IDEAS home Printed from https://ideas.repec.org/a/ire/issued/v27n022024p275-302.html
   My bibliography  Save this article

Development of a Land Price Model for a Medium Sized Indian City

Author

Listed:
  • V S Sanjay Kumar

    (KSCSTE – NATPAC)

  • Shabana Yoonus

    (KSCSTE – NATPAC)

  • M V L R Anjaneyulu

    (NIT Calicut)

Abstract

Land price plays a crucial role in the development of a region, which serves as an indicator of the features of a property. The primary goal of this paper is to explore the relationship between land price and various geographical and accessibility parameters and thereby arrive at a model to predict the land price. Based on literature surveys, the parameters that influence land price are identified further through which data collection from primary surveys, the creation of a road network map, a geographic information system (GIS) analysis to determine the distance to the central business district (CBD), measurement of road density and access road width, assessment of employment opportunities through establishment surveys, and identification of various land use parcels in the study region are accomplished. The land prices are collected from recently sold parcels in each of the zones in the study region. A negative and significant correlation is observed between land price and distance to the CBD. Positive correlations are observed between land price and other factors considered, such as road density, availability of educational facilities, employment opportunities, and the extent of commercial and residential land use areas. A non-linear regression model is developed that can predict land price depending on the significant parameters.

Suggested Citation

  • V S Sanjay Kumar & Shabana Yoonus & M V L R Anjaneyulu, 2024. "Development of a Land Price Model for a Medium Sized Indian City," International Real Estate Review, Global Social Science Institute, vol. 27(2), pages 275-302.
    • Handle: RePEc:ire:issued:v:27:n:02:2024:p:275-302
    as

    Download full text from publisher

    File URL: http://www.gssinst.org/irer/wp-content/uploads/2024/07/4-MS23081001-Land-Price-Model-for-Indian-City-Yoonus.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David A. Swanson, 2015. "On the Relationship among Values of the Same Summary Measure of Error when it is used across Multiple Characteristics at the Same Point in Time: An Examination of MALPE and MAPE," Review of Economics & Finance, Better Advances Press, Canada, vol. 5, pages 1-14, August.
    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. Raihan Tanvir & Md Tanvir Rouf Shawon & Md. Golam Rabiul Alam, 2023. "DSE Stock Price Prediction using Hidden Markov Model," Papers 2302.08911, arXiv.org.
    2. Hüseyin İlker Erçen & Hüseyin Özdeşer & Turgut Türsoy, 2022. "The Impact of Macroeconomic Sustainability on Exchange Rate: Hybrid Machine-Learning Approach," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    3. Jeff Tayman & David A. Swanson & Jack Baker, 2021. "Using Synthetic Adjustments and Controlling to Improve County Population Forecasts from the Hamilton–Perry Method," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 40(6), pages 1355-1383, December.
    4. Jeff Tayman & David A. Swanson, 2017. "Using modified cohort change and child-woman ratios in the Hamilton–Perry forecasting method," Journal of Population Research, Springer, vol. 34(3), pages 209-231, September.
    5. Felix Lokin & Fenghui Yu, 2024. "Fill Probabilities in a Limit Order Book with State-Dependent Stochastic Order Flows," Papers 2403.02572, arXiv.org.
    6. Rosa Francesca De Masi & Gerardo Maria Mauro & Silvia Ruggiero & Francesca Villano, 2023. "Predicting Building Energy Demand and Retrofit Potentials Using New Climatic Stress Indices and Curves," Energies, MDPI, vol. 16(16), pages 1-23, August.
    7. Manshadi, Zahra Dehghan & Parivar, Parastoo & Sotoudeh, Ahad & Morovati Sharifabadi, Ali, 2024. "Modeling urban growth effects on carrying capacity in arid and semi-arid regions using system dynamics," Ecological Modelling, Elsevier, vol. 487(C).
    8. Jack Baker & David Swanson & Jeff Tayman, 2021. "The Accuracy of Hamilton–Perry Population Projections for Census Tracts in the United States," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 40(6), pages 1341-1354, December.
    9. Steinegger, Josef & Wallner, Stefan & Greiml, Matthias & Kienberger, Thomas, 2023. "A new quasi-dynamic load flow calculation for district heating networks," Energy, Elsevier, vol. 266(C).

    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:ire:issued:v:27:n:02:2024:p:275-302. 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: IRER Graduate Assistant/Webmaster (email available below). General contact details of provider: https://www.gssinst.org/gssinst/index.html .

    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.