Accessible On-Line Floor Plans

Goncu, Cagatay; Madugalla, Anuradha; Marinai, Simone; Marriott, Kim

doi:10.1145/2736277.2741660

Cited by 27 publications

(16 citation statements)

References 25 publications

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“…al. [12] have created an application that leverages text and geometry recognition software to convert floor plan images into a vector file with contrasting colors. The goal is to provide a simplified, more accessible version of floor plans to sight-impaired users.…”

Section: Image Analysis In Real Estatementioning

confidence: 99%

“…For example, the relationship between rooms and spaces, as well as the overall layout are typically only available from the floor plan image itself. Information on floor plans is typically also not available in textual form as the vast majority of platforms either do not include an alternative text description of the floor plan or the text is very general and not helpful [12].…”

Section: Introductionmentioning

confidence: 99%

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Integrating Floor Plans into Hedonic Models for Rent Price Appraisal

Solovev

Pröllochs

2021

Proceedings of the Web Conference 2021

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Online real estate platforms have become significant marketplaces facilitating users' search for an apartment or a house. Yet it remains challenging to accurately appraise a property's value. Prior works have primarily studied real estate valuation based on hedonic price models that take structured data into account while accompanying unstructured data is typically ignored. In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. We propose a tailored two-staged deep learning approach to learn price-relevant designs of floor plans from historical price data. Subsequently, we integrate the floor plan predictions into hedonic rent price models that account for both structural and locational characteristics of an apartment. Our empirical analysis based on a unique dataset of 9,174 real estate listings suggests that current hedonic models underutilize the available data. We find that (1) the visual design of floor plans has significant explanatory power regarding rent prices -even after controlling for structural and locational apartment characteristics, and (2) harnessing floor plans results in an up to 10.56% lower out-of-sample prediction error. We further find that floor plans yield a particularly high gain in prediction performance for older and smaller apartments. Altogether, our empirical findings contribute to the existing research body by establishing the link between the visual design of floor plans and real estate prices. Moreover, our approach has important implications for online real estate platforms, which can use our findings to enhance user experience in their real estate listings.

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Section: Image Analysis In Real Estatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating Floor Plans into Hedonic Models for Rent Price Appraisal

Solovev

Pröllochs

2021

Proceedings of the Web Conference 2021

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“…Goncu et. al [GMMM15] developed a web service that generates an accessible floor plan. Ferres et al [FLST13] proposed a system that verbally describes line charts.…”

Section: Visualization For the Non-visualmentioning

confidence: 99%

Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization

Choi

Jung

Park

et al. 2019

Computer Graphics Forum

104

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The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep‐neural‐network‐based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back‐end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.

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“…These information are then added to a symbolic representation of the graphic so that they can be presented in various formats. In this paper, we explored their use with the GraVVITAS system that we already adopted to present floor plans to visually impaired users [3].…”

Section: Introductionmentioning

confidence: 99%

Line Recognition for Generating Accessible Line Plots

Lombardi

Goncu

Marinai

2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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Existing technologies identify individual lines in a line plot by matching the symbols in the legend. Identifying each symbol is crucial to create accessible graphics for people who are blind so that they provide the best resemblance possible to the original plot. This resemblance provides better communication between sighted and vision impaired users. In our work we use an existing network architecture and improve it in a way that it will get more semantic information out of the lines and their symbology. We present these line graphs in the GraVVITAS system, an approach for providing dynamic and refreshable accessible graphics, to be used by people who are blind.

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Accessible On-Line Floor Plans

Cited by 27 publications

References 25 publications

Integrating Floor Plans into Hedonic Models for Rent Price Appraisal

Integrating Floor Plans into Hedonic Models for Rent Price Appraisal

Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization

Line Recognition for Generating Accessible Line Plots

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