The above data for agricultural leases comprise only about 10% of estimated transactions. Making entry of price data mandatory in the software, data entry masks to eliminate erroneous entries, and issuance of simple instructions to registrars on how to deal with doubtful cases could thus quickly generate large amounts of data to provide more granular information to market participants, allowing them to make better decisions, e.g., on whether to lease or sell once land markets are open.

Beyond benefits to individuals, this would allow to improve local government revenue by replacing normative with market-based mass valuation of land. If taxes are assessed based on normative land values instead of market prices, local Governments will have no incentive to invest in public goods that would increase land values (and thus future tax revenues). Given the volume of transactions, quick actions by MoJ to institutionalize price reporting together with analytical work could allow to have a mass valuation model in place latest by end of 2121.

Available market price data can be used to adjust regulations. As access to agricultural land price information is a key to NBU reducing the haircut for valuation of land as collateral, this information is readily available and software to systematically collect it exists, it will be important to discuss with NBU how such data could be used to come up with a more systematic valuation of agricultural land and whether additional historical data (which could in principle be digitized from a random sample of registered lease contracts) would be needed to provide a longer time series. Given the importance of this issue for credit access by farmers with land market opening, coming up with clear principles and agreements would be essential. If there is need, the Bank would be happy to explore supporting the required analytical work.

 Table 1: Median prices for agricultural land leases by oblast

Name Price/ha (UAH) Area ha Duration Number of obs.
Panel A: Prices for agricultural leases, national and by oblast
Vinnitsya 1,973 2.09 10.0 5,142
Volyn 1,200 0.34 10.0 984
Dnipropetrovsk 2,220 5.33 10.1 1,675
Donetsk 2,183 4.33 7.0 942
Zhytomyr 993 1.95 10.0 1,352
Zakarpatie 2,054 0.46 10.1 3,447
Zaporozhje 2,256 3.84 10.0 762
Ivano-Frankivsk 2,000 0.62 11.0 61
Kiev 2,858 2.23 10.0 3,682
Kirovohrad 1,760 3.46 10.9 2,650
Luhansk 979 2.47 15.0 949
Lviv 2,210 0.74 9.4 4,072
Mykolayiv 975 4.53 10.1 557
Odessa 2,488 3.43 10.7 168
Poltava 4,346 3.40 10.1 2,324
Rivne 1,202 1.10 10.0 1,801
Sumskaya 2,943 1.88 11.6 1,709
Ternopil 2,013 1.28 10.0 226
Kharkiv 2,176 4.49 10.1 5,090
Kherson 1,213 3.82 10.0 2,354
Khmelnytsky 2,153 2.00 10.1 268
Cherkasy 2,885 2.08 10.0 1,273
Chernivtsi 3,020 1.05 10.0 755
Chernihiv 1,300 1.66 10.0 800
Total 1,995 2.00 10.0 43,045
Panel B: Annuity (8% interest) for emphyteutic leases, national level
Total 1,176 3.27 49.7 3,829
Panel C: Value of mortgages (UAH/m2) for non-agricultural land, national level
Total 1,009 953 2.03 3,185

Source: Own computation from NAIS land price data.

Table 2: Determinants of lease price for agricultural land: 2019-2020

  Oblast FE Rayon FE
Area (ha; ln) -0.165*** -0.193***
(0.00547) (0.00541)
Area squared 0.141*** 0.142***
(0.00264) (0.00252)
Contract length (ln years) 1.401*** 1.635***
(0.0557) (0.0561)
Contract length squared -0.243*** -0.274***
(0.0110) (0.0110)
Year dummy 0.0298*** 0.0600***
(0.0103) (0.0110)
Constant 5.510*** 5.142***
(0.0705) (0.0715)
Number of observation 31,982 31,982
R-squared 0.162 0.198
Mean of dependent variable 7.562 7.562
SD of dependent variable (in logs UAH) 0.936 0.936
Mean of dependent variable (UAH) 3362.1 3362.1
SD of dependent variable (UAH) 7182.8 7182.8
Mean area in ha 2.546 2.546
Mean length of contract in years 11.90 11.90

Note: The dependent variable is the log of the lease price/ha (in UAH).

Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.010

 Table 3: Rayon fixed effect price per ha: 2019-2020 

Predicted Rayon FE
Herfindahl-Hirschman cultivated land concentration index -1.068***
Ln unregistered cultivated area in ha -0.135**
Constant 1.265**
N 365
R-squared 0.0353
Mean of dependent variable -0.0360
SD of dependent variable 0.657
Mean of Herfindahl-Hirschman concentration index 0.113
Mean of unregistered cultivated land in ha 8298.7

Standard errors in parentheses

* p<0.10, ** p<0.05, *** p<0.010

 Table 4: Rayon level HHI and registered agricultural land

Name of oblast HHI Unregistered cultivated land in ha Number of Rayons
Vinntsya 0.10 4,739 26
Volyn 0.28 7,168 7
Dnipropetrovsk 0.04 6,391 21
Donetsk 0.15 7,846 11
Zhytomyr 0.11 4,263 21
Zakarpatie 0.42 2,657 6
Zaporozhje 0.05 14,579 16
Ivano-Frankivsk 0.18 4,853 6
Kiev 0.15 4,445 26
Kirovohrad 0.06 10,655 18
Luhansk 0.06 15,589 8
Lviv 0.17 9,655 18
Mykolayiv 0.04 21,185 13
Odessa 0.05 15,384 13
Poltava 0.14 5,594 23
Rivne 0.15 5,623 14
Sumskaya 0.22 7,485 16
Ternopil 0.10 4,286 6
Kharkiv 0.07 9,254 27
Kherson 0.07 13,841 19
Khmelnytsky 0.11 5,340 12
Cherkasy 0.06 4,586 17
Chernivtsi 0.13 4,451 4
Chernihiv 0.13 8,299 17
Total 0.11 8,299 365

Source: Own computation Form 29 state statistics and EOS land cover database

[1] For a more detailed description of methodology and data see Deininger, K., Ali, D; Kussul, N.; Lavreniuk, M.; Nivievskyi, O. 2020. Using Machine Learning to Assess Yield Impacts of Crop Rotation: Combining Satellite and Statistical Data for Ukraine. Policy Research Working Paper 9306, The World Bank. Anecdotal evidence suggests that inability to register such land reflects a weakness of local institutions relative to powerful interests. Also, as more concentrated operational ownership may reduce landowners’ bargaining power in lease markets.

[2] Descriptive statistics in table 4 show that means of concentration of operational holdings at rayon level are high in Zakarpatie (0.42) and Sumskaya (0.22) and that, with a mean of close to 9,000 ha -ranging between 2,600 in Zakarpatie and 21,000 in Mykolaiv, mean area of unregistered cultivated land varies considerably across oblasts.