Climate change-induced sea level rise (SLR) will affect a range of coastal assets and prompt difficult decisions about coastal land use across the world. Several recent studies find that current and projected SLR is associated with relatively lower property values. We contribute to this growing body of research with a case study of O‘ahu, Hawai‘i, which is famed for its beaches as well as valuable coastal real estate. We leverage a dataset that unpacks multiple types of SLR exposure and coastal parcel attributes. We apply property transaction data for the island of O‘ahu through 2019 to investigate the effect of current and expected SLR exposure on residential property prices. We find that exposed properties have already experienced declines in transaction prices, at 9 to 14%, attributed to expectations of exposure to chronic inundation (as opposed to seasonal flooding). The price declines are mainly for multi-dwelling homes as opposed to single family homes. The market response of residential properties to SLR has important implications for coastal management strategies, in particular the viability and timing of programs for retreat.
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House Price Prediction via Visual Cues and Estate Attributes
The price of a house depends on many factors, such as its size, location, amenities, surrounding establishments, and the season in which the house is being sold, just to name a few of them. As a seller, it is absolutely essential to price the property competitively else it will not attract any buyers. This problem has given rise to multiple companies as well as past research works that try to enhance the predictability of property prices using relevant mathematical models and machine learning techniques. In this research, we investigate the usage of machine learning in predicting the house price based on related estate attributes and visual images. To this end, we collect a dataset of 2,000 houses across different cities in the United States. For each house, we annotate 14 estate attributes and five visual images for exterior, interior-living room, kitchen, bedroom, and bathroom. Following the dataset collection, different features are extracted from the input data. Furthermore, a multi-kernel regression approach is used to predict the house price from both visual cues and estate attributes. The extensive experiments demonstrate the superiority of the proposed method over the baselines.
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- Award ID(s):
- 2025234
- NSF-PAR ID:
- 10428263
- Date Published:
- Journal Name:
- ISVC 2022
- Page Range / eLocation ID:
- 91=103
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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