Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach
Although deep learning-based valuation models are spreading throughout the real estate industry following the artificial intelligence boom, property owners and investors continue to doubt the accuracy of the results. In this study, we specify a neural network for predicting house prices. We suggest...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2020-12-01
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| Series: | Real Estate Management and Valuation |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/remav-2020-0028 |
| _version_ | 1818449506733129728 |
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| author | Lee Changro Park Keith Key-Ho |
| author_facet | Lee Changro Park Keith Key-Ho |
| author_sort | Lee Changro |
| collection | DOAJ |
| description | Although deep learning-based valuation models are spreading throughout the real estate industry following the artificial intelligence boom, property owners and investors continue to doubt the accuracy of the results. In this study, we specify a neural network for predicting house prices. We suggest a standard feed-forward network with two hidden layers, and show that it is sufficiently reasonable to apply its prediction to real-world projects such as property valuation. In addition, we propose a Bayesian neural network for describing uncertainty in house price predictions while providing a means to quantify uncertainty for each prediction. We choose Gangnam-gu, Seoul for the analysis, and predict house prices in the area using both networks. Although the Bayesian neural network did not perform better than the conventional network, it could provide a tool to measure the uncertainty inherent in predicted prices. The findings of this study show that a Bayesian approach can model uncertainty in property valuation, thereby promoting the adoption of deep learning tools in the real estate industry. |
| first_indexed | 2024-12-14T20:36:30Z |
| format | Article |
| id | doaj.art-b091b7c319de4de6bbc4c391f6965b9f |
| institution | Directory Open Access Journal |
| issn | 2300-5289 |
| language | English |
| last_indexed | 2024-12-14T20:36:30Z |
| publishDate | 2020-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Real Estate Management and Valuation |
| spelling | doaj.art-b091b7c319de4de6bbc4c391f6965b9f2022-12-21T22:48:23ZengSciendoReal Estate Management and Valuation2300-52892020-12-01284152310.1515/remav-2020-0028remav-2020-0028Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning ApproachLee Changro0Park Keith Key-Ho1Department of Real Estate, Kangwon National UniversityDepartment of Geography, Seoul National University, Institute for Korean Regional StudiesAlthough deep learning-based valuation models are spreading throughout the real estate industry following the artificial intelligence boom, property owners and investors continue to doubt the accuracy of the results. In this study, we specify a neural network for predicting house prices. We suggest a standard feed-forward network with two hidden layers, and show that it is sufficiently reasonable to apply its prediction to real-world projects such as property valuation. In addition, we propose a Bayesian neural network for describing uncertainty in house price predictions while providing a means to quantify uncertainty for each prediction. We choose Gangnam-gu, Seoul for the analysis, and predict house prices in the area using both networks. Although the Bayesian neural network did not perform better than the conventional network, it could provide a tool to measure the uncertainty inherent in predicted prices. The findings of this study show that a Bayesian approach can model uncertainty in property valuation, thereby promoting the adoption of deep learning tools in the real estate industry.https://doi.org/10.1515/remav-2020-0028deep learningbayesian neural networkuncertaintyproperty valuatione37l85r00c11c45 |
| spellingShingle | Lee Changro Park Keith Key-Ho Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach Real Estate Management and Valuation deep learning bayesian neural network uncertainty property valuation e37 l85 r00 c11 c45 |
| title | Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach |
| title_full | Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach |
| title_fullStr | Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach |
| title_full_unstemmed | Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach |
| title_short | Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach |
| title_sort | representing uncertainty in property valuation through a bayesian deep learning approach |
| topic | deep learning bayesian neural network uncertainty property valuation e37 l85 r00 c11 c45 |
| url | https://doi.org/10.1515/remav-2020-0028 |
| work_keys_str_mv | AT leechangro representinguncertaintyinpropertyvaluationthroughabayesiandeeplearningapproach AT parkkeithkeyho representinguncertaintyinpropertyvaluationthroughabayesiandeeplearningapproach |