Housing GANs: Deep Generation of Housing Market Data

Author:

Yilmaz BilgiORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)

Reference46 articles.

1. Al-Homoud, M., Al-Oun, S., & Al-Hindawi, A. M. (2009). The low-income housing market in Jordan. International Journal of Housing Markets and Analysis, 2, 233–252.

2. Arjovsky, M., Chintala, S., Bottou, L. (2017). Wasserstein generative adversarial networks. In: International conference on machine learning, PMLR, pp 214–223

3. Bendaoud, N. M. M., Farah, N., & Ahmed, S. B. (2021). Comparing generative adversarial networks architectures for electricity demand forecasting. Energy and Buildings, 247(111), 152.

4. Bishop, C. M. (2006). Pattern recognition and machine learning (information science and statistics). Berlin: Springer.

5. Boyle, M., & Kiel, K. (2001). A survey of house price hedonic studies of the impact of environmental externalities. Journal of Real Estate Literature, 9(2), 117–144.

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