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Understanding house price appreciation using multi-source big geo-data and machine learning

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  • Kang, Yuhao
  • Zhang, Fan
  • Peng, Wenzhe
  • Gao, Song
  • Rao, Jinmeng
  • Duarte, Fabio
  • Ratti, Carlo

Abstract

Understanding house price appreciation benefits place-based decision makings and real estate market analyses. Although large amounts of interests have been paid in the house price modeling, limited work has focused on evaluating the price appreciation rate. In this study, we propose a data-fusion framework to examine how well house price appreciation potentials can be predicted by combining multiple data sources. We used data sets including house structural attributes, house photos, locational amenities, street view images, transportation accessibility, visitor patterns, and socioeconomic attributes of neighborhoods to enrich our understanding of the real estate appreciation and its predictive modeling. As a case study, we investigate more than 20,000 houses in the Greater Boston Area, and discuss the spatial dependency of house price appreciations, influential variables and their relationships. In detail, we extract deep features from street view images and house photos using a deep learning model, merging features from multi-source data and modeling house price appreciation using machine learning models and the geographically weighted regression at two spatial scales: fine-scale point level and aggregated neighborhood level. Results show that the house price appreciation rate can be modeled with high accuracy using the proposed framework (R2=0.74 for gradient boosting machine at neighborhood-scale). We discovered that houses with low house prices and small house areas may have a higher house appreciation potential. Our results provide insights into how multi-source big geo-data can be employed in machine learning frameworks to characterize real estate price trends and help understand human settlements for policy-making.

Suggested Citation

  • Kang, Yuhao & Zhang, Fan & Peng, Wenzhe & Gao, Song & Rao, Jinmeng & Duarte, Fabio & Ratti, Carlo, 2021. "Understanding house price appreciation using multi-source big geo-data and machine learning," Land Use Policy, Elsevier, vol. 111(C).
  • Handle: RePEc:eee:lauspo:v:111:y:2021:i:c:s0264837719316746
    DOI: 10.1016/j.landusepol.2020.104919
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    References listed on IDEAS

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    1. Qiwei Song & Yifeng Liu & Waishan Qiu & Ruijun Liu & Meikang Li, 2022. "Investigating the Impact of Perceived Micro-Level Neighborhood Characteristics on Housing Prices in Shanghai," Land, MDPI, vol. 11(11), pages 1-21, November.
    2. Zhou Huang & Ganmin Yin & Xia Peng & Xiao Zhou & Quanhua Dong, 2023. "Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data," Environment and Planning B, , vol. 50(9), pages 2470-2490, November.
    3. 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).
    4. Gong, Wenjing & Rui, Jin & Li, Tianyu, 2024. "Deciphering urban bike-sharing patterns: An in-depth analysis of natural environment and visual quality in New York's Citi bike system," Journal of Transport Geography, Elsevier, vol. 115(C).
    5. Doan, Quang Cuong, 2023. "Determining the optimal land valuation model: A case study of Hanoi, Vietnam," Land Use Policy, Elsevier, vol. 127(C).
    6. Jin, Ting & Liang, Feiyan & Dong, Xiaoqi & Cao, Xiaojuan, 2023. "Research on land resource management integrated with support vector machine —Based on the perspective of green innovation," Resources Policy, Elsevier, vol. 86(PB).

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