A Local Gaussian Process Regression Approach to Mass Appraisal of Residential Properties
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11146-024-09980-5.pdf
Reference11 articles.
1. Arribas, I., García, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2016). Mass appraisal of residential real estate using multilevel modelling. International Journal of Strategic Property Management, 20(1), 77–87.
2. Chen, J., & Stein, M. L. (2021). Linear-cost covariance functions for Gaussian random fields. Journal of the American Statistical Association, 1–18. https://doi.org/10.1080/01621459.2021.1919122
3. Davis, M. A., Larson, W. D., Oliner, S. D., & Shui, J. (2021). The price of residential land for counties, ZIP codes, and census tracts in the United States. Journal of Monetary Economics, 118, 413–431.
4. Dearmon, J. & Smith, T. E. (2021) A hierarchical approach to scalable Gaussian process regression for spatial data. Journal of Spatial Econometrics (forthcoming). https://doi.org/10.1007/s43071-021-00012-5
5. Kauko, T., & d’Amato, M. (Eds.). (2009). Mass appraisal methods: An international perspective for property valuers. John Wiley & Sons.
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1. Empirical Study on Real Estate Mass Appraisal Based on Dynamic Neural Networks;Buildings;2024-07-16
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