IDEAS home Printed from https://ideas.repec.org/a/eee/lauspo/v54y2016icp487-498.html

The assessment of land valuation in land consolidation schemes: The need for a new land valuation framework

Author

Listed:
  • Demetriou, Demetris

Abstract

There are principally two ways for quantifying the land value of parcels in land consolidation schemes. The first approach involves assigning an agronomic value based on soil quality and land productivity represented by a score while the second method determines the market value signified in monetary terms. In Cyprus, the market value is employed, which is defined through an empirical process based on visual inspection of all parcels and hence it constitutes a type of mass land appraisal. This process presents weaknesses regarding time, costs, transparency, accuracy, reliability, consistency and fairness. In addition, the lack of adequate sales transactions in rural areas further complicates the whole process. Consequently, these deficiencies have adverse effects in the preparation of land consolidation plans and cause arguments between landowners and the authorities carrying out each scheme. Although experts are aware of this issue, there is a lack of research investigating land valuation factors and the quality of this traditional process. Therefore, this paper discusses, explores and assesses the land valuation undertaken by the Land Valuation Committee (LVC) in a case study area in Cyprus and proposes a new framework for carrying out this process. The assessment of the current process is undertaken by employing advanced spatial analysis techniques, including multiple regression analysis (MRA) and geographically weighted regression (GWR) within a GIS. Results show that eight out of fourteen land valuation factors related to parcel location characteristics, legal factors, physical attributes and economic conditions are the most significant. In addition, although the basic regression fits are quite good, some of the assumptions required for testing the hypothesis are not met, indicating unreliability and inconsistency in the relationships modelled. Furthermore, the presence of spatial autocorrelation reveals important regional variation in these factors suggesting significant inconsistencies in the valuation policy applied by the LVC. The latter two findings confirm experts’ concerns and suggest the need for a new land valuation framework that is designed to overcome the problems of the current process. The application of this framework and the investigation of various critical relevant issues is the core of ongoing further research.

