Fine-Grained Property Value Assessment using Probabilistic Disaggregation
This work addresses the need for fine-grained property value estimates for applications like insurance and urban planning, but it appears incremental as it builds on existing methods for disaggregation.
The paper tackled the problem of estimating property value at high spatial resolution from remote sensing imagery, and the results showed that the proposed method significantly improved over baseline approaches on a real-world urban dataset.
The monetary value of a given piece of real estate, a parcel, is often readily available from a geographic information system. However, for many applications, such as insurance and urban planning, it is useful to have estimates of property value at much higher spatial resolutions. We propose a method to estimate the distribution over property value at the pixel level from remote sensing imagery. We evaluate on a real-world dataset of a major urban area. Our results show that the proposed approaches are capable of generating fine-level estimates of property values, significantly improving upon a diverse collection of baseline approaches.