Image Analyses and Real Estate: Evaluation of the Quality of Location Using Remotely Sensed Imagery

A growing number of applied studies examine the impact of urban space quality on property price. Especially the planning and development of the immediate neighborhood (micro location) is an important influencing factor in regional economics. An image-based method for the estimation of location quality, in the context of property valuation, does not exist yet. We develop method for the determination of the quality of location using image processing, taking at the same time into account the classification in quality classes based on regional structural characteristics. With the help of automatic image analysis, a new information source is leveraged, which previously could not be taken into account within the scope of evaluation of location quality or within the scope of automated valuation models (e.g. hedonic models). In the field of image analysis, the extraction of parameters related to location quality is a new task. It is so far not clear to which degree meaningful parameters can be found autonomously by machine learning. This dissertation will investigate this question in detail and is to our knowledge the first approach for the automatic image-based valuation of location quality.