Optimizing housing price estimation through image segmentation and geographically weighted regression: an empirical study in Nanjing, China
Author:
Funder
National Natural Science Foundation of China
China Scholarship Council
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10901-024-10133-6.pdf
Reference53 articles.
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2. Bohman, H., & Nilsson, D. (2016). The impact of regional commuter trains on property values: Price segments and income. Journal of Transport Geography, 56, 102–109. https://doi.org/10.1016/j.jtrangeo.2016.09.003.
3. Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x.
4. Cetintahra, G. E., & Cubukcu, E. (2015). The influence of environmental aesthetics on economic value of housing: An empirical research on virtual environments. Journal of Housing and the Built Environment, 30(2), 331–340. https://doi.org/10.1007/s10901-014-9413-6.
5. Cordera, R., Chiarazzo, V., Ottomanelli, M., dell’Olio, L., & Ibeas, A. (2019). The impact of undesirable externalities on residential property values: Spatial regressive models and an empirical study. Transport Policy, 80, 177–187. https://doi.org/10.1016/j.tranpol.2018.04.010.
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