Suggested Citation

  • Demetriou, Demetris, 2016. "The assessment of land valuation in land consolidation schemes: The need for a new land valuation framework," Land Use Policy, Elsevier, vol. 54(C), pages 487-498.
    • Handle: RePEc:eee:lauspo:v:54:y:2016:i:c:p:487-498
    DOI: 10.1016/j.landusepol.2016.03.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264837716301934
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.landusepol.2016.03.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Nghiep Nguyen & Al Cripps, 2001. "Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks," Journal of Real Estate Research, Taylor & Francis Journals, vol. 22(3), pages 313-336, January.
    2. Oldenburg, Philip, 1990. "Land consolidation as land reform, in India," World Development, Elsevier, vol. 18(2), pages 183-195, February.
    3. Robert Lafrance & Lawrence L. Schembri, 2002. "Purchasing-Power Parity: Definition, Measurement, and Interpretation," Bank of Canada Review, Bank of Canada, vol. 2002(Autumn), pages 27-33.
    4. Nghiep Nguyen & Al Cripps, 2001. "Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks," Journal of Real Estate Research, American Real Estate Society, vol. 22(3), pages 313-336.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sagi, Alon & Gal, Avigdor & Broitman, Dani & Czamanski, Daniel, 2024. "An unsupervised machine learning approach to the spatial analysis of urban systems through neighbourhoods’ dynamics," Land Use Policy, Elsevier, vol. 144(C).
    2. Demetriou, Demetris, 2018. "Automating the land valuation process carried out in land consolidation schemes," Land Use Policy, Elsevier, vol. 75(C), pages 21-32.
    3. Cihan Çılgın & Hadi Gökçen, 2025. "A Hybrid Machine Learning Model Architecture with Clustering Analysis and Stacking Ensemble for Real Estate Price Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 127-178, July.
    4. Minh-Thang Ha & Thi-Cham Nguyen & Thanh-Huyen Pham & Van-Hau Nguyen, 2025. "Using Machine Learning Regression Algorithms to Predict House Prices in Vietnam," International Real Estate Review, Global Social Science Institute, vol. 28(4), pages 505-527.
    5. Silviu-IonuÈ› BÄ‚BÈšAN, 2024. "AUTOMATED EVALUATION MODELS in real estate market: A comparative analysis between linear regression and XGBoost," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(26), pages 1-3.
    6. Christopher Ragan, 2005. "The Exchange Rate and Canadian Inflation Targeting," Staff Working Papers 05-34, Bank of Canada.
    7. Kitova, Olga & Dyakonova, Ludmila & Savinova, Victoria, 2020. "Prediction of Socio-Economic Indicators of the Megapolis Development on the Basis of the Intellectual Forecasting Information System “SHM Horizon”," MPRA Paper 104234, University Library of Munich, Germany, revised 19 Nov 2020.
    8. Nguyen, Huy, 2014. "The effect of land fragmentation on labor allocation and the economic diversity of farm households: The case of Vietnam," MPRA Paper 57521, University Library of Munich, Germany.
    9. Tien Foo Sing & Jesse Jingye Yang & Shi Ming Yu, 2022. "Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM)," The Journal of Real Estate Finance and Economics, Springer, vol. 65(4), pages 649-674, November.
    10. Maurizio d’Amato, 2007. "Comparing Rough Set Theory with Multiple Regression Analysis as Automated Valuation Methodologies," International Real Estate Review, Global Social Science Institute, vol. 10(2), pages 42-65.
    11. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    12. Shawn L. Robey & Mark A McKnight & Misty R. Price & Rachel N. Coleman, 2019. "Considerations for a Regression-Based Real Estate Valuation and Appraisal Model: A Pilot Study," Accounting and Finance Research, Sciedu Press, vol. 8(2), pages 1-99, May.
    13. Giller, Ken E. & Andersson, Jens & Delaune, Thomas & Silva, João Vasco & Descheemaeker, Katrien & van de Ven, Gerrie & Schut, Antonius G.T. & van Wijk, Mark & Hammond, Jim & Hochman, Zvi & Taulya, God, "undated". "IFAD Research Series 83: The future of farming: who will produce our food?," IFAD Research Series 322005, International Fund for Agricultural Development (IFAD).
    14. Koen Koning & Tatiana Filatova & Okmyung Bin, 2018. "Improved Methods for Predicting Property Prices in Hazard Prone Dynamic Markets," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 69(2), pages 247-263, February.
    15. Craig Ellis & Patrick J. Wilson & Ralf Zurbruegg, 2007. "Real Estate ‘Value’ Stocks and International Diversification," Journal of Property Research, Taylor & Francis Journals, vol. 24(3), pages 265-287, September.
    16. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    17. Alla Koblyakova & Larisa Fleishman & Orly Furman, 2022. "Accuracy of Households’ Dwelling Valuations, Housing Demand and Mortgage Decisions: Israeli Case," The Journal of Real Estate Finance and Economics, Springer, vol. 65(1), pages 48-74, July.
    18. Gang-Zhi Fan & Seow Eng Ong & Hian Chye Koh, 2006. "Determinants of House Price: A Decision Tree Approach," Urban Studies, Urban Studies Journal Limited, vol. 43(12), pages 2301-2315, November.
    19. van Ruijven, Bas & Urban, Frauke & Benders, René M.J. & Moll, Henri C. & van der Sluijs, Jeroen P. & de Vries, Bert & van Vuuren, Detlef P., 2008. "Modeling Energy and Development: An Evaluation of Models and Concepts," World Development, Elsevier, vol. 36(12), pages 2801-2821, December.
    20. Li, You & Hewitt, C.N., 2008. "The effect of trade between China and the UK on national and global carbon dioxide emissions," Energy Policy, Elsevier, vol. 36(6), pages 1907-1914, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:lauspo:v:54:y:2016:i:c:p:487-498. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joice Jiang (email available below). General contact details of provider: https://www.journals.elsevier.com/land-use-policy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.