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Real Estate Ranking via Mixed Land-use Latent Models

Published: 10 August 2015 Publication History

Abstract

Mixed land use refers to the effort of putting residential, commercial and recreational uses in close proximity to one another. This can contribute economic benefits, support viable public transit, and enhance the perceived security of an area. It is naturally promising to investigate how to rank real estate from the viewpoint of diverse mixed land use, which can be reflected by the portfolio of community functions in the observed area. To that end, in this paper, we develop a geographical function ranking method, named FuncDivRank, by incorporating the functional diversity of communities into real estate appraisal. Specifically, we first design a geographic function learning model to jointly capture the correlations among estate neighborhoods, urban functions, temporal effects, and user mobility patterns. In this way we can learn latent community functions and the corresponding portfolios of estates from human mobility data and Point of Interest (POI) data. Then, we learn the estate ranking indicator by simultaneously maximizing ranking consistency and functional diversity, in a unified probabilistic optimization framework. Finally, we conduct a comprehensive evaluation with real-world data. The experimental results demonstrate the enhanced performance of the proposed method for real estate appraisal.

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    cover image ACM Conferences
    KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    August 2015
    2378 pages
    ISBN:9781450336642
    DOI:10.1145/2783258
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 10 August 2015

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    Author Tags

    1. functional diversity
    2. human mobility
    3. mixed-land using
    4. real estate ranking
    5. urban geography

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    • Research-article

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    • National Institutes of Health
    • National Science Foundation
    • Natural Science Foundation of China

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    KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    • (2022)A plug-in memory network for trip purpose classificationProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560969(1-12)Online publication date: 1-Nov-2022
    • (2022)PATE: Property, Amenities, Traffic and Emotions Coming Together for Real Estate Price Prediction2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA54385.2022.10032416(1-10)Online publication date: 13-Oct-2022
    • (2022)Towards robust and speculation-reduction real estate pricing models based on a data-driven strategyJournal of the Operational Research Society10.1080/01605682.2021.202367273:12(2794-2807)Online publication date: 18-Jan-2022
    • (2021)Mixed Land Use Evaluation and Its Impact on Housing Prices in Beijing Based on Multi-Source Big DataLand10.3390/land1010110310:10(1103)Online publication date: 18-Oct-2021
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    • (2021)Developing an Evaluation Model for Forecasting of Real Estate PricesApplications of Artificial Intelligence and Machine Learning10.1007/978-981-16-3067-5_46(619-627)Online publication date: 27-Jul-2021
    • (2020)AR2NetACM Transactions on Knowledge Discovery from Data10.1145/337240614:2(1-28)Online publication date: 9-Feb-2020
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