“What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features”, Shunyuan Zhang, Dokyun Lee, Param Vir Singh, Kannan Srinivasan2017-05-25 (CNN, data visualization, ads; similar)⁠:

[see also NIMA, Murray & Gordo2017, Porzi et al 2015/Dubey et al 2016/Fu et al 2018, CLIP prompts] We study how Airbnb property demand changed after the acquisition of verified images (taken by Airbnb’s photographers) and explore what makes a good image for an Airbnb property.

Using deep learning and difference-in-difference analyses on an Airbnb panel dataset spanning 7,423 properties over 16 months, we find that properties with verified images had 8.98% higher occupancy than properties without verified images (images taken by the host).

To explore what constitutes a good image for an Airbnb property, we quantify 12 human-interpretable image attributes that pertain to 3 artistic aspects—composition, color, and the figure-ground relationship—and we find systematic differences between the verified and unverified images. We also predict the relationship between each of the 12 attributes and property demand, and we find that most of the correlations are statistically-significant and in the theorized direction.

Our results provide actionable insights for both Airbnb photographers and amateur host photographers who wish to optimize their images. Our findings contribute to and bridge the literature on photography and marketing (eg. staging), which often either ignores the demand side (photography) or does not systematically characterize the images (marketing).

[Keywords: sharing economy, Airbnb, property demand, computer vision, deep learning, image feature extraction, content engineering]

…One of our key objectives is to determine what makes a good image for an Airbnb property. Our CNN model is highly accurate at predicting image quality, but the CNN-extracted features are uninterpretable.

To provide better guidance for managers, we use the photography literature to identify 12 human-interpretable image attributes that are relevant to image quality in the real estate context. We theorize the relationship between each of the 12 image attributes and property demand.

The 12 attributes fall under 3 key artistic aspects: composition, color, and the figure-ground relationship. Composition is the arrangement of visual elements in the photograph; ideally, the composition leads the viewer’s eyes to the center of focus (Freeman2007).

We capture composition with 4 attributes: diagonal dominance, the rule of thirds, visual balance of color, and visual balance of intensity.

Color can affect the viewer’s emotional arousal. The marketing literature has studied the impact of color on consumer behavior particularly in the context of web design, product packaging design, and advertisement design (Gorn et al 1997_27ya, Gorn et al 2004_20ya; Miller & Kahn2005). We include 5 aspects related to color: warm hue, saturation, brightness, contrast of brightness, and image clarity.

The principle of the figure-ground relationship is one of the most basic laws of perception and is used extensively by expert photographers to plan their photographs. In visual art, the figure refers to the key region (ie. foreground), and the ground refers to the background; photographs in which the figure is inseparable from the ground do not retain the viewer’s attention. We include 3 attributes: the area difference, texture difference, and color difference between the figure and ground.

…Of the 12 image attributes, the visual balance of color is most strongly related to property demand, followed by image clarity and the contrast of brightness. The visual balance of color refers to color symmetry, which can be affected by both the property itself and the position from which the image is captured.

Image clarity refers to the extent to which the image conveys visual information. The unverified low-quality images scored poorly on image clarity; the verified photos scored almost twice as high. Even without employing a professional photographer, hosts can improve image clarity through the effective use of lighting and access to a good camera.

Finally, the contrast of brightness captures the difference in illumination between the brightest and dimmest points in the image; a low contrast of brightness indicates that illumination is relatively even across the image. The verified photos have a substantially lower contrast of brightness than unverified high-quality images. Interestingly, several hosts on the Airbnb community forums complained that the contrast of brightness is so low in the verified photos that they appear washed out, but we find the predicted negative relationship between the contrast of brightness and property demand. In other words, consumers seem to prefer the low contrast of brightness that appears in verified photos.

1. Diagonal Dominance

2. Rule of Thirds

3. Visual Balance of Intensity

4. Visual Balance of Color

5. Warm Hue

6. Saturation

7. Brightness

8. Contrast of Brightness

9. Image Clarity

10. Area Difference

11. Color Difference

12. Texture Difference

See Also:

• “Learning Transferable Visual Models From Natural Language Supervision”

• “DALL·E 1: Creating Images from Text: We’ve trained a neural network called DALL·E 1 that creates images from text captions for a wide range of concepts expressible in natural language”

• “Multimodal Neurons in Artificial Neural Networks [CLIP]”

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