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Related Experiment Videos

Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning

Gergo Pinter1, Amir Mosavi1,2,3, Imre Felde1

  • 1John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.

Entropy (Basel, Switzerland)
|December 19, 2020
PubMed
Summary

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Mathematical Modeling: Problem Solving01:29

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Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
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This study uses artificial intelligence (AI) and call detail records (CDR) to predict real estate prices. Mobility patterns, particularly workers' entropy and dwellers' work distances, significantly impact property values.

Area of Science:

  • Urban Planning and Real Estate Economics
  • Data Science and Artificial Intelligence
  • Mobility Studies

Background:

  • Accurate real estate price prediction is crucial for urban development and economic functions.
  • Real estate modeling is complex due to dynamic variables and uncertainties.
  • Call detail records (CDR) offer rich data for analyzing human mobility patterns.

Purpose of the Study:

  • To propose a novel machine learning (ML) method for real estate price prediction.
  • To investigate the potential of CDR data in conjunction with AI for real estate modeling.
  • To identify key mobility factors influencing real estate prices.

Main Methods:

  • Utilized mobility entropy factors derived from CDR as input variables (dweller entropy, dweller gyration, workers' entropy, worker gyration, dwellers' work distance, workers' home distance).
Keywords:
5GIoTartificial intelligencecall detail recordscellular networkcomputational sciencemachine learningreal estate pricesmart citiestelecommunicationsurban development

Related Experiment Videos

  • Developed a prediction model using a multi-layered perceptron (MLP) machine learning algorithm.
  • Optimized the MLP model with the particle swarm optimization (PSO) evolutionary algorithm.
  • Evaluated model performance using mean square error (MSE), sustainability index (SI), and Willmott's index (WI).

    • Main Results:

    • Workers' entropy and dwellers' work distances were identified as direct influencers of real estate prices.
    • Dweller gyration, dweller entropy, workers' gyration, and workers' home distance showed minimal impact on price.
    • Regions with high activity flow and mobility entropy correlated with lower real estate prices.

    Conclusions:

    • Mobility characteristics derived from CDR data, analyzed via AI, can effectively predict real estate prices.
    • Specific mobility metrics like workers' entropy and dwellers' work distances are key predictors.
    • Understanding mobility patterns offers insights into real estate market dynamics and urban planning strategies.