Visual Estimation of Building Condition with Patch-level ConvNets
This addresses the need for objective building condition assessment in real estate valuation, though it is incremental as it builds on existing ConvNet methods.
The paper tackled the problem of subjective building condition estimation by real estate appraisers by proposing a vision-based approach using patch-level ConvNets, achieving results that serve as a proxy for appraiser estimates.
The condition of a building is an important factor for real estate valuation. Currently, the estimation of condition is determined by real estate appraisers which makes it subjective to a certain degree. We propose a novel vision-based approach for the assessment of the building condition from exterior views of the building. To this end, we develop a multi-scale patch-based pattern extraction approach and combine it with convolutional neural networks to estimate building condition from visual clues. Our evaluation shows that visually estimated building condition can serve as a proxy for condition estimates by appraisers.