We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Housing GANs: Deep Generation of Housing Market Data.
- Authors
Yilmaz, Bilgi
- Abstract
Modeling housing markets is a challenging and central research area since they are highly related to the economy. However, the limited available data prevents researchers from improving models. As an alternative, this study introduces Housing GANs, a data-driven modeling approach inspired by the recent success of generative adversarial networks (GANs). The Housing GANs include a generator and discriminator function utilizing Wasserstein GAN with gradient penalty and mitigate original housing datasets, including continuous and discrete data. The generator function predicts the real data distribution and generates realistic housing data. The empirical analysis highlights that the Housing GANs successfully learns the distribution and generate realistic housing data in high fidelity.
- Subjects
GENERATIVE adversarial networks; MACHINE learning; HOUSING market; DATA distribution; RESEARCH personnel
- Publication
Computational Economics, 2024, Vol 64, Issue 1, p579
- ISSN
0927-7099
- Publication type
Article
- DOI
10.1007/s10614-023-10456-6