IDEAS home Printed from https://ideas.repec.org/a/eee/lauspo/v94y2020ics0264837719306507.html
   My bibliography  Save this article

Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques

Author

Listed:
  • Ma, Jun
  • Cheng, Jack C.P.
  • Jiang, Feifeng
  • Chen, Weiwei
  • Zhang, Jingcheng

Abstract

Land value plays a vital role in the real estate market. It is a critical reference for urban planners to reallocate land resources and introduce valid policies. Studying the influential factors on land value can help better understand the spatial-temporal variation of land values and design effective control policies. This attracted a number of scholars to study the spatial and temporal relationships between land value and its possible influential factors from the perspective of macro and micro. However, the majority of the existing studies have the problems of linear assumption and multicollinearity in research models. Limited features and the lack of feature selection procedure are another two commonly seen limitations. To overcome the gaps, this paper adopts non-linear machine learning (ML) methods to investigate the influential factors on land values per square foot based on “big data” in New York City. More than one thousand potential factors are considered, covering from the land attribute, point of interest, demographics, housing, to economic, education, and social. They are further selected using a feature extraction model named Recursive Feature Elimination (RFE). Six ML algorithms, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Multi Linear Regression (MLR), Linear Support Vector Regression (SVR), Multilayer Perceptron (MLP) Regression, and K-Nearest Neighbor (KNN) Regression are evaluated and compared. The optimal one with an R-square value of 0.933 is used to calculate the feature importance further. Several important impact features are disclosed, including the number of newsstands, and the vacant housing percentage.

Suggested Citation

  • Ma, Jun & Cheng, Jack C.P. & Jiang, Feifeng & Chen, Weiwei & Zhang, Jingcheng, 2020. "Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques," Land Use Policy, Elsevier, vol. 94(C).
    • Handle: RePEc:eee:lauspo:v:94:y:2020:i:c:s0264837719306507
    DOI: 10.1016/j.landusepol.2020.104537
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264837719306507
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.landusepol.2020.104537?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Capozza, Dennis R. & Helsley, Robert W., 1989. "The fundamentals of land prices and urban growth," Journal of Urban Economics, Elsevier, vol. 26(3), pages 295-306, November.
    2. Ma, Jun & Cheng, Jack C.P., 2016. "Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology," Applied Energy, Elsevier, vol. 183(C), pages 182-192.
    3. Kok, Nils & Monkkonen, Paavo & Quigley, John M., 2014. "Land use regulations and the value of land and housing: An intra-metropolitan analysis," Journal of Urban Economics, Elsevier, vol. 81(C), pages 136-148.
    4. Daniel P. McMillen, 2003. "Neighborhood house price indexes in Chicago: a Fourier repeat sales approach," Journal of Economic Geography, Oxford University Press, vol. 3(1), pages 57-73, January.
    5. Ma, Jun & Cheng, Jack C.P., 2016. "Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests," Applied Energy, Elsevier, vol. 183(C), pages 193-201.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sesil Koutra & Christos S. Ioakimidis, 2022. "Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges," Land, MDPI, vol. 12(1), pages 1-19, December.
    2. Kim, Daehwan & Seo, Ducksu & Kwon, Youngsang, 2021. "Novel trends in SNS customers in food and beverage patronage: An empirical study of metropolitan cities in South Korea," Land Use Policy, Elsevier, vol. 101(C).
    3. Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Farbod Farhangi & Soo-Mi Choi, 2021. "COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms," IJERPH, MDPI, vol. 18(18), pages 1-21, September.
    4. Sisman, S. & Aydinoglu, A.C., 2022. "Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis," Land Use Policy, Elsevier, vol. 119(C).
    5. Maowen Sun & Boyi Liang & Xuebin Meng & Yunfei Zhang & Zong Wang & Jia Wang, 2024. "Study on the Evolution of Spatial and Temporal Patterns of Carbon Emissions and Influencing Factors in China," Land, MDPI, vol. 13(6), pages 1-24, June.
    6. Gao, Feng & Yi, Shiyi & Li, Xiaonuo & Chen, Weiping, 2024. "Ensemble intelligence algorithms and soil environmental quality to model economic quantity of land resource allocation and spatial inequality," Land Use Policy, Elsevier, vol. 141(C).
    7. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    8. Stéphane C. K. Tékouabou & Jérôme Chenal & Rida Azmi & Hamza Toulni & El Bachir Diop & Anastasija Nikiforova, 2022. "Identifying and Classifying Urban Data Sources for Machine Learning-Based Sustainable Urban Planning and Decision Support Systems Development," Data, MDPI, vol. 7(12), pages 1-19, November.
    9. Sun, Jianing & Zhou, Tao & Wang, Di, 2022. "Relationships between urban form and air quality: A reconsideration based on evidence from China’s five urban agglomerations during the COVID-19 pandemic," Land Use Policy, Elsevier, vol. 118(C).
    10. Doan, Quang Cuong, 2023. "Determining the optimal land valuation model: A case study of Hanoi, Vietnam," Land Use Policy, Elsevier, vol. 127(C).
    11. Zhenwei Wang & Xiaochun Wang & Zijin Dong & Lisan Li & Wangjun Li & Shicheng Li, 2023. "More Urban Elderly Care Facilities Should Be Placed in Densely Populated Areas for an Aging Wuhan of China," Land, MDPI, vol. 12(1), pages 1-13, January.
    12. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.
    13. Qing Lu & Jing Ning & Hong You & Liyan Xu, 2023. "Urban Intensity in Theory and Practice: Empirical Determining Mechanism of Floor Area Ratio and Its Deviation from the Classic Location Theories in Beijing," Land, MDPI, vol. 12(2), pages 1-16, February.
    14. Kopczewska, Katarzyna & Ćwiakowski, Piotr, 2021. "Spatio-temporal stability of housing submarkets. Tracking spatial location of clusters of geographically weighted regression estimates of price determinants," Land Use Policy, Elsevier, vol. 103(C).
    15. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.

    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. Han, Wenjing & Zhang, Xiaoling & Zheng, Xian, 2020. "Land use regulation and urban land value: Evidence from China," Land Use Policy, Elsevier, vol. 92(C).
    2. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    3. Or Levkovich & Jan Rouwendal, 2016. "Spatial Planning and Segmentation of the Land Market," Tinbergen Institute Discussion Papers 16-018/VIII, Tinbergen Institute.
    4. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    5. Gluszak, Michal & Zygmunt, Robert, 2018. "Development density, administrative decisions, and land values: An empirical investigation," Land Use Policy, Elsevier, vol. 70(C), pages 153-161.
    6. Liu, Xue & Ding, Yong & Tang, Hao & Fan, Lingxiao & Lv, Jie, 2022. "Investigating the effects of key drivers on energy consumption of nonresidential buildings: A data-driven approach integrating regularization and quantile regression," Energy, Elsevier, vol. 244(PA).
    7. Peplinski, McKenna & Dilkina, Bistra & Chen, Mo & Silva, Sam J. & Ban-Weiss, George A. & Sanders, Kelly T., 2024. "A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets," Applied Energy, Elsevier, vol. 357(C).
    8. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    9. Zhang, Yan & Teoh, Bak Koon & Zhang, Limao, 2023. "Exploring driving force factors of building energy use and GHG emission using a spatio-temporal regression method," Energy, Elsevier, vol. 269(C).
    10. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    11. Bigelow, Daniel P. & Plantinga, Andrew J., 2017. "Town mouse and country mouse: Effects of urban growth controls on equilibrium sorting and land prices," Regional Science and Urban Economics, Elsevier, vol. 65(C), pages 104-115.
    12. Jason Allen & Robert Amano & David P. Byrne & Allan W. Gregory, 2009. "Canadian city housing prices and urban market segmentation," Canadian Journal of Economics, Canadian Economics Association, vol. 42(3), pages 1132-1149, August.
    13. Sadayuki, Taisuke, 2018. "Measuring the spatial effect of multiple sites: An application to housing rent and public transportation in Tokyo, Japan," Regional Science and Urban Economics, Elsevier, vol. 70(C), pages 155-173.
    14. Patric H. Hendershott & Jesse M. Abraham, 1992. "Patterns and Determinants of Metropolitan House Prices, 1977-91," NBER Working Papers 4196, National Bureau of Economic Research, Inc.
    15. T. Gries & R. Grundmann & I. Palnau & M. Redlin, 2017. "Innovations, growth and participation in advanced economies - a review of major concepts and findings," International Economics and Economic Policy, Springer, vol. 14(2), pages 293-351, April.
    16. Carrión-Flores, Carmen E. & Flores-Lagunes, Alfonso & Guci, Ledia, 2018. "An estimator for discrete-choice models with spatial lag dependence using large samples, with an application to land-use conversions," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 77-93.
    17. Caruso, Geoffrey & Peeters, Dominique & Cavailhes, Jean & Rounsevell, Mark, 2007. "Spatial configurations in a periurban city. A cellular automata-based microeconomic model," Regional Science and Urban Economics, Elsevier, vol. 37(5), pages 542-567, September.
    18. John Landis & Vincent J. Reina, 2021. "Do Restrictive Land Use Regulations Make Housing More Expensive Everywhere?," Economic Development Quarterly, , vol. 35(4), pages 305-324, November.
    19. Dominique Prunetti & Alexandre Muzy & Eric Innocenti & Xavier Pieri, 2014. "Utility-based Multi-agent System with Spatial Interactions: The Case of Virtual Estate Development," Computational Economics, Springer;Society for Computational Economics, vol. 43(3), pages 271-299, March.
    20. Diao, Xinshen & Magalhaes, Eduardo & Silver, Jed, 2019. "Cities and rural transformation: A spatial analysis of rural livelihoods in Ghana," World Development, Elsevier, vol. 121(C), pages 141-157.

    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:94:y:2020:i:c:s0264837719306507. 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.