USING EYE-TRACKING TO SUPPORT BIG DATA DRIVEN PROPERTY VALUATION TOOLS

Soundararaj, B.; Pettit, C.

Building and using large scale, Automated Valuation Models (AVM) is one of the key multi-disciplinary pursuits in the study of cities and their economies. The methods used in building these AVMs such as ‘hedonic price modelling’ require a ‘co-design’ approach which needs significant collaboration and feedback between the modellers and the users of these models. The success of this collaborative approach depends crucially on our ability to capture the inputs and feedback from users without the bias and uncertainties present in traditional data collecting methods. In this paper, we explore and demonstrate the use of ‘eye-tracking’ technology in devising an objective methodology for collecting user feedback for co-design exercises. We employed a remote eye tracker in conjunction with traditional questionnaires to capture the decision making process of participants as buyers while selecting a property among a set of available options. We then compared the factors they reported to be important in their decision-making process to the factors they actually considered when looking at property listings. In our experiments, we found that pictures and maps captured more than 95% of the attention from buyers compared to the descriptive or statistical information showing the significance of the interface and medium of the valuation process. When responding to questionnaires, participants as property buyers reported that the attributes of a property such as number of beds, baths, quality of construction from pictures and location are equally important in selecting one over others. In contrast, when measured by an eye-tracker, we found that the participants gave significantly more attention to the quality of construction and location of the property compared to other factors. These preliminary results, though not definitive, demonstrate the value and usefulness of eye-tracking as a technique for capturing and measuring the factors that influence the desirability and in turn the price of a property. This methodology when controlled for characteristics of the participants, the properties and the medium of communication has the potential to help us to identifying and quantifying the relevance of parameters during property valuation and hence improve the accuracy and effectiveness of the corresponding hedonic price models.

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Soundararaj, B. / Pettit, C.: USING EYE-TRACKING TO SUPPORT BIG DATA DRIVEN PROPERTY VALUATION TOOLS. 2021. Copernicus Publications.

